Learning Analytics Governance: Using Student Data Responsibly

Learning analytics promise to help schools identify struggling students, personalize instruction, and improve outcomes. But without proper governance, these same systems can create privacy violations, embed bias, and damage student-teacher relationships.

This guide helps schools implement learning analytics responsibly.

Executive Summary

• Learning analytics collect and analyze student data to improve educational outcomes
• Without governance, analytics can violate privacy, embed bias, and create surveillance culture
• Key principles: purpose limitation, transparency, human oversight, fairness
• Students should benefit from analytics, not just be subjects of them
• Governance should address what data is collected, how it's used, and who decides
• Regular audits ensure analytics serve educational purposes, not just administrative convenience
• Parents and students have rights regarding analytics about them

What Are Learning Analytics?

Learning analytics is the measurement, collection, analysis, and reporting of data about learners to understand and optimize learning.

Common applications:

• At-risk identification (predicting which students may struggle)
• Adaptive learning (adjusting content based on performance)
• Engagement tracking (monitoring participation and completion)
• Performance dashboards (visualizing student progress)
• Early warning systems (alerting when indicators decline)

Data sources:

• Learning management systems (assignment completion, grades)
• Student information systems (demographics, attendance, historical data)
• Assessment platforms (test scores, item-level performance)
• Behavioral data (login times, time on task, click patterns)

The Governance Challenge

Learning analytics create tension between beneficial use and potential harm:

Governance balances these tensions.

Governance Principles

Principle 1: Purpose Limitation

Analytics should serve educational purposes—student learning and wellbeing.

Questions to ask:

• Does this analytics use case directly serve student learning?
• Would we be comfortable explaining this to parents?
• Is this the minimum data needed for the purpose?

Principle 2: Transparency

Students, parents, and teachers should understand how analytics work.

Questions to ask:

• Do parents know what data is collected and how it's used?
• Do students understand when they're being tracked?
• Are predictions and scores explainable?

Principle 3: Human Oversight

Analytics should inform human decisions, not replace them.

Questions to ask:

• Who reviews analytics outputs before action?
• Can a human override algorithmic recommendations?
• Are teachers trained to interpret analytics critically?

Principle 4: Fairness

Analytics should not disadvantage student groups.

Questions to ask:

• Do predictions perform equally across demographic groups?
• Could this system embed or amplify existing biases?
• Are we testing for differential impact?

Principle 5: Student Agency

Students should benefit from and have appropriate control over their data.

Questions to ask:

• Do students have access to their own analytics?
• Can students/parents challenge inaccurate data or predictions?
• Are we building student data literacy?

Governance Policy Template

[School Name] Learning Analytics Governance Policy

Purpose: This policy governs the collection, analysis, and use of student learning data.

Scope: All systems that collect or analyze data about student learning behaviors, performance, or outcomes.

1. Permitted Uses

Learning analytics may be used to:

• Identify students who may benefit from additional support
• Personalize learning experiences
• Inform instructional decisions
• Evaluate program effectiveness
• Support student goal-setting and self-reflection

2. Prohibited Uses

Learning analytics shall not be used to:

• Make automated decisions about student discipline
• Share identifiable student data for commercial purposes
• Create permanent student profiles that follow them across schools
• Make high-stakes decisions without human review
• Evaluate teacher performance based solely on student analytics

3. Data Minimization

• Collect only data necessary for specified educational purposes
• Review data collection annually and eliminate unnecessary collection
• Retain analytics data only as long as needed for educational purposes

4. Transparency

• Inform parents/students about learning analytics in privacy notice
• Make dashboards available to students and parents where appropriate
• Explain how predictive models work in accessible language

5. Human Oversight

• Require teacher review before acting on predictive analytics
• Train teachers on appropriate interpretation and use
• Document decisions influenced by analytics

6. Fairness

• Conduct bias audits on predictive models annually
• Monitor for differential impact across student groups
• Investigate and address any identified disparities

7. Student Rights

Students/parents may:

• Access analytics data about them
• Request correction of inaccurate data
• Opt out of specific analytics programs (with alternative arrangements)
• Challenge decisions influenced by analytics

8. Oversight

• [Designate responsible person/committee] oversees this policy
• Annual review of analytics programs against this policy
• Report significant concerns to school leadership

Implementation Checklist

Assessment

• Inventoried all learning analytics systems in use
• Documented what data each system collects
• Identified how analytics outputs are used
• Assessed alignment with governance principles

Policy

• Adopted learning analytics governance policy
• Communicated policy to staff, parents, students
• Integrated into data protection framework

Operations

• Trained teachers on appropriate analytics use
• Established human oversight procedures
• Created process for handling parent/student requests
• Scheduled annual bias audits

Monitoring

• Tracking analytics usage patterns
• Monitoring for differential impact
• Collecting feedback from teachers and families
• Annual policy review scheduled

Frequently Asked Questions

Q1: Do we need governance if we're just using built-in LMS analytics?

Yes. Even basic analytics (login times, completion rates) create governance needs. The question is proportionate governance—simpler analytics need simpler governance.

Q2: How do we explain predictions to parents who ask "why is my child flagged"?

Train staff to explain: "The system looks at patterns like [attendance, assignment completion, grade trends] that have historically been associated with needing support. It's a tool that helps us pay attention, not a judgment. Let's talk about what we've observed and how we can help."

Q3: What if analytics show something we don't want to act on?

If you can't or won't act on insights, reconsider collecting the data. Analytics without action create responsibility without benefit.

Q4: Can students opt out of learning analytics?

Consider making opt-out available for non-essential analytics. Essential learning tools may require participation, but students should understand what's happening.

Q5: What about analytics vendors—do they have governance?

Vendor governance is your responsibility through contracts. Require appropriate data use terms, auditing rights, and deletion on termination.

Next Steps

Start with an inventory of your current learning analytics. Assess against governance principles. Address the highest-risk gaps first.

Need help establishing learning analytics governance?

→ Book an AI Readiness Audit with Pertama Partners. We'll assess your analytics practices and help you build responsible governance.

References

1. Sclater, N. (2017). Learning Analytics Explained. Routledge.
2. Jisc. (2020). Code of Practice for Learning Analytics.
3. UNESCO. (2024). Data Governance in Education.