what is description of study area in research

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HOW TO WRITE DESCRIPTION OF STUDY AREA IN RESEARCH

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DESCRIPTION OF THE STUDY AREA

Description of the study area is the act of describing the characteristics, qualities and physical features of an area, location, neighbourhood, city or community that is being studied or investigated for research purposes. Describing a study area is an important part of research, not just to the researcher, but to all concerned both now and in the future.

The study area is sometimes referred to as a study site in research, some higher institutions refer to the description of the study area as the “ profile of the study area “. To properly give a detailed description of an area, the researcher should have an in-depth knowledge of the study area in the project , and this can only be possible by reviewing other related materials in the form of published Journals, textbooks, etc., by academicians within a particular field of study or when the researcher is familiar with the neighbourhood or area in question.

In a dissertation, the description of a study area usually comes under Chapter Three (in most cases) except in some institutions with special formats for presenting research papers. But the key point is that the captions on whichever chapter this subject is mentioned are usually written as “A Brief Description of Study Area” . This is not to limit the information provided but to apply some sort of concision in that it should be skillfully couched to enable a good flow in the presentation without creating confusion.

In summary, there are three aspects to the description of study area – (brief) (description) (study area)

The fact that it is termed brief does not necessarily mean it shouldn’t be detailed.

Your description should cover a broad spectrum of information; this would include location, geography, climatic condition, social infrastructure, vegetation, density, humidity, temperature, topography, terrain and so on.

The study area should be your area of coverage that is, your case study. Introducing information from other areas or regions will have no significance on the subject matter, hence your primary focus would be on the area your research is covering. At this point, you are expected to include maps of the study area (in colour).

What you’re expected to write is a detailed description of your study area to give your reader an idea of what your study area looks like. Get Samples 

Depending on the research topic, the pattern of describing the study area could vary;

If a project topic is focused on investigating issues or problems that concern a state or province the description will bother around everything that makes up the location. For example “ Evaluation of the Issues Affecting Girl-Child Education in FCT Abuja ” is a broad topic that focuses on FCT-Abuja as a case study, hence to describe the study area which in this case is FCT-Abuja, adequate data on everything that makes up or best describes Abuja as a Federal Capital Territory has to be brought in and properly described. The description should include the vegetation in Abuja, the population of Abuja, Abuja’s topography, its climatic condition, the culture of people living in Abuja, the terrain (that is, the flatness or sloppiness of Roads in Abuja), the nature of business, market data, rainfall, electricity, common food, the number of ethnic group in the territory, religion of residents among others.

The essence of the description of the study area is to enable an outsider to have an idea of the area or neighbourhood that is being researched, this knowledge will also help the readers to understand the body of your work and try to envision what your study is trying to pass across. It will also influence people’s judgment of the topic being researched.

If the project topic is centred on a particular catchment or neighbourhood for example “ A Critical Examination of Facilities Management Strategy on Public Properties – A Case Study of Central Bank of Nigeria Staff Quarters ” the description will take a different shape. In this case, it should include a brief summary of the neighborhood where the CBN staff quarters are located followed by a broad description of the CBN staff quarters environment and premises. Let’s look at the following example;

“ The CBN staff quarters is a large purpose-built residential estate for senior, intermediate and junior CBN staff members. It is properly fenced around its perimeter solid block walls with a giant double two-way gate at the main entrance and another small pedestrian gate by the side. The estate comprises of the following; 16 Blocks of 8 units of 2bedroom flats each totalling 128 flats, 12 Block of 8 units of 3bedroom flats each totalling another 97 flats, and 6 Blocks of 6 units of 3bedroom flats each totalling 36 flats with additional buildings at the rear for intermediate and junior staff. The senior staff also have a large garage for parking vehicles .”

In addition, a detailed description of the site and construction details of the buildings will beef up your work. For example:

“ The site is rectangular, it has a flat or table surface and properly drains off water during heavy downpours. The site measures approximately 22.32 hectares .”

The construction details should encompass; the type of floor, wall, doors, windows, ceiling, fittings, roof and the materials used in constructing them. For example, a brief description of the floor can be written like this “ The floor is made of mass concrete on hardcore filling well rammed over consolidated laterite and finished with terrazzo material ” The other building components (windows, doors, ceiling, wall, fitting and roof) should have their description proper done like the “floor”.

The facilities in the CBN staff estate should also come in the description. A short write-up can be done to explain to the reader or supervisor the available facilities installed and used in the CBN staff quarters, for example, “ The facilities provided in the CBN staff quarters are; water treatment plant, cameras, sewage treatment plant, generator house, heavy duty generator set to illuminate the premises, pumping machine (Sumo) to circulate water to all apartments, borehole, external lighting points and lawn tennis court for exercise .”

Sometimes an institution could be a case study of a project. Let’s use this project topic as an example “ An Analysis of the Maintenance and Management Problem of the University of Lagos Hostel Buildings ”.

To describe this study area the following sub-headings should be developed and expanded:

This involves the description of the University’s location, including the city and local government area where it is situated.

The History, Origin and Growth of the Study Area:

Tracing the historical background of the University of Lagos, its various campuses, colleges (college of medicine), the total size of the school premises, total number of staff and students (undergraduates and postgraduate students), annual enrollment of students, the various faculties and departments and other facilities attached to the universities and subsidiary campuses or learning institutions within and outside the state, or country is paramount.

Important : Make sure to include a colour map in your description to guide your readers and supervisor further.

SAMPLE OF DESCRIPTION OF STUDY AREA

Frequently Asked Questions about the Description of Study Area.

What is the general description of the study area.

There is no such thing as a general description of the study area, the pattern of describing a particular area might differ from the way other areas are described. This is dependent on the type and nature of the area that is going to be described. No two locations have the same features, hence you cannot give a written account of locations A and B the same way, which is why the researcher will need to either visit the area of the study or source materials with comprehensive and recent information on a particular area to be described in the research paper.

Examples of areas of study?

The determination of a study area is dependent on the type or nature of the researchable problem that the researcher wants to solve. For example, an ideal study area for “Impact of indiscriminate dumps on children’s health” would be a neighbourhood that experiences a high volume of indiscriminate dumps such as ghettos, slum neighbourhoods, high-density or populated neighbourhoods etc.

Also, if a project topic is   “Impact of social media on junior secondary school subjects” the area of study will comprise a certain number of junior secondary schools in a particular area not necessarily the entire State, Region or Province. The area of study must be connected with the project topic, this is because the research problem is first identified before developing a topic around the problem. So, the research has to identify a problem, search for areas affected by the identified problem and then develop a topic that captures the problem and the area of study.

The following can represent an area of study; Primary and Secondary Schools, Communities, Organizations, Provinces, Streets, Local Government Areas, hospitals, Banks, TV and Radio Stations, Government Agencies, Military Barracks, Police Stations, Specialized Buildings, Events, Shrines, Layouts etc.

Some people may want to carry out simple research about their home or certain areas or components in their home, this could be academic or personal research about an identified problem in the home. Hence, examples of study areas at home would include, the premises, building structures, the environment, farmland or plantation farm, auxiliary facilities, recreational areas in the home, pool sites, cooking or baking area etc. depending on the project topic.

What is a study area in research?

Study areas are locations where a researcher plans to carry out an in-depth study about a topic or existing problem. This is usually indicated in the research proposal for the supervisor to vet and approve. If approved, the researcher or student is expected to visit the study area to observe and gather information related to the existing problem in that neighbourhood. A study area is also referred to as a study site or research site.

What is the importance of the study area in research?

The importance of the study area cannot be over-emphasized. I have taken time to explain this question in the article “ Reasons for Choosing a Study Area in Research ”.

Must a description of the study area in a project be broad?

No.  I mentioned earlier in this article that most research papers or projects require a brief description of your study area, so you could write a brief account of your study area in about one to three pages depending on how vast the area is. You don’t need to write more than is required, just provide the relevant information needed and you’re good.

Get complete samples of the Description of Study Areas here  

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20 Replies to “HOW TO WRITE DESCRIPTION OF STUDY AREA IN RESEARCH” .

South Africa has been experiencing load-shedding

its educative

I am interested in your website. Currently I am preparing my thesis for completion of my MBA n Marketing. Thank you for your help.

Thank you Zelalem, you can send a message if you need further guide. I wish you success!

What if I choose a Senior high school in Enugu state? Am I going to write about the school or the State itself?

If your project topic is about a particular high school then the description should focus on the School itself not the State.

very good concept. Really I appreciate it.

Awesome, thanks Ray.

Helpful information .Thanks

Thank you Peres Bett.

How to write description of the study Area

This is great information just as I wanted it to be. thanks a lot man

Thanks Richard.

What important information should I include in the research area description? Regard Telkom University

What’s your study area?

My study area is Mbabane in Eswatini. However, am doing my thesis in an Asian University. Should my focus be with the Town or the entire Eswatini?

You may not be able to cover the entire Eswatini. So, delimit your study area to Mbabane. By the way, what’s your Thesis topic?

enquiry was very helpful

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3. Methodology of Research 3.1. Study Area and Target Population

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection  

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Related Reads:

• Dangling Modifiers and How to Avoid Them in Your Writing 
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• How to Paraphrase Research Papers Effectively
• What is a Literature Review? How to Write It (with Examples)

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What Is Research Methodology?

I f you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Learn More About Methodology

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Research Limitations 101: What You Need To Know

Learn everything you need to know about research limitations (AKA limitations of the study). Includes practical examples from real studies.

In Vivo Coding 101: Full Explainer With Examples

Learn about in vivo coding, a popular qualitative coding technique ideal for studies where the nuances of language are central to the aims.

Process Coding 101: Full Explainer With Examples

Learn about process coding, a popular qualitative coding technique ideal for studies exploring processes, actions and changes over time.

Qualitative Coding 101: Inductive, Deductive & Hybrid Coding

Inductive, Deductive & Abductive Coding Qualitative Coding Approaches Explained...

📄 FREE TEMPLATES

Research Topic Ideation

Proposal Writing

Literature Review

Methodology & Analysis

Academic Writing

Referencing & Citing

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199 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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What is a study area in research?  

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A study area in research refers to the specific geographical location where a research project is conducted, encompassing various characteristics like climate, topography, resources, and land use patterns. It serves as the primary field for data collection and analysis. Study areas can vary widely, from forest-steppe ecotones in northern China [1] to valleys in the tropical Andes of southern Ecuador [5] . Researchers focus on describing the geographical profile, physiography, resources, and socio-economic aspects of the study area [2] . Field studies are often conducted along gradients to capture variations in environmental conditions, as seen in research areas along the coasts of the North Sea and the Baltic Sea [3] . The choice of study area is crucial for achieving research goals and providing valuable information for policymakers, especially in dynamic urban settings like Tehran [4] .

