literature review on student performance

• DOI: 10.4236/OALIB.1107423
• Corpus ID: 236395944

A Literature Review of Academic Performance, an Insight into Factors and their Influences on Academic Outcomes of Students at Senior High Schools

• E. Brew , Benjamin Nketiah , R. Koranteng
• Published in OALib 1 June 2021

24 Citations

Key influences on students' academic success: insights from scholarly research, relationship between teachers’ attitude towards curriculum and students’ academic achievement at secondary school level, exploring college academic performance of k to12 it and non-it-related strands to reduce academic deficiencies, an assessment of teacher motivation in determining academic performance of secondary school learners in kenya, parental involvement and students' engagement in modular learning environments, factors affecting learners’ academic performance in selected districts, division of gingoog city, the influence of social support on academic performance: the mediating role of cognitive engagement, teaching performance, student self-efficacy and creativity fostering teacher: a structural model on academic success of students, exploring the impact of quality education management on pupils’ academic performance. a case study of basic schools in ghana, relationship between digital capabilities and academic performance: the mediating effect of self-efficacy, 77 references, library use and students academic achievement: implication for counseling, what factors determine academic achievement in high achieving undergraduate medical students a qualitative study, students’ views about secondary school science lessons: the role of practical work, factors affecting female students' academic achievement at bahir dar university (special issue : study results of the africa-asia university dialogue for educational development network second phase), effects of availability and use of laboratories on students performance in science subjects in community secondary schools, e-textbooks usage by students at andrews university: a study of attitudes, perceptions, and behaviors, students’ failure in academic environment☆, e‐textbooks and students’ learning experiences, homework type, parental occupational status and academic performance of primary school pupils in english and mathematics in ijebu north local government, ogun state, nigeria, textbook research in mathematics education: development status and directions, related papers.

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Analysing the Impact of Social Media on Students’ Academic Performance: A Comparative Study of Extraversion and Introversion Personality

Sourabh sharma.

International Management Institute (IMI), Bhubaneswar, India

Ramesh Behl

Associated data.

Complete data and material is available to support transparency.

The advent of technology in education has seen a revolutionary change in the teaching–learning process. Social media is one such invention which has a major impact on students’ academic performance. This research analyzed the impact of social media on the academic performance of extraversion and introversion personality students. Further, the comparative study between these two personalities will be analysed on education level (postgraduate and undergraduate) and gender (male and female). The research was initiated by identifying the factors of social media impacting students’ academic performance. Thereafter, the scale was developed, validated and tested for reliability in the Indian context. Data were collected from 408 students segregated into 202 males and 206 females. Two hundred and thirty-four students are enrolled in postgraduation courses, whereas 174 are registered in the undergraduate programme. One-way ANOVA has been employed to compare the extraversion and introversion students of different education levels and gender. A significant difference is identified between extraversion and introversion students for the impact of social media on their academic performance.

Introduction

Social Networking Sites (SNS) gained instant popularity just after the invention and expansion of the Internet. Today, these sites are used the most to communicate and spread the message. The population on these social networking sites (SNS) has increased exponentially. Social networking sites (SNS) in general are called social media (Boyd & Ellison, 2008 ). Social media (SM) is used extensively to share content, initiate discussion, promote businesses and gain advantages over traditional media. Technology plays a vital role to make SM more robust by reducing security threats and increasing reliability (Stergiou et al., 2018 ).

As of January 2022, more than 4.95 billion people are using the Internet worldwide, and around 4.62 billion are active SM users (Johnson, 2022 ). In India, the number of Internet users was 680 million by January 2022, and there were 487 million active social media users (Basuray, 2022 ). According to Statista Research Department ( 2022 ), in India, SM is dominated by two social media sites, i.e. YouTube and Facebook. YouTube has 467 million users followed by Facebook with 329 million users.

