Improving Student Modeling through Partial Credit and Problem Difficulty

Autor/inn/en	Ostrow, Korinn; Donnelly, Christopher; Adjei, Seth; Heffernan, Neil
Titel	Improving Student Modeling through Partial Credit and Problem Difficulty
Quelle	(2015), (10 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
DOI	10.1145/2724660.2724667
Schlagwörter	Intelligent Tutoring Systems; Models; Difficulty Level; Scores; Prediction; Student Evaluation; Statistical Analysis + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Analogiemodell; Schwierigkeitsgrad; Vorhersage; Schulnote; Studentische Bewertung; Statistische Analyse
Abstract	Student modeling within intelligent tutoring systems is a task largely driven by binary models that predict student knowledge or next problem correctness (i.e., Knowledge Tracing (KT)). However, using a binary construct for student assessment often causes researchers to overlook the feedback innate to these platforms. The present study considers a novel method of tabling an algorithmically determined partial credit score and problem difficulty bin for each student's current problem to predict both binary and partial next problem correctness. This study was conducted using log files from ASSISTments, an adaptive mathematics tutor, from the 2012-2013 school year. The dataset consisted of 338,297 problem logs linked to 15,253 unique student identification numbers. Findings suggest that an efficiently tabled model considering partial credit and problem difficulty performs about as well as KT on binary predictions of next problem correctness. This method provides the groundwork for modifying KT in an attempt to optimize student modeling. [The paper was published in: "L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale," (pp. 11-20).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01