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Research on artificial intelligence (AI) spans various domains, highlighting its transformative potential and diverse applications. A comprehensive study by Sharma emphasizes AI's role in sectors such as healthcare, finance, and robotics, focusing on knowledge representation and deep learning's implications for innovation and operational efficiency. In the educational sector, Titko et al. explored academic staff's perceptions of AI, revealing a positive attitude towards its use for teaching materials, yet concerns about plagiarism and a lack of AI skills among staff. Cico and Cico conducted a tertiary study on AI in software engineering, identifying key subfields like Machine Learning and Natural Language Processing that support essential software processes. Ofosu-Ampong's review of AI literature highlights a predominance of technological issues while noting gaps in contextual knowledge and security concerns. Lastly, a study on moral education illustrates AI's potential to enhance educational methods and student outcomes. Collectively, these studies underscore the multifaceted impact of AI across various fields, while also identifying areas for further research and development.

Recent studies have focused on various aspects related to areas of interest (AOIs) in different fields. Ma et al. (2021) proposed a systematic framework to analyze road-constrained AOIs and their semantic attractiveness, highlighting the impact of urban structures on human activity patterns and urban functions . Chastine et al. (Year) presented a technique for visualizing AOIs in molecular modeling environments, emphasizing the challenges posed by complex molecular models and the need for efficient visualization methods . Additionally, Gregor (Year) conducted analyses in the western part of Slovakia, specifically in the Nitra river catchment area, showcasing the importance of studying AOIs in geographical contexts . These studies collectively contribute to a better understanding of AOIs across urban, scientific, and geographical domains, offering insights into spatial distributions, semantic attractiveness, and visualization techniques.

The study areas discussed in the provided contexts encompass diverse geographical locations with unique characteristics. These areas include the north-east region of India, specifically Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, and Tripura, known for its rugged relief, heavy rainfall, and rich biodiversity . Additionally, Tehran city in Iran stands out due to its dynamic nature and significant land cover changes, making it a challenging area for research with potential implications for land policy . The Faget area in Romania boasts high tourist potential based on its natural landscapes, historical sites, and accommodation structures, with a focus on economic development through tourism promotion . Furthermore, the Sanjiang Plain in China is highlighted for its vast freshwater wetlands and the impact of human activities leading to a reduction in natural wetland area .

The specific areas of study mentioned in the provided contexts include the Rio San Francisco Valley in southern Ecuador, the north-eastern states of India (Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, and Tripura), South Florida, Miyun County in Beijing, and a study conducted in the periphery of Glasgow. These areas are characterized by unique geographical features, biodiversity hotspots, environmental challenges, and societal dynamics. The Rio San Francisco Valley is highlighted for its biodiversity and mesoscale atmospheric phenomena . The north-eastern states of India are known for their rugged relief, heavy rainfall, and rich tropical vegetation . South Florida faces flood risks due to its low elevation and limestone bedrock . Miyun County influences Beijing's water safety and faces water pollution challenges from stockbreeding . The study in Glasgow's periphery focuses on the role of schools in the community .

The specific area of study mentioned in the provided contexts includes diverse geographical regions such as South Florida, the Rio San Francisco Valley in southern Ecuador, the seven states of north east India, and a study conducted in the periphery of Glasgow. These areas offer unique characteristics for research, such as the susceptibility to sea-level rise in South Florida , the biodiversity hotspot in the Rio San Francisco Valley , the rugged terrain and heavy rainfall in north east India , and the community dynamics in the housing schemes near Glasgow . Additionally, the study of benthic foraminifera off the southwest Atlantic coast contributes to the understanding of marine ecosystems in the region .

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The agricultural ditches draining former wetlands on the Delmarva Peninsula support a variety of plant species, influenced by hydrology, soil conditions, and management practices. These ditches often mimic wetland functions, promoting diverse vegetation. ## Dominant Plant Species - **Phragmites australis**: Commonly found in wetland areas, this species thrives in shallow moisture environments, indicating its adaptability to the conditions in agricultural ditches. - **Suaeda salsa**: This halophyte shows resilience to varying salinity levels, making it suitable for transitional areas within ditches. - **Herbaceous Plants**: Restored wetlands exhibit higher herbaceous diversity, particularly in ditches with shorter hydroperiods, which can enhance overall biodiversity. ## Ecological Functions - Vegetated ditches can effectively mitigate nutrient and heavy metal runoff, similar to constructed wetlands, thus supporting plant growth and ecological health. - The diversity of plant species in these ditches contributes to nutrient cycling and habitat stability, although they differ from natural wetlands in composition and structure. While agricultural ditches can support diverse plant communities, they may not fully replicate the ecological functions of natural wetlands, raising questions about restoration efficacy and management strategies.

Biogenic volatile organic compounds (BVOCs) emissions in Southeast Asia are significantly influenced by land cover changes, climate variations, and vegetation types. The region's tropical forests and oil palm plantations are major sources of BVOCs, particularly isoprene and monoterpenes, which play crucial roles in atmospheric chemistry. ## Land Cover Changes and BVOC Emissions - The expansion of oil palm plantations has led to increased isoprene emissions, with estimates showing a rise of 6.5 TgC/year due to land cover changes from 1990 to 2010. - Oil palms emit isoprene at rates significantly higher than natural forests, with maximum emissions recorded at 13 mg m−2 h−1 compared to 2.5 mg m−2 h−1 from rainforests. ## Climate Change Impact - Climate change is projected to increase isoprene emissions by 22% under certain scenarios, although higher CO₂ levels may offset this increase. - The interplay between temperature and CO₂ concentrations is critical in determining future BVOC emissions in the region. ## Biomass Burning Contributions - Biomass burning also contributes to BVOC emissions, with significant emissions from forest fires and agricultural practices, averaging 3,831 Gg of non-methane volatile organic compounds (NMVOCs) from 2001 to 2010. In summary, while oil palm expansion and climate change are primary drivers of BVOC emissions in Southeast Asia, biomass burning remains a significant contributor, highlighting the complex interactions affecting atmospheric chemistry in the region. However, the potential for mitigation through sustainable land management practices is an area for further exploration.

The damage pattern of Typhoon Enteng on residential areas typically involves a combination of structural failures and vegetation loss, influenced by various environmental factors. Understanding these patterns is crucial for improving resilience in affected neighborhoods. ## Structural Damage - Residential buildings often suffer from external and internal wind pressures, leading to failures in vulnerable structures such as bungalows and light steel buildings. - The damage is exacerbated in areas where structural connections are weak, particularly in enclosures and stress concentration points. ## Vegetation Impact - Urban vegetation, particularly residential greenspaces and street trees, is highly susceptible to typhoon disturbances, showing significant spatial heterogeneity in damage. - The intensity of wind plays a critical role in the extent of vegetation damage, with urban areas experiencing more pronounced effects compared to natural landscapes. ## Flooding Concerns - Flooding is another significant risk, with high-resolution simulations indicating that residential areas can face severe inundation, leading to substantial economic losses. - Vulnerability factors such as proximity to water bodies and population density further exacerbate flood risks in residential neighborhoods. While the focus is often on structural and vegetation damage, it is essential to consider the broader implications of flooding and the cumulative effects of these disasters on community resilience and recovery strategies.

Hypoxia in eutrophication is primarily driven by anthropogenic nutrient inputs, which lead to excessive algal blooms and subsequent oxygen depletion in aquatic ecosystems. This process is influenced by various environmental factors and feedback mechanisms. ## Nutrient Enrichment - **Anthropogenic Sources**: Increased nitrogen and phosphorus from agricultural runoff and wastewater significantly contribute to eutrophication, particularly in coastal and estuarine systems. - **Algal Blooms**: The proliferation of algae consumes dissolved oxygen during decomposition, leading to hypoxic conditions. ## Environmental Conditions - **Thermal Stratification**: In stratified water bodies, oxygen depletion is exacerbated as warmer surface waters limit the vertical mixing of oxygen-rich water. - **Water Circulation**: Poor circulation, especially in coastal lagoons, restricts oxygen replenishment, intensifying hypoxia during periods of high nutrient loading. ## Feedback Mechanisms - **Bacterial Activity**: Increased organic matter from algal blooms enhances bacterial respiration, further depleting oxygen levels. - **Climate Influence**: Climate change can exacerbate these conditions by altering nutrient dynamics and promoting stratification. While the primary drivers of hypoxia are well understood, the complex interactions between nutrient loading, environmental conditions, and biological responses highlight the need for integrated management strategies to mitigate these effects.

The extraction of chlorophyll-a (Chl-a) concentration using remote sensing data is crucial for monitoring aquatic ecosystems, particularly in fisheries. Various methodologies have been developed to enhance the accuracy of Chl-a retrieval from satellite imagery. ## Remote Sensing Techniques - **Satellite Data Utilization**: Studies have shown that Landsat-8/OLI data significantly outperforms other satellite sources for Chl-a retrieval, particularly in summer months. - **Machine Learning Approaches**: A machine learning model combining satellite observations with atmospheric and oceanic data has been effective in predicting Chl-a concentrations, achieving a notable R² metric of 0.578. ## Correlation with Fisheries - **Phytoplankton Dynamics**: Research indicates a positive correlation between Chl-a concentration and fish landings, particularly in the southeastern Arabian Sea, where fluctuations in Chl-a influence sardine populations. ## Challenges and Innovations - **Optical Interference**: The retrieval of Chl-a in inland waters faces challenges due to optical interferences, but advanced models like support vector regression (SVR) have shown improved accuracy in various environments. While remote sensing offers powerful tools for Chl-a monitoring, challenges such as atmospheric correction and optical interferences remain significant hurdles that require ongoing research and innovation.

Selecting Research Area

Selecting a research area is the very first step in writing your dissertation. It is important for you to choose a research area that is interesting to you professionally, as well as, personally. Experienced researchers note that “a topic in which you are only vaguely interested at the start is likely to become a topic in which you have no interest and with which you will fail to produce your best work” [1] . Ideally, your research area should relate to your future career path and have a potential to contribute to the achievement of your career objectives.

The importance of selecting a relevant research area that is appropriate for dissertation is often underestimated by many students. This decision cannot be made in haste. Ideally, you should start considering different options at the beginning of the term. However, even when there are only few weeks left before the deadline and you have not chosen a particular topic yet, there is no need to panic.

There are few areas in business studies that can offer interesting topics due to their relevance to business and dynamic nature. The following is the list of research areas and topics that can prove to be insightful in terms of assisting you to choose your own dissertation topic.

Globalization can be a relevant topic for many business and economics dissertations. Forces of globalization are nowadays greater than ever before and dissertations can address the implications of these forces on various aspects of business.