Although almost all age groups are using SM platforms to interact and communicate with their known community (Whiting & Williams, 2013 ), it has been found that social media sites are more popular among youngsters and specifically among students. They use SM for personal as well as academic activities extensively (Laura et al., 2017 ). Other than SM, from the last two years, several online platforms such as Microsoft Teams, Zoom and Google Meet are preferred to organize any kind of virtual meetings, webinars and online classes. These platforms were used worldwide to share and disseminate knowledge across the defined user community during the pandemic. Social media sites such as Facebook, YouTube, Instagram, WhatsApp and blogs are comparatively more open and used to communicate with public and/or private groups. Earlier these social media platforms were used only to connect with friends and family, but gradually these platforms became one of the essential learning tools for students (Park et al., 2009 ). To enhance the teaching–learning process, these social media sites are explored by all types of learning communities (Dzogbenuku et al., 2019 ). SM when used in academics has both advantages and disadvantages. Social media helps to improve academic performance, but it may also distract the students from studies and indulge them in other non-academic activities (Alshuaibi et al., 2018 ).

Here, it is important to understand that the personality traits of students, their education level and gender are critical constructs to determine academic performance. There are different personality traits of an individual such as openness, conscientiousness, extraversion and introversion, agreeableness and neuroticism (McCrae & Costa, 1987 ). This cross-functional research is an attempt to study the impact of social media on the academic performance of students while using extraversion and introversion personality traits, education levels and gender as moderating variables.

Literature Review

There has been a drastic change in the internet world due to the invention of social media sites in the last ten years. People of all age groups now share their stories, feelings, videos, pictures and all kinds of public stuff on social media platforms exponentially (Asur & Huberman, 2010 ). Youth, particularly from the age group of 16–24, embraced social media sites to connect with their friends and family, exchange information and showcase their social status (Boyd & Ellison, 2008 ). Social media sites have many advantages when used in academics. The fun element of social media sites always helps students to be connected with peers and teachers to gain knowledge (Amin et al., 2016 ). Social media also enhances the communication between teachers and students as this are no ambiguity and miscommunication from social media which eventually improves the academic performance of the students (Oueder & Abousaber, 2018 ).

When social media is used for educational purposes, it may improve academic performance, but some associated challenges also come along with it (Rithika & Selvaraj, 2013 ). If social media is incorporated into academics, students try to also use it for non-academic discussions (Arnold & Paulus, 2010 ). The primary reason for such distraction is its design as it is designed to be a social networking tool (Qiu et al., 2013 ). According to Englander et al. ( 2010 ), the usage of social media in academics has more disadvantages than advantages. Social media severely impacts the academic performance of a student. The addiction to social media is found more among the students of higher studies which ruins the academic excellence of an individual (Nalwa & Anand, 2003 ). Among the social media users, Facebook users’ academic performance was worse than the nonusers or users of any other social media network. Facebook was found to be the major distraction among students (Kirschner & Karpinski, 2010 ). However, other studies report contrary findings and argued that students benefited from chatting (Jain et al., 2012 ), as it improves their vocabulary and writing skills (Yunus & Salehi, 2012 ). Social media can be used either to excel in academics or to devastate academics. It all depends on the way it is used by the students. The good or bad use of social media in academics is the users’ decision because both the options are open to the students (Landry, 2014 ).

Kaplan and Haenlein ( 2010 ) defined social media as user-generated content shared on web 2.0. They have also classified social media into six categories:

• Social Networking Sites: Facebook, Twitter, LinkedIn and Instagram are the social networking sites where a user may create their profile and invite their friends to join. Users may communicate with each other by sharing common content.
• Blogging Sites: Blogging sites are individual web pages where users may communicate and share their knowledge with the audience.
• Content Communities and Groups: YouTube and Slideshare are examples of content communities where people may share media files such as pictures, audio and video and PPT presentations.
• Gaming Sites: Users may virtually participate and enjoy the virtual games.
• Virtual Worlds: During COVID-19, this type of social media was used the most. In the virtual world, users meet with each other at some decided virtual place and can do the pre-decided things together. For example, the teacher may decide on a virtual place of meeting, and students may connect there and continue their learning.
• Collaborative Content Sites: Wikipedia is an example of a collaborative content site. It permits many users to work on the same project. Users have all rights to edit and add the new content to the published project.

Massive open online courses (MOOCs) are in trend since 2020 due to the COVID-19 pandemic (Raja & Kallarakal, 2020 ). MOOCs courses are generally free, and anyone may enrol for them online. Many renowned institutions have their online courses on MOOCs platform which provides a flexible learning opportunity to the students. Students find them useful to enhance their knowledge base and also in career development. Many standalone universities have collaborated with the MOOCs platform and included these courses in their curriculum (Chen, 2013 ).

