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What are metacognitive skills? Examples in everyday life

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What are metacognitive skills?

Examples of metacognitive skills, how to improve metacognitive skills, take charge of your mind.

When facing a career change or deciding to switch jobs, you might update the hard and soft skills on your resume. You could even take courses to upskill and expand your portfolio.

But some growth happens off the page. Your metacognitive skills contribute to your learning process and help you look inward to self-reflect and monitor your growth. They’re like a golden ticket toward excellence in both academia and your career path , always pushing you further.

A deeper understanding of metacognition, along with effective strategies for developing related skills, opens the door to heightened personal and professional development . Metacognitive thinking might just be the tool you need to reach your academic and career goals

Metacognitive skills are the soft skills you use to monitor and control your learning and problem-solving processes , or your thinking about thinking. This self-understanding is known as metacognition theory, a term that the American developmental psychologist John H. Flavell coined in the 1970s .

It might sound abstract, but these skills are mostly about self-awareness , learning, and organizing your thoughts. Metacognitive strategies include thinking out loud and answering reflective questions. They’re often relevant for students who need to memorize concepts fast or absorb lots of information at once.

But metacognition is important for everyone because it helps you retain information more efficiently and feel more confident about what you know. One meta-analysis of many studies showed that being aware of metacognitive strategies has a strong positive impact on teaching and learning , and that knowing how to plan ahead was a key indicator of future success.

Understanding your cognition and how you learn is a fundamental step in optimizing your educational process. To make the concept more tangible, here are a few cognitive skills examples:

Goal setting

One of the foremost metacognitive skills is knowing how to set goals — recognizing what your ambitions are and fine-tuning them into manageable and attainable objectives. The SMART goal framework is a good place to start because it dives deeper into what you know you can realistically achieve.

Whether it's a personal goal of grasping a complex concept, a professional goal of developing a new skill set, or a financial goal of achieving a budgeting milestone , setting a concrete goal helps you know what you’re working toward. It’s the first step to self-directed learning and achievement, giving you a destination for your path.

Planning and organization

Planning is an essential metacognition example because it sketches out the route you'll take to reach your goal, as well as identifying and collecting the specific strategies, resources, and support mechanisms you'll need along the way. It’s an in-demand skill for many jobs, but it also helps you learn new things.

Creating and organizing a plan is where you contemplate the best methods for learning, evaluate the materials and resources at your disposal, and determine the most efficient time management strategies. Even though it’s a concrete skill, it falls under the umbrella of metacognition because it involves self-awareness about your learning style and abilities.

Problem-solving

Central to metacognition is problem-solving, a higher-order cognitive process requiring both creative and critical thinking skills . Solving problems both at work and during learning begins with recognizing the issue at hand, analyzing the details, and considering potential solutions. The next step is selecting the most promising solution from the pool of possibilities and evaluating the results after implementation. 

The problem-solving process gives you the opportunity to grow from your mistakes and practice trial and error. It also helps you reflect and refine your approach for future endeavors. These qualities make it central to metacognition’s inward-facing yet action-oriented processes.

Concentration

Concentration allows you to fully engage with the information you’re processing and retain new knowledge. It involves a high degree of mental fitness , which you can develop with metacognition. Most tasks require the ability to ignore distractions , resist procrastination , and maintain a steady focus on the task at hand. 

This skill is paramount when it comes to work-from-home settings or jobs with lots of moving parts where countless distractions are constantly vying for your attention. And training your mind to focus better in general can also increase your learning efficacy and overall productivity.

Self-reflection

The practice of self-reflection involves continually assessing your performance, cognitive strategies, and experiences to foster self-improvement . It's a type of mental debriefing where you look back on your actions and outcomes, examining them critically to gain insight and experience valuable lessons. 

Reflective practice can help you identify what worked well, what didn't, and why, giving you the opportunity to make necessary adjustments for future actions. This continuous process enhances your learning and helps you adapt to new changes and strategies. 

Metacognition turns you into a self-aware problem solver, empowering you to take control of your education and become a more efficient thinker. Although it’s helpful for students, you can also apply it in the workplace while brainstorming and discovering new ways to fulfill your roles and responsibilities .

Here are some examples of metacognitive strategies and how to cultivate your abilities:

1. Determine your learning style

Are you a visual learner who thrives on images, diagrams, and color-coded notes? Are you an auditory learner who benefits more from verbal instructions, podcasts , or group discussions? Or are you a kinesthetic learner who enjoys hands-on experiences, experiments, or physical activities?

Metacognition in education is critical because it teaches you to recognize the way you intake information — the first step to effective strategies that help you truly retain information. By identifying your learning style, you can tailor your goals and study strategies to suit your strengths, maximizing your cognitive potential and improving your understanding of new material.

2. Find deeper meaning in what you read

Merely skimming the surface of the text you read won't lead to profound understanding or long-term retention. Instead, dive deep into the material. Employ reading strategies like note-taking, highlighting, and summarizing to help information enter your brain. 

If that process doesn’t work for you, try using brainstorming techniques like mind mapping to tease out the underlying themes and messages. This depth of processing enhances comprehension and allows you to connect new information to prior knowledge, promoting meaningful learning.

3. Write organized plans

Deconstruct your tasks into manageable units and create a comprehensive, step-by-step plan. Having a detailed guide breaks down large, intimidating tasks into bite-sized, achievable parts, reduces the risk of procrastination, and helps manage cognitive load. This process frees up your mental energy for higher-order thinking.

4. Ask yourself open-ended questions

Metacognitive questioning is a powerful tool for fostering self-awareness. Asking good questions like “What am I trying to achieve?” and “Why did this approach work or not work?” facilitates a deeper understanding of your education style, promotes critical thinking, and enables self-directed learning. Your answers will pave the way for improved processes.

5. Ask for feedback

External perspectives offer valuable insights into your thinking patterns and strategies. Seek feedback from teachers, peers, or mentors and earn the metacognitive knowledge you need to identify strengths to harness and weaknesses to address. Remember, the objective isn’t to nitpick or micromanage. It’s constructive criticism to help refine your learning process.

6. Self-evaluate

Cultivate a habit of self-assessment and self-monitoring, whether you’re experiencing something new or working on an innovative project. Check in on progress regularly, and compare current performance with your goals. This continuous self-evaluation helps you maintain focus on your objectives and identify when you're going off track, allowing for timely adjustments when necessary. 

Introspection is a powerful tool, and you can’t overstate the importance of knowing yourself . After all, building your metacognitive skills begins with a strong foundation of self-awareness and accountability .

7. Focus on solutions

It's easy to let problems and obstacles discourage you during the learning process. But metacognitive skills encourage a solutions-oriented mindset. Instead of fixating on the challenges, shift your focus to identifying, analyzing, and implementing creative solutions . 

This proactive approach fosters resilience and adaptability skills in the face of adversity, helping you overcome whatever comes your way. Cultivating this mindset — sometimes known as a growth mindset — also boosts your problem-solving prowess and transforms challenges into opportunities for growth.

The simple act of writing about your learning experiences can heighten your metacognitive awareness. Journaling provides a space to reflect on your thought processes, emotions, and struggles, which can reveal patterns and trends in your behavior. It’s a springboard for improvement that helps you recognize and solve problems as they come.

In the journey of learning and career advancement, metacognitive skills are your compass toward improvement. They empower you to understand your cognitive processes, enhance your strategies, and become a more effective thinker. They’re useful whether you’re just starting a master’s degree or upskilling to earn a promotion.

Remember, the journey to gain metacognitive skills isn’t a race. It’s a personal voyage of self-discovery and growth. Each stride you take toward honing your metacognitive skills is a step toward a more successful, fulfilling, and self-aware life.

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Elizabeth Perry is a Coach Community Manager at BetterUp. She uses strategic engagement strategies to cultivate a learning community across a global network of Coaches through in-person and virtual experiences, technology-enabled platforms, and strategic coaching industry partnerships. With over 3 years of coaching experience and a certification in transformative leadership and life coaching from Sofia University, Elizabeth leverages transpersonal psychology expertise to help coaches and clients gain awareness of their behavioral and thought patterns, discover their purpose and passions, and elevate their potential. She is a lifelong student of psychology, personal growth, and human potential as well as an ICF-certified ACC transpersonal life and leadership Coach.

What’s convergent thinking? How to be a better problem-solver

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Center for Teaching

Metacognition.

Thinking about One’s Thinking |   Putting Metacognition into Practice

Thinking about One’s Thinking

Initially studied for its development in young children (Baker & Brown, 1984; Flavell, 1985), researchers soon began to look at how experts display metacognitive thinking and how, then, these thought processes can be taught to novices to improve their learning (Hatano & Inagaki, 1986).  In How People Learn , the National Academy of Sciences’ synthesis of decades of research on the science of learning, one of the three key findings of this work is the effectiveness of a “‘metacognitive’ approach to instruction” (Bransford, Brown, & Cocking, 2000, p. 18).

Metacognitive practices increase students’ abilities to transfer or adapt their learning to new contexts and tasks (Bransford, Brown, & Cocking, p. 12; Palincsar & Brown, 1984; Scardamalia et al., 1984; Schoenfeld, 1983, 1985, 1991).  They do this by gaining a level of awareness above the subject matter : they also think about the tasks and contexts of different learning situations and themselves as learners in these different contexts.  When Pintrich (2002) asserts that “Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them” (p. 222), notice the students must “know about” these strategies, not just practice them.  As Zohar and David (2009) explain, there must be a “ conscious meta-strategic level of H[igher] O[rder] T[hinking]” (p. 179).

Metacognitive practices help students become aware of their strengths and weaknesses as learners, writers, readers, test-takers, group members, etc.  A key element is recognizing the limit of one’s knowledge or ability and then figuring out how to expand that knowledge or extend the ability. Those who know their strengths and weaknesses in these areas will be more likely to “actively monitor their learning strategies and resources and assess their readiness for particular tasks and performances” (Bransford, Brown, & Cocking, p. 67).

The absence of metacognition connects to the research by Dunning, Johnson, Ehrlinger, and Kruger on “Why People Fail to Recognize Their Own Incompetence” (2003).  They found that “people tend to be blissfully unaware of their incompetence,” lacking “insight about deficiencies in their intellectual and social skills.”  They identified this pattern across domains—from test-taking, writing grammatically, thinking logically, to recognizing humor, to hunters’ knowledge about firearms and medical lab technicians’ knowledge of medical terminology and problem-solving skills (p. 83-84).  In short, “if people lack the skills to produce correct answers, they are also cursed with an inability to know when their answers, or anyone else’s, are right or wrong” (p. 85).  This research suggests that increased metacognitive abilities—to learn specific (and correct) skills, how to recognize them, and how to practice them—is needed in many contexts.

Putting Metacognition into Practice

In “ Promoting Student Metacognition ,” Tanner (2012) offers a handful of specific activities for biology classes, but they can be adapted to any discipline. She first describes four assignments for explicit instruction (p. 116):

• Preassessments—Encouraging Students to Examine Their Current Thinking: “What do I already know about this topic that could guide my learning?”

• Retrospective Postassessments—Pushing Students to Recognize Conceptual Change: “Before this course, I thought evolution was… Now I think that evolution is ….” or “How is my thinking changing (or not changing) over time?”
• Reflective Journals—Providing a Forum in Which Students Monitor Their Own Thinking: “What about my exam preparation worked well that I should remember to do next time? What did not work so well that I should not do next time or that I should change?”