Following are few examples of research areas in globalization:

• A study of implications of COVID-19 pandemic on economic globalization
• Impacts of globalization on marketing strategies of beverage manufacturing companies: a case study of The Coca-Cola Company
• Effects of labour migration within EU on the formation of multicultural teams in UK organizations
• A study into advantages and disadvantages of various entry strategies to Chinese market
• A critical analysis of the effects of globalization on US-based businesses

Corporate Social Responsibility (CSR) is also one of the most popular topics at present and it is likely to remain so for the foreseeable future. CSR refers to additional responsibilities of business organizations towards society apart from profit maximization. There is a high level of controversy involved in CSR. This is because businesses can be socially responsible only at the expense of their primary objective of profit maximization.

Perspective researches in the area of CSR may include the following:

• The impacts of CSR programs and initiatives on brand image: a case study of McDonald’s India
• A critical analysis of argument of mandatory CSR for private sector organizations in Australia
• A study into contradictions between CSR programs and initiatives and business practices: a case study of Philip Morris Philippines
• A critical analysis into the role of CSR as an effective marketing tool
• A study into the role of workplace ethics for improving brand image

Social Media and viral marketing relate to increasing numbers of various social networking sites such as Facebook, Twitter, Instagram, YouTube etc. Increasing levels of popularity of social media among various age groups create tremendous potential for businesses in terms of attracting new customers.

The following can be listed as potential studies in the area of social media:

• A critical analysis of the use of social media as a marketing strategy: a case study of Burger King Malaysia
• An assessment of the role of Instagram as an effective platform for viral marketing campaigns
• A study into the sustainability of TikTok as a marketing tool in the future
• An investigation into the new ways of customer relationship management in mobile marketing environment: a case study of catering industry in South Africa
• A study into integration of Twitter social networking website within integrated marketing communication strategy: a case study of Microsoft Corporation

Culture and cultural differences in organizations offer many research opportunities as well. Increasing importance of culture is directly related to intensifying forces of globalization in a way that globalization forces are fuelling the formation of cross-cultural teams in organizations.

Perspective researches in the area of culture and cultural differences in organizations may include the following:

• The impact of cross-cultural differences on organizational communication: a case study of BP plc
• A study into skills and competencies needed to manage multicultural teams in Singapore
• The role of cross-cultural differences on perception of marketing communication messages in the global marketplace: a case study of Apple Inc.
• Effects of organizational culture on achieving its aims and objectives: a case study of Virgin Atlantic
• A critical analysis into the emergence of global culture and its implications in local automobile manufacturers in Germany

Leadership and leadership in organizations has been a popular topic among researchers for many decades by now. However, the importance of this topic may be greater now than ever before. This is because rapid technological developments, forces of globalization and a set of other factors have caused markets to become highly competitive. Accordingly, leadership is important in order to enhance competitive advantages of organizations in many ways.

The following studies can be conducted in the area of leadership:

• Born or bred: revisiting The Great Man theory of leadership in the 21 st century
• A study of effectiveness of servant leadership style in public sector organizations in Hong Kong
• Creativity as the main trait for modern leaders: a critical analysis
• A study into the importance of role models in contributing to long-term growth of private sector organizations: a case study of Tata Group, India
• A critical analysis of leadership skills and competencies for E-Commerce organizations

COVID-19 pandemic and its macro and micro-economic implications can also make for a good dissertation topic. Pandemic-related crisis has been like nothing the world has seen before and it is changing international business immensely and perhaps, irreversibly as well.

The following are few examples for pandemic crisis-related topics:

• A study into potential implications of COVID-19 pandemic into foreign direct investment in China
• A critical assessment of effects of COVID-19 pandemic into sharing economy: a case study of AirBnb.
• The role of COVID-19 pandemic in causing shifts in working patterns: a critical analysis

Moreover, dissertations can be written in a wide range of additional areas such as customer services, supply-chain management, consumer behaviour, human resources management, catering and hospitality, strategic management etc. depending on your professional and personal interests.

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

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What's the difference between 'research topic' and 'research area'?

I am writing an essay to apply for a summer research project and is supposed to write about 'general research topic that interests me' and 'area I would like to focus'. I'm kind of confused about these two terms. What's the difference?

For example, if I'm interested in computer science, where should I write it?

p.s. I have asked this question in English Language & Usage site but didn't get answer. So I suppose that these two words may only have difference in academic field?

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2 Answers 2

A research area is what a research topic is placed into, but is much broader than the scope of the topic. For example a research area can be human physiology, computer science (as you mentioned) or even relate to a specific field within these broader terms such as cardiac electrophysiology or machine learning respectively.

A research topic would be a specific question, hypothesis or problem you wish to investigate and answer which is under the scope of your research area. That is to say, my research area is in neuroscience/neurophysiology and my research topic is investigating the mechanisms of neuronal communication, as an example.

You would want to say topics that interest you which relate to a certain problem that you may be aware of, whereas in the research area you would want to outline your inclinations towards a particular field of academia.

While a topic is narrower than an area (for example, your area may be "solid state physics" and your topic "semiconductor tuning based on dopage"), it's probably true that for most people there is little difference between the two terms as far as colloquial usage is concerned.

In other words, don't obsess about the difference -- though, if you want, consider the "area" a broader term.

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Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

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Conflict of interest: None

How to write the perfect study description

The secret to getting quality data for your research study is recruiting quality participants – who are eager, enthusiastic, and engaged. But just how do you do that?

Your study description can help. It's an often-overlooked aspect of study design that can greatly influence who you bring on board, and the results they give you.

Why your study description matters

Your title and description are the first things potential participants see when they’re scrolling through, looking for their next study.

If they’re compelling, they won’t just catch a participant’s eye. They’ll also provide all the information they need to decide whether they’ll participate in your study.

What’s the aim of the study? What does the participant need to do? Will they have to give any sensitive information? Is there anything in the study that might make them uncomfortable? These are the kinds of things a participant will want to know before getting involved.

Written well, a study description makes your instructions clearer. And it can make your participants feel more motivated. After all, participants are more likely to immerse themselves in your study if they understand its purpose and what’s expected of them.

What’s more, a good study description can help you meet certain ethical requirements, such as gaining informed consent from participants.

The key here is to include just the right amount of information the participant will need to decide whether to partake.

Too much, and you risk not only giving away the aims of your study, but also boring the reader – driving them to scroll on. Too little, and they won’t know what they’re possibly getting themselves into, or fully understand what’s expected of them.

Here are the elements we recommend you include:

The aim of the study

Include a clear and concise statement about what the study is trying to achieve – without divulging too much, so as not to influence responses.

Participant requirements

Clearly outline what the participant will need to do. This includes any instructions, materials, or equipment they’ll require.

Sensitive information

If you need any sensitive data from a participant, let them know. This includes personal or medical details.

Uncomfortable tasks

Warn participants about any sections they may find uncomfortable. This could be viewing disturbing images or videos, for instance.

Unusual requests

Warn participants of anything unexpected they will need to do, such as downloading software or requiring headphones.

Rejection prevention

Instruct participants on what they must do to avoid their submission being rejected, such as completing all tasks or answering all questions.

Reward details

Give participants an estimate of how long it’ll take to receive a reward after submission. If you plan to use bonus payments, or if it’s a longitudinal study with a payment schedule, then state this clearly.

Opt-out instructions

Discuss how a participant can opt out of the study, and what will happen if they do.

Data removal information

Let participants know whether they can remove their data from the dataset, and provide instructions on how to do so.

Data accessibility information

Explain whether anonymized data will be made accessible to other researchers, and how the data will be used (e.g., to publish a research study or guide government policy).

Contact details

Provide your contact details in case participants have questions. If you have ethics approval, include the contact details of the ethics board in question.

Don’t forget to debrief

At the end of your study, it's important to provide participants with a debriefing. This serves three purposes: it provides closure for the participant, it makes sure they leave the study with a positive impression, and it allows you to address any issues that arose during the study.

If you used deception or a cover story during the study, make sure to resolve this in the debriefing. The debriefing should consist of a short thank you message, as well as information about any deception that was used in the study.

In some cases, you might also want to give contact details for relevant helplines. An addiction support helpline would be pertinent after a study on drug abuse, for example.

In conclusion

A well-written study description is a win-win for you and the people taking part. It ensures participants know exactly what they need to do and have the motivation to do it. And it helps you gather valuable, quality data.

By providing participants with clear instructions and information about the study's purpose, you’ll set them up to give the best responses they can, and minimize the risk of incomplete or inaccurate data.

To learn more tips and tricks for running effective research and receiving high-quality data, download our best practice guide today.

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3. The Study Area: Definition and Characteristics

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study

• Open access
• Published: 07 September 2024

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• Riina Kleimola   ORCID: orcid.org/0000-0003-2091-2798 1 ,
• Laura Hirsto   ORCID: orcid.org/0000-0002-8963-3036 2 &
• Heli Ruokamo   ORCID: orcid.org/0000-0002-8679-781X 1  

Learning analytics provides a novel means to support the development and growth of students into self-regulated learners, but little is known about student perspectives on its utilization. To address this gap, the present study proposed the following research question: what are the perceptions of higher education students on the utilization of a learning analytics dashboard to promote self-regulated learning? More specifically, this can be expressed via the following threefold sub-question: how do higher education students perceive the use of a learning analytics dashboard and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of self-regulated learning? Data for the study were collected from students ( N  = 16) through semi-structured interviews and analyzed using a qualitative content analysis. Results indicated that the students perceived the use of the learning analytics dashboard as an opportunity for versatile learning support, providing them with a means to control and observe their studies and learning, while facilitating various performance phase processes. Insights from the analytics data could also be used in targeting the students’ development areas as well as in reflecting on their studies and learning, both individually and jointly with their educators, thus contributing to the activities of forethought and reflection phases. However, in order for the learning analytics dashboard to serve students more profoundly across myriad studies, its further development was deemed necessary. The findings of this investigation emphasize the need to integrate the use and development of learning analytics into versatile learning processes and mechanisms of comprehensive support and guidance.

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1 Introduction

Promoting students to become autonomous, self-regulated learners is a fundamental goal of education (Lodge et al., 2019 ; Puustinen & Pulkkinen, 2001 ). The importance of doing so is particularly highlighted in higher education (HE) contexts that strive to prepare its students for highly demanding and autonomous expert tasks (Virtanen, 2019 ). In order to perform successfully in diverse educational and professional settings, students need to take an active, self-initiated role in managing their learning processes, thereby assuming primary responsibility for their educational pursuits. Self-regulated learning (SRL) invites students to actively monitor, control, and regulate their cognition, motivation, and behavior in relation to their learning goals and contextual conditions (Pintrich, 2000 ). In an effort to create a favorable foundation for the development of SRL, many HE institutions have begun to explore and exploit the potential of emerging educational technologies, such as learning analytics (LA).