Security and privacy are the two major concerns associated with social media. Teachers are quite apprehensive in using social media for knowledge sharing due to the same concerns (Fedock et al., 2019 ). It was found that around 72% teachers were reluctant to use social media platforms due to integrity issues and around 63% teachers confirmed that security needs to be tightened before using social media in the classroom (Surface et al., 2014 ). Proper training on security and privacy, to use social media platforms in academics, is needed for  students and teachers (Bhatnagar & Pry, 2020 ).

The personality traits of a student also play a significant role in deciding the impact of social media on students’ academic performance. Personality is a dynamic organization which simplifies the way a person behaves in a situation (Phares, 1991 ). Human behaviour has further been described by many renowned researchers. According to Lubinski ( 2000 ), human behaviour may be divided into five factors, i.e. cognitive abilities, personality, social attitudes, psychological interests and psychopathology. These personality traits are very important characteristics of a human being and play a substantial role in work commitment (Macey & Schneider, 2008 ). Goldberg ( 1993 ) elaborated on five dimensions of personality which are commonly known as the Big Five personality traits. The traits are “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”.

It has been found that among all personality traits, the “extraversion vs. introversion” personality trait has a greater impact on students’ academic performance (Costa & McCrae, 1999 ). Extrovert students are outgoing, talkative and assertive (Chamorro et al., 2003 ). They are positive thinkers and comfortable working in a crowd. Introvert students are reserved and quiet. They prefer to be isolated and work in silos (Bidjerano & Dai, 2007 ). So, in the present study, we have considered only the “extraversion vs. introversion” personality trait. This study is going to analyse the impact of social media platforms on students’ academic performance by taking the personality trait of extraversion and introversion as moderating variables along with their education level and gender.

Research Gap

Past research by Choney ( 2010 ), Karpinski and Duberstein ( 2009 ), Khan ( 2009 ) and Kubey et al. ( 2001 ) was done mostly in developed countries to analyse the impact of social media on the students’ academic performance, effect of social media on adolescence, and addictiveness of social media in students. There are no published research studies where the impact of social media was studied on students’ academic performance by taking their personality traits, education level and gender all three together into consideration. So, in the present study, the impact of social media will be evaluated on students’ academic performance by taking their personality traits (extraversion and introversion), education level (undergraduate and postgraduate) and gender (male and female) as moderating variables.

Objectives of the Study

Based on the literature review and research gap, the following research objectives have been defined:

• To identify the elements of social media impacting student's academic performance and to develop a suitable scale
• To test the  validity and reliability of the scale
• To analyse the impact of social media on students’ academic performance using extraversion and introversion personality trait, education level and gender as moderating variables

Research Methodology

Sampling technique.

Convenience sampling was used for data collection. An online google form was floated to collect the responses from 408 male and female university students of undergraduation and postgraduation streams.

Objective 1 To identify the elements of social media impacting student's academic performance and to develop a suitable scale.

A structured questionnaire was employed to collect the responses from 408 students of undergraduate and postgraduate streams. The questionnaire was segregated into three sections. In section one, demographic details such as gender, age and education stream were defined. Section two contained the author’s self-developed 16-item scale related to the impact of social media on the academic performance of students. The third section had a standardized scale developed by John and Srivastava ( 1999 ) of the Big Five personality model.

Demographics

There were 408 respondents (students) of different education levels consisting of 202 males (49.5%) and 206 females (50.5%). Most of the respondents (87%) were from the age group of 17–25 years. 234 respondents (57.4) were enrolled on postgraduation courses, whereas 174 respondents (42.6) were registered in the undergraduate programme. The result further elaborates that WhatsApp with 88.6% and YouTube with 82.9% are the top two commonly used platforms followed by Instagram with 76.7% and Facebook with 62.3% of students. 65% of students stated that Google doc is a quite useful and important application in academics for document creation and information dissemination.

Validity and Reliability of Scale

Objective 2 Scale validity and reliability.

Exploratory factor analysis (EFA) and Cronbach’s alpha test were used to investigate construct validity and reliability, respectively.