Next are recommendations for developing a “classroom culture grounded in metacognition” (p. 116-118):

• Giving Students License to Identify Confusions within the Classroom Culture:  ask students what they find confusing, acknowledge the difficulties
• Integrating Reflection into Credited Course Work: integrate short reflection (oral or written) that ask students what they found challenging or what questions arose during an assignment/exam/project
• Metacognitive Modeling by the Instructor for Students: model the thinking processes involved in your field and sought in your course by being explicit about “how you start, how you decide what to do first and then next, how you check your work, how you know when you are done” (p. 118)

To facilitate these activities, she also offers three useful tables:

• Questions for students to ask themselves as they plan, monitor, and evaluate their thinking within four learning contexts—in class, assignments, quizzes/exams, and the course as a whole (p. 115)
• Prompts for integrating metacognition into discussions of pairs during clicker activities, assignments, and quiz or exam preparation (p. 117)
• Questions to help faculty metacognitively assess their own teaching (p. 119)

Weimer’s “ Deep Learning vs. Surface Learning: Getting Students to Understand the Difference ” (2012) offers additional recommendations for developing students’ metacognitive awareness and improvement of their study skills:

“[I]t is terribly important that in explicit and concerted ways we make students aware of themselves as learners. We must regularly ask, not only ‘What are you learning?’ but ‘How are you learning?’ We must confront them with the effectiveness (more often ineffectiveness) of their approaches. We must offer alternatives and then challenge students to test the efficacy of those approaches. ” (emphasis added)

She points to a tool developed by Stanger-Hall (2012, p. 297) for her students to identify their study strategies, which she divided into “ cognitively passive ” (“I previewed the reading before class,” “I came to class,” “I read the assigned text,” “I highlighted the text,” et al) and “ cognitively active study behaviors ” (“I asked myself: ‘How does it work?’ and ‘Why does it work this way?’” “I wrote my own study questions,” “I fit all the facts into a bigger picture,” “I closed my notes and tested how much I remembered,” et al) .  The specific focus of Stanger-Hall’s study is tangential to this discussion, 1 but imagine giving students lists like hers adapted to your course and then, after a major assignment, having students discuss which ones worked and which types of behaviors led to higher grades. Even further, follow Lovett’s advice (2013) by assigning “exam wrappers,” which include students reflecting on their previous exam-preparation strategies, assessing those strategies and then looking ahead to the next exam, and writing an action plan for a revised approach to studying. A common assignment in English composition courses is the self-assessment essay in which students apply course criteria to articulate their strengths and weaknesses within single papers or over the course of the semester. These activities can be adapted to assignments other than exams or essays, such as projects, speeches, discussions, and the like.

As these examples illustrate, for students to become more metacognitive, they must be taught the concept and its language explicitly (Pintrich, 2002; Tanner, 2012), though not in a content-delivery model (simply a reading or a lecture) and not in one lesson. Instead, the explicit instruction should be “designed according to a knowledge construction approach,” or students need to recognize, assess, and connect new skills to old ones, “and it needs to take place over an extended period of time” (Zohar & David, p. 187).  This kind of explicit instruction will help students expand or replace existing learning strategies with new and more effective ones, give students a way to talk about learning and thinking, compare strategies with their classmates’ and make more informed choices, and render learning “less opaque to students, rather than being something that happens mysteriously or that some students ‘get’ and learn and others struggle and don’t learn” (Pintrich, 2002, p. 223).

• What to Expect (when reading philosophy)
• The Ultimate Goal (of reading philosophy)
• Basic Good Reading Behaviors
• Important Background Information, or discipline- and course-specific reading practices, such as “reading for enlightenment” rather than information, and “problem-based classes” rather than historical or figure-based classes
• A Three-Part Reading Process (pre-reading, understanding, and evaluating)
• Flagging, or annotating the reading
• Linear vs. Dialogical Writing (Philosophical writing is rarely straightforward but instead “a monologue that contains a dialogue” [p. 365].)

What would such a handout look like for your discipline?

Students can even be metacognitively prepared (and then prepare themselves) for the overarching learning experiences expected in specific contexts . Salvatori and Donahue’s The Elements (and Pleasures) of Difficulty (2004) encourages students to embrace difficult texts (and tasks) as part of deep learning, rather than an obstacle.  Their “difficulty paper” assignment helps students reflect on and articulate the nature of the difficulty and work through their responses to it (p. 9).  Similarly, in courses with sensitive subject matter, a different kind of learning occurs, one that involves complex emotional responses.  In “ Learning from Their Own Learning: How Metacognitive and Meta-affective Reflections Enhance Learning in Race-Related Courses ” (Chick, Karis, & Kernahan, 2009), students were informed about the common reactions to learning about racial inequality (Helms, 1995; Adams, Bell, & Griffin, 1997; see student handout, Chick, Karis, & Kernahan, p. 23-24) and then regularly wrote about their cognitive and affective responses to specific racialized situations.  The students with the most developed metacognitive and meta-affective practices at the end of the semester were able to “clear the obstacles and move away from” oversimplified thinking about race and racism ”to places of greater questioning, acknowledging the complexities of identity, and redefining the world in racial terms” (p. 14).

Ultimately, metacognition requires students to “externalize mental events” (Bransford, Brown, & Cocking, p. 67), such as what it means to learn, awareness of one’s strengths and weaknesses with specific skills or in a given learning context, plan what’s required to accomplish a specific learning goal or activity, identifying and correcting errors, and preparing ahead for learning processes.

————————

1 Students who were tested with short answer in addition to multiple-choice questions on their exams reported more cognitively active behaviors than those tested with just multiple-choice questions, and these active behaviors led to improved performance on the final exam.

• Adams, Maurianne, Bell, Lee Ann, and Griffin, Pat. (1997). Teaching for diversity and social justice: A sourcebook . New York: Routledge.
• Bransford, John D., Brown Ann L., and Cocking Rodney R. (2000). How people learn: Brain, mind, experience, and school . Washington, D.C.: National Academy Press.
• Baker, Linda, and Brown, Ann L. (1984). Metacognitive skills and reading.  In Paul David Pearson, Michael L. Kamil, Rebecca Barr, & Peter Mosenthal (Eds.), Handbook of research in reading: Volume III (pp. 353–395).  New York: Longman.
• Brown, Ann L. (1980). Metacognitive development and reading. In Rand J. Spiro, Bertram C. Bruce, and William F. Brewer, (Eds.), Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education (pp. 453-482). Hillsdale, NJ: Erlbaum.
• Chick, Nancy, Karis, Terri, and Kernahan, Cyndi. (2009). Learning from their own learning: how metacognitive and meta-affective reflections enhance learning in race-related courses . International Journal for the Scholarship of Teaching and Learning, 3(1). 1-28.
• Commander, Nannette Evans, and Valeri-Gold, Marie. (2001). The learning portfolio: A valuable tool for increasing metacognitive awareness . The Learning Assistance Review, 6 (2), 5-18.
• Concepción, David. (2004). Reading philosophy with background knowledge and metacognition . Teaching Philosophy , 27 (4). 351-368.
• Dunning, David, Johnson, Kerri, Ehrlinger, Joyce, and Kruger, Justin. (2003) Why people fail to recognize their own incompetence . Current Directions in Psychological Science, 12 (3). 83-87.
• Flavell,  John H. (1985). Cognitive development. Englewood Cliffs, NJ: Prentice Hall.
• Hatano, Giyoo and Inagaki, Kayoko. (1986). Two courses of expertise. In Harold Stevenson, Azuma, Horishi, and Hakuta, Kinji (Eds.), Child development and education in Japan, New York: W.H. Freeman.
• Helms, Janet E. (1995). An update of Helms’ white and people of color racial identity models . In J.G. Ponterotto, Joseph G., Casas, Manuel, Suzuki, Lisa A., and Alexander, Charlene M. (Eds.), Handbook of multicultural counseling (pp. 181-198) . Thousand Oaks, CA: Sage.
• Lovett, Marsha C. (2013). Make exams worth more than the grade. In Matthew Kaplan, Naomi Silver, Danielle LaVague-Manty, and Deborah Meizlish (Eds.), Using reflection and metacognition to improve student learning: Across the disciplines, across the academy . Sterling, VA: Stylus.
• Palincsar, Annemarie Sullivan, and Brown, Ann L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities . Cognition and Instruction, 1 (2). 117-175.
• Pintrich, Paul R. (2002). The Role of metacognitive knowledge in learning, teaching, and assessing . Theory into Practice, 41 (4). 219-225.
• Salvatori, Mariolina Rizzi, and Donahue, Patricia. (2004). The Elements (and pleasures) of difficulty . New York: Pearson-Longman.
• Scardamalia, Marlene, Bereiter, Carl, and Steinbach, Rosanne. (1984). Teachability of reflective processes in written composition . Cognitive Science , 8, 173-190.
• Schoenfeld, Alan H. (1991). On mathematics as sense making: An informal attack on the fortunate divorce of formal and informal mathematics. In James F. Voss, David N. Perkins, and Judith W. Segal (Eds.), Informal reasoning and education (pp. 311-344). Hillsdale, NJ: Erlbaum.
• Stanger-Hall, Kathrin F. (2012). Multiple-choice exams: An obstacle for higher-level thinking in introductory science classes . Cell Biology Education—Life Sciences Education, 11(3), 294-306.
• Tanner, Kimberly D.  (2012). Promoting student metacognition . CBE—Life Sciences Education, 11, 113-120.
• Weimer, Maryellen.  (2012, November 19). Deep learning vs. surface learning: Getting students to understand the difference . Retrieved from the Teaching Professor Blog from http://www.facultyfocus.com/articles/teaching-professor-blog/deep-learning-vs-surface-learning-getting-students-to-understand-the-difference/ .
• Zohar, Anat, and David, Adi Ben. (2009). Paving a clear path in a thick forest: a conceptual analysis of a metacognitive component . Metacognition Learning , 4 , 177-195.

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• Metacognition

Metacognitive thinking skills are important for instructors and students alike. This resource provides instructors with an overview of the what and why of metacognition and general “getting started” strategies for teaching for and with metacognition.

In this page:

What is metacognition?

Why use metacognition, getting started: how to teach both for and with metacognition, metacognition at columbia.

Cite this resource: Columbia Center for Teaching and Learning (2018). Metacognition Resource. Columbia University. Retrieved [today’s date] from https://ctl.columbia.edu/resources-and-technology/resources/metacognition/

• assess the task.
• plan for and use appropriate strategies and resources.
• monitor task performance.
• evaluate processes and products of their learning and revise their goals and strategies accordingly.

The Center for Teaching and Learning encourages instructors to teach metacognitively. This means to teach “ with and for metacognition.” To teach with metacognition involves instructors “thinking about their own thinking regarding their teaching” (Hartman, 2001: 149). To teach for metacognition involves instructors thinking about how their instruction helps to elucidate learning and problem solving strategies to their students (Hartman, 2001).

Learners with metacognitive skills are:

• More self-aware as critical thinkers and problem solvers, enabling them to actively approach knowledge gaps and problems and to rely on themselves.
• Able to monitor, plan, and control their mental processes.
• Better able to assess the depth of their knowledge.
• Able to transfer/apply their knowledge and skills to new situations.
• Able to choose more effective learning strategies.
• More likely to perform better academically.

Instructors who teach metacognitively / think about their teaching are:

• More self-aware of their instructional capacities, and know what teaching strategies they rely upon, when and why these use these strategies, and how to use them effectively and inclusively.
• Better able to regulate their instruction before, during, and after conducting a class session (i.e., to plan what and how to teach, monitor how lessons are going and make adjustments, and evaluate how a lesson went afterwards).
• Better able to communicate, helping students understand the what, why, and how of their learning, which can lead to better learning outcomes.
• Able to use their knowledge of students’ metacognitive skills to plan instruction designed to improve students’ metacognition and to create inclusive course climates.

Teaching for metacognition — Metacognitive strategies that serve students and their learning:

Design homework assignments that ask students to focus on their learning process. This includes having students monitor progress, identify and correct mistakes, and plan next steps.

Provide structures to guide students in creating implementable action plans for improvement.

Show students how to move stepwise from reflection to action. Use appropriate technology to support student self-regulation. Many platforms such as CourseWorks provide tools that students can use to keep up with their course work and monitor their progress.

Teaching with metacognition — Metacognitive strategies that serve the course and the instructor’s teaching practice:

Create an evaluation plan to periodically evaluate one’s teaching and course design, set-up, and content.

Structure the course to provide time for students to give feedback on the course and teaching. Evaluate course progress and successes of teaching Use course and instructional objectives to measure progress.

Schedule mid-course feedback surveys with students.

Request a mid-course review (offered as a service for graduate students).

Review end-of-course evaluations and reflect on the changes that will be made to maximize student learning. Build in time for metacognitive work Set aside time before, during, and after a course to reflect on one’s teaching practice, relationship with students, course climate and dynamics, as well as assumptions about the course material and its accessibility to students.

Metacognition and Memory Lab  |  Dr. Janet Metcalfe (Professor of Psychology and of Neurobiology and Behavior) runs a lab that focuses on how people use their metacognition to improve self-awareness and to guide their own learning and behavior. Dr. Metcalfe is author of Metacognition: A Textbook for Cognitive, Educational, Life Span & Applied Psychology (2009), co-authored with John Dunlosky.

