Clustering Introductory Computer Science Exercises Using Topic Modeling Methods

Autor/inn/en	Oliveira Moraes, Laura; Pedreira, Carlos Eduardo
Titel	Clustering Introductory Computer Science Exercises Using Topic Modeling Methods
Quelle	In: IEEE Transactions on Learning Technologies, 14 (2021) 1, S.42-54 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Oliveira Moraes, Laura) ORCID (Pedreira, Carlos Eduardo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2021.3056907
Schlagwörter	Computer Science Education; Semantics; Coding; Matrices; Data; Models; Computation + Suchen Sie Ihr Suchwort? Computer science lessons; Informatikunterricht; Semantik; Codierung; Programmierung; Matrizenrechnung; Daten; Analogiemodell
Abstract	Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines is required. In this article, we investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying nonnegative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors' data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01