An Analysis of Student Decision Making for Educational Recommender Systems

Autor/inn/en	Songer, Robert Wesley; Yamamoto, Tomohito
Titel	An Analysis of Student Decision Making for Educational Recommender Systems
Quelle	In: Educational Research and Reviews, 18 (2023) 4, S.54-62 (9 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Artificial Intelligence; Decision Making; Decision Support Systems; Engineering Education; College Students; Foreign Countries; Independent Study; Student Projects; Selection Criteria; Japan + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Decision-making; Entscheidungsfindung; Entscheidungshilfe; Ingenieurausbildung; Collegestudent; Ausland; Selbststudium; Schulprojekt; Selection criterion; Auslesekriterium
Abstract	Recommender systems in education aim to help students make good decisions about the direction of their learning. The design of such systems in conventional research has treated the decision making process of students as a black box and assumes the best recommendations to be those that accurately predict student choices. Such an approach overlooks potentially valuable use cases for supporting optimal decision making, especially in self-directed learning contexts which present such challenges as identifying all available options, accurately evaluating the options against selection criteria, and selecting the best choice. This qualitative study aims to understand the areas where students struggle in the context of planning an open-ended project in order to inform the design of educational recommender systems. Data from interviews with 7 students at an international engineering school in Japan are analyzed to examine choice behaviors, influences on choice, and difficulty to choose in a self-directed learning context. The results illustrate considerations for designing educational recommender systems that can support