Despite the growing interest in adopting LA for educational purposes (Van Leeuwen et al., 2022 ), little is known about students’ perspectives on its utilization (Jivet et al., 2020 ; Wise et al., 2016 ). Additionally, there is only limited evidence on using LA to support SRL (Heikkinen et al., 2022 ; Jivet et al., 2018 ; Matcha et al., 2020 ; Viberg et al., 2020 ). Thus, more research is inevitably needed to better understand how students themselves consider the potential of analytics applications from the perspective of SRL. Involving students in the development of LA is particularly important, as they represent primary stakeholders targeted to benefit from its utilization (Dollinger & Lodge, 2018 ; West et al., 2020 ). LA should not only be developed for users but also with them in order to adapt its potential to their needs and expectations (Dollinger & Lodge, 2018 ; Klein et al., 2019 ).

LA is thought to provide a promising means to enhance student SRL by harnessing the massive amount of data stored in educational systems and facilitating appropriate means of support (Lodge et al., 2019 ). It is generally defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (Conole et al., 2011 , para. 4). The reporting of such data is typically conducted through learning analytics dashboards (LADs) that aggregate diverse types of indicators about learners and learning processes in a visualized form (Corrin & De Barba, 2014 ; Park & Jo, 2015 ; Schwendimann et al., 2017 ). Recently, there has been a rapid movement into LADs that present analytics data directly to students themselves (Schwendimann et al., 2017 ; Teasley, 2017 ; Van Leeuwen et al., 2022 ). Such analytics applications generally aim to provide students with insights into their study progress as well as support for optimizing learning outcomes (Molenaar et al., 2019 ; Sclater et al., 2016 ; Susnjak et al., 2022 ).

The purpose of this qualitative study is to examine how HE students perceive the use and development of an LAD to promote the different phases and processes of SRL. Instead of taking a course-level approach, this study addresses a less-examined study path perspective that covers the entirety of studies, from the start of an HE degree to its completion. A specific emphasis is placed on such an LAD that students could use both independently across studies and together with their tutor teachers as a component of educational guidance. As analytics applications are largely still under development (Sclater et al., 2016 ), and mainly in the exploratory phase (Schwendimann et al., 2017 ; Susnjak et al., 2022 ), it is essential to gain an understanding of how students perceive the use of these applications as a form of learning support. Preparing students to become efficient self-regulated learners is increasingly—and simultaneously—a matter of helping them develop into efficient users of analytics data.

2 Theoretical framework

2.1 enhancing srl in he.

SRL, which has been the subject of wide research interest over the last two decades (Panadero, 2017 ), is referred to as “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 1999 , p. 14). Self-regulated students are proactive in their endeavors to learn, and they engage in diverse, personally initiated metacognitive, motivational, and behavioral processes to achieve their goals (Zimmerman, 1999 ). They master their learning through covert, cognitive means but also through behavioral, social, and environmental approaches that are reciprocally interdependent and interrelated (Zimmerman, 1999 , 2015 ), thus emphasizing the sociocognitive views on SRL (Bandura, 1986 ).

When describing and modelling SRL, researchers have widely agreed on its cyclical nature and its organization into several distinct phases and processes (Panadero, 2017 ; Puustinen & Pulkkinen, 2001 ). In the well-established model by Zimmerman and Moylan ( 2009 ), SRL occurs in the cyclic phases of forethought, performance, and self-reflection that take place before, during, and after students’ efforts to learn. In the forethought phase, students prepare themselves for learning and approach the learning tasks through the processes of planning and goal setting, and the activation of self-motivation beliefs, such as self-efficacy perceptions, outcome expectations, and personal interests. Next, in the performance phase, they carry out the actual learning tasks and make use of self-control processes and strategies, such as self-instruction, time management, help-seeking, and interest enhancement. Moreover, they keep records of their performance and monitor their learning, while promoting the achievement of desired goals. In the final self-reflection phase, students participate in the processes of evaluating their learning and reflecting on the perceived causes of their successes and failures, which typically results in different types of cognitive and affective self-reactions as responses to such activity. This phase also forms the basis for the approaches to be adjusted for and applied in the subsequent forethought phase, thereby completing the SRL cycle. The model suggests that the processes in each phase influence the following ones in a cyclical and interactive manner and provide feedback for subsequent learning efforts (Zimmerman & Moylan, 2009 ; Zimmerman, 2011 ). Participation in these processes allows students to become self-aware, competent, and decisive in their learning approaches (Kramarski & Michalsky, 2009 ).

Although several other prevalent SRL models with specific emphases also exist (e.g., Pintrich, 2000 ; Winne & Hadwin, 1998 ; for a review, see Panadero, 2017 ), the one presented above provides a comprehensive yet straightforward framework for identifying and examining the key phases and processes related to SRL (Panadero & Alonso-Tapia, 2014 ). Developing thorough insights into the student SRL is especially needed in an HE context, where the increase in digitized educational settings and tools requires students to manage their learning in a way that is autonomous and self-initiated. When pursuing an HE degree, students are expected to engage in the cyclical phases and processes of SRL as a continuous effort throughout their studies. Involvement in SRL is needed not only to successfully perform a single study module, course, or task but also to actively promote the entirety of studies throughout semesters and academic years. It therefore plays a central role in the successful completion of HE studies.

From the study path perspective, the forethought phase requires HE students to be active in the directing and planning of their studies and learning—that is, setting achievable goals, making detailed plans, finding personal interests, and trusting in their abilities to complete the degree. The performance phase, in turn, invites students to participate in the control and observation of their studies and learning. While completing their studies, they must regularly track study performance, visualize relevant study information, create functional study environments, maintain motivation and interest, and seek and receive productive guidance. The reflection phase, on the other hand, involves students in evaluating and reflecting on their studies and learning—that is, analyzing their learning achievements and processing resulting responses. These activities typically occur as overlapping, cyclic, and connected processes and as a continuum across studies. Additionally, the phases may appear simultaneously, as students strive to learn and receive feedback from different processes (Pintrich, 2000 ). The processes may also emerge in more than one phase (Panadero & Alonso-Tapia, 2014 ), and boundaries between the phases are not always that precise.

SRL is shown to benefit HE students in various ways. Research has evidenced, for instance, that online students who use their time efficiently, are aware of their learning behavior, think critically, and show efforts to learn despite challenges are likely to achieve academic success when studying in online settings (Broadbent & Poon, 2015 ). SRL is also shown to contribute to many non-academic outcomes in HE blended environments (for a review, see Anthonysamy et al., 2020 ). Despite this importance, research (e.g., Azevedo et al., 2004 ; Barnard-Brak et al., 2010 ) has indicated that students differ in their ways to self-regulate, and not all are competent self-regulated learners by default. As such, many students would require and benefit from support to develop their SRL (Moos, 2018 ; Wong et al., 2019 ).

Supporting student SRL is generally considered the responsibility of a teaching staff (Callan et al., 2022 ; Kramarski, 2018 ). It can also be a specific task given to tutor teachers assigned to each student or to a group of students for particular academic years. Sometimes referred to as advisors, they are often teachers of study programs who aim to help students in decision-making, study planning, and career reflection (De Laet et al., 2020 ), while offering them guidance and support for the better management of learning. In recent years, efforts have also been made to promote student SRL with educational technologies such as LA (e.g., Marzouk et al., 2016 ; Wise et al., 2016 ). LA is used to deliver insights for students themselves to better self-regulate their learning (e.g., Jivet et al., 2021 ; Molenaar et al., 2019 ), and also to facilitate the interaction between students and guidance personnel (e.g., Charleer et al., 2018 ). It is generally thought to promote the development of future competences needed by students in education and working life (Kleimola & Leppisaari, 2022 ), and to offer novel insights into their motivational drivers (Kleimola et al., 2023 ).

2.2 LA as a potential tool to promote SRL

Much of the recent development in the field of LA has focused on the design and implementation of LADs. In general, their purpose is to support sensemaking and encourage students and teachers to make informed decisions about learning and teaching processes (Jivet et al., 2020 ; Verbert et al., 2020 ). Schwendimann and colleagues ( 2017 ) refer to an LAD as a “display that aggregates different indicators about learner(s), learning process(es) and/or learning context(s) into one or multiple visualizations” (p. 37). Such indicators may provide information, for instance, about student actions and use of learning contents on a learning platform, or the results of one’s learning performance, such as grades (Schwendimann et al., 2017 ). Data can also be extracted from educational institutions’ student information systems to provide students with snapshots of their study progress and access to learning support (Elouazizi, 2014 ). While visualizations enable intuitive and quick interpretations of educational data (Papamitsiou & Economides, 2015 ), they additionally require careful preparation, as not all users may necessarily interpret them uniformly (Aguilar, 2018 ).

LADs can target various stakeholders, and recently there has been a growing interest in their development for students’ personal use (Van Leeuwen et al., 2022 ). Such displays, also known as student-facing dashboards, are thought to increase students’ knowledge of themselves and to assist them in achieving educational goals (Eickholt et al., 2022 ). They are also believed to promote student autonomy by encouraging students to take control of their learning and by supporting their intrinsic motivation to succeed (Bodily & Verbert, 2017 ). However, simply making analytics applications available to students does not guarantee that they will be used productively in terms of learning (Wise, 2014 ; Wise et al., 2016 ). Moreover, they may not necessarily cover or address the relevant aspects of learning (Clow, 2013 ). Thus, to promote the widespread acceptance and adoption of LADs, it is crucial to consider students’ perspectives on their use as a means of learning support (Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ). If students’ needs are not adequately examined and met, such analytics applications may fail to encourage or even hinder the process of SRL (Schumacher & Ifenthaler, 2018 ).

Although previous research on students’ perceptions of LA to enhance their SRL appears to be limited, some studies have addressed such perspectives. Schumacher and Ifenthaler ( 2018 ) found that HE students appreciated LADs that help them plan and initiate their learning activities with supporting elements such as reminders, to-do lists, motivational prompts, learning objectives, and adaptive recommendations, thus promoting the forethought phase of SRL. The students in their study also expected such analytics applications to support the performance phase by providing analyses of their current situation and progress towards goals, materials to meet their individual learning needs, and opportunities for learning exploration and social interaction. To promote the self-reflection phase, the students anticipated LADs to allow for self-assessment, real-time feedback, and future recommendations but were divided as to whether they should receive comparative information about their own or their peers’ performance (Schumacher & Ifenthaler, 2018 ). Additionally, the students desired analytics applications to be holistic and advanced, as well as adaptable to individual needs (Schumacher & Ifenthaler, 2018 ).

