Data analytics empowers EdTech by turning information such as attendance, assessments, learning-platform activity and student records into feedback that educators and school leaders can act on. Used well, it can help tailor instruction, spot students who may need support, evaluate teaching approaches and plan resources. It is not a substitute for educator judgment: useful results depend on reliable, connected data, privacy safeguards, staff training and ongoing evaluation.
How data analytics is changing education technology
Learning analytics is the use of data to understand and improve teaching and learning. EdTech systems can collect information from learning management systems (LMSs), virtual learning environments, assessment tools and student information systems (SISs). Analytics brings those records together to reveal patterns that may be hard to see in isolated assignments or attendance sheets.
The value comes from connecting a signal to a meaningful response. A dashboard showing missed activities, for example, is only useful if an educator can investigate what is happening and decide whether to offer clarification, a different resource or a conversation with the student. Analytics can inform that decision; it cannot make the educational context disappear.
How learning analytics can improve education
Personalized learning and formative feedback
Assessment results and activity-level LMS data can help educators identify concepts a student has not yet mastered. They can use that evidence to give more specific feedback, revisit a misconception or sequence suitable practice and learning materials. The goal is not simply to serve each student more content, but to make feedback and instruction more responsive to what the student needs next.
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Earlier support for students who may be struggling
Learning analytics can surface patterns associated with disengagement or difficulty, such as a change in participation or a run of missed assessments. UNESCO’s 2023 Global Education Monitoring Report describes early detection as one use of learning analytics. It also gives Course Signals as an example of a system that flags students who may not pass, giving educators a chance to intervene.
A flag is a prompt to check in, not proof that a student will fail or an explanation of why they are struggling. Educators should review the underlying information, consider circumstances the system may not capture and speak with the student before deciding what support is appropriate.
Evaluating instruction and EdTech products
Data from LMSs and virtual learning environments can be examined alongside learning outcomes to understand engagement patterns and assess pedagogical practices. That can help a school ask whether a teaching approach or digital resource is supporting its intended learning goals. A pattern or association alone does not establish that a tool caused an outcome, so evaluation should use clear questions and appropriate evidence rather than treating usage counts as proof of impact.
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Planning for students and institutions
Longitudinal student-information data can support analysis of enrolment, attendance, educational pathways, graduation, examinations, credits and grade progression. At the school or system level, dashboards and linked records can help leaders identify needs and consider how to allocate support and resources. Comparisons are most useful when systems use compatible definitions and leaders account for differences in student populations and context.
What OECD data shows about analytics infrastructure
OECD’s 2023 figures illustrate both the reach of digital learning platforms and the limits of the systems used to monitor student progress. The percentages below refer to systems tracking individual student trajectories, not to all jurisdictions.
| OECD measure | Reported figure | What it indicates |
|---|---|---|
| Jurisdictions with online learning platforms | 26 of 29 jurisdictions | Online learning platforms were widely reported across the jurisdictions covered. |
| Jurisdictions with systems tracking individual student trajectories | 19 of 29 jurisdictions | Tracking individual progress was less universal than platform availability. |
| Trajectory-tracking systems integrating standardized national-evaluation results | 45% | Less than half of these systems included those results. |
| Trajectory-tracking systems providing dashboards or visualisations | 31% | About three in ten of these systems offered this reporting feature. |
| Trajectory-tracking systems linking student and teacher data | 31% | About three in ten connected those data types. |
These figures describe the jurisdictions and systems in OECD’s 2023 reporting; they do not establish how effective a platform is in a particular school. OECD’s broader point is that coherent system-wide approaches to tools, technologies and the people who use them are needed to realize digital technology’s potential to improve learning outcomes.
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What dashboards and analytics tools schools need
There is no single dashboard that every school needs. A useful setup starts with the educational decision the school wants to support, then checks whether the necessary data can be accessed safely and interpreted by the people responsible for acting on it. Depending on that purpose, a deployment may involve an LMS or learning analytics platform, an SIS, an education-data dashboard, or connections among them.
- For classroom feedback: assessment and learning-activity information that helps teachers see progress and respond to misconceptions.
- For early support: understandable indicators or alerts that prompt an educator to review a student’s situation, with a clear path to follow up.
- For school planning: longitudinal attendance, enrolment, pathway and outcome information suited to the planning question.
- For system improvement: reporting that can connect relevant student and teacher information while respecting access limits and local privacy requirements.
When comparing platforms or deployments, assess the following before choosing a product:
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- Data sources and interoperability: Can the system use the school’s relevant LMS, assessment and student-record data without creating conflicting or duplicate records?
- Dashboard and alert usefulness: Do reports make the signal, its basis and the possible next step clear to the educator who needs it?
- Evidence of learning impact: Is there credible evidence that the proposed use improves an educational outcome, rather than merely increasing activity or screen time?
- Privacy and control: Are consent, retention, role-based access and data minimization addressed for the school’s context?
- Bias, explainability and human override: Can staff understand why an alert appears, consider possible bias and correct or disregard a result when context calls for it?
- Accessibility and equity: Can students and educators with different access needs use the system, and could gaps in connectivity or device access distort the data?
- Implementation effort: What integration work, staff training and ongoing support will be needed?
- Exportability and vendor lock-in: Can the school retrieve its data in a usable form if it changes systems?
Procurement should connect the tool to a specific educational purpose. UNESCO’s 2023 report found that around two-thirds of education software licences were unused in the United States. That figure is a warning about adoption and purchasing, not a universal estimate for other countries or proof that every unused licence is wasteful.
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Privacy, equity and other risks of student-data analytics
Student analytics can involve sensitive records and inferences about a child’s learning or behaviour. UNESCO’s 2023 report states that only 16% of countries explicitly guarantee data privacy in education by law. It also found that 89% of 163 education technology products recommended during the pandemic could survey children. These findings describe the policy and product landscape reported by UNESCO; they do not mean that every product collected the same information or that every country has the same legal protections.
Risks include collecting more information than necessary, exposing data to people who do not need it, using indicators that unfairly disadvantage some students, and treating a model’s prediction as a settled judgment. De-identifying information can support research, but it does not eliminate the possibility of re-identification. A system may also miss factors that matter to a student’s circumstances, making human review essential.
Safeguards schools should put in place
- Define the use: State what educational decision the data will support and avoid collecting information without a clear need.
- Limit access: Use role-based permissions so each person can see only the information needed for their responsibilities.
- Set retention and consent practices: Explain what is collected, how it is used, who can access it and how long it is kept, in line with applicable law and school policy.
- Check for bias and errors: Monitor how indicators perform across student groups and provide a way for educators to correct inaccurate records or challenge an alert.
- Keep people accountable: Make sure an educator reviews consequential signals and can override or disregard an automated recommendation.
- Build data literacy: Train staff to interpret dashboards, understand uncertainty and communicate clearly with students and families.
- Evaluate continuously: Check whether the system is achieving its stated educational goal and whether unintended harms are emerging.
UNESCO’s policy test is that “technology serves education, not the other way round.” That means privacy, equity and learning goals should shape the analytics deployment rather than being treated as afterthoughts.
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Making analytics useful in practice
A school can reduce the risk of buying an elaborate system that no one uses by starting with a narrowly defined problem and involving the educators who will act on its outputs. For example, a school concerned about missed assignments might first determine which records are reliable, who should receive a signal, what a responsible follow-up looks like and how it will tell whether the intervention helped. Only then can it judge whether a new dashboard or integration is needed.
Analytics is most empowering when it improves a real decision without reducing students to data points. Interoperable systems, trustworthy information, staff capability and human oversight turn data from a record of activity into a tool for better support and planning.
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