Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Liu, Ran; Koedinger, Kenneth R.
Institution	International Educational Data Mining Society
Titel	Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Models; Regression (Statistics); Learning; Classification; Error Patterns; Accuracy; Predictive Validity; Pretests Posttests; Statistical Analysis; Students; Geometry; Algebra; English; Grammar; Bayesian Statistics + Suchen Sie Ihr Suchwort? Analogiemodell; Regression; Regressionsanalyse; Lernen; Classification system; Klassifikation; Klassifikationssystem; Fehlertyp; Statistische Analyse; Student; Schüler; Schülerin; Studentin; Geometrie; English language; Englisch; Grammatik
Abstract	A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a parameter that individualizes for overall student ability but not for student learning rate. Here, we show that adding a per-student learning rate parameter to AFM overall does not improve predictive accuracy. In contrast, classifying students into three "learning rate" groups using residual error patterns, and adding a per-group learning rate parameter to AFM, substantially and consistently improves predictive accuracy across 8 datasets spanning the domains of Geometry, Algebra, English grammar, and Statistics. In a subset of datasets for which there are pre- and post-test data, we observe a systematic relationship between learning rate group and pre-topost-test gains. This suggests there is both predictive power and external validity in modeling these distinct learning rate groups. [For complete proceedings, see ED560503.] (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