Somewhat similar notions were made by Divjak and colleagues ( 2023 ), who discovered that students welcomed LADs that promote short-term planning and organization of learning but were wary of making comparisons or competing with peers, as they might demotivate learners. Correspondingly, De Barba et al. ( 2022 ) noted that students perceived goal setting and monitoring of progress from a multiple-goals approach as key features in LADs, but they were hesitant to view peer comparisons, as they could promote unproductive competition between students and challenge data privacy. In a similar vein, Rets et al. ( 2021 ) reported that students favored LADs that provide them with study recommendations but did not favor peer comparison unless additional information was included. Roberts et al. ( 2017 ), in turn, stressed that LADs should be customizable by students and offer them some level of control to support their SRL. Silvola et al. ( 2023 ) found that students perceived LADs as supportive for their study planning and monitoring at a study path level but also associated some challenges with them in terms of SRL. Further, Bennett ( 2018 ) found that students’ responses to receiving analytics data varied and were highly individual. There were different views, for instance, on the potential of analytics to motivate students: although it seemed to inspire most students, not all students felt the same way (Bennett, 2018 ; see also Corrin & De Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). Moreover, LADs were reported to evoke varying affective responses in students (Bennett, 2018 ; Lim et al., 2021 ).

To promote student SRL, it is imperative that LADs comprehensively address and support all phases of SRL (Schumacher & Ifenthaler, 2018 ). However, a systematic literature review conducted by Jivet et al. ( 2017 ) indicated that students were often offered only limited support for goal setting and planning, and comprehensive self-monitoring, as very few of the LADs included in their study enabled the management of self-set learning goals or the tracking of study progress over time. According to Jivet et al. ( 2017 ), this might indicate that most LADs were mainly harnessed to support the reflection and self-evaluation phase of SRL, as the other phases were mostly ignored. Somewhat contradictory results were obtained by Viberg et al. ( 2020 ), whose literature review revealed that most studies aiming to measure or support SRL with LA were primarily focused on the forethought and performance phases and less on the reflection phase. Heikkinen et al. ( 2022 ) discovered that not many of the studies combining analytics-based interventions and SRL processes covered all phases of SRL.

It appears that further development is inevitably required for LADs to better promote student SRL as a whole. Similarly, there is a demand for their tight integration into pedagogical practices and learning processes to encourage their productive use (Wise, 2014 ; Wise et al., 2016 ). One such strategy is to use these analytics applications as a part of guidance activity and as a joint tool for both students and guidance personnel. In the study by Charleer et al. ( 2018 ), the LAD was shown to trigger conversations and to facilitate dialogue between students and study advisors, improve the personalization of guidance, and provide insights into factual data for further interpretation and reflection. However, offering students access to an LAD only during the guidance meeting may not be sufficient to meet their requirements for the entire duration of their studies. For instance, Charleer and colleagues ( 2018 ) found that the students were also interested in using the LAD independently, outside of the guidance context. Also, it seems that encouraging students to actively advance their studies with such analytics applications necessitates a student-centered approach and holistic development through research. According to Rets et al. ( 2021 ), there is a particular call for qualitative insights, as many previous LAD studies that included students have primarily used quantitative approaches (e.g., Beheshitha et al., 2016 ; Divjak et al., 2023 ; Kim et al., 2016 ).

2.3 Research questions

The purpose of this qualitative study is to examine how HE students perceive the utilization of an LAD in SRL. A specific emphasis was placed on its utilization as part of the forethought, performance, and reflection phase processes, considered central to student SRL. The main research question (RQ) and the threefold sub-question are as follows:

RQ: What are the perceptions of HE students on the utilization of an LAD to promote SRL?

How do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL?

3.1 Context

The study was conducted in a university of applied sciences (UAS) in Finland that had launched an initial version of an LAD to be piloted together with its students and tutor teachers as a part of the guidance process. The LAD was descriptive in nature and consisted of commonly available analytics data and simple analytics indicators showing an individual student’s study progress and success in a study path. As is typical for descriptive analytics, it offered insights to better understand the past and present (Costas-Jauregui et al., 2021 ) while informing the future action (Van Leeuwen et al., 2022 ). The data were extracted from the UAS’ student information system and presented using Microsoft Power BI tools. No predictive or comparative information was included. The main display of the LAD consisted of three data visualizations and an information bar (see Fig.  1 , a–d), all presented originally in Finnish. Each visualization could also be expanded into a single display for more accurate viewing.

An example of the main display of the piloted LAD with data visualizations ( a – c ) and an information bar ( d )

First, the LAD included a data visualization that illustrated a student’s study progress and success per semester using a line chart (Fig.  1 , a). It displayed the scales for total number of credit points (left) and grade point averages (right) for courses completed on a semester timeline. Data points on the chart displayed an individual student’s study performance with respect to these indicators in each semester and were connected to each other with a line. Pointing to one of these data points also opened a data box that indicated the student name and information about courses (course name, scope, grade, assessment date) from which the credit points and grade point averages were obtained.

Second, the LAD contained another type of line chart that indicated a student’s individual study progress over time in more detail (Fig.  1 , b). The chart displayed a timeline with three-month periods and illustrated a scale for the accumulated credit points. Data points on the chart indicated the accumulated number of credit points obtained from the courses and appeared in blue if the student had passed the course(s) and in red if the student had failed the course(s) at that time. As with the line chart above it, the data points in this chart also provided more detailed information about the courses behind the credit points and were intertwined with a line.

Third, the LAD offered information related to a student’s study success through a radar chart (Fig.  1 , c). The chart represented the courses taken by the student and displayed a scale for the grades received from them. The lowest grade was placed in the center of the chart and the highest one on its outer circle. The grades in between were scaled on the chart accordingly, and the courses performed with a similar grade were displayed close to each other. Data points on the chart represented the grades obtained from numerically evaluated courses and were merged with a line. Each data point also had a data box with the course name and the grade obtained.

Fourth, the LAD included an information bar (Fig.  1 , d) that displayed the student number and the student name (removed from the figure), the total number of accumulated credit points, the grade point average for passed courses, and the amount of credit points obtained from practical training.

The LAD was piloted in authentic guidance meetings in which a tutor teacher and a student discussed topical issues related to the completion of studies. Such meetings were a part of the UAS’ standard guidance discussions that were typically held 1–2 times during the academic year, or more often if needed. In the studied meetings, the students and tutor teachers collectively reviewed the LAD to support the discussion. Only the tutor teachers were commonly able to access the LAD, as it was still under development and in the pilot phase. However, the students could examine its use as presented by the tutor teacher. In addition to the LAD, the meeting focused on reviewing the student’s personal study plan, which contained information about their studies to be completed and could be viewed through the student information system. Most of the meetings were organized online, and their duration varied according to an individual student’s needs. A researcher (first author) attended the meetings as an observer.

3.2 Participants and procedures

Participants were HE students ( N  = 16) pursuing a bachelor’s degree at the Finnish University of Applied Sciences (UAS), ranging from 21 to 49 years of age (mean = 30.38, median = 29.5); 11 (68.75%) were female, and 5 (31.25%) were male. HE studies commenced between 2016 and 2020, and comprised different academic fields, including business administration, culture, engineering, humanities and education, and social services and health care. Depending on the degree, study scope ranged from 210 to 240 ECTS credit points, which take approximately three and a half to four years to complete. However, the students could also proceed at a faster or slower pace under certain conditions. The students were selected to represent different study fields and study stages, and to have studied for more than one academic year. Informed consent to participate in the study was obtained from all students, and their participation was voluntary. The research design was approved by the respective UAS.

Data for this qualitative study was collected through semi-structured, individual student interviews conducted in April–September 2022. To address certain topics in each interview, an interview guide was used. The interview questions incorporated into the guide were tested in two student test interviews to simulate a real interview situation and to assure intelligibility, as also suggested by Chenail ( 2011 ). Findings indicated that the questions were largely usable, functional, and understandable, but some had to be slightly refined to ensure their conciseness and to improve clarity and familiarity of expressions vis-à-vis the target group. Also, the order of questions was partly reshaped to support the flow of discussion.

In the interviews, the students were asked to provide information about their demographic and educational backgrounds as well as their overall opinions of educational practices and the use of LA. In particular, they were invited to share their views on the use of the piloted LAD and its development as promoting different phases and processes of SRL. Students’ perceptions were generally based on the assumption that they could use the LAD both independently during their studies and collectively with their tutor teachers as a component of the guidance process.

Interviews were conducted immediately or shortly after the guidance meeting. Interview duration ranged from 42 to 70 min. The graphical presentation of the LAD was commonly shown to the students to provide stimuli and evoke discussion, as suggested by Kwasnicka et al. ( 2015 ). The interviews were conducted by the same researcher (first author) who observed the guidance meetings. They were primarily held online, and only one was organized face-to-face. All interviews were video recorded for subsequent analysis.

3.3 Data analysis

Interview recordings were transcribed verbatim, accumulating a total of 187 pages of textual material for analysis (Times New Roman, 12-point font, line spacing 1). A qualitative content analysis method was used to analyze the data (see Mayring, 2000 ; Schreier, 2014 ) to enhance in-depth understanding of the research phenomenon and to inform practical actions (Krippendorf, 2019 ). Also, data were approached both deductively and inductively (see Elo & Kyngäs, 2008 ; Merriam & Tisdell, 2016 ), and the analysis was supported using the ATLAS.ti program.

Analysis began with a thorough familiarization with the data in order to develop a general understanding of the students’ perspectives. First, the data were deductively coded using Zimmerman and Moylan’s ( 2009 ) SRL model as a theoretical guide for analysis and as applied to the study path perspective. All relevant units of analysis—such as paragraphs, sentences, or phrases that addressed the use of the LAD or its development in relation to the processes of SRL presented in the model—were initially identified from the data, and then sorted into meaningful units with specific codes. The focus was placed on instances in the data that were applicable and similar to the processes represented in the model, but the analysis was not limited to those that fully corresponded to them. The preliminary analysis involved several rounds of coding that ultimately led to the formation of main categories, grouped into the phases of SRL. The forethought phase consisted of processes that emphasized the planning and directing of studies and learning with the LAD. The performance phase, in turn, involved processes that addressed the control and observation of studies and learning through the LAD. Finally, the reflection phase included processes that focused on evaluating and reflecting on studies and learning with the LAD.

Second, the data were approached inductively by examining the use of the LAD and its development as distinct aspects within each phase and process of SRL (i.e., the main categories). The aim was, on the one hand, to identify how the use of the LAD was considered to serve the students in the phases and processes of SRL in its current form, and on the other hand, how it should be improved to better support them. The analysis not only focused on the characteristics of the LAD but also on the practices that surrounded its use and development. The units of analysis were first condensed from the data and then organized into subcategories for similar units. As suggested by Schreier ( 2014 ), the process was continued until a saturation point was reached—that is, no additional categories could be found. As a result, subcategories for all of the main categories were identified.

Following Schreier’s ( 2014 ) recommendation, the categories were named and described with specific data examples. Additionally, some guidelines were added to highlight differences between categories and to avoid overlap. Using parts of this categorization framework as a coding scheme, a portion of the data (120 text segments) was independently coded into the main categories by the first and second authors. The results were then compared, and all disagreements were resolved through negotiation until a shared consensus was reached. After minor changes were made to the coding scheme, the first author recoded all data. The number of students who had provided responses to each subcategory was counted and added to provide details on the study. For study integrity, the results are supported by data examples with the students’ aliases and the study fields they represented. The quotations were translated from Finnish to English.

