An Interpretable Pipeline for Identifying At-Risk Students

Autor/inn/en	Pei, Bo; Xing, Wanli
Titel	An Interpretable Pipeline for Identifying At-Risk Students
Quelle	In: Journal of Educational Computing Research, 60 (2022) 2, S.380-405 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Pei, Bo) ORCID (Xing, Wanli)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0735-6331
DOI	10.1177/07356331211038168
Schlagwörter	At Risk Students; Learning Analytics; Information Retrieval; Models; Prediction; Student Behavior; Predictor Variables; Dropouts; Student Characteristics; Social Sciences; STEM Education; Online Courses; Accountability + Suchen Sie Ihr Suchwort? Analogiemodell; Vorhersage; Student behaviour; Schülerverhalten; Prädiktor; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; STEM; Online course; Online-Kurs; Verantwortung
Abstract	This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for maintaining the consecutiveness among learning activities. To demonstrate the efficacy of our model in identifying at-risk students, we compare the weekly AUCs and averaged performance (i.e., accuracy, precision, recall, and f1-score) over each course with the baseline models (i.e., Random Forest, Support Vector Machine, and Decision Tree), respectively. Furthermore, we adopt a Top-"K" metric to examine the number of at-risk students that the model is able to identify with high precision during each week. Finally, the model output is interpreted through a model-agnostic interpretation approach to support instructors to make informed recommendations for students' learning. The experimental results demonstrate the capability and interpretability of our model in identifying at-risk students in online learning settings. In addition to that our work also provides significant implications in building accountable machine learning pipelines that can be used to automatically generated individualized learning interventions while considering fairness between different learning groups. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11