A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward.
Background: While formative assessment is widely regarded as essential for improving teaching and learning, it remains difficult to operationalize due to systemic misalignment with other instructional practices, limited teacher capacity, low feedback quality, inferential uncertainty, domain‐general...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 3; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | editorial tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185452197&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185452197 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Jun2025 vid: 41 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 185452197 185452197 185452197 10.1111/jcal.70056 185452197 ppf: 1 ppct: 9 formats: tig: atl: A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward. aug: au: Banihashem, Seyyed Kazem Gašević, Dragan Noroozi, Omid affil: Open Universiteit, Heerlen, the Netherlands sug: subj: Learning Methods Data Analytics Educational Measurement Serial Publications Prediction Models Feedback Quality Improvement Artificial Intelligence, Generative ab: Background: While formative assessment is widely regarded as essential for improving teaching and learning, it remains difficult to operationalize due to systemic misalignment with other instructional practices, limited teacher capacity, low feedback quality, inferential uncertainty, domain‐general approaches, and validity concerns. Objectives: This editorial introduces a special issue that critically examines how learning analytics can contribute to advancing formative assessment by addressing persistent challenges in its design and implementation. Results and Conclusion: The twelve studies featured in this issue demonstrate several innovations such as adaptive feedback, multimodal analytics, predictive modeling, dashboard design, and evidence‐centered assessment frameworks. Collectively, these studies demonstrate how learning analytics can enhance formative assessment by personalizing feedback, scaling dialogic feedback, understanding the nature of feedback, improving assessment validity, automating assessment, uncovering deeper learning patterns, and improving assessment alignment with instructional goals. However, the issue also highlights several underexplored gaps, including the limited disciplinary adaptation of analytics tools, a lack of ongoing student involvement in feedback design, insufficient attention to ethical concerns and the physiological and motivational dimensions of assessment, and a limited understanding of the role of emerging technologies, in particular, Generative AI (GenAI). This editorial argues for a more critical, inclusive, and context‐sensitive approach to learning analytics in formative assessment—one that centers pedagogy, teacher and student agency, and long‐term educational value. The contributions of this special issue lay essential groundwork for future research, policy, and practice aimed at transforming formative assessment through learning analytics. pubtype: Academic Journal doctype: editorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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