The results are reported by first answering the threefold sub-question, that is, how do HE students perceive the use of an LAD and its development as promoting the (1) forethought, (2) performance, and (3) reflection phase processes of SRL. The subsequent results are then summarized to address the main RQ, that is, what are the main findings on HE students’ perceptions on the utilization of an LAD to promote SRL.

4.1 LAD as a part of the forethought phase processes

The students perceived the use of the LAD and its development as related to the forethought phase processes of SRL through the categorization presented in Table  1 below.

Regarding the process of goal setting , almost all students ( n  = 15) emphasized that the use of the LAD promoted the targeting of goal-oriented study completion and competence development. Analytics indicators—such as grades, grade point averages, and accumulated credit points—adequately informed the students of areas they should aim for, further improve, or put more effort into. Only one student ( n  = 1) considered the analytics data too general for establishing goals. However, some students ( n  = 7) specifically mentioned their desire to set and enter individual goals in the LAD. The students were considered to have individual intentions, which should also be made visible in the LAD:

For example, someone might complete [their studies] in four years, someone might do [them] even faster, so maybe in a way that there is the possibility…to set…that, well, I want or my goal is to graduate in this time, and then it would kind of show in it. (Sophia, Humanities and Education student)

Moreover, some students ( n  = 6) wanted to obtain information on the degree program’s overall target times, study requirements, or pace recommendations through the LAD.

In relation to the process of study planning , the use of the LAD provided many students ( n  = 8) grounds to plan and structure the promotion and completion of their studies, such as which courses and types of studies to choose, and what kind of study pace and schedule to follow. However, an even greater set of students ( n  = 12) hoped that the LAD could provide them with more sophisticated tools for planning. For instance, it could inform them about studies to be completed, analyze their study performance in detail, or make predictions for the future. Moreover, it should offer them opportunities to choose courses, make enrollments, set schedules, get reminders, and take notes. One example of such an advanced analytics application was described as follows: ‟It would be a bit like a conversational tool with the student as well, that you would first put…your studies in the program, so it would [then] remind you regularly that hey, do this” (James, Humanities and Education student).

When discussing the use of the LAD, most students ( n  = 12) emphasized the critical role of personal interests and preferences , which was found to not only guide studying and learning in general but to also drive and shape the utilization of the LAD. According to the students, using such an analytics application could particularly benefit those students who, for instance, prefer monitoring of study performance, perceive information in a visualized form, are interested in analytics or themselves, or find it relevant for their studies. Prior familiarization was also considered useful: ‟Of course, there are those who use this kind of thing more and those who use this kind of thing in daily life, so they could especially benefit from this, probably more than I do” (Olivia, Social Services and Health Care student). Even though the LAD was considered to offer pertinent insights for many types of learners, it might not be suitable for all. For instance, it could be challenging for some students to comprehend analytics data or to make effective use of them in their studies. In the development of the LAD, such personal aspects should be noted. The students ( n  = 7) believed the LAD might better adapt to students’ individual needs if it allows them to customize its features and displays or to use it voluntarily based on one’s personal interests and needs.

When describing the use of LAD, half of the students ( n  = 8) discussed its connections with self-efficacy . Making use of analytics data appeared to strengthen the students’ beliefs in their abilities to study and learn in a targeted manner, even if their own feelings suggested otherwise. As one of the students stated:

It’s nice to see that progress, that it has happened although it feels that it hasn’t. So, you can probably set goals based on [an idea] that you’re likely to progress, you could set [them] that you could graduate sometime. (Emma, Engineering student)

On the other hand, the use of the LAD also seemed to require students to have sufficient self-efficacy. It was perceived as vital especially when the analytics data showed unfavorable study performance, such as failed or incomplete courses, or gaps in the study performance with respect to peers. One student ( n  = 1) suggested that the LAD could include praises as evidence of and support for appropriate study performance. Such incentives may help improve the students’ self-confidence as learners. Apart from this, however, the students had no other recommendations for developing the use of the LAD to support self-efficacy.

4.2 LAD as a part of the performance phase processes

The students discussed the use of the LAD and its development in relation to the performance phase processes of SRL according to the categories described in Table  2 below.

The students ( n  = 16) widely agreed that using the LAD benefited them in the process of metacognitive monitoring. By indicating the progress and success of study performance, the LAD was thought to be well suited for observing the course of studies and the development of competences. Moreover, it helped the students to gain awareness of their individual strengths and weaknesses, as well as successes and failures, in a study path. Tracking individual study performance was also found to contribute to purposeful study completion, as the following data example demonstrates:

It’s important especially when there is a target time to graduate, so of course you must follow and stay on track in many ways as there are many such pitfalls to easily fall into, [and] as I’ve fallen quite many times, it’s good [to monitor]. (Sarah, Culture student)

Additionally, the insights of monitoring could be used in future job searches to provide information about acquired competences to potential employers. The successful promotion of studies was generally perceived to require regular monitoring by both students and their educators. However, one of the students considered it a particular responsibility of the students themselves, as the studies were completed at an HE level and were thus voluntary for them. To provide more in-depth insights, many students ( n  = 12) recommended the incorporation of a course-level monitoring opportunity in the LAD. More detailed information was needed, for instance, about course descriptions, assignments completed, and grades received. The rest of the students ( n  = 4), however, wanted to keep the course-level monitoring within the learning management system. One of them stated that it could also be a place through which the students could use the LAD. Some students ( n  = 6) emphasized the need to reconsider current assessment practices to enable better tracking of study performance. Specifically, assessments could be made in greater detail and grades given immediately after course completion. The variation in scales and time points of assessments between the courses and degree programs posed potential challenges for monitoring, thus prompting the need to unify educational practices at the organizational level.

As an activity closely related to metacognitive monitoring, the process of imaging and visualizing was emphasized by the students as helping them to advance in their educational pursuits. Most students ( n  = 15) mentioned that using the LAD allowed them to easily image their study path and clarify their study situation. As one of them stated, ‟This is quite clear, this like, that you can see the overall situation with a quick glance” (Anna, Business Administration student). The visualizations were perceived as informative, tangible, and understandable. However, they were also thought to carry the risk of students neglecting some other relevant aspects of studying and learning in the course of attracting such focused attention. Although the visualizations were generally considered clear, some students ( n  = 11) noted that they could be further improved to better organize the analytics data. For instance, the students suggested the attractive use of colors and the categorization of different types of courses. Visual symbols, in turn, may be particularly effective in course-level data. Technical aspects should also be carefully considered to avoid false visualizations.

Regarding the process of environmental structuring , the LAD appeared to be a welcome addition to the study toolkit and overall study environment. A few students ( n  = 4) considered it appropriate to utilize the LAD as a separate PowerBI application alongside other (Microsoft O365) study tools, but they also felt that it could be utilized through other systems if necessary. However, one student ( n  = 1) raised the need for overall system integrations and some students ( n  = 8) expressed a specific wish to use the LAD as an integrated part of the student information system that was thought to improve its accessibility. A few students ( n  = 6) also wanted to receive some additional analytics data as related to the information stored in such a system. For instance, the students could be informed about their study progress or offered feedback on their overall performance in relation to the personal study plan. Other students ( n  = 10), in turn, did not consider the need for this or did not mention it. It was generally emphasized that the LAD should remain sufficiently clear and simple, as too much information can make its use ineffective:

I think there is just enough information in this. Of course, if you would want to add something small, you could, but I don’t know how much, because I feel that when there is too much information, so it’s a bit like you can’t get as much out of it as you could get. (Olivia, Social Services and Health Care student)

Moreover, the analytics data must be kept private and protected. The students generally desired personal access to the LAD; if given such an opportunity, almost all ( n  = 15) believed they would utilize it in the future, and only one ( n  = 1) was unsure about this prospect. The analytics data were believed to be of particular use when studies were actively promoted. Hence they should be made available to the students from the start of their studies.

Regarding the process of interest and motivation enhancement , all students ( n  = 16) mentioned that using the LAD stimulated their interest or enhanced their motivation, although to varying degrees. For some students, a general tracking of studies was enough to encourage them to continue their pursuits, while others were particularly inspired by seeing either high or low study performance. The development of motivation and interest was generally thought to be a hindrance if the students perceived the analytics data as unfavorable or lacking essential information. As one of students mentioned, ‟If your [chart] line was downward, and if there were only ones and zeros or something like that, it could in a way decrease the motivation” (Helen, Humanities and Education student). It appeared that enhancing interest and motivation was mainly dependent on the students’ own efforts to succeed in their course of study and thus to generate favorable analytics data. However, some students ( n  = 7) felt that it could be additionally enhanced by diversifying and improving the analytics tools in the LAD. For example, the opportunities for more detailed analyses and future study planning or comparisons of study performance with that of peers might further increase these students’ motivation and interest in their studies. Even so, it was also considered possible that especially comparisons between students might have the opposite, demotivating and discouraging effect.

All students ( n  = 16) mentioned that using the LAD facilitated the process of seeking and accessing help . It enabled the identification of potential support needs—for instance, if several courses were failed or left unfinished. As noted, they were perceived as alarming signals for the students themselves to seek help and for the guidance personnel to provide targeted support. As one of the students emphasized, it was important that not only ‟a teacher [tutor] gets interested in looking at what the situation is but also that a student would understand to communicate regarding the promotion of studies and situations” (Emily, Social Services and Health Care student). Some students ( n  = 9) suggested that the students, tutor teachers, or both could receive automated alerts if concerns were to arise. On the other hand, the impact of such automated notifications on changing the course of study was considered somewhat questionable. Above all, the students ( n  = 16) preferred human contact and personal support by the guidance personnel, who would use a sensitive approach to address possibly delicate issues. Support would be important to include in existing practices, as the tutor teachers should not be overburdened. One of the students also stated that the automated alerts could be sufficient if they just worked effectively.

4.3 LAD as a part of the reflection phase and processes

The students addressed the use of the LAD and its development as a part of the reflection phase processes of SRL through categories outlined in Table  3 .