In Fall 2018, the CTL and the Science of LEarning Research (SOLER) initiative co-organized the inaugural Science of Learning Symposium “Metacognition: From Research to Classroom” which brought together Columbia faculty, staff, graduate students, and experts in the science of learning to share the research on metacognition in learning, and to translate it into strategies that maximize student learning. View video recording of the event here .

Ambrose, S. A., Lovett, M., Bridges, M. W., DiPietro, M., & Norman, M. K. (2010). How Learning Works: Seven Research-Based Principles for Smart Teaching . San Francisco: John Wiley & Sons.

Dunlosky, J. and Metcalfe, J. (2009). Metacognition. Thousand Oaks, CA: Sage.

Flavell, J.H. (1976). Metacognitive Aspects of Problem Solving. In L.B. Resnick (Ed.), The Nature of Intelligence (pp. 231-236). Hillsdale, NJ: Erlbaum.

Hacker, D.J. (1998). Chapter 1. Definitions and Empirical Foundations. In Hacker, D.J.; Dunlosky, J.; and Graesser, A.C. (1998). Metacognition in Educational Theory and Practice. Mahwah, N.J.: Routledge.

Hartman, H.J. (2001). Chapter 8: Teaching Metacognitively. In Metacognition in Learning and Instruction. Kluwer Academic Publishers, 149 – 172.

Lai, E.R. (2011). Metacognition: A Literature Review. Pearson’s Research Reports. Retrieved from https://images.pearsonassessments.com/images/tmrs/Metacognition_Literature_Review_Final.pdf

McGuire, S.Y. (2015). Teach Students How to Learn: Strategies You Can Incorporate Into Any Course to Improve Student Metacognition, Study Skills, and Motivation. Sterling, VA: Stylus.

National Research Council (2000). How People Learn: Brain, Mind, Experience, and School . Expanded Edition . Washington, DC: The National Academies Press. https://doi.org/10.17226/9853

Nilson, L. (2013). Creating Self-Regulated Learners: Strategies to Strengthen Students’ Self-Awareness and Learning Skills. Sterling, VA: Stylus.

Schraw, G. and Dennison, R.S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology. 19(4): 460-475.

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What Is Metacognition? How Does It Help Us Think?

Metacognitive strategies like self-reflection empower students for a lifetime..

Posted October 9, 2020 | Reviewed by Abigail Fagan

Metacognition is a high order thinking skill that is emerging from the shadows of academia to take its rightful place in classrooms around the world. As online classrooms extend into homes, this is an important time for parents and teachers to understand metacognition and how metacognitive strategies affect learning. These skills enable children to become better thinkers and decision-makers.

Metacognition: The Neglected Skill Set for Empowering Students is a new research-based book by educational consultants Dr. Robin Fogarty and Brian Pete that not only gets to the heart of why metacognition is important but gives teachers and parents insightful strategies for teaching metacognition to children from kindergarten through high school. This article summarizes several concepts from their book and shares three of their thirty strategies to strengthen metacognition.

What Is Metacognition?

Metacognition is the practice of being aware of one’s own thinking. Some scholars refer to it as “thinking about thinking.” Fogarty and Pete give a great everyday example of metacognition:

Think about the last time you reached the bottom of a page and thought to yourself, “I’m not sure what I just read.” Your brain just became aware of something you did not know, so instinctively you might reread the last sentence or rescan the paragraphs of the page. Maybe you will read the page again. In whatever ways you decide to capture the missing information, this momentary awareness of knowing what you know or do not know is called metacognition.

When we notice ourselves having an inner dialogue about our thinking and it prompts us to evaluate our learning or problem-solving processes, we are experiencing metacognition at work. This skill helps us think better, make sound decisions, and solve problems more effectively. In fact, research suggests that as a young person’s metacognitive abilities increase, they achieve at higher levels.

Fogarty and Pete outline three aspects of metacognition that are vital for children to learn: planning, monitoring, and evaluation. They convincingly argue that metacognition is best when it is infused in teaching strategies rather than taught directly. The key is to encourage students to explore and question their own metacognitive strategies in ways that become spontaneous and seemingly unconscious .

Metacognitive skills provide a basis for broader, psychological self-awareness , including how children gain a deeper understanding of themselves and the world around them.

Metacognitive Strategies to Use at Home or School

Fogarty and Pete successfully demystify metacognition and provide simple ways teachers and parents can strengthen children’s abilities to use these higher-order thinking skills. Below is a summary of metacognitive strategies from the three areas of planning, monitoring, and evaluation.

1. Planning Strategies

As students learn to plan, they learn to anticipate the strengths and weaknesses of their ideas. Planning strategies used to strengthen metacognition help students scrutinize plans at a time when they can most easily be changed.

One of ten metacognitive strategies outlined in the book is called “Inking Your Thinking.” It is a simple writing log that requires students to reflect on a lesson they are about to begin. Sample starters may include: “I predict…” “A question I have is…” or “A picture I have of this is…”

Writing logs are also helpful in the middle or end of assignments. For example, “The homework problem that puzzles me is…” “The way I will solve this problem is to…” or “I’m choosing this strategy because…”

2. Monitoring Strategies

Monitoring strategies used to strengthen metacognition help students check their progress and review their thinking at various stages. Different from scrutinizing, this strategy is reflective in nature. It also allows for adjustments while the plan, activity, or assignment is in motion. Monitoring strategies encourage recovery of learning, as in the example cited above when we are reading a book and notice that we forgot what we just read. We can recover our memory by scanning or re-reading.

One of many metacognitive strategies shared by Fogarty and Pete, called the “Alarm Clock,” is used to recover or rethink an idea once the student realizes something is amiss. The idea is to develop internal signals that sound an alarm. This signal prompts the student to recover a thought, rework a math problem, or capture an idea in a chart or picture. Metacognitive reflection involves thinking about “What I did,” then reviewing the pluses and minuses of one’s action. Finally, it means asking, “What other thoughts do I have” moving forward?

Teachers can easily build monitoring strategies into student assignments. Parents can reinforce these strategies too. Remember, the idea is not to tell children what they did correctly or incorrectly. Rather, help children monitor and think about their own learning. These are formative skills that last a lifetime.

3. Evaluation Strategies

According to Fogarty and Pete, the evaluation strategies of metacognition “are much like the mirror in a powder compact. Both serve to magnify the image, allow for careful scrutiny, and provide an up-close and personal view. When one opens the compact and looks in the mirror, only a small portion of the face is reflected back, but that particular part is magnified so that every nuance, every flaw, and every bump is blatantly in view.” Having this enlarged view makes inspection much easier.

When students inspect parts of their work, they learn about the nuances of their thinking processes. They learn to refine their work. They grow in their ability to apply their learning to new situations. “Connecting Elephants” is one of many metacognitive strategies to help students self-evaluate and apply their learning.

In this exercise, the metaphor of three imaginary elephants is used. The elephants are walking together in a circle, connected by the trunk and tail of another elephant. The three elephants represent three vital questions: 1) What is the big idea? 2) How does this connect to other big ideas? 3) How can I use this big idea? Using the image of a “big idea” helps students magnify and synthesize their learning. It encourages them to think about big ways their learning can be applied to new situations.

Metacognition and Self-Reflection

Reflective thinking is at the heart of metacognition. In today’s world of constant chatter, technology and reflective thinking can be at odds. In fact, mobile devices can prevent young people from seeing what is right before their eyes.

John Dewey, a renowned psychologist and education reformer, claimed that experiences alone were not enough. What is critical is an ability to perceive and then weave meaning from the threads of our experiences.

The function of metacognition and self-reflection is to make meaning. The creation of meaning is at the heart of what it means to be human.

Everyone can help foster self-reflection in young people.

Marilyn Price-Mitchell, Ph.D., is an Institute for Social Innovation Fellow at Fielding Graduate University and author of Tomorrow’s Change Makers.

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Metacognitive strategies (how people learn).

Metacognitive strategies are techniques to help students develop an awareness of their thinking processes as they learn. These techniques help students focus with greater intention, reflect on their existing knowledge versus information they still need to learn, recognize errors in their thinking, and develop practices for effective learning.

Some metacognitive strategies are easy to implement:

• ask students to submit a reflection on a topic before reading a text and then revisit that reflection after the reading to consider how it informed their thinking
• introduce a problem and have students participate in a think-pair-share on the strategy they would use to solve it; then share your strategy too
• ask students to write a reflection on how they figured out an answer to a question (Bransford, Brown, & Cocking, 2000)
• Pre-Assessment of Knowledge: Use a pre-class survey, homework assignment, polling questions in class, or a short reflective writing piece as a way for students to explore their existing knowledge about a topic. Asking how the topic relates to students’ experiences or interests can highlight pre-existing knowledge and boost engagement. Comment on the reflections or share some themes with the class.
• Debrief with the class and share a list of common strategies
• Preview your reading (title, abstract, headings, charts, diagrams, questions, terms highlighted in bold text, italicized words, etc.)
• Based on your preview, develop some questions that you think the text will answer
• Write down any questions you have
• Read a paragraph, paraphrase it, and check to see if it answered any of your questions
• Repeat this process with the entire document to ensure you understand the material and can answer your questions
• After you finish reading, test yourself on your questions
• Make a note of what is still unclear
• What is one question you still have about the reading?
• What is one thing you are curious about?
• How can you best prepare for class?
• What can you do in class to help yourself learn?
• Explain two ideas in the reading that you found confusing.
• Did working with your group help you learn? Why or why not?
• What advice would you give yourself now if you were to start this project again?
• What went well?
• What could have gone better?
• What could you do to improve things in the future?

These strategies offer a great opportunity to teach students about metacognition. Explaining that this reflection process can help them integrate new knowledge and take control of their learning experience.

For more information on using metacognitive strategies, please contact CTI for a consultation.

Selected Resources

• Assessing Prior Knowledge
• Classroom Assessment Techniques
• Bransford, J., National Research Council (U.S.)., & National Research Council (U.S.). (2000).  How people learn: Brain, mind, experience, and school . Washington, D.C: National Academy Press.
• Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning.
• Lang, J. M. (2016). Small teaching: Everyday lessons from the science of learning.
• Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9, 287–307.
• McGuire, S. Y., & McGuire, S. (2015). Teach students how to learn: Strategies you can incorporate into any course to improve student metacognition, study skills, and motivation.

Initial Thoughts

Perspectives & resources, what is high-quality mathematics instruction and why is it important.

• Page 1: The Importance of High-Quality Mathematics Instruction
• Page 2: A Standards-Based Mathematics Curriculum
• Page 3: Evidence-Based Mathematics Practices

What evidence-based mathematics practices can teachers employ?

• Page 4: Explicit, Systematic Instruction
• Page 5: Visual Representations
• Page 6: Schema Instruction

Page 7: Metacognitive Strategies

• Page 8: Effective Classroom Practices
• Page 9: References & Additional Resources
• Page 10: Credits

However, teaching students cognitive strategies alone is not enough to ensure that those strategies will be implemented correctly or independently. This is especially the case for students with mathematics difficulties and disabilities, who tend to implement the same strategy for every problem, implement strategies without considering the problem type, or fail to use a strategy at all. If students are to be more successful, teachers should pair instruction on cognitive strategies with that of metacognitive strategies —strategies that enable students to become aware of how they think when solving mathematics problems. This combined strategy instruction teaches students how to consider the appropriateness of the problem-solving approach, make sure that all procedural steps are implemented, and check for accuracy or to confirm that their answers makes sense. More specifically, metacognitive strategies help students learn to:

How does this practice align?

High-leverage practice (hlp).

• HLP14 : Teach cognitive and metacognitive strategies to support learning and independence

CCSSM: Standards for Mathematical Practice

• MP1 : Make sense of problems and persevere in solving them.
• Plan — Students decide how to approach the mathematical problem, first determining what the problem is asking and then selecting and implementing an appropriate strategy to solve it.
• Monitor — As students solve a mathematical problem, they check to see whether their problem-solving approach is working. After completing the problem, they consider whether the answer makes sense.
• Modify — If, as they work to solve a mathematical problem, students determine that their problem-solving approach is not working or that their answer is incorrect, they can adjust their approach.