The author’s self-designed scale of ‘social media impacting students’ academic performance’ consisting of 16 items was validated using exploratory factor analysis. The principle component method with varimax rotation was applied to decrease the multicollinearity within the items. The initial eigenvalue was set to be greater than 1.0 (Field, 2005 ). Kaiser–Meyer–Olkin (KMO) with 0.795 and Bartlett’s test of sphericity having significant values of 0.000 demonstrated the appropriateness of using exploratory factor analysis.

The result of exploratory factor analysis and Cronbach’s alpha is shown in Table ​ Table1. 1 . According to Sharma and Behl ( 2020 ), “High loading on the same factor and no substantial cross-loading confirms convergent and discriminant validity respectively”.

Exploratory factor analysis and Cronbach’s alpha for the self-developed scale of “Social media impact on academic performance”

The self-developed scale was segregated into four factors, namely “Accelerating Impact”, “Deteriorating Impact”, “Social Media Prospects” and “Social Media Challenges”.

The first factor, i.e. “Accelerating Impact”, contains items related to positive impact of social media on students’ academic performance. Items in this construct determine the social media contribution in the grade improvement, communication and knowledge sharing. The second factor “Deteriorating Impact” describes the items which have a negative influence of social media on students’ academic performance. Items such as addiction to social media and distraction from studies are an integral part of this factor. “Social Media Prospects” talk about the opportunities created by social media for students’ communities. The last factor “Social Media Challenges” deals with security and privacy issues created by social media sites and the threat of cyberbullying which is rampant in academics.

The personality trait of an individual always influences the social media usage pattern. Therefore, the impact of social media on the academic performance of students may also change with their personality traits. To measure the personality traits, the Big Five personality model was used. This model consists of five personality traits, i.e. “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”. To remain focussed on the scope of the study, only a single personality trait, i.e. “extraversion vs. introversion” with 6 items was considered for analysis. A reliability test of this existing scale using Cronbach’s alpha was conducted. Prior to the reliability test, reverse scoring applicable to the associated items was also calculated. Table ​ Table2 2 shows the reliability score, i.e. 0.829.

Cronbach’s alpha test for the scale of extraversion vs. introversion personality traits

Objective 3 To analyse the impact of social media on students’ academic performance using extraversion and introversion personality traits, education level and gender as moderating variables.

The research model shown in Fig.  1 helps in addressing the above objective.

Social media factors impacting academic performances of extraversion and introversion personality traits of students at different education levels and gender

As mentioned in Fig.  1 , four dependent factors (Accelerating Impact, Deteriorating Impact, Social Media Prospects and Social Media Challenges) were derived from EFA and used for analysing the impact of social media on the academic performance of students having extraversion and introversion personality traits at different education levels and gender.

Students having a greater average score (more than three on a scale of five) for all personality items mentioned in Table ​ Table2 2 are considered to be having extraversion personality or else introversion personality. From the valid dataset of 408 students, 226 students (55.4%) had extraversion personality trait and 182 (44.6%) had introversion personality trait. The one-way ANOVA analysis was employed to determine the impact of social media on academic performance for all three moderators, i.e. personality traits (Extraversion vs. Introversion), education levels (Undergraduate and Postgraduate) and gender (Male and Female). If the sig. value for the result is >  = 0.05, we may accept the null hypothesis, i.e. there is no significant difference between extraversion and introversion personality students for the moderators; otherwise, null hypothesis is rejected which means there is a significant difference for the moderators.

Table ​ Table3 3 shows the comparison of the accelerating impact of social media on the academic performance of all students having extraversion and introversion personality traits. It also shows a comparative analysis on education level and gender for these two personality traits of students. In the first comparison of extraversion and introversion students, the sig. value is 0.001, which indicates that there is a significant difference among extraversion and introversion students for the “Accelerating Impact” of social media on academic performance. Here, 3.781 is the mean value for introversion students which is higher than the mean value 3.495 of extraversion students. It clearly specifies that the accelerating impact of social media is more prominent in the students having introversion personality traits. Introversion students experienced social media as the best tool to express thoughts and improve academic grades. The result is also consistent with the previous studies where introvert students are perceived to use social media to improve their academic performance (Amichai-Hamburger et al., 2002 ; Voorn & Kommers, 2013 ). Further at the education level, there was a significant difference in postgraduate as well as undergraduate students for the accelerating impact of social media on the academic performance among students with extraversion and introversion, and introverts seem to get better use of social media. The gender-wise significant difference was also analysed between extraversion and introversion personalities. Female introversion students were found to gain more of an accelerating impact of social media on their academic performance.

