Imagine discovering that you spend hours revising a subject but repeatedly struggle with the same examination questions. You attend lectures, complete assignments and read your notes, yet your results do not reflect the effort you put in. What if you could identify exactly where your learning is breaking down? This is one of the opportunities offered by learning analytics.
Educational institutions increasingly use digital platforms to collect and interpret information about learning activities. When used responsibly, these insights can help students understand their progress, identify areas requiring attention and make more informed decisions about how they study.
But learning analytics is not a magic system that guarantees better grades. Its value depends on the quality of the data, the interpretation of the results and the actions taken afterwards.
1. What is learning analytics?
Learning analytics refers to the collection, measurement, analysis and reporting of information about learners and their learning environments, with the aim of understanding and improving learning.
Information may come from:
- Online learning platform activity.
- Quiz and assessment results.
- Assignment submissions.
- Participation in learning activities.
- Completion of course materials.
- Patterns of engagement with digital resources.
- Student feedback and learning reflections.
For example, a university learning platform may show that a student repeatedly struggles with a particular topic or has not completed several essential activities.
An instructor could use this information to offer additional explanations or support.
The purpose should be to improve learning—not simply to monitor students.
2. Five ways learning analytics can help students
1. Identifying knowledge gaps
Assessment data can help students recognise which topics they understand and which require further revision.
A student who performs well in one area but repeatedly makes errors in another can adjust their study plan accordingly.
2. Making progress visible
A learning dashboard may show completed modules, upcoming deadlines, assessment scores and learning activities.
Seeing progress can help students organise their work and recognise when they are falling behind.
3. Supporting personalised feedback
Teachers may use assessment patterns to provide more targeted explanations or recommend additional practice.
However, recommendations should be checked against the student’s actual needs rather than accepted automatically.
4. Identifying students who may need support
Institutions can examine patterns that may indicate difficulties, such as missed activities or declining assessment performance.
These indicators should lead to supportive conversations, not automatic judgements about a student’s ability or motivation.
5. Improving course design
When educators examine patterns across a class, they may discover that many students struggle with the same topic.
This could indicate a need to improve teaching materials, provide more examples or reconsider the way a concept is introduced.
3. How students can use analytics to improve their own learning
You do not need access to sophisticated university software to apply the principles of learning analytics.
A simple study tracker can help you monitor:
- Topics studied.
- Time spent on each topic.
- Practice questions attempted.
- Accuracy of answers.
- Concepts that remain difficult.
- Revision activities completed.
- Performance in later assessments.
The aim is to identify useful patterns.
For example, if you repeatedly make mistakes in a particular topic, simply spending more time rereading the notes may not solve the problem. You may need additional examples, guided practice or an explanation from your teacher.
4. A simple example: Turning assessment results into a better study plan
Imagine a student studying five topics.
| Topic | Practice score | Suggested next step |
|---|---|---|
| Topic A | 85% | Continue periodic revision |
| Topic B | 55% | Review key concepts and practise |
| Topic C | 90% | Test understanding with harder questions |
| Topic D | 60% | Seek feedback and identify errors |
| Topic E | 75% | Use retrieval practice |
These figures are illustrative, not universal performance standards.
The important lesson is that study time should be allocated according to learning needs, not simply divided equally between topics.
5. The risks: When student data becomes a problem
Learning analytics can raise serious ethical and practical questions.
Privacy: Students should understand what information is collected and how it is used.
Consent and transparency: Institutions should explain relevant data practices and provide appropriate information about students’ rights.
Bias: Data may reflect unequal access to technology, disability-related barriers or other circumstances rather than genuine academic ability.
Misinterpretation: A student who accesses fewer online resources may be studying through printed materials or offline methods.
Excessive monitoring: Tracking every interaction can create pressure and undermine trust.
Research on the ethics of digital trace data in education highlights concerns about privacy, informed consent, data ownership, validity and accountability.
6. What universities should do
Institutions should establish clear policies explaining:
- What student information is collected.
- Why it is collected.
- Who can access it.
- How long it is retained.
- How students can question inaccurate information.
- How automated predictions are reviewed.
- What safeguards protect student privacy.
Most importantly, learning analytics should support human educational judgement rather than replace it.
7. Can learning analytics guarantee better grades?
No.
Data can help identify possible problems, but students still need effective study methods, appropriate teaching, motivation, feedback and access to support.
A dashboard showing that a student is behind does not automatically explain why the student is struggling or how best to help.
Learning analytics should therefore be treated as a decision-support tool, not a substitute for teaching or learning.
8. Use learning data to make better decisions, not to define your ability
Learning analytics offers opportunities for students and educators to understand progress, identify difficulties and make more informed learning decisions.
But the most useful information is not necessarily the largest amount of data. It is information that leads to practical, appropriate improvements.
Do not study simply to increase your hours or complete more activities. Study to understand what you know, identify what you do not know and take action to improve.
Recommended further reading
- OECD Digital Education Outlook 2021: Learning Analytics for School and System Management
- Utilising Learning Analytics to Support Study Success in Higher Education: A Systematic Review
- The Ethics of Using Digital Trace Data in Education
- Mapping the Ethics of Learning Analytics in Higher Education
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Disclaimer
This article is intended for general educational and informational purposes. Learning analytics tools differ between institutions, and their predictions or recommendations are not guaranteed to be accurate. Institutions should follow applicable privacy, data protection, equality and education requirements. Scholars Careers is an independent educational information platform and is not affiliated with or endorsed by the OECD or the research organisations mentioned.
