KC-Finder: Automated Knowledge Component Discovery for Programming Problems [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas
Titel	KC-Finder: Automated Knowledge Component Discovery for Programming Problems [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Computer Science Education; Data Analysis; Programming; Coding; Assignments; Teaching Methods; Learning Analytics; Classification; Introductory Courses; Programming Languages; Goodness of Fit; Evaluation Methods; Artificial Intelligence; Models; Decision Making; Undergraduate Students; State Universities; Student Evaluation; Virginia + Suchen Sie Ihr Suchwort? Computer science lessons; Informatikunterricht; Auswertung; Programmierung; Codierung; Assignment; Auftrag; Zuweisung; Teaching method; Lehrmethode; Unterrichtsmethode; Classification system; Klassifikation; Klassifikationssystem; Einführungskurs; Künstliche Intelligenz; Analogiemodell; Decision-making; Entscheidungsfindung; Staatliche Universität; Schulnote; Studentische Bewertung
Abstract	Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1) generate learning curves following the power law of practice; and (2) are predictive of response correctness. We train a neural architecture (named KC-Finder) that classifies the correctness of student code submissions and captures problem-KC relationships. Our evaluation on data from 351 students in an introductory Java course shows that the learned KCs can generate reasonable learning curves and predict code submission correctness. At the same time, some KCs can be interpreted to identify programming skills. We compare the learning curves described by our model to four baselines, showing that: (1) identifying KCs with naive methods is a difficult task; and (2) our learning curves exhibit a substantially better curve fit. Our work represents a first step in solving the data-driven KC discovery problem in computing education. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01