Research Shows

• When paired with cognitive strategies, metacognitive strategies have been shown to increase the understanding and ability of students with mathematics learning difficulties and disabilities to solve mathematics problems. (Pfannenstiel, Bryant, Bryant, & Porterfield, 2015)
• Middle school students who received cognitive and metacognitive strategy instruction outperformed peers who received typical math instruction. (Montague, Enders, & Dietz, 2011; Pfannenstiel, Bryant, Bryant, & Porterfield, 2015)

Types of Metacognitive Strategies

Metacognitive strategies that help students plan, monitor, and modify their mathematical problem-solving include self-instruction and self-monitoring . Not only are these strategies relatively easy for students to implement, but they also help students to become better independent problem solvers.

Teaching Metacognitive Strategies

Teachers should use explicit instruction to help students understand how to use self-instruction and self-monitoring during the problem-solving process. To do this, teachers can:

• Example questions: What information is relevant? Have I solved a problem like this before?
• Example prompts: Identify the relevant information. Use a visual to solve the problem.
• Model working through a problem using “think alouds,” during which the teacher verbalizes her thoughts as she demonstrates using self-instruction and self-monitoring throughout the problem-solving process.
• Provide sufficient opportunities for students to practice these metacognitive strategies with corrective feedback.
• Encourage students to use these strategies independently, once they have achieved mastery.

Examples of Students Using Metacognitive Strategies

The videos below illustrate students using metacognitive strategies to solve mathematics problems. In the first video, in addition to self-instruction, an elementary student uses an age-appropriate self-monitoring checklist that includes visual cues for each step. Note that the student was explicitly taught how to use this checklist before using it to solve problems independently. In the second video, a high-school student uses self-instruction and self-monitoring to solve a word problem.

Elementary School Example (time: 1:49)

View Transcript

Transcript: Metacognative Strategies: Elementary School

Narrator: In this video, an elementary student uses metacognitive strategies while solving an addition problem. More specifically, he uses self-instruction and a self-monitoring checklist to guide himself through the problem-solving process. By doing so, he actively plans and monitors his work.

Student: I can’t figure out what 3 + 5 is. What is it? Well, let me look at my checklist. First, it says, “read the problem.” The problem says 3 + 5, so I’ve checked that. Now what is…now it says…my checklist says, “What is the problem asking?” It’s asking me to add 3 + 5.

Now, to draw a picture. One, two, three. One, two, three, four, five. Now it says, “Does my drawing match the problem?” Up here it says 3 + 5, so down here it says one, two, three, one, two, three, four, five. Now I have to solve it. So one, two, three, four, five, six, seven, eight. The answer to 3 + 5 is 8.

Click here to view the self-monitoring checklist used by the elementary student in the video above.

High School Example (time: 2:54)

Transcript: Metacognative Strategies: High School

Narrator : In this video, a high school student uses metacognitive strategies while solving a word problem. By using self-instruction and self-monitoring, she actively plans and monitors her work.

Student : First, I’m going to read the problem. “Mr. Smith, the principal, is standing on top of the high school. He is looking at a tree in the courtyard that is 30 feet away from the school. The angle from Mr. Smith’s feet to the base of the tree is 43 degrees. Using this information, determine the height of the high school.”

So what am I missing? The problem says that the angle from Mr. Smith’s feet to the base of the tree is 43 degrees. I’ve noticed that, if you connect this point to this point, we have a right triangle. So, while this angle is 43 degrees, this angle right here is a right angle that’s 90 degrees.

There’s a trick that I’m going to use that’s called SOHCAHTOA that you can use to find the sides and angles in a right triangle. The opposite side to 43 degrees is 30 feet, right here. So what do I need to find? I need to find the adjacent side. I’ll label it with an “A.” I look at SOHCAHTOA, and I know that I need to find the tangent, because tangent equals opposite over adjacent.

Now all I have to do is plug in the information that I have in order to find “A.” Tangent of 43 degrees, the angle, equals 30—that’s the opposite side—over “A.” And I find that 30 over 0.93 equals 32.25. So the height of this building is 32.25 feet.

Now that I’ve solved the problem, I ask does my answer make sense? Given the information from the problem, and with what I know about most buildings, 32 feet seems like a reasonable answer.

Diane Bryant discusses the importance of teaching students cognitive and metacognitive strategies and how they benefit students (time: 2:22).

Diane Pedrotty Bryant, PhD Project Director, Mathematics Institute for Learning Disabilities and Difficulties University of Texas at Austin

Transcript: Diane Pedrotty Bryant, PhD

It really is important to pair metacognitive strategies with cognitive strategies. Metacognitive strategies simply refers to thinking about thinking. It’s beneficial and certainly validated in research that they have a series of cognitive steps to employ to solve problems, whatever the problem might be. The metacognitive strategies help students think about what steps that they’re supposed to be using—that’s the self-instruction—and then pausing to check about whether they are indeed using those various cognitive strategies steps, which really refers to the self-monitoring. For students with mathematics learning disabilities, we want them to become independent learners and to use strategies for solving various problems and to be able to pause and ask themselves questions about how they’re proceeding and back up and check on a particular step. Through the use of cognitive strategies paired with metacognitive strategies, the goal is to empower them to be more independent learners, and that’s definitely something we strive for when we teach students with learning disabilities. I think that there is difficulty in students learning how to implement metacognitive strategies independently, because they may not know how to approach the learning task. They may not be aware of their own ability to self-monitor, to self-instruct, to use self-talk, self-verbalizations for tackling tasks. Usually students with mathematics learning disabilities really need to be taught to use metacognitive strategies and to learn the metacognitive strategies to mastery before being able to use them independently.

For Your Information

Although teachers can provide students with a generic list of questions or prompts to guide them through the problem-solving process, some students, such as those with mathematics difficulties and disabilities, might need more individualized support to address their specific learning challenges. The teacher can identify the student’s common error patterns by conducting an error analysis —a process by which instructors identify the types of errors made by students when working mathematical problems. Using this information, teachers can develop a list of questions or prompts that students can use to address their specific needs. To begin with, many of these students might require a self-monitoring checklist, such as the one below, to guide them through the problem-solving process.

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Metacognitive strategies improve learning

Metacognition refers to thinking about one's thinking and is a skill students can use as part of a broader collection of skills known as self-regulated learning. Metacognitive strategies for learning include planning and goal setting, monitoring, and reflecting on learning. Students can be instructed in the use of metacognitive strategies. Classroom interventions designed to improve students’ metacognitive approaches are associated with improved learning (Cogliano, 2021; Theobald, 2021).

Strategies to encourage students to use metacognitive techniques

• Prompt students to develop study plans and to evaluate their approaches to planning for, monitoring, and evaluating their learning. Early in the term, advise and support students in making a study plan. After receiving feedback on the first and subsequent assessments, ask students to reflect on their performance and determine which study strategies worked and which did not. Encourage them to revise their study plans if needed. One way to support this is to ask students to identify their personal learning environment .  This is an activity where students identify the various resources and support available to them.
• Offer practice tests. Explain to students the benefits of practice testing for improving retention and performance on exams. Create practice tests with an answer key to help students prepare for exams. Use practice questions for in-class formative feedback throughout the term. Consider creating a bank of practice questions from previous exams to share with students (Stanton, 2021).
• Call attention to strategies students can adopt to space their practice. This can include explaining the benefits of spaced practice and encouraging students to map out weekly study sessions for your course on their calendar. These study sessions should include the most recent material and revisit older material, perhaps in the form of practice tests (Stanton, 2021).
• Model your metacognitive processes with students. Show students the thinking process behind your approach to solving problems (Ambrose, 2010). This can take the form of a think-aloud where you talk through the steps you would take to plan, monitor, and reflect on your problem-solving approach.
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The Meta Model

The Meta Model Problem Solving Strategies

The Meta model is a model for changing our maps of the world. It provides a number of problem solving strategies. We cause many of our problems by our unconscious rule governed behavior.

We have problems not because the world isn’t rich enough, but because our maps aren’t. Alfred Korzybski’s work demonstrated that we don’t operate on the world directly but through our maps or models

This model is the foundation of NLP. It evolved from watching extraordinary therapists and the kinds of interactions they had with clients that got results.

Our nervous system deletes and distorts whole portions of reality in order to make the world manageable. Our maps determine our behavioral options by creating rules and programs for how we do things.

We delete information to avoid being overwhelmed. We don’t see all the choices we have available. We attend to our priorities and overlook other things that might be valuable.

We generalize information in order to summarize and synthesize. Dealing with categories is much less demanding than dealing with individual cases. For example, we talk about dogs as a category rather than all the individual dogs that we have met.

Lastly we distort information, for instance when we plan or visualize the future.

How we build the maps that control our behavior

We use three universal modeling processes to build our maps or models. The Meta model uses these three processes. Its terminology is from the field of linguistics and may seem quite strange.

Meta Model Deletions

We pay attention to some parts of our experiences and not others. The millions of sights, sounds, smells and feelings in the external environment and our internal world would overwhelm us if we didn’t delete most of them.

Deleting enables us for instance to talk on the phone in the middle of a crowded room. We tune in to what is important like hearing our name mentioned at a party. We are also deleting information when we think of ourselves as having limited choices. We often overlook problem-solving strategies that recover deleted choices.

Deletion Patterns

• Unspecified Nouns – Who or What
• Unspecified Verbs – Understanding the Process

Simple deletions

• Comparative deletions
• Ly Adverbs – Obviously this is Useful

Meta Model Generalizations

We categorize and summarize in order to manage our experience. We do this by choosing a representative experience, so one particular dog (real or a combination) will represent our category of dogs.

Generalizing enables us to transfer learning from one area to another. We learn the doorknob principle and use it to open doors we’ve never seen before.

Generalization Patterns

• Universal quantifiers – a Meta Model Generalization
• Modal operators – a Meta Model Generalization
• Complex equivalences – a Meta model generalization

The Meta Model Distortions

Our ability to distort experiences enables us to imagine new things and plan for the future. Distortion is useful in planning a trip, choosing new clothes and decorating a room.

On the other hand, distortions probably cause us the most problems. It can be limiting when we imagine negative events and become unresourceful. For instance, jealousy can be a response to imagining a partner being unfaithful and then responding as though it is real.

Distortion Patterns

• Nominalizations – Recipe for Misunderstanding
• Mind reading – Jumping to Conclusions
• Cause effects – How our world works
• Lost Performatives – Not my Beliefs
• Linguistic Presuppositions – Accepting What I Say

Using the Model

This model provides a way to recover deleted information, uncover our rules and untangle misunderstandings in our own and others’ communication. It is particularly useful in business communication where clear unambiguous directions can be critical.

Meta model questions

The model is the questions. By listening for how someone has created his or her maps, we can ask an appropriate question to recover what has been deleted, generalized or distorted. This then expands and enriches the person’s choices for solving the problem.

Further Reading: The Secrets of Magic by L.Michael Hall  reprinted as Communication Magic

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Ly adverbs – obviously this is useful, cause effects – how our world works, lost performatives – not my beliefs, mind reading – jumping to conclusions, complex equivalences – a meta model generalization, privacy overview.

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Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Introduction.

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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Metacognitive Study Strategies

Do you spend a lot of time studying but feel like your hard work doesn’t help your performance on exams? You may not realize that your study techniques, which may have worked in high school, don’t necessarily translate to how you’re expected to learn in college. But don’t worry—we’ll show you how to analyze your current strategies, see what’s working and what isn’t, and come up with new, more effective study techniques. To do this, we’ll introduce you to the idea of “metacognition,” tell you why metacognition helps you learn better, and introduce some strategies for incorporating metacognition into your studying.

What is metacognition and why should I care?

Metacognition is thinking about how you think and learn. The key to metacognition is asking yourself self-reflective questions, which are powerful because they allow us to take inventory of where we currently are (thinking about what we already know), how we learn (what is working and what is not), and where we want to be (accurately gauging if we’ve mastered the material). Metacognition helps you to be a self-aware problem solver and take control of your learning. By using metacognition when you study, you can be strategic about your approach. You will be able to take stock of what you already know, what you need to work on, and how best to approach learning new material.

Strategies for using metacognition when you study

Below are some ideas for how to engage in metacognition when you are studying. Think about which of these resonate with you and plan to incorporate them into your study routine on a regular basis.

Use your syllabus as a roadmap

Look at your syllabus. Your professor probably included a course schedule, reading list, learning objectives or something similar to give you a sense of how the course is structured. Use this as your roadmap for the course. For example, for a reading-based course, think about why your professor might have assigned the readings in this particular order. How do they connect? What are the key themes that you notice? What prior knowledge do you have that could inform your reading of this new material? You can do this at multiple points throughout the semester, as you gain additional knowledge that you can piece together.

