Practical Guide for Higher Education - Building Learning Dashboards: Which Metrics to Use

1. What is a learning dashboard and why does it matter?

A learning dashboard is a visual tool that integrates academic, pedagogical, and student experience data to support informed decision-making in universities and higher education institutions.

A well-designed dashboard is not a decorative report, but a strategic instrument that helps institutions:

• Detect early risks of student dropout

• Evaluate course and program effectiveness

• Improve the student learning experience

• Align learning outcomes with institutional goals

• Support quality assurance and accreditation processes

👉 Key principle: It’s not about showing more data, but about showing the right data for each audience.

2. Before choosing metrics: key questions

Before building the dashboard, institutions should answer:

1. Who will use it?

   • Senior leadership / Vice-rectorate

   • Deans and program directors

   • Faculty

   • Academic support and student success teams

2. What decisions should it enable?

   • Early interventions

   • Curriculum redesign

   • Resource allocation

   • Teaching improvement

3. How frequently will actions be taken?

   • Real time

   • Weekly

   • Per term / semester

Metrics must always be linked to concrete decisions.

3. Key metric categories for learning dashboards

Below are the most relevant metrics, organized by dimension.

3.1 Engagement and participation metrics

These measure the level of student interaction with the learning environment.

Recommended metrics:

• LMS login frequency

• Weekly active learning time

• Resources viewed vs. available

• Participation in forums and collaborative activities

• On-time assignment submission

Strategic use:

• Identify disengaged students

• Evaluate instructional design

• Trigger early alerts

⚠️ Warning: high activity does not necessarily mean deep learning.

3.2 Academic progress metrics

These track student progress in relation to course objectives.

Recommended metrics:

• Percentage of activities completed

• Progress by unit or competency

• Learning pace compared to the cohort

• Number of assessment attempts

Strategic use:

• Identify course bottlenecks

• Adjust academic workload

• Personalize academic support

• 
  

3.3 Performance and achievement metrics

These evaluate academic outcomes.

Recommended metrics:

• Average grade per course

• Grade distribution

• Pass / fail rates

• Results by learning outcome

Strategic use:

• Quality assurance

• Curriculum review

• Accreditation evidence

👉 Ideally, these metrics should be linked to competencies, not just final grades.

3.4 Retention and persistence metrics

Critical for institutional sustainability.

Recommended metrics:

• Early dropout rate

• Semester-to-semester retention

• Re-enrollment rates

• Academic interruptions or stop-outs

Strategic use:

• Design student retention strategies

• Measure the impact of support programs

• Justify investment in advising and tutoring

• 
  

3.5 Student experience metrics

These complement quantitative data with perception and satisfaction indicators.

Recommended metrics:

• Course satisfaction surveys

• Academic Net Promoter Score (NPS)

• Categorized qualitative feedback

• Perceived workload

Strategic use:

• Continuous improvement

• Faculty development

• Student-centered course design

  
  

3.6 Equity and inclusion metrics (when applicable)

These help identify systemic gaps and inequalities.

Recommended metrics:

• Performance by entry profile

• Progress gaps across student groups

• Differential use of academic support services

⚠️ Important: always apply ethical principles and regulatory compliance.

4. Composite indicators and traffic-light systems

The most effective dashboards include synthetic indicators, such as:

• Academic risk index

• Engagement level (high / medium / low)

• Dropout probability

Recommended visualizations:

• Traffic-light indicators

• Trend lines over time

• Comparisons with historical cohorts

👉 These features support rapid understanding for non-technical decision-makers.

5. Common mistakes when defining metrics

❌ Measuring only what is easy to capture❌ Mixing operational and strategic indicators❌ Using the same dashboard for all user profiles❌ Failing to define actions linked to each metric❌ Overloading dashboards with charts and no narrative

6. Dashboard design best practices

• Limit to 8–12 key metrics per view

• Clear and consistent visualizations

• Progressive drill-down capabilities

• Temporal and comparative context

• Plain, non-technical language

• 
  

7. Conclusion

An effective learning dashboard in higher education:

• Is aligned with institutional strategy

• Combines academic, pedagogical, and experience data

• Prioritizes action over visualization

• Evolves with the educational model

Learning analytics is not about monitoring students, but about creating better conditions for their success.

Would you like to turn this guide into a strategy tailored to your organization?

👉 Let’s talk at www.analytikus.com