Who Should Attend
Faculty, learning analytics teams, academic advisors and teaching center staff.
Learning Objectives
- Identify useful learning indicators and limitations of available data.
- Interpret learning patterns without overclaiming causation.
- Evaluate educational technologies for usability, accessibility and privacy.
- Propose a responsible learning analytics intervention and evaluation.
Curriculum & Instructional Hours
| Module | Key Topics | Hours |
|---|---|---|
| Learning Analytics Foundations | Indicators, learning questions, data provenance | 6 |
| Data Literacy for Educators | Descriptive analysis, interpretation, uncertainty | 6 |
| Educational Technology Evaluation | Selection criteria, usability, accessibility, integration | 6 |
| Ethics and Student Privacy | Consent, bias, minimization, governance | 6 |
| Intervention and Evaluation Design | Student support, monitoring, effectiveness metrics | 6 |
| Analytics Capstone | Dashboard mockup, action plan, risk assessment | 6 |
Teaching & Assessment
Learning Methods
Interactive workshops, guided labs, peer review, case discussions and applied capstone projects.
Assessment
Workshop engagement (25%), applied exercises and draft artifacts (35%), final capstone project (40%); proposed.
Program Outcomes
- Learning indicator and data dictionary
- Educational technology evaluation matrix
- Privacy-aware analytics dashboard prototype
- Student support intervention and evaluation plan
Program planning notice: Curriculum, duration, faculty, delivery, visits, and completion arrangements are proposed and subject to final institutional confirmation. University visits, visiting scholar placements, academic credit, and partner-issued credentials are not guaranteed.