Literaturnachweis - Detailanzeige
| Autor/inn/en | Khosravi, Hassan; Shabaninejad, Shiva; Bakharia, Aneesha; Sadiq, Shazia; Indulska, Marta; Gasevic, Dragan |
|---|---|
| Titel | Intelligent Learning Analytics Dashboards: Automated Drill-Down Recommendations to Support Teacher Data Exploration |
| Quelle | In: Journal of Learning Analytics, 8 (2021) 3, S.133-154 (22 Seiten)Infoseite zur Zeitschrift
PDF als Volltext kostenfreie Datei |
| Zusatzinformation | ORCID (Khosravi, Hassan) ORCID (Shabaninejad, Shiva) ORCID (Bakharia, Aneesha) ORCID (Sadiq, Shazia) ORCID (Indulska, Marta) ORCID (Gasevic, Dragan) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1929-7750 |
| Schlagwörter | Learning Analytics; Visual Aids; Artificial Intelligence; Information Retrieval; Foreign Countries; College Faculty; Teacher Attitudes; Prediction; At Risk Students; Predictor Variables; Data Analysis; Decision Making; Undergraduate Students; College Mathematics; Mathematics Instruction; Student Characteristics; Performance; Australia |
| Abstract | Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs. (As Provided). |
| Anmerkungen | Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
Literaturbeschaffung und Bestandsnachweise in Bibliotheken prüfen
Permalink als QR-Code
Inhalt auf sozialen Plattformen teilen (nur vorhanden, wenn Javascript eingeschaltet ist)
Teile diese Seite:
