Using Latent Variable Models to Observe Academic Pathways [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Gruver, Nate; Malik, Ali; Capoor, Brahm; Piech, Chris; Stevens, Mitchell L.; Paepcke, Andreas
Titel	Using Latent Variable Models to Observe Academic Pathways [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Models; Course Selection (Students); Enrollment; Decision Making; Probability; Classification; College Students; Student Records; Inferences; Management Systems; Comparative Analysis; Student Interests; Private Colleges; Mathematical Models; Visualization; Statistical Analysis + Suchen Sie Ihr Suchwort? Analogiemodell; Course selection; Student; Students; Kurswahl; Einschulung; Decision-making; Entscheidungsfindung; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Classification system; Klassifikation; Klassifikationssystem; Collegestudent; Schülerakte; Inference; Inferenz; Studieninteresse; Privathochschule; Mathematical model; Mathematisches Modell; Visualisation; Visualisierung; Statistische Analyse
Abstract	Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modelling course enrollment decisions, drawing inspiration from multilabel classification and mixture models. We use ten years of anonymized student transcripts from a large university to construct a Gaussian latent variable model that learns the joint distribution over course enrollments. The models allow for a diverse set of inference queries and robustness to data sparsity. We demonstrate the efficacy of this approach in comparison to others, including deep learning architectures, and demonstrate its ability to infer the underlying student interests that guide enrollment decisions. [For the full proceedings, see ED599096.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01