The students widely appreciated the support provided by the use of the LAD for the process of evaluation and reflection. The majority ( n  = 15) mentioned that it allowed them to individually reflect on the underlying aspects of their study performance, such as what kind of learners they are, what type of teaching or learning methods suit them, and what factors impact their learning. Similarly, the students ( n  = 16) valued the possibility of examining the analytics data together with the guidance personnel, such as tutor teachers, and commonly expressed a desire to revisit the LAD in future guidance meetings. It was thought to promote the interpretation of analytics data and to facilitate collective reflection on the reasons behind one’s study success or failure. However, this might require a certain orientation from the guidance personnel, as the student describes below:

I feel that it’s possible to address such themes that what may perhaps cause this. Of course, a lot depends on how amenable the teacher [tutor] is, like are we focusing on how the studies are going but in a way, not so much on what may cause it. (Sophia, Humanities and Education student)

Some students ( n  = 8) proposed incorporating familiarization with analytics insights into course implementations of the degree programs. Additionally, many students ( n  = 11) expressed a desire to examine the student group’s general progress in tutoring classes together with the tutor teacher and peers, particularly if the results were properly targeted and anonymized, and presented in a discreet manner. However, some students ( n  = 5) found this irrelevant. The students were generally wary to evaluate and compare an individual student’s study performance in relation to the peer average through the LAD. While some students ( n  = 4) welcomed such an opportunity, others ( n  = 6) considered it unnecessary. A few students ( n  = 5) emphasized that such comparisons between students should be optional and visible if desired, and one student ( n  = 1) did not have a definite view about it. Rather than competing with others, the students stressed the importance of challenging themselves and evaluating study performance against their own goals or previous achievements.

According to the students ( n  = 16), the use of the LAD was associated with a wide range of affective reactions . Positive responses such as joy, relief, and satisfaction were considered to emerge if the analytics data displayed by the LAD was perceived as favorable and expected, and supportive of future learning. Similarly, negative responses such as anxiety, pressure, or stress were likely to occur if such data indicated poor performance, thus challenging the learning process. On the other hand, such self-reactions could also appear as neutral or indifferent, depending on the student and the situation. Individual responses were related not only to the current version of the LAD but also to its further development targets. Some students ( n  = 3) pointed out the importance of guidance and support, through which the affective reactions could be processed together with professionals. As one of the students underlined, it is important “that there is also that support for the studies, that it isn’t just like you have this chart, and it looks bad, that try to manage. Perhaps there is that support, support in a significant role as well” (Sophia, Humanities and Education student). It seemed critical that the students were not left alone with the LAD but rather were given assistance to deal with the various responses its use may elicit.

4.4 Summary of findings on LAD utilization to promote SRL among HE students

In summary, HE students’ perceptions on the utilization of an LAD to promote SRL phases and processes were largely congruent, but nonetheless partly varied. In particular, the students agreed on the support provided by the LAD during the performance phase and for the purpose of metacognitive monitoring. Such activity was thought to not only enable the students to observe their studies and learning, but to also create the basis for the emergence of all other processes, which were facilitated by the monitoring. That is, while the students familiarized themselves with the course of their studies via the analytics data, they could further apply these insights—for instance, to visualize study situations, enhance motivation, and identify possible support needs. Monitoring with the LAD was also perceived to partly promote the students to the forethought and reflection phases and processes by giving them grounds to target their development areas as well as to reflect on their studies and learning individually and jointly with their tutor teachers. However, it was clear that less emphasis was placed on using the LAD for study planning, addressing individual interests, activating self-efficacy, and supporting environmental structuring, thus giving incentives for their further investigation and future improvement.

Although the LAD used in this study seemed to serve many functions as such, its holistic development was deemed necessary for more thorough SRL support. In particular, the students agreed on the need to improve such an analytics application to further strengthen the performance phase processes—particularly monitoring—by, for instance, developing it for the students’ independent use, and by integrating it with instructional and guidance practices provided by their educators. Moreover, the students commonly wished for more advanced analytics tools that could more directly contribute to the planning of studies and joint reflection of group-level analytics data. To better support the various processes of SRL, new features were generally welcomed into the LAD, although the students’ views and emphases on them also varied. Mixed perspectives were related, for instance, to the need to enrich data or compare students within the LAD. Thus, it seemed important to develop the LAD to conform to the preferences of its users. Along with improving the LAD, students also paid attention to the development of pedagogical practices and guidance processes that together could create appropriate conditions for the emergence of SRL.

5 Discussion

The purpose of this study was to gain insights into HE students’ perceptions on the utilization of an LAD to promote their SRL. The investigation extended the previous research by offering in-depth descriptions of the specific phases and processes of SRL associated with the use of an LAD and its development targets. By applying a study path perspective, it also provided novel insights into how to promote students to become self-regulated learners and effective users of analytics data as an integral part of their studies in HE.

The students’ perspectives on the use of LAD and its development were initially explored as a part of the forethought phase processes of SRL, with a particular focus on the planning and directing of studies and learning. In line with previous research (e.g., Divjak et al., 2023 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students in this study appreciated an analytics application that helped them prepare for their future learning endeavors—that is, the initial phase of the SRL cycle (see Zimmerman & Moylan, 2009 ). Using the LAD specifically allowed the students to recognize their development areas and offered a basis to organize their future coursework. However, improvements to allow students to set individual goals and make plans directly within the LAD, as well as to increase awareness of general degree goals, were also desired. These seem to be pertinent avenues for development, as goals may inspire the students not only to invest greater efforts in learning but also to track their achievements against these goals (Wise, 2014 ; Wise et al., 2016 ). While education is typically entered with individual starting points, it is important to allow the students to set personal targets and routes for their learning (Wise, 2014 ; Wise et al., 2016 ).

The results of this study indicate that the use of LADs is primarily driven and shaped by students’ personal interests and preferences, which commonly play a crucial role in the development of SRL (see Zimmerman & Moylan, 2009 ; Panadero & Alonso-Tapia, 2014 ). It might particularly benefit those students for whom analytics-related activities are characteristic and of interest, and who consider them personally meaningful for their studies. It has been argued that if students consider analytics applications serve their learning, they are also willing to use them (Schumacher & Ifenthaler, 2018 ; Wise et al., 2016 ). On the other hand, it has also been stated that not all students are necessarily able to maximize its possible benefits on their own and might need support in understanding its purpose (Wise, 2014 ) and in finding personal relevance for its use. The findings of this study suggest that a more individual fit of LADs could be promoted by allowing students to customize its functionalities and displays. Comparable results have also been obtained from other studies (e.g., Bennett, 2018 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), thus highlighting the need to develop customized LADs that better meet the needs of diverse students and that empower them to control their analytics data. More attention may also be needed to promote the use and development of LADs to support self-efficacy, as it appeared to be an unrecognized potential still for many students in this study. According to Rets et al. ( 2021 ), using LADs for such a purpose might particularly benefit online learners and part-time students, who often face various requirements and thus may forget the efforts put into learning and giving themselves enough credit. By facilitating students’ self-confidence, it could also promote the necessary changes in study behavior, at least for those students with low self-efficacy (Rets et al., 2021 ).

Second, the students’ views on the use of the LAD and its development were investigated in terms of the performance phase processes of SRL, with an emphasis on the control and observation of studies and learning. In line with the results of other studies (De Barba et al., 2022 ; Rets et al., 2021 ; Schumacher & Ifenthaler, 2018 ; Silvola et al., 2023 ), the students preferred using the LAD to monitor their study performance—they wanted to follow their progress and success over time and keep themselves and their educators up to date. According to Jivet et al. ( 2017 ), such functionality directly promotes the performance phase of SRL. Moreover, it seemed to serve as a basis for other activities under SRL, all of which were heavily dependent and built on the monitoring. The results of this study, however, imply that monitoring opportunities should be further expanded to provide even more detailed insights. Moreover, they indicate the need to develop and refine pedagogical practices at the organizational level in order to better serve student monitoring. As monitoring plays a crucial role in SRL (Zimmerman & Moylan, 2009 ), it is essential to examine how it is related to other SRL processes and how it can be effectively promoted with analytics applications (Viberg et al., 2020 ).

In this study, the students used the LAD not only to monitor but also to image and visualize their learning. In accordance with the views of Papamitsiou and Economides ( 2015 ), the visualizations transformed the analytics data into an easily interpretable visual form. The visualizations were not considered to generate information overload, although such a concern has sometimes been associated with the use of LADs (e.g., Susnjak et al., 2022 ). However, the students widely preferred even more descriptive and nuanced illustrations to clarify and structure the analytics data. At the same time, care must be taken to ensure that the visualizations do not divert too much attention from other relevant aspects of learning, as was also found important in prior research (e.g., Charleer et al., 2018 ; Wise, 2014 ). It seems critical that an LAD inform but not overwhelm its users (Susnjak et al., 2022 ). As argued by Klein et al. ( 2019 ), confusing visualizations may not only generate mistrust but also lead to their complete nonuse.

Although the LAD piloted in the study was considered to be a relatively functional application, it could be even more accessible and usable if it was incorporated into the student information system and enriched with the data from it. Even then, however, the LAD should remain simple to use and its data privacy ensured. It has been argued that more information is not always better (Aguilar, 2018 ), and the analytics indicators must be carefully considered to truly optimize learning (Clow, 2013 ). While developing their SRL, students would particularly benefit from a well-structured environment with fewer distractions and more facilitators for learning (Panadero & Alonso-Tapia, 2014 ). The smooth promotion of studies also seems to require personal access to the analytics data. Similar to the learners in Charleer and colleagues’ ( 2018 ) study, the students in this study desired to take advantage of the LAD autonomously, beyond the guidance context. It was believed to be especially used when they were actively promoting their studies. This is seen as a somewhat expected finding given the significant role of study performance indicators in the LAD. However, the question is also raised as to whether such an analytics application would be used mainly by those students who progress diligently but would be ignored by those who advance only a little or not at all. Ideally, the LAD would serve students in different situations and at various stages of studies.

Using the LAD offered the students a promising means to enhance motivation and interest in their studies through the monitoring of analytics data. However, not all students were inspired in the same manner or similar analytics data displayed by the LAD. Although the LAD was seen as inspiring and interesting in many ways, it also had the potential to demotivate or even discourage. This finding corroborates the results of other studies reporting mixed results on the power of LADs to motivate students (e.g., Bennett, 2018 ; Corrin & de Barba, 2014 ; Schumacher & Ifenthaler, 2018 ). As such, it would be essential that the analytics applications consider and address students with different performance levels and motivational factors (Jivet et al., 2017 ). Based on the results of this study, diversifying the tools included in the LAD might also be necessary. On the other hand, the enhancement of motivation was also found to be the responsibility of the students themselves—that is, if the students wish the analytics application to display favorable analytics data and thus motivate them, they must first display concomitant effort in their studies.

The use of the LAD provided a convenient way to intervene if the students’ study performance did not meet expectations. With the LAD, both the students and their tutor teachers could detect signs of possible support needs and address them with guidance. In the future, such needs could also be reported through automated alerts. Overall, however, the students in this study preferred human contact and personal support over automated interventions, contrary to the findings obtained by Roberts and colleagues ( 2017 ). Being identified to their educators did not seem to be a particular concern for them, although it has been found to worry students in other contexts (e.g., Roberts et al., 2017 ). Rather, the students felt they would benefit more from personal support that was specifically targeted to them and sensitive in its approach. The students generally demanded delicate, ethical consideration when acting upon analytics data and in the provision of support, which was also found to be important in prior research (e.g., Kleimola & Leppisaari, 2022 ). Additionally, Wise and colleagues ( 2016 ) underlined the need to foster student agency and to prevent students from becoming overly reliant on analytics-based interventions: if all of the students’ mistakes are pointed out to them, they may no longer learn to recognize mistakes on their own. Therefore, to support SRL, it is essential to know when to intervene and when to let students solve challenges independently (Kramarski & Michalsky, 2009 ).