One-way ANOVA: determining “Accelerating Impact” among extraversion and introversion personality traits students at different education levels and genders

Significant at the 0.05 level

Like Table ​ Table3, 3 , the first section of Table ​ Table4 4 compares the deteriorating impact of social media on the academic performance of all students having extraversion and introversion personality traits. Here, the sig. value 0.383 indicates no significant difference among extraversion and introversion students for the “Deteriorating Impact” of social media on academic performance. The mean values show the moderating deteriorating impact of social media on the academic performance of extraversion and introversion personality students. Unlimited use of social media due to the addiction is causing a distraction in academic performance, but the overall impact is not on the higher side. Further, at the education level, the sig. values 0.423 and 0.682 of postgraduate and undergraduate students, respectively, show no significant difference between extraversion and introversion students with respect to “Deteriorating Impact of Social Media Sites”. The mean values again represent the moderate impact. Gender-wise, male students have no difference between the two personality traits, but at the same time, female students have a significant difference in the deteriorating impact, and it is more on extroverted female students.

One-way ANOVA: Examining “Deteriorating Impact” among extraversion and introversion personality traits students at different education levels and genders

The significant value, i.e. 0.82, in Table ​ Table5 5 represents no significant difference between extraversion and introversion personality students for the social media prospects. The higher mean value of both personality students indicates that they are utilizing the opportunities of social media in the most appropriate manner. It seems that all the students are using social media for possible employment prospects, gaining knowledge by attending MOOCs courses and transferring knowledge among other classmates. At the education level, postgraduation students have no significant difference between extraversion and introversion for the social media prospects, but at the undergraduate level, there is a significant difference among both the personalities, and by looking at mean values, extroverted students gain more from the social media prospects. Gender-wise comparison of extraversion and introversion personality students found no significant difference in the social media prospects for male as well as female students.

One-way ANOVA: Examining “Social Media Prospects” among extraversion and introversion personality traits students at different education levels and genders

Table ​ Table6 6 shows the comparison of the social media challenges of all students having extraversion and introversion personality traits. It is also doing a comparative analysis on education level and gender for these two personality traits of students. All sig. values in Table ​ Table6 6 represent no significant difference between extraversion and introversion personality students for social media challenges. Even at the education level and gender-wise comparison of the two personalities, no significant difference is derived. The higher mean values indicate that the threat of cyberbullying, security and privacy is the main concern areas for extraversion and introversion personality students. Cyberbullying is seen to be more particularly among female students (Snell & Englander, 2010 ).

One-way ANOVA: Examining “Social Media Challenges” among extraversion and introversion personality traits students at different education levels and genders

The use of social media sites in academics is becoming popular among students and teachers. The improvement or deterioration in academic performance is influenced by the personality traits of an individual. This study has tried to analyse the impact of social media on the academic performance of extraversion and introversion personality students. This study has identified four factors of social media which have an impact on academic performance. These factors are: accelerating impact of social media; deteriorating impact of social media; social media prospects; and social media challenges.

Each of these factors has been used for comparative analysis of students having extraversion and introversion personality traits. Their education level and gender have also been used to understand the detailed impact between these two personality types. In the overall comparison, it has been discovered that both personalities (extraversion and introversion) have a significant difference for only one factor, i.e. “Accelerating Impact of Social Media Sites” where students with introversion benefited the most. At the education level, i.e. postgraduate and undergraduate, there was a significant difference between extraversion and introversion personalities for the first factor which is the accelerating impact of social media. Here, the introversion students were found to benefit in postgraduate as well as undergraduate courses. For the factors of deteriorating impact and social media challenges, there was no significant difference between extraversion and introversion personality type at the different education levels.

Surprisingly, for the first factor, i.e. the accelerating impact of social media, in gender-wise comparison, no significant difference was found between extraversion and introversion male students. Whereas a significant difference was found in female students. The same was the result for the second factor, i.e. deteriorating impact of social media of male and female students. For social media prospects and social media challenges, no significant difference was identified between extraversion and introversion students of any gender.

