Identifying Informatively Easy and Informatively Hard Concepts

Autor/inn/en	Wiegand, R. Paul; Bucci, Anthony; Kumar, Amruth N.; Albert, Jennifer; Gaspar, Alessio
Titel	Identifying Informatively Easy and Informatively Hard Concepts
Quelle	In: ACM Transactions on Computing Education, 22 (2022) 1, Artikel 7 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1946-6226
Schlagwörter	Concept Formation; Difficulty Level; Computer Science Education; Problem Solving; Intelligent Tutoring Systems; College Students + Suchen Sie Ihr Suchwort? Concept learning; Begriffsbildung; Schwierigkeitsgrad; Computer science lessons; Informatikunterricht; Problemlösen; Intelligentes Tutorsystem; Collegestudent
Abstract	In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of "informatively" easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student-problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called "problets." The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01