Lastly, the students’ perceptions on the use and development of the LAD were examined from the perspective of the reflection phase processes of SRL, with particular attention given to evaluation and reflection on studies and learning. The use of the LAD provided the students with a basis to individually reflect on the potential causes behind their study performance, for better or worse. Moreover, they could address such issues together with guidance personnel and thus make better sense of the analytics data. Corresponding to the results of Charleer et al.’s ( 2018 ) study, collective reflection on analytical data provided the students with new insights and supported their understanding. Engaging in such reflective practices offered the students the opportunity to complete the SRL cycle and draw the necessary conclusions regarding their performance for subsequent actions (see Zimmerman & Moylan, 2009 ). In the future, analytics-based reflection could also be implemented in joint tutoring classes and courses included in the degree programs. This would likely promote the integration of LADs into the activity flow of educational environments, as recommended by Wise and colleagues ( 2016 ). In sum, using LADs should be a regular part of pedagogical practices and learning processes (Wise et al., 2016 ).

When evaluating and reflecting on their studies and learning, the students preferred to focus on themselves and their own development as learners. Similar to earlier findings (e.g., Divjak et al., 2023 ; Rets et al., 2021 ; Roberts et al., 2017 ; Schumacher & Ifenthaler, 2018 ), the students felt differently about the need to develop LADs to compare their study performance with that of other students. Although this function could help some of the students to position themselves in relation to their peers, others thought it should be optional or completely avoided. In agreement with the findings of Divjak et al. ( 2023 ), it seemed that the students wanted to avoid mutual competition comparisons; however, it might not be harmful for everyone and in every case. Consequently, care is required when considering the kind of features in the LAD that offer real value to students in a particular context (Divjak et al., 2023 ). Rather than limiting the point of reference only to peers, it might be useful to also offer students other targets for comparative activity, such as individual students’ previous progress or goals set for the activity (Wise, 2014 ; Wise et al., 2016 ; see also Bandura, 1986 ). In addition, it is important that students not be left alone to face and cope with the various reactions that may be elicited by such evaluation and reflection with analytics data (Kleimola & Leppisaari, 2022 ). As the results of this study and those of others (e.g., Bennett, 2018 ; Lim et al., 2021 ) generally indicate, affective responses evoked by LADs may vary and are not always exclusively positive. Providing a safe environment for students to reflect on successes and failures and to process the resulting responses might not only encourage necessary changes in future studies but also promote the use of an LAD as a learning support.

In summary, the results of this study imply that making an effective use of an analytics application—even with a limited amount of analytics data and functionality available—may facilitate the growth of students into self-regulated learners. That is, even if the LAD principally addresses some particular phase or process of SRL, it can act as a catalyst to encourage students in the development of SRL on a wider scale. This finding also emphasizes the interdependent and interactive nature of SRL (see Zimmerman, 2011 ; Zimmerman & Moylan, 2009 ) that similarly seems to characterize the use of an LAD. However, the potential of LADs to promote SRL may be lost unless students themselves are (pro)active in initiating and engaging with such activity or receive appropriate pedagogical support for it. There appears to be a specific need for guidance that is sensitive to the students’ affective reactions and would help students learn and develop with analytics data. Providing the students with adequate support is particularly critical if their studies have not progressed favorably or as planned. It seems important that the LAD would not only target those students who are already self-regulated learners but, with appropriate support and guidance, would also serve those students who are gradually growing in that direction.

5.1 Limitations and further research

This study has some limitations. First, it involved a relatively small number of HE students who were examined in a pilot setting. Although the sample was sufficient to provide in-depth insights and the saturation point was reached, it might be useful in further research to use quantitative approaches and diverse groups of students to improve the generalizability of results to a larger student population. Also, addressing the perspectives of guidance personnel, specifically tutor teachers, could provide additional insights into the use and development of LADs to promote SRL.

Second, the LAD piloted and investigated in this study was not yet widely in use or accessible by the students. Moreover, it was examined for a relatively brief time, so the students’ perceptions were shaped not only by their experiences but also by their expectations of its potential. Future research on students and tutor teachers with more extensive user experience could build an even more profound picture of the possibilities and limitations of the LAD from a study path perspective. Such investigation might also benefit from trace data collected from the students’ and tutor teachers’ interactions with the LAD. It would be valuable to examine how the students and tutor teachers make use of the LAD in the long term and how it is integrated into learning activities and pedagogical practices.

Third, due to the emphasis on an HE institution and the analytics application used in this specific context, the transferability of results may be limited. However, the results of this study offer many important and applicable perspectives to consider in various educational environments where LADs are implemented and aimed at supporting students across their studies.

6 Conclusions

The results of this study offer useful insights for the creation of LADs that are closely related to the theoretical aspects of learning and that meet the particular needs of their users. In particular, the study increases the understanding of how such analytics applications should be connected to the entirety of studies—that is, what kind of learning processes and pedagogical support are needed alongside them to best serve students in their learning. Consequently, it encourages a comprehensive consideration and promotion of pedagogy, educational technology, and related practices in HE. The role of LA in supporting learning and guidance seems significant, so investments must be made in its appropriate use and development. In particular, the voice of the students must be listened to, as it promotes their commitment to the joint development process and fosters the productive use of analytics applications in learning. At its best, LA becomes an integral part of HE settings, one that helps students to complete their studies and contributes to their development into self-regulated learners.

Data availability

Not applicable.

Abbreviations

Higher education

• Learning analytics
• Learning analytics dashboard

Research question

• Self-regulated learning

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Language process: In the preparation process of the manuscript, the Quillbot Paraphraser tool was used to improve language clarity in some parts of the text (e.g., word choice). The manuscript was also proofread by a professional. After using this tool and service, the authors reviewed and revised the text as necessary, taking full responsibility for the content of this manuscript.

The authors also thank the communications and information technology specialists of the UAS under study for their support in editing Fig. 1 for publication.

This research was partly funded by Business Finland through the European Regional Development Fund (ERDF) project “Utilization of learning analytics in the various educational levels for supporting self-regulated learning (OAHOT)” (Grant no. 5145/31/2019). The article was completed with grants from the Finnish Cultural Foundation’s Central Ostrobothnia Regional Fund (Grant no. 25221232) and The Emil Aaltonen Foundation (Grant no. 230078), which were awarded to the first author.

Open Access funding provided by University of Lapland.

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Riina Kleimola & Heli Ruokamo

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Conceptualization: RK, LH. Data collection: RK. Formal analysis: RK, LH. Writing—original draft: RK. Writing—review and editing: RK, LH, HR. Supervision: LH, HR. All authors read and approved the final manuscript.

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Kleimola, R., Hirsto, L. & Ruokamo, H. Promoting higher education students’ self-regulated learning through learning analytics: A qualitative study. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12978-4

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Tap water study detects PFAS ‘forever chemicals’ across the US

Usgs estimates at least 45% of tap water could have one or more pfas.

At least 45% of the nation’s tap water is estimated to have one or more types of the chemicals known as per- and polyfluorinated alkyl substances, or PFAS, according to a new study by the U.S. Geological Survey. There are more than 12,000 types of PFAS, not all of which can be detected with current tests; the USGS study tested for the presence of 32 types. 

This USGS research marks the first time anyone has tested for and compared PFAS in tap water from both private and government-regulated public water supplies on a broad scale throughout the country. Those data were used to model and estimate PFAS contamination nationwide. This USGS study can help members of the public to understand their risk of exposure and inform policy and management decisions regarding testing and treatment options for drinking water. 

PFAS are a group of synthetic chemicals used in a wide variety of common applications, from the linings of fast-food boxes and non-stick cookware to fire-fighting foams and other purposes. High concentrations of some PFAS may lead to adverse health risks in people, according to the U.S. Environmental Protection Agency . Research is still ongoing to better understand the potential health effects of PFAS exposure over long periods of time. Because they break down very slowly, PFAS are commonly called “forever chemicals.” Their persistence in the environment and prevalence across the country make them a unique water-quality concern. 

"USGS scientists tested water collected directly from people’s kitchen sinks across the nation, providing the most comprehensive study to date on PFAS in tap water from both private wells and public supplies,” said USGS research hydrologist Kelly Smalling, the study’s lead author. “The study estimates that at least one type of PFAS – of those that were monitored – could be present in nearly half of the tap water in the U.S. Furthermore, PFAS concentrations were similar between public supplies and private wells.”  

The EPA regulates public water supplies, and homeowners are responsible for the maintenance, testing and treatment of private water supplies. Those interested in testing and treating private wells should contact their local and state officials for guidance. Testing is the only way to confirm the presence of these contaminants in wells. For more information about PFAS regulations, visit the EPA’s website on addressing PFAS . 

The study tested for 32 individual PFAS compounds using a method developed by the USGS National Water Quality Laboratory. The most frequently detected compounds in this study were PFBS, PFHxS and PFOA. The interim health advisories released by the EPA in 2022 for PFOS and PFOA were exceeded in every sample in which they were detected in this study. 

Scientists collected tap water samples from 716 locations representing a range of low, medium and high human-impacted areas. The low category includes protected lands; medium includes residential and rural areas with no known PFAS sources; and high includes urban areas and locations with reported PFAS sources such as industry or waste sites.  

Most of the exposure was observed near urban areas and potential PFAS sources. This included the Great Plains, Great Lakes, Eastern Seaboard, and Central/Southern California regions. The study’s results are in line with previous research concluding that people in urban areas have a higher likelihood of PFAS exposure. USGS scientists estimate that the probability of PFAS not being observed in tap water is about 75% in rural areas and around 25% in urban areas.  

Learn more about USGS research on PFAS by reading the USGS strategy for the study of PFAS and visiting the PFAS Integrated Science Team’s website . The new study builds upon previous research by the USGS and partners regarding human-derived contaminants, including PFAS, in drinking water and PFAS in groundwater . 

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Per- and polyfluoroalkyl substances (PFAS) in United States tapwater: Comparison of underserved private-well and public-supply exposures and associated health implications

Pfas in us tapwater interactive dashboard.

Drinking-water quality and potential exposures to per- and poly-fluoroalkyl substances (PFAS) at the point-of-use (tapwater) are a rising concern in the United States (US).

USGS Releases Strategy for Addressing PFAS Science Gaps

RESTON, Va. – The U.S. Geological Survey released a strategy today that outlines the agency’s future scientific role in the study of perfluoroalkyl...

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