Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Nazaretsky, Tanya; Hershkovitz, Sara; Alexandron, Giora
Titel	Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Intelligent Tutoring Systems; Item Response Theory; Measurement; Testing; Test Items; Mastery Learning; Mathematics Tests; Computer Assisted Testing + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Item-Response-Theorie; Messverfahren; Testdurchführung; Testen; Test content; Testaufgabe
Abstract	Sequencing items in adaptive learning systems typically relies on a large pool of interactive question items that are analyzed into a hierarchy of skills, also known as Knowledge Components (KCs). Educational data mining techniques can be used to analyze students response data in order to optimize the mapping of items to KCs, with similarity-based clustering as one of the two main approaches for this type of analysis. However, current similarity-based methods make the implicit assumption that students' performance on items that belong to the same KC should be similar. This assumption holds if the latent trait (mastery of the underlying skill) is relatively fixed during students' activity, as in the context of testing, which is the primary context in which these methods were developed and applied. However, in adaptive learning systems that aim for learning, and address subject matters such as K-6 Math that consist of multiple sub-skills, this assumption does not hold. In this paper we propose a new item-similarity measure, termed Kappa Learning (KL), which aims to address this gap. KL identifies similarity between items under the assumption of learning, namely, that learners' mastery of the underlying skills changes as they progress through the items. We evaluate KL on data from a K-6 Math Intelligent Tutoring System, with experts' tagging as ground truth, and on simulated data. Our results show that clustering that is based on KL outperforms clustering that is based on commonly used similarity measures (Cohen's Kappa, Yule, and Pearson), and that KL is also superior in the task of discovering the number of KCs. [For the full proceedings, see ED599096.] (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