A self-service course-level framework lowers the infrastructure barrier to piloting learning analytics interventions¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? SPPA enables academics to pilot LAIs in their courses without institutional investment in LAI infrastructure, addressing the main reported obstacle to uptake. → Alalawi 2024

Evidence¶

Alalawi 2024¶

Alalawi, K., Athauda, R., & Chiong, R. (2024). An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00429-7

q2 · i? · design · r2

Design-argument and evaluation in six tertiary courses with four academics piloting interventions; quantitative and qualitative data were collected and analysed. The article argues this self-service access "addressed a major obstacle that inhibits academics from piloting LAIs."

"SPPA provided a self-service model for academics to pilot LAI in their courses, allowing seamless access to its features via a web browser without the need for extensive efforts in developing institution-centric LAI infrastructure."

Discussion¶

Related Claims¶

• Academics piloting SPPA provided effective interventions and held positive views of the framework — related
• Course-specific prediction models may outperform institution-wide one-size-fits-all models because instructional conditions vary — related
• Grounding LAI feedback in constructive alignment and the Hattie–Timperley feedback model structures personalised interventions — related
• Equity commitments in technology adoption face ongoing constraints from infrastructure, funding, and organizational capacity — a broader claim this one bears on
• Integrating cognitive apprenticeship methods in a Web-based course required a significant and largely unbounded instructor time investment — related