Learning Analytics
for Educators

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Dashboard

Module 01 · Student Success Analytics Simulator

Understanding Learning Analytics
& Student Performance

You are the Academic Success Coordinator for Introduction to Educational Technology. Analyze the real-time dashboard for your 12 students, identify who is at risk, and decide where to intervene — before it's too late.

COMING SOON
From Data to Decisions: The Learning Analytics Framework ~20 min · Module 1 · Introduction
"Learning Analytics is the measurement, collection, analysis and reporting of data about learners, for the purpose of understanding and optimizing learning."
— George Siemens, 1st International Conference on Learning Analytics (2011)
3×
Higher success rates with
early warning systems
— UNESCO IITE, 2021
40%
At-risk students who never
receive timely intervention
— EDUCAUSE, 2023

The 4-Step Analytics Cycle

①

Collect

Gather attendance, grades, submissions, logins

②

Analyze

Find patterns and trends across dimensions

③

Interpret

Apply pedagogical context to the data

④

Act

Design targeted, timely interventions

Your Course Dashboard — Week 6

Introduction to Educational Technology · 12 students enrolled · Week 6 of 15

Student Completion % Avg Grade Late Subs Absences Last Active Trend Risk

Be specific: reference student IDs and which metrics concern you most.

Analyzing student data and detecting risk patterns…

AI Risk Analysis — Complete

Critical
High Risk
Medium Risk
On Track

Detected Risk Patterns

Prioritized Intervention Plan

Key Insight

✓ Risk Analysis Complete

You identified at-risk students and mapped intervention priorities using data-driven analysis.

Module 02 · Dashboard Builder Studio

Dashboard Design &
Educational Data Visualization

Build your own learning analytics dashboard. Select the KPIs and metrics that matter most for your context — then get an AI expert review of your design's effectiveness and gaps.

COMING SOON
Designing Dashboards That Drive Decisions ~18 min · Module 2 · Dashboard Design
"A dashboard that shows everything shows nothing. The discipline of KPI selection is the discipline of knowing what decisions you actually need to make."
— Adapted from Davenport & Harris, Competing on Analytics (2007)
7±2
The cognitive limit for meaningful
simultaneous data dimensions
— Miller's Law, applied to dashboard design

Dashboard Design Principles

Purpose First

Define the decision the dashboard must support before selecting any metric.

Audience-Aware

An instructor dashboard and a department head dashboard serve different questions.

Action-Oriented

Every metric should trigger a possible action. If you can't act on it, remove it.

Trend Over Snapshot

Direction of change is more predictive than current value alone.

Define your dashboard context

Build your dashboard

Select the KPIs and metrics for your dashboard. Aim for 5–8 for optimal readability.

Select at least 3 KPIs to continue.

Evaluating your dashboard design…
Effectiveness

Dashboard Assessment

✓ Strengths

⚠ Gaps to Address

Recommended Additions

Design Tips

Module 03 · Student Retention Crisis Simulator

Data-Driven Intervention
& Continuous Improvement

A real retention crisis just hit your institution. Course completion has dropped 23 points in 3 weeks. You have the data. Now you need a plan — and AI will build it with you.

COMING SOON
From Crisis to Recovery: Evidence-Based Intervention Planning ~22 min · Module 3 · Intervention Design
"The value of learning analytics is not in the data. It is in the decision it enables — and the action it inspires."
— Adapted from George Siemens & Phil Long, Penetrating the Fog (2011)
W4
Optimal intervention week —
before compound deficit sets in
— Educational research consensus
87%
Of preventable dropouts had
detectable signals 3+ weeks prior
— Retention analytics studies
⚠ RETENTION CRISIS ALERT Week 6 — Immediate Action Required

Introduction to Data Science · DS-101

78 students enrolled · 8 dropouts in the last 2 weeks

Course Completion Rate
87%
64%
−23pts
Average Grade
74
61
−13pts
Late Submissions
12
31
+158%
Weekly Logins / Student
4.2
1.8
−57%
Discussion Posts (total)
145
38
−74%
Students "Silent" >10 days
2
19
+850%

What are the root causes?

Select all that apply based on the data you analyzed. Consider multiple contributing factors.

Design your intervention strategy

Select the interventions you would implement. Align them with the root causes you identified.

Building your Student Retention Recovery Plan…

AI Recovery Plan — Generated

Root Cause Analysis

Recovery Phases

Success Metrics

MetricBaselineTargetTimeframe

Projected Improvement

Final Assessment

Learning Analytics
Knowledge Check

10 questions covering the three modules. You need 7 or more correct to earn your credential. You can review your answers after submitting.

🎓

You did it, .

You have successfully completed the Learning Analytics for Educators microcredential.

Credential

Learning Analytics Specialist for Educators

Recipient
Credential ID
InstitutionAtlantis University
Hours15 hours
Download Certificate PDF