Findings and Implications

The personality trait of a student plays a vital role in analysing the impact of social media on their academic performance. The present study was designed to find the difference between extraversion and introversion personality types in students for four identified factors of social media and their impact on students’ academic performance. The education level and gender were also added to make it more comprehensive. The implications of this study are useful for institutions, students, teachers and policymakers.

This study will help the institutions to identify the right mix of social media based on the personality, education level and gender of the students. For example, technological challenges are faced by all students. It is important for the institutions to identify the challenges such as cyberbullying, security and privacy issues and accordingly frame the training sessions for all undergraduate and postgraduate students. These training sessions will help students with extraversion and introversion to come out from possible technological hassles and will create a healthy ecosystem (Okereke & Oghenetega, 2014 ).

Students will also benefit from this study as they will be conscious of the possible pros and cons that exist because of social media usage and its association with students’ academic performance. This learning may help students to enhance their academic performance with the right use of social media sites. The in-depth knowledge of all social media platforms and their association with academics should be elucidated to the students so that they may explore the social media opportunities in an optimum manner. Social media challenges also need to be made known to the students to improve upon and overcome with time (Boateng & Amankwaa, 2016 ).

Teachers are required to design the curriculum by understanding the learning style of students with extraversion and introversion personality type. Innovation and customization in teaching style are important for the holistic development of students and to satisfy the urge for academic requirements. Teachers should also guide the students about the adverse impacts of each social media platform, so that these can be minimized. Students should also be guided to reduce the time limit of using social media (Owusu-Acheaw & Larson, 2015 ).

Policymakers are also required to understand the challenges faced by the students while using social media in academics. All possible threats can be managed by defining and implementing transparent and proactive policies. As social media sites are open in nature, security and privacy are the two major concerns. The Government of India should take a strong stand to control all big social media companies so that they may fulfil the necessary compliances related to students’ security and privacy (Kumar & Pradhan, 2018 ).

The overall result of these comparisons gives a better insight and deep understanding of the significant differences between students with extraversion and introversion personality type towards different social media factors and their impact on students’ academic performance. Students’ behaviour according to their education level and gender for extraversion and introversion personalities has also been explored.

Limitation and Future Scope of Research

Due to COVID restrictions, a convenient sampling technique was used for data collection which may create some response biases where the students of introversion personality traits may have intentionally described themselves as extroversion personalities and vice versa. This study also creates scope for future research. In the Big Five personality model, there are four other personality traits which are not considered in the present study. There is an opportunity to also use cross-personality comparisons for the different social media parameters. The other demographic variables such as age and place may also be explored in future research.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. SS and Prof. RB. The first draft of the manuscript was written by Dr. SS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

No funds, grants, or other support was received.

Availability of data and material

Declarations.

The authors declare that they have no conflict of interest.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Verbal informed consent was obtained from the participants.

Verbal consent is obtained for publication

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sourabh Sharma, Email: ni.ude.hbimi@hbaruos .

Ramesh Behl, Email: ude.imi@lhebr .

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A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

2021, Education Sciences

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to unde...

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Predicting student performance in introductory programming courses.

1. Introduction

2. methodology, 2.1. datasets.

• Questionnaires
• Academic Records
• Personal Data

2.2. Evaluation Metrics

• Accuracy-is the most common evaluation metric. It works as an indicator of the model’s overall performance by determining the percentage of correctly classified instances [ 38 ].
• Precision-is an extremely useful metric, in situations where false positives are more important than false negatives because it allows us to evaluate the model and determine what percentage of instances classified as positive are positive [ 38 ].
• Recall-is a metric that can be very useful when false negatives are more important than false positives, as it allows us to calculate the percentage of true positive instances that have been correctly classified [ 38 ].
• F1-Score-is created when the precision and recall metrics are both critical. This metric combines precision and recall through a harmonic mean, so by maximizing the value of the F1-Score, we simultaneously maximize the precision and recall [ 38 ].
• Specificity-is the metric that allows calculating the percentage of truly negative instances that have been classified as negative; in other words, it assesses the ability of the model to correctly identify negative cases [ 38 ].
• Sensitivity-is the metric that allows calculating the percentage of truly positive instances that have been classified as positive [ 38 ].
• Root Mean Square Error (RMSE)-is commonly used to evaluate model performance. This metric measures the average squared difference between the values predicted by the model and the actual values. The lower the RMSE value, the better the model’s performance [ 38 ].