Summon your prior knowledge

Before you read your textbook or attend a lecture, look at the topic that is covered and ask yourself what you know about it already. What questions do you have? What do you hope to learn? Answering these questions will give context to what you are learning and help you start building a framework for new knowledge. It may also help you engage more deeply with the material.

Think aloud

Talk through your material. You can talk to your classmates, your friends, a tutor, or even a pet. Just verbalizing your thoughts can help you make more sense of the material and internalize it more deeply. Talking aloud is a great way to test yourself on how well you really know the material. In courses that require problem solving, explaining the steps aloud will ensure you really understand them and expose any gaps in knowledge that you might have. Ask yourself questions about what you are doing and why.

Ask yourself questions

Asking self-reflective questions is key to metacognition. Take the time to be introspective and honest with yourself about your comprehension. Below are some suggestions for metacognitive questions you can ask yourself.

• Does this answer make sense given the information provided?
• What strategy did I use to solve this problem that was helpful?
• How does this information conflict with my prior understanding?
• How does this information relate to what we learned last week?
• What questions will I ask myself next time I’m working these types of problems?
• What is confusing about this topic?
• What are the relationships between these two concepts?
• What conclusions can I make?

Try brainstorming some of your own questions as well.

Use writing

Writing can help you organize your thoughts and assess what you know. Just like thinking aloud, writing can help you identify what you do and don’t know, and how you are thinking about the concepts that you’re learning. Write out what you know and what questions you have about the learning objectives for each topic you are learning.

Organize your thoughts

Using concept maps or graphic organizers is another great way to visualize material and see the connections between the various concepts you are learning. Creating your concept map from memory is also a great study strategy because it is a form of self-testing.

Take notes from memory

Many students take notes as they are reading. Often this can turn notetaking into a passive activity, since it can be easy to fall into just copying directly from the book without thinking about the material and putting your notes in your own words. Instead, try reading short sections at a time and pausing periodically to summarize what you read from memory. This technique ensures that you are actively engaging with the material as you are reading and taking notes, and it helps you better gauge how much you’re actually remembering from what you read; it also engages your recall, which makes it more likely you’ll be able to remember and understand the material when you’re done.

Review your exams

Reviewing an exam that you’ve recently taken is a great time to use metacognition. Look at what you knew and what you missed. Try using this handout to analyze your preparation for the exam and track the items you missed, along with the reasons that you missed them. Then take the time to fill in the areas you still have gaps and make a plan for how you might change your preparation next time.

Take a timeout

When you’re learning, it’s important to periodically take a time out to make sure you’re engaging in metacognitive strategies. We often can get so absorbed in “doing” that we don’t always think about the why behind what we are doing. For example, if you are working through a math problem, it’s helpful to pause as you go and think about why you are doing each step, and how you knew that it followed from the previous step. Throughout the semester, you should continue to take timeouts before, during or after assignments to see how what you’re doing relates to the course as a whole and to the learning objectives that your professor has set.

Test yourself

You don’t want your exam to be the first time you accurately assess how well you know the material. Self-testing should be an integral part of your study sessions so that have a clear understanding of what you do and don’t know. Many of the methods described are about self-testing (e.g., thinking aloud, using writing, taking notes from memory) because they help you discern what you do and don’t actually know. Other common methods include practice tests and flash cards—anything that asks you to summon your knowledge and check if it’s correct.

Figure out how you learn

It is important to figure out what learning strategies work best for you. It will probably vary depending on what type of material you are trying to learn (e.g. chemistry vs. history), but it will be helpful to be open to trying new things and paying attention to what is effective for you. If flash cards never help you, stop using them and try something else instead. Making an appointment with an academic coach at the Learning Center is a great chance to reflect on what you have been doing and figuring out what works best for you.

Works consulted

McGuire, S.Y. and McGuire, S. (2016). Teach Students How to Learn: Strategies You Can Incorporate in Any Course to Improve Student Metacognition, Study Skills, and Motivation. Sterling, Virginia: Stylus Publishing, LLC.

Centre for Innovation and Excellence in Learning. Ten Metacognitive Teaching Strategies. Vancouver Island University. Retrieved from https://ciel.viu.ca/sites/default/files/ten_metacognitive_teaching_strategies.docx

Anderson, J. (2017, May 09). A Stanford researcher’s 15-minute study hack lifts B+ students into the As. Quartz. Retrieved from https://qz.com/978273/a-stanford-professors-15-minute-study-hack-improves-test-grades-by-a-third-of-a-grade/

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Metacognitive Strategies and Development of Critical Thinking in Higher Education

Silvia f. rivas.

1 Departamento de Psicología Básica, Psicobiología y Metodología de CC, Facultad de Psicología, Universidad de Salamanca, Salamanca, Spain

Carlos Saiz

Carlos ossa.

2 Departamento de Ciencias de la Educación, Facultad de Educación y Humanidades, Universidad del Bío-Bío, Sede Chillán, Chile

Associated Data

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

More and more often, we hear that higher education should foment critical thinking. The new skills focus for university teaching grants a central role to critical thinking in new study plans; however, using these skills well requires a certain degree of conscientiousness and its regulation. Metacognition therefore plays a crucial role in developing critical thinking and consists of a person being aware of their own thinking processes in order to improve them for better knowledge acquisition. Critical thinking depends on these metacognitive mechanisms functioning well, being conscious of the processes, actions, and emotions in play, and thereby having the chance to understand what has not been done well and correcting it. Even when there is evidence of the relation between metacognitive processes and critical thinking, there are still few initiatives which seek to clarify which process determines which other one, or whether there is interdependence between both. What we present in this study is therefore an intervention proposal to develop critical thinking and meta knowledge skills. In this context, Problem-Based Learning is a useful tool to develop these skills in higher education. The ARDESOS-DIAPROVE program seeks to foment critical thinking via metacognition and Problem-Based Learning methodology. It is known that learning quality improves when students apply metacognition; it is also known that effective problem-solving depends not only on critical thinking, but also on the skill of realization, and of cognitive and non-cognitive regulation. The study presented hereinafter therefore has the fundamental objective of showing whether instruction in critical thinking (ARDESOS-DIAPROVE) influences students’ metacognitive processes. One consequence of this is that critical thinking improves with the use of metacognition. The sample was comprised of first-year psychology students at Public University of the North of Spain who were undergoing the aforementioned program; PENCRISAL was used to evaluate critical thinking skills and the Metacognitive Activities Inventory (MAI) for evaluating metacognition. We expected an increase in critical thinking scores and metacognition following this intervention. As a conclusion, we indicate actions to incentivize metacognitive work among participants, both individually via reflective questions and decision diagrams, and at the interactional level with dialogues and reflective debates which strengthen critical thinking.

Introduction

One of the principal objectives which education must cover is helping our students become autonomous and effective. Students’ ability to use strategies which help them direct their motivation toward action in the direction of the meta-proposal is a central aspect to keep at the front of our minds when considering education. This is where metacognition comes into play—knowledge about knowledge itself, a component which is in charge of directing, monitoring, regulating, organizing, and planning our skills in a helpful way, once these have come into operation. Metacognition helps form autonomous students, increasing consciousness about their own cognitive processes and their self-regulation so that they can regulate their own learning and transfer it to any area of their lives. As we see, it is a conscious activity of high-level thinking which allows us to look into and reflect upon how we learn and to control our own strategies and learning processes. We must therefore approach a problem which is increasing in our time, that of learning and knowledge from the perspective of active participation by students. To achieve these objectives of “learning to learn” we must use adequate cognitive learning strategies, among which we can highlight those oriented toward self-learning, developing metacognitive strategies, and critical thinking.

Metacognition is one of the research areas, which has contributed the most to the formation of the new conceptions of learning and teaching. In this sense, it has advanced within the constructivist conceptions of learning, which have attributed an increasing role to student consciousness and to the regulation which they exercise over their own learning ( Glaser, 1994 ).

Metacognition was initially introduced by John Flavell in the early 1970s. He affirmed that metacognition, on one side, refers to “the knowledge which one has about his own cognitive processes products, or any other matter related with them” and on the other, “to the active supervision and consequent regulation and organization of these processes in relation with the objects or cognitive data upon which they act” ( Flavell, 1976 ; p. 232). Based on this, we can differentiate two components of metacognition: one of a declarative nature, which is metacognitive knowledge, referring to knowledge of the person and the task, and another of a procedural nature, which is metacognitive control or self-regulated learning, which is always directed toward a goal and controlled by the learner.

Different authors have pointed out that metacognition presents these areas of thought or skills, aimed knowledge or toward the regulation of thought and action, mainly proposing a binary organization in which attentional processes are oriented, on occasions, toward an object or subject, and the other hand, toward to interact with objects and/or subjects ( Drigas and Mitsea, 2021 ). However, it is possible to understand metacognition from another approach that establishes more levels of use of metacognitive thinking to promote knowledge, awareness, and intelligence, known as the eight pillars of metacognition model ( Drigas and Mitsea, 2020 ). These pillars allow thought to promote the use of deep knowledge, cognitive processes, self-regulation, functional adaptation to society, pattern recognition and operations, and even meaningful memorization ( Drigas and Mitsea, 2020 ).

In addition to the above, Drigas and Mitsea’s model establishes different levels where metacognition could be used, in a complex sequence from stimuli to transcendental ideas, in which each of the pillars could manifest a different facet of the process metacognitive, thus establishing a dialectical and integrative approach to learning and knowledge, allowing it to be understood as an evolutionary and complex process in stages ( Drigas and Mitsea, 2021 ).

All this clarifies the importance of and need for metacognition, not only in education but also in our modern society, since this need to “teach how to learn” and the capacity to “learn how to learn” in order to achieve autonomous learning and transfer it to any area of our lives will let us face problems more successfully. This becomes a relevant challenge, especially today where it is required to have a broad view regarding reflection and consciousness, and to transcend simplistic and reductionist models that seek to center the problem of knowledge only around the neurobiological or the phenomenological scope ( Sattin et al., 2021 ).

Critical thinking depends largely on these mechanisms functioning well and being conscious of the processes used, since this gives us the opportunity to understand what has not been done well and correct it in the future. Consciousness for critical thinking would imply a continuous process of reuse of thought, in escalations that allow thinking to be oriented both toward the objects of the world and toward the subjective interior, allowing to determine the ideas that give greater security to the person, and in that perspective, the metacognitive process, represents this use of Awareness, also allowing the generation of an identity of knowing being ( Drigas and Mitsea, 2021 ).

We know that thinking critically involves reasoning and deciding to effectively solve a problem or reach goals. However, effective use of these skills requires a certain degree of consciousness and regulation of them. The ARDESOS-DIAPROVE program seeks precisely to foment critical thinking, in part, via metacognition ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, it is not only centered on developing cognitive components, as this would be an important limitation. Since the 1990s, it has been known that non-cognitive components play a crucial role in developing critical thinking. However, there are few studies focusing on this relation. This intervention therefore considers both dimensions, where metacognitive processes play an essential role by providing evaluation and control mechanisms over the cognitive dimension.

Metacognition and Critical Thinking

Critical Thinking is a concept without a firm consensus, as there have been and still are varying conceptions regarding it. Its nature is so complex that it is hard to synthesize all its aspects in a single definition. While there are numerous conceptions about critical thinking, it is necessary to be precise about which definition we will use. We understand that “ critical thinking is a knowledge-seeking process via reasoning skills to solve problems and make decisions which allows us to more effectively achieve our desired results” ( Saiz and Rivas, 2008 , p. 131). Thinking effectively is desirable in all areas of individual and collective action. Currently, the background of the present field of critical thinking is also based in argumentation. Reasoning is used as the fundamental basis for all activities labeled as thinking. In a way, thinking cannot easily be decoupled from reasoning, at least if our understanding of it is “deriving something from another thing.” Inference or judgment is what we essentially find behind the concept of thinking. The question, though, is whether it can be affirmed that thinking is only reasoning. Some defend this concept ( Johnson, 2008 ), while others believe the opposite, that solving problems and making decisions are activities which also form part of thinking processes ( Halpern, 2003 ; Halpern and Dunn, 2021 , 2022 ). To move forward in this sense, we will return to our previous definition. In that definition, we have specified intellectual activity with a goal intrinsic to all mental processes, namely, seeking knowledge. Achieving our ends depends not only on the intellectual dimension, as we may need our motor or perceptive activities, so it contributes little to affirm that critical thinking allows us to achieve our objectives as we can also achieve them by doing other activities. It is important for us to make an effort to identify the mental processes responsible for thinking and distinguish them from other things.

Normally, we think to solve our problems. This is the second important activity of thought. A problem can be solved by reasoning, but also by planning course of action or selecting the best strategy for the situation. Apart from reasoning, we must therefore also make decisions to resolve difficulties. Choosing is one of the most frequent and important activities which we do. Because of this, we prefer to give it the leading role it deserves in a definition of thinking. Solving problems demands multiple intellectual activities, including reasoning, deciding, planning, etc. The final characteristic goes beyond the mechanisms peculiar to inference. What can be seen at the moment of delineating what it means to think effectively is that concepts are grouped together which go beyond the nuclear ideas of what has to do with inferring or reasoning. The majority of theoreticians in the field ( APA, 1990 ; Ennis, 1996 ; Halpern, 1998 , 2003 ; Paul and Elder, 2001 ; Facione, 2011 ; Halpern and Dunn, 2021 , 2022 ) consider that, in order to carry out this type of thinking effectively, apart from having this skill set, the intervention of other types of components is necessary, such as metacognition and motivation. This is why we consider it necessary to speak about the components of critical thinking, as we can see in Figure 1 :

Components of critical thinking ( Saiz, 2020 ).

In the nature of thinking, there are two types of components: the cognitive and the non-cognitive. The former include perception, learning, and memory processes. Learning is any knowledge acquisition mechanism, the most important of which is thinking. The latter refer to motivation and interests (attitudes tend to be understood as dispositions, inclinations…something close to motives); with metacognition remaining as a process which shares cognitive and non-cognitive aspects as it incorporates aspects of both judgment (evaluation) and disposition (control/efficiency) about thoughts ( Azevedo, 2020 ; Shekhar and Rahnev, 2021 ). Both the cognitive and non-cognitive components are essential to improve critical thinking, as one component is incomplete without the other, that is, neither cognitive skills nor dispositions on their own suffice to train a person to think critically. In general, relations are bidirectional, although for didactic reasons only unidirectional relations appear in Figure 1 ( Rivas et al., 2017 ). This is because learning is a dynamic process which is subject to all types of influence. For instance, if a student is motivated, they will work more and better—or at least, this is what is hoped for. If they can achieve good test scores as well, it can be supposed that motivation is reinforced, so that they will continue existing behaviors in the same direction that is, working hard and well on their studies. This latter point appears to arise at least because of an adjustment between expectations and reality which the student achieves thanks to metacognition, which allows them to effectively attribute their achievements to their efforts ( Ugartetxea, 2001 ).

Metacognition, which is our interest in this paper, should also have bidirectional relations with critical thinking. Metacognition tends to be understood as the degree of consciousness which we have about our own mental processes and similar to the capacity for self-regulation, that is, planning and organization ( Mayor et al., 1993 ). We observe that these two ideas have very different natures. The former is simpler, being the degree of consciousness which we reach about an internal mechanism or process. The latter is a less precise idea, since everything which has to do with self-regulation is hard to differentiate from a way of understanding motivation, such as the entire tradition of intrinsic motivation and self-determination from Deci, his collaborators, and other authors of this focus (see, e.g., Deci and Ryan, 1985 ; Ryan and Deci, 2000 ). The important thing is to emphasize the executive dimension of metacognition, more than the degree of consciousness, for practical reasons. It can be expected that this dimension has a greater influence on the learning process than that of consciousness, although there is little doubt that we have to establish both as necessary and sufficient conditions. However, the data must speak in this regard. Due to all of this, and as we shall see hereinafter, the intervention designed incorporates both components to improve critical thinking skills.

We can observe, though, that the basic core of critical thinking continues to be topics related to skills, in our case, reasoning, problem-solving, and decision-making. The fact that we incorporate concepts of another nature, such as motivation, in a description of critical thinking is justified because it has been proven that, when speaking about critical thinking, the fact of centering solely on skills does not allow for fully gathering its complexity. The purpose of the schematic in Figure 2 is to provide conceptual clarity to the adjective “critical” in the expression critical thinking . If we understand critical to refer to effective , we should also consider that effectiveness is not, as previously mentioned, solely achieved with skills. They must be joined together with other mechanisms during different moments. Intellectual skills alone cannot achieve the effectiveness assumed within the term “critical.” First, for said skills to get underway, we must want to do so. Motivation therefore comes into play before skills and puts them into operation. For its part, metacognition allows us to take advantage of directing, organizing, and planning our skills and act once they have begun to work. Motivation thus activates our abilities, while metacognition lets them be more effective. The final objective should always be to gain proper knowledge of reality to resolve our problems.

Purpose of critical thinking ( Saiz, 2020 , p.27).

We consider that the fact of referring to components of critical thinking while differentiating the skills of motivation and metacognition aids with the conceptual clarification we seek. On one side, we specify the skills which we discuss, and on another, we mention which other components are related to, and even overlap with them. We must be conscious of how difficult it is to find “pure” mental processes. Planning a course of action, an essential trait of metacognition, demands reflection, prediction, choice, comparison, and evaluation… And this, evidently, is thinking. The different levels or dimensions of our mental activity must be related and integrated. Our aim is to be able to identify what is substantial in thinking to know what we are able to improve and evaluate.

It is widely known that for our personal and professional functioning, thinking is necessary and useful. When we want to change a situation or gain something, all our mental mechanisms go into motion. We perceive the situation, identify relevant aspects of the problem, analyze all the available information, and appraise everything we analyze. We make judgments about the most relevant matters, decide about the options or pathways for resolution, execute the plan, obtain results, evaluate the results, estimate whether we have achieved our purpose and, according to the level of satisfaction following this estimation, consider our course of action good, or not.

The topic we must pose now is what things are teachable. It is useful to specify that what is acquired is clearly cognitive and some of the non-cognitive, because motivation can be stimulated or promoted, but not taught. The concepts of knowledge and wisdom are its basis. Mental representation and knowledge only become wisdom when we can apply it to reality, when we take it out of our mind and adequately situate it in the world. For our teaching purposes, we only have to take a position about whether knowledge is what makes critical thinking develop, or vice versa. For us, skills must be directly taught, and dominion is secondary. Up to now, we have established the components of critical thinking, but these elements still have to be interrelated properly. What we normally find are skills or components placed side by side or overlapping, but not the ways in which they influence each other. Lipman (2003) may have developed the most complete theory of critical and creative thinking, along Paul and his group, in second place, with their universal thought structures ( Paul and Elder, 2006 ). However, a proposal for the relation between the elements is lacking.

To try to explain the relation between the components of thought, we will use Figure 2 as an aid.

The ultimate goal of critical thinking is change that is, passing from one state of wellbeing into a better state. This change is only the fruit of results, which must be the best. Effectiveness is simple achieving our goals in the best way possible. There are many possible results, but for our ends, there are always some which are better than others. Our position must be for effectiveness, the best response, the best solution. Reaching a goal is resolving or achieving something, and for this, we have mechanisms available which tell us which are the best course of action. Making decisions and solving problems are fundamental skills which are mutually interrelated. Decision strategies come before a solution. Choosing a course of action always comes before its execution, so it is easy to understand that decisions contribute to solutions.

Decisions must not come before reflection, although this often can and does happen. As we have already mentioned, the fundamental skills of critical thinking, in most cases, have been reduced to reasoning, and to a certain degree, this is justified. There is an entire important epistemological current behind this, within which the theory of argumentation makes no distinction, at least syntactically, between argumentation and explanation. However, for us this distinction is essential, especially in practice ( Saiz, 2020 ). We will only center on an essential difference for our purpose. Argumentation may have to do with values and realities, but explanation only has to do with the latter. We can argue about beliefs, convictions, and facts, but we can only explain realities. Faced with an explanation of reality, any argumentation would be secondary. Thus, explanation will always be the central skill in critical thinking.

The change which is sought is always expressed in reality. Problems always are manifested and resolved with actions, and these are always a reality. An argument about realities aids in explaining them. An argument about values upholds a belief or a conviction. However, beliefs always influence behavior; thus, indirectly, the argument winds up being about realities. One may argue, for example, only for or against the death penalty, and reach the conviction that it is good or bad and ultimately take a position for or against allowing it. This is why we say that deciding always comes before resolving; furthermore, resolution always means deciding about something in a particular direction—it always means choosing and taking an option; furthermore, deciding is often only from two possibilities, the better or that which is not better, or which is not as good. Decisions are made based on the best option possible of all those which can be presented. Resolution is a dichotomy. Since our basic end lies within reality, explanation must be constituted as the basic pillar to produce change. Argumentation must therefore be at the service of causality (explanation), and both must be in the service of solid decisions leading us to the best solution or change of situation. We now believe that the relation established in Figure 2 can be better understood. From this relation, we propose that thinking critically means reaching the best explanation for an event, phenomenon, or problem in order to know how to effectively resolve it ( Saiz, 2017 , p.19). This idea, to our judgment, is the best summary of the nature of critical thinking. It clarifies details and makes explicit the components of critical thinking.

Classroom Activities to Develop Metacognition

We will present a set of strategies to promote metacognitive work in the classroom in this section, aimed at improving critical thinking skills. These strategies can be applied both at the university level and the secondary school level; we will thus focus on these two levels, although metacognitive strategies can be worked on from an earlier age ( Jaramillo and Osses, 2012 ; Tamayo-Alzate et al., 2019 ) and some authors have indicated that psychological maturity has a greater impact on effectively achieving metacognition ( Sastre-Riba, 2012 ; García et al., 2016 ).

At the individual level, metacognition can be worked on via applying questions aimed at the relevant tasks which must be undertaken regarding a task (meta-knowledge questions), for example:

• Do I know how much I know about this subject?
• Do I have clear instructions and know what action is expected from me?
• How much time do I have?
• Am I covering the proper and necessary subjects, or is there anything important left out?
• How do I know that my work is right?
• Have I covered every point of the rubric for the work to gain a good grade or a sufficient level?

These reflective questions facilitate supervising knowledge level, resource use, and the final product achieved, so that the decisions taken for said activities are the best and excellent learning results are achieved.

Graphs or decision diagrams can also be used to aid in organizing these questions during the different phases of executing a task (planning, progress, and final evaluation), which is clearly linked with the knowledge and control processes of metacognition ( Mateos, 2001 ). These diagrams are more complex and elaborate strategies than the questions, but are effective when monitoring the steps considered in the activity ( Ossa et al., 2016 ). Decision diagrams begin from a question or task, detailing the principal steps to take, and associating an alternative (YES or NO) to each step, which leads to the next step whenever the decision is affirmative, or to improve or go further into the step taken if the decision is negative.

Finally, we can work on thinking aloud, a strategy which facilitates making the thoughts explicit and conscious, allowing us to monitor their knowledge, decisions, and actions to promote conscious planning, supervision and evaluation ( Ávila et al., 2017 ; Dahik et al., 2019 ). For example:

• While asking a question, the student thinks aloud: I am having problems with this part of the task, and I may have to ask the teacher to know whether I am right.

Thinking aloud can be done individually or in pairs, allowing for active monitoring of decisions and questions arising from cognitive and procedural work done by the student.

Apart from the preceding strategies, it is also possible to fortify metacognitive development via personal interactions based on dialogue between both the students themselves and between the teacher and individual students. One initial strategy, similar to thinking out loud in pairs, is reflective dialogue between teacher and student, a technique which allows for exchanging deep questions and answers, where the student becomes conscious of their knowledge and practice thanks to dialogical interventions by the teacher ( Urdaneta, 2014 ).

Reflective dialogue can also be done via reflective feedback implemented by the teacher for the students to learn by themselves about the positive and negative aspects of their performance on a task.

Finally, another activity based on dialogue and interaction is related to metacognitive argumentation ( Sánchez-Castaño et al., 2015 ), a strategy which uses argumentative resources to establish a valid argumentative structure to facilitate responding to a question or applying it to a debate. While argumentative analysis is based on logic and the search for solid reasons, these can have higher or lower confidence and reliability as a function of the data which they provide. Thus, if a reflective argumentative process is performed, via questioning reasons or identifying counterarguments, there is more depth and density in the argumentative structure, achieving greater confidence and validity.

We can note that metacognition development strategies are based on reflective capacity, which allow thought to repeatedly review information and decisions to consider, without immediately taking sides or being carried away by superficial or biased ideas or data. Critical thought benefits strongly from applying this reflective process, which guides both data management and cognitive process use. These strategies can also be developed in various formats (written, graphic, oral, individual, and dialogical), providing teachers a wide range of tools to strengthen learning and thinking.

Metacognitive Strategies to Improve Critical Thinking

In this section, we will describe the fundamental metacognitive strategies addressed in our critical thinking skills development program ARDESOS-DIAPROVE.

First, one of the active learning methodologies applied is Problem-Based Learning (PBL). This pedagogical strategy is student-centered and encourages autonomous and participative learning, orienting students toward more active and decisive learning. In PBL each situation must be approached as a problem-solving task, making it necessary to investigate, understand, interpret, reason, decide, and resolve. It is presented as a methodology which facilitates joint knowledge acquisition and skill learning. It is also good for working on daily problems via relevant situations, considerably reducing the distance between learning context and personal/professional life and aiding the connection between theory and practice, which promote the highly desired transference. It favors organization and the capacity to decide about problem-solving, which also improves performance and knowledge about the students’ own learning processes. Because of all this, this methodology aids in reflection and analysis processes, which in turn promotes metacognitive skill development.

The procedure which we carried out in the classroom with all the activities is based on the philosophy of gradual learning control transference ( Mateos, 2001 ). During instruction, the teacher takes on the role of model and guide for students’ cognitive and metacognitive activity, gradually bringing them into participating in an increasing level of competency, and slowly withdrawing support in order to attain control over the students’ learning process. This methodology develops in four phases: (1) explicit instruction, where the teacher directly explains the skills which will be worked on; (2) guided practice, where the teacher acts as a collaborator to guide and aid students in self-regulation; and (3) cooperative practice, where cooperative group work facilitates interaction with a peer group collaborating to resolve the problem. By explaining, elaborating, and justifying their own points of view and alternative solutions, greater consciousness, reflection, and control over their own cognitive processes is promoted. Finally, (4) individual practice is what allows students to place their learning into practice in individual evaluation tasks.

Regarding the tasks, it is important to highlight that the activities must be aimed not only at acquiring declarative knowledge, but also at procedural knowledge. The objective of practical tasks, apart from developing fundamental knowledge, is to develop CT skills among students in both comprehension and expression in order to favor their learning and its transference. The problems used must be common situations, close to our students’ reality. The important thing in our task of teaching critical thinking is its usefulness to our students, which can only be achieved during application since we only know something when we are capable of applying it. We are not interested in students merely developing critical skills; they must also be able to generalize their intellectual skills, for which they must perceive them as useful in order to want to acquire them. Finally, they will have to actively participate to apply them to solving problems. Furthermore, if we study the different ways of reasoning without context, via overly academic problems, their application to the personal sphere becomes impossible, leading them to be considered hardly useful. This makes it important to contextualize skills within everyday problems or situations which help us get students to use them regularly and understand their usefulness.

Reflecting on how one carries things out in practice and analyzing mistakes are ways to encourage success and autonomy in learning. These self-regulation strategies are the properly metacognitive part of our study. The teacher has various resources to increase these strategies, particularly feedback oriented toward task resolution. Similarly, one of the most effective instruments to achieve it is using rubrics, a central tool for our methodology. These guides, used in student performance evaluations, describe the specific characteristics of a task at various performance levels, in order to clarify expectations for students’ work, evaluate their execution, and facilitate feedback. This type of technique also allows students to direct their own activity. We use them with this double goal in mind; on the one hand, they aid students in carrying out tasks, since they help divide the complex tasks they have to do into simpler jobs, and on the other, they help evaluate the task. Rubrics guide students in the skills and knowledge they need to acquire as well as facilitating self-evaluation, thereby favoring responsibility in their learning. Task rubrics are also the guide for evaluation which teachers carry out in classrooms, where they specify, review, and correctly resolve the tasks which students do according to the rubric criteria. Providing complete feedback to students is a crucial aspect for the learning process. Thus, in all sessions time is dedicated to carrying it out. This is what will allow them to move ahead in self-regulated skill learning.

According to what we have seen, there is a wide range of positions when it comes to defining critical thinking. However, there is consensus in the fact that critical thinking involves cognitive, attitudinal, and metacognitive components, which together favor proper performance in critical thinking ( Ennis, 1987 ; Facione, 1990 ). This important relation between metacognition and critical thinking has been widely studied in the literature ( Berardi-Coletta et al., 1995 ; Antonietti et al., 2000 ; Kuhn and Dean, 2004 ; Black, 2005 ; Coutinho et al., 2005 ; Orion and Kali, 2005 ; Schroyens, 2005 ; Akama, 2006 ; Choy and Cheah, 2009 ; Magno, 2010 ; Arslan, 2014 ) although not always in an applied way. Field studies indicate the existence of relations between teaching metacognitive strategies and progress in students’ higher-order thinking processes ( Schraw, 1998 ; Kramarski et al., 2002 ; Van der Stel and Veenman, 2010 ). Metacognition is thus considered one of the most relevant predictors of achieving a complex higher-order thought process.

Along the same lines, different studies show the importance of developing metacognitive skills among students as it is related not only with developing critical thinking, but also with academic achievement and self-regulated learning ( Klimenko and Alvares, 2009 ; Magno, 2010 ; Doganay and Demir, 2011 ; Özsoy, 2011 ). Klimenko and Alvares (2009) indicated that one way for students to acquire necessary tools to encourage autonomous learning is making cognitive and metacognitive strategies explicit and well-used and that teachers’ role is to be mediators and guides. Inspite of this evidence, there is less research about the use of metacognitive strategies in encouraging critical thinking. The principal reason is probably that it is methodologically difficult to gather direct data about active metacognitive processes which are complex by nature. Self-reporting is also still very common in metacognition evaluation, and there are few studies which have included objective measurements aiding in methodological precision for evaluating metacognition.

However, in recent years, greater importance has been assigned to teaching metacognitive skills in the educational system, as they aid students in developing higher-order thinking processes and improving their academic success ( Flavell, 2004 ; Larkin, 2009 ). Because of this, classrooms have seen teaching and learning strategies emphasizing metacognitive knowledge and regulation. Returning to our objective, which is to improve critical thinking via the ARDESOS-DIAPROVE program, we have achieved our goal in an acceptable way ( Saiz and Rivas, 2011 , 2012 , 2016 ).

However, we need to know which specific factors contribute to this improvement. We have covered significant ground through different studies, one of which we present here. In this one, we attempt to find out the role of metacognition in critical thinking. This is the central objective of the study. Our program includes motivational and metacognitive variables. Therefore, we seek to find out whether metacognition improves after this instruction program focused on metacognition. Therefore, our hypothesis is simple: we expect that the lesson will improve our students’ metacognition. The idea is to know whether applying metacognition helps us achieve improved critical thinking and whether after this change metaknowledge itself improves. In other words, improved critical thinking performance will make us think better about thinking processes themselves. If this can be improved, we can expect that in the future it will have a greater influence on critical thinking. The idea is to be able to demonstrate that applying specifically metacognitive techniques, the processes themselves will subsequently improve in quality and therefore contribute better volume and quality to reasoning tasks, decision-making and problem-solving.

Materials and Methods

Participants.

In the present study, we used a sample of 89 students in a first-year psychology course at Public University of the North of Spain. 82% (73) were women, and the other 18% (16) were men. Participants’ median age was 18.93 ( SD 1.744).

Instruments

Critical thinking test.

To measure critical thinking skills, we applied the PENCRISAL test ( Saiz and Rivas, 2008 ; Rivas and Saiz, 2012 ). The PENCRISAL is a battery consisting of 35 production problem situations with an open-answer format, composed of five factors: Deductive Reasoning , Inductive Reasoning , Practical Reasoning , Decision-Making , and Problem-Solving , with seven items per factor. Items for each factor gather the most representative structures of fundamental critical thinking skills.

The items’ format is open, so that the person has to answer a concrete question, adding a justification for the reasons behind their answer. Because of this, there are standardized correction criteria assigning values between 0 and 2 points as a function of answer quality. This test offers us a total score of critical thinking skills and another five scores referring to the five factors. The value range is located between 0 and 72 points as a maximum limit for total test scoring, and between 0 and 14 for each of the five scales. The reliability measures present adequate precision levels according to the scoring procedures, with the lowest Cronbach’s alpha values at 0.632, and the test–retest correlation at 0.786 ( Rivas and Saiz, 2012 ). PENCRISAL administration was done over the Internet via the evaluation platform SelectSurvey.NET V5: http://24.selectsurvey.net/pensamiento-critico/Login.aspx .

Metacognitive Skill Inventory

Metacognitive skill evaluation was done via the metacognitive awareness inventory from Schraw and Dennison (1994) (MAI; Huertas Bustos et al., 2014 ). This questionnaire has 52 Likert scale-type items with five points. The items are distributed in two general dimensions: cognitive knowledge (C) and regulation of cognition (R). This provides ample coverage for the two aforementioned ideas about metaknowledge. There are also eight defined subcategories within each general dimension. For C, these are: declarative knowledge (DK), procedural knowledge (PK), and conditional knowledge (CK). In R, we find: organization (O), monitoring (M), and evaluation (E). This instrument comprehensively, and fairly clearly, brings together essential aspects of metacognition. On one side, there is the level of consciousness, containing types of knowledge—declarative, procedural, and strategic. On the other, it considers everything important in the processes of self-regulation, planning, organization, direction or control (monitoring), adjustment (troubleshooting), and considering the results achieved (evaluation). It provides a very complete vision of everything important in this dimension. Cronbach’s alpha for this instrument is 0.94, showing good internal consistency.

Intervention Program

As previously mentioned, in this study, we applied the third version of the ARDESOS_DIAPROVE program ( Saiz and Rivas, 2016 ; Saiz, 2020 ), with the objective of improving thinking skills. This program is centered on directly teaching the skills which we consider essential to develop critical thinking and for proper performance in our daily affairs. For this, we must use reasoning and good problem-solving and decision-making strategies, with one of the most fundamental parts of our intervention being the use of everyday situations to develop these abilities.

DIAPROVE methodology incorporates three new and essential aspects: developing observation, the combined use of facts and deduction, and effective management of de-confirmation procedures, or discarding hypotheses. These are the foundation of our teaching, which requires specific teaching–learning techniques.

The intervention took place over 16 weeks and is designed to be applied in classrooms over a timeframe of 55–60 h. The program is applied in classes of around 30–35 students divided into groups of four for classwork in collaborative groups, and organized into six activity blocks: (1) nature of critical thinking, (2) problem-solving and effectiveness, (3) explanation and causality, (4) deduction and explanation, (5) argumentation and deduction, and (6) problem-solving and decision-making. These blocks are assembled maintaining homogeneity, facilitating a global integrated skill focus which helps form comprehension and use of the different structures in any situation as well as a greater degree of ability within the domain of each skill.

Our program made an integrated use of problem-based learning (PBL) and cooperative learning (CL) as didactic teaching and learning strategies in the critical thinking program. These methodologies jointly exert a positive influence on the students, allowing them to participate more actively in the learning process, achieve better results in contextualizing content and developing skills and abilities for problem-solving, and improve motivation.

To carry out our methodology in the classrooms, we have designed a teaching system aligned with these directives. Two types of tasks are done: (1) comprehension and (2) production. The materials we used to carry out these activities are the same for all the program blocks. One key element in our aim of teaching how to think critically must be its usefulness to our students, which is only achieved through application. This makes it important to contextualize reasoning types within common situations or problems, aiding students to use them regularly and understand their usefulness. Our intention with the materials we use is to face the problems of transference, usefulness, integrated skills, and how to produce these things. Accordingly, the materials used for the tasks are: (1) common situations and (2) professional/personal problems.

The tasks which the students perform take place over a week. They work in cooperative groups in class, and then review, correct, and clarify together, promoting reflection on their achievements and errors, which fortifies metacognition. Students get the necessary feedback on the work performed which will help them progressively acquire fundamental procedural contents. Our goal here is that students become conscious of their own thought processes in order to improve them. In this way, via the dialogue achieved between teachers and students as well as between the students themselves in their cooperative work, metacognition is developed. For conscious performance of tasks, the students will receive rubrics for each and every task to guide them in their completion.

Application of the ARDESOS-DIAPROVE program was done across a semester in the Psychology Department of the Public University of the North of Spain. One week before teaching began; critical thinking and metacognition evaluations were done. This was also done 1 week after the intervention ended, in order to gather the second measurement for PENCRISAL and MAI. The timelapse between the pre-treatment and post-treatment measurements was 4 months. The intervention was done by instructors with training and good experience in the program.

To test our objective, we used a quasi-experimental pre-post design with repeated measurements.

Statistical Analysis

For statistical analysis, we used the IBM SPSS Statistics 26 statistical packet. The statistical tools and techniques used were: frequency and percentage tables for qualitative variables, exploratory and descriptive analysis of quantitative variables with a goodness of fit test to the normal Gaussian model, habitual descriptive statistics (median, SD, etc.) for numerical variables, and Student’s t -tests for significance of difference.

To begin, a descriptive analysis of the study variables was carried out. Tables 1 , ​ ,2 2 present the summary of descriptions for the scores obtained by students in the sample, as well as the asymmetry and kurtosis coefficients for their distribution.

Description of critical thinking measurement (PENCRISAL).

TOT_PRE, PENCRISAL pre-test; RD_PRE, Deductive reasoning pre-test; RI_PRE, Inductive reasoning pre-test; RP_PRE, Practical reasoning pre-test; TD_PRE, Decision making pre-test; SP_PRE, Problem solving pre-test; TOT_POST, PENCRISAL post-test; RD_ POST, Deductive reasoning post-test; RI_ POST, Inductive reasoning post-test; RP_ POST, Practical reasoning post-test; TD_ POST, Decision making post-test; SP_ POST, Problem solving post-test; Min, minimum, Max, maximum, Asym, asymmetry; and Kurt, kurtosis.

Description of metacognition measurement (MAI).

TOT_MAI_PRE, MAI pre-test; Decla_PRE, Declarative pre-test; Proce_PRE, Procedural pre-test; Condi_PRE, Conditional pre-test; CONO_PRE, Knowledge pre-test; Plani_PRE, Planning pre-test; Orga_PRE, Organization pre-test; Moni_PRE, Monitoring pre-test; Depu_PRE, Troubleshooting pre-test; Eva_PRE, Evaluation pre-test; REGU_PRE, Regulation pre-test; TOT_MAI_POST, MAI post-test; Decla_ POST, Declarative post-test; Proce_ POST, Procedural post-test; Condi_ POST, Conditional post-test; CONO_ POST, Knowledge post-test; Plani_ POST, Planning post-test; Orga_POST, Organization post-test; Moni_ POST, Monitoring post-test; Depu_ POST, Troubleshooting post-test; Eva_ POST, Evaluation post-test; and REGU_ POST, Regulation post-test;

As we see in the description of all study variables, the evidence is that the majority of them adequately fit the normal model, although some present significant deviations which can be explained by sample size.

Next, to verify whether there were significant differences in the metacognition variable based on measurements before and after the intervention, we contrasted medians for samples related with Student’s t -test (see Table 3 ).

Comparison of the METAKNOWLEDGE variable as a function of PRE-POST measurements.

The results show that there are significant differences in the metaknowledge scale total and in most of its dimensions, where all the post medians for both the scale overall and for the three dimensions of the knowledge factor (declarative, procedural, and conditional) are higher than the pre-medians. However, in the cognition regulation dimension, there are only significant differences in the total and in the planning, organization, and monitoring dimensions. The medians are also greater in the post-test than the pre-test. However, the troubleshooting and evaluation dimensions do not differ significantly after intervention.

Finally, for critical thinking skills, the results show significant differences in the scale total and in the five factors regarding the measurement time, where performance medians rise after intervention (see Table 4 ).

Comparison of the CRITICAL THINKING variable as a function of PRE-POST measurements.

These results show how metacognition improves due to CT intervention, as well as how critical thinking also improves with metacognitive intervention and CT skills intervention. Thus, it improves how people think about thinking as well as about the results achieved, since metacognition supports decision-making and final evaluation about proper strategies to solve problems.

Discussion and Conclusions

The general aim of our study was to know whether a critical thinking intervention program can also influence metacognitive processes. We know that our teaching methodology improves cross-sectional skills in argumentation, explanation, decision-making, and problem-solving, but we do not know if this intervention also directly or indirectly influences metacognition. In our study, we sought to shed light on this little-known point. If we bear in mind the centrality of how we think about thinking for our cognitive machinery to function properly and reach the best results possible in the problems we face, it is hard to understand the lack of attention given to this theme in other research. Our study aimed to remedy this deficiency somewhat.

As said in the introduction, metacognition has to do with consciousness, planning, and regulation of our activities. These mechanisms, as understood by many authors, have a blended cognitive and non-cognitive nature, which is a conceptual imprecision; what is known, though, is the enormous influence they exert on fundamental thinking processes. However, there is a large knowledge gap about the factors which make metacognition itself improve. This second research lacuna is what we have partly aimed to shrink here as well with this study. Our guide has been the idea of knowing how to improve metacognition from a teaching initiative and from the improvement of fundamental critical thinking skills.

Our study has shed light in both directions, albeit in a modest way, since its design does not allow us to unequivocally discern some of the results obtained. However, we believe that the data provide relevant information to know more about existing relations between skills and metacognition, something which has seen little contrast. These results allow us to better describe these relations, guiding the design of future studies which can better discern their roles. Our data have shown that this relation is bidirectional, so that metacognition improves thinking skills and vice versa. It remains to establish a sequence of independent factors to avoid this confusion, something which the present study has aided with to be able to design future research in this area.

As the results show, total differences in almost all metaknowledge dimensions are higher after intervention; specifically, we see how in the knowledge factor the declarative, procedural, and conditional dimensions improve in post-measurements. This improvement moves in the direction we predicted. However, the cognitive regulation dimension only shows differences in the total, and in the planning, organization, and regulation dimensions. We can see how the declarative knowledge dimensions are more sensitive than the procedural ones to change, and within the latter, the dimensions over which we have more control are also more sensitive. With troubleshooting and evaluation, no changes are seen after intervention. We may interpret this lack of effects as being due to how everything referring to evaluating results is highly determined by calibration capacity, which is influenced by personality factors not considered in our study. Regarding critical thinking, we found differences in all its dimensions, with higher scores following intervention. We can tentatively state that this improved performance can be influenced not only by interventions, but also by the metacognitive improvement observed, although our study was incapable of separating these two factors, and merely established their relation.

As we know, when people think about thinking they can always increase their critical thinking performance. Being conscious of the mechanisms used in problem-solving and decision-making always contributes to improving their execution. However, we need to go into other topics to identify the specific determinants of these effects. Does performance improve because skills are metacognitively benefited? If so, how? Is it only the levels of consciousness which aid in regulating and planning execution, or do other factors also have to participate? What level of thinking skills can be beneficial for metacognition? At what skill level does this metacognitive change happen? And finally, we know that teaching is always metacognitive to the extent that it helps us know how to proceed with sufficient clarity, but does performance level modify consciousness or regulation level of our action? Do bad results paralyze metacognitive activity while good ones stimulate it? Ultimately, all of these open questions are the future implications which our current study has suggested. We believe them to be exciting and necessary challenges, which must be faced sooner rather than later. Finally, we cannot forget the implications derived from specific metacognitive instruction, as presented at the start of this study. An intervention of this type should also help us partially answer the aforementioned questions, as we cannot obviate what can be modified or changed by direct metacognition instruction.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SR and CS contributed to the conception and design of the study. SR organized the database, performed the statistical analysis, and wrote the first draft of the manuscript. SR, CS, and CO wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

This study was partly financed by the Project FONDECYT no. 11220056 ANID-Chile.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Ready for Meta Connect 2024? Here’s what expecting to see

Llama, AR and chatbots

Meta Quest 3S

Orion ar glasses prototype, ai chatbots and avatars, the meta horizon ecosystem.

Meta has just announced that its annual Meta Connect conference will take place in its usual venue at Menlo Park between September 25-26, 2024. The event, now in its third year, is expected to showcase Meta's latest advancements in AI , VR, and the expanding vision of the Metaverse.

It has been a big. year for Meta in the AI space, ramping up launches of its own dedicated MetaAI chatbot , new versions of its Llama family of large language models and integration of its various generative AI models into Instagram, the Quest and WhatsApp.

More of this is expected at Meta Connect. Registrations for the event are already open online, with options to attend both in public at Menlo Park and online via Facebook and Horizon Worlds. 

But with several major hardware and software announcements competing for time during this year’s event, you’re probably wondering what’s really on the cards. Read on for a list of potential developments that we can expect from Meta Connect, based on various rumors, leaks, and press releases circulating online.

How to attend Meta Connect 2024

Meta Connect is Meta's flagship technology conference, bringing together developers, creators, and tech enthusiasts to explore the company's vision for the future. Meta Connect 2024 will be a hybrid event, with an in-person component at Meta's headquarters in Menlo Park as well as a virtual livestream accessible worldwide through Facebook and Horizon Worlds.

As usual, registration for Connect 2024 is available free of charge on Meta’s official website . Once signed up on the website, you will receive regular updates and announcements leading up to the actual event on September 25 and 26. 

When it’s time to attend, you can follow along with the conference using one of the below options:

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• Join the event IRL at Meta’s campus in Menlo Park, California.
• Follow along virtually, in real-time or on-demand, using the @MetaforDevelopers Facebook Page .
• View the entire live keynote in VR using the Horizon Worlds app on your Meta Quest headset. (Make sure you’re in the list of 13 supported countries where Horizon Worlds is available, first.)

What to expect from Meta Connect this year

Meta Connect 2024 comes at a pivotal time as Meta doubles down on AI while facing increased competition in the XR space from rivals like Apple. 

One of the most anticipated announcements at Meta Connect 2024 is the potential unveiling of the Meta Quest 3S, a more affordable version of the Quest 3 VR headset launched last year by Meta. A recent store listing leak hinted at the existence of this "lite" model even though Meta has not officially confirmed it.

The Quest 3S could help boost adoption of Meta's VR ecosystem by lowering the entry price point while still offering immersive VR experiences. Key specs and features remain to be seen, but given the lower price tag, you can expect some tradeoffs compared to the higher-end Quest 3.

This is also likely to come complete with Llama 3.1, Meta's powerful new family of artificial intelligence models offering deeper AI assistant integration and vision features similar to those we've seen in the Ray-Ban smart glasses.

Meta Connect 2024 may give us our first real glimpse at Meta's long-awaited augmented reality glasses, codenamed Project Orion. Unlike the Ray-Ban Stories smart glasses which mainly focused on audio and camera functionality, the Orion AR glasses are expected to offer true visual AR experiences with digital overlays integrated into the real-world view.

Meta has been developing the Orion glasses for nearly a decade, representing a significant leap in AR technology. While a finished consumer product is still likely a few years away, Meta may demo an early prototype to showcase its progress and get creators started building AR apps for the platform.

Again, this is a technology made possible thanks to integration with artificial intelligence models like Llama 3.1, bringing rapid processing of data to the device. It will also utilize vision models from Meta to identify real-world objects and places.

Expect a major focus on artificial intelligence at Meta Connect 2024, as Meta doubles down on AI across its products and platforms. Meta previewed some of its AI advancements earlier this year, like an AI assistant for WhatsApp, Instagram and Quest, as well as AI tools for image editing and avatar creation.

At the event, Meta will likely dive deeper into its conversational AI technology. It would be particularly exciting to see a deeper dive into AI Studio, Meta’s latest platform for creating no-code AI chatbots without writing a line of code.

We will also see a focus on the Llama family of models, and while it is an outside chance this could include the first look at a multimodal version of Llama or the opening up of Meta's image, video and audio generation models.

Of course, Meta Connect would not be complete without significant updates on the company's progress in building the metaverse. Expect Mark Zuckerberg's keynote to lay out Meta's vision for the metaverse over the next year and beyond.

We'll likely get a status update on Horizon Worlds, Meta's social VR platform, which aims to integrate avatars, inventory, and identity across different virtual experiences. Meta may announce new tools and standards to help developers create more interoperable metaverse content.

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Ritoban Mukherjee is a freelance journalist from West Bengal, India whose work on cloud storage, web hosting, and a range of other topics has been published on Tom's Guide, TechRadar, Creative Bloq, IT Pro, Gizmodo, Medium, and Mental Floss.

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