A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward DOI
Seyyed Kazem Banihashem, Dragan Gašević, Omid Noroozi

и другие.

Journal of Computer Assisted Learning, Год журнала: 2025, Номер 41(3)

Опубликована: Май 5, 2025

ABSTRACT Background While formative assessment is widely regarded as essential for improving teaching and learning, it remains difficult to operationalize due systemic misalignment with other instructional practices, limited teacher capacity, low feedback quality, inferential uncertainty, domain‐general approaches, validity concerns. Objectives This editorial introduces a special issue that critically examines how learning analytics can contribute advancing by addressing persistent challenges in its design implementation. Results Conclusion The twelve studies featured this demonstrate several innovations such adaptive feedback, multimodal analytics, predictive modeling, dashboard design, evidence‐centered frameworks. Collectively, these enhance personalizing scaling dialogic understanding the nature of validity, automating assessment, uncovering deeper patterns, alignment goals. However, also highlights underexplored gaps, including disciplinary adaptation tools, lack ongoing student involvement insufficient attention ethical concerns physiological motivational dimensions role emerging technologies, particular, Generative AI (GenAI). argues more critical, inclusive, context‐sensitive approach assessment—one centers pedagogy, agency, long‐term educational value. contributions lay groundwork future research, policy, practice aimed at transforming through analytics.

Язык: Английский

Looking Beyond the Hype: Understanding the Effects of AI on Learning DOI Creative Commons
Elisabeth Bauer, Samuel Greiff,

Arthur C. Graesser

и другие.

Educational Psychology Review, Год журнала: 2025, Номер 37(2)

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0
A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward DOI
Seyyed Kazem Banihashem, Dragan Gašević, Omid Noroozi

и другие.

Journal of Computer Assisted Learning, Год журнала: 2025, Номер 41(3)

Опубликована: Май 5, 2025

ABSTRACT Background While formative assessment is widely regarded as essential for improving teaching and learning, it remains difficult to operationalize due systemic misalignment with other instructional practices, limited teacher capacity, low feedback quality, inferential uncertainty, domain‐general approaches, validity concerns. Objectives This editorial introduces a special issue that critically examines how learning analytics can contribute advancing by addressing persistent challenges in its design implementation. Results Conclusion The twelve studies featured this demonstrate several innovations such adaptive feedback, multimodal analytics, predictive modeling, dashboard design, evidence‐centered frameworks. Collectively, these enhance personalizing scaling dialogic understanding the nature of validity, automating assessment, uncovering deeper patterns, alignment goals. However, also highlights underexplored gaps, including disciplinary adaptation tools, lack ongoing student involvement insufficient attention ethical concerns physiological motivational dimensions role emerging technologies, particular, Generative AI (GenAI). argues more critical, inclusive, context‐sensitive approach assessment—one centers pedagogy, agency, long‐term educational value. contributions lay groundwork future research, policy, practice aimed at transforming through analytics.

Язык: Английский

Процитировано

0