3. Related Work

4.1. models, 4.2. most-used algorithms, 4.3. most-used evaluation metrics, 4.4. most-used dataset, 4.5. datasets analysis, 4.6. algorithms with best results, 4.7. deep learning approach, 4.8. challenges and gaps.

• Data Quality Assurance
• Selection of the Algorithms
• Evaluation Metrics Application

5. Discussion

6. threats to validity, 7. future research directions.

• Use of deep learning techniques
• Integration of various data sources

8. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Pires, J.P.J.; Brito Correia, F.; Gomes, A.; Borges, A.R.; Bernardino, J. Predicting Student Performance in Introductory Programming Courses. Computers 2024 , 13 , 219. https://doi.org/10.3390/computers13090219

Pires JPJ, Brito Correia F, Gomes A, Borges AR, Bernardino J. Predicting Student Performance in Introductory Programming Courses. Computers . 2024; 13(9):219. https://doi.org/10.3390/computers13090219

Pires, João P. J., Fernanda Brito Correia, Anabela Gomes, Ana Rosa Borges, and Jorge Bernardino. 2024. "Predicting Student Performance in Introductory Programming Courses" Computers 13, no. 9: 219. https://doi.org/10.3390/computers13090219

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Development and validation of the high school students’ Mathematics Discourse Feedback Skills Scale (MDFSS)

• Published: 07 September 2024

Cite this article

• Hao Chen 1 , 2 ,
• Sanyi Tang 3 ,
• Shang Zhang 1 ,
• Jie Xu 1 &
• Guangsheng Wang 3  

This study aimed to develop an instrument for assessing high school students’ mathematics discourse feedback skills (MDFS) in order to measure their feedback literacy performance in mathematics. First, the researcher constructed a theoretical framework of MDFS, including comparative analysis, expressing communication, mathematical reasoning, monitor and adjust, diagnostic evaluation, and implementation capacity, through literature review, and designed the mathematics discourse feedback skills scale (MDFSS) questions accordingly. Subsequently, 9 experts conducted two rounds of content validity tests on the theoretical framework and scale questions, while 32 high school student volunteers conducted surface validity tests. Then, 273 high school students participated in the item analysis of the scale. Ultimately, 1681 high school students assessed the structural validity of the scale. In these assessments, exploratory factor analysis was conducted on 841 high school students, and confirmatory factor analysis with first-order and second-order models was conducted on 840 students. The study also conducted reliability, validity, and measurement invariance tests on the survey questionnaire. Based on the results of these analyses, the researcher confirmed that the final version of the scale consisted of 24 items. The results of the study indicated that the scale provided a valid evidence for measuring the MDFS of high school students. The study is of great significance to academic and educational practice, as it not only deepens the research on student feedback literacy in mathematics, but also provides a valuable reference tool for improving the academic quality of mathematics among high school students in China and other Asian countries.

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Data availability

The data generated during the current study are not publicly available since they constitute an excerpt of research in progress. Data are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank all those who participated in this study. They are also grateful to Professor Robert A. Cheke from the UK for language editing, and to the Editor-in-Chief, Associate Editors, and anonymous reviewers for their valuable comments on this paper.

This study is supported by the National Natural Science Foundation of China (NSFC: 12031010).

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Hao Chen drafted the manuscript. Sanyi Tang and Guangsheng Wang served as the research advisor. Guangsheng Wang and Hao Chen contributed significantly to the conception, the data analysis, and manuscript revision. Shang Zhang and Jie Xu collected the data and worked as writer’s assistant. All authors contributed to the article and approved the submitted version.

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Chen, H., Tang, S., Zhang, S. et al. Development and validation of the high school students’ Mathematics Discourse Feedback Skills Scale (MDFSS). Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06578-1

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Accepted : 15 August 2024

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DOI : https://doi.org/10.1007/s12144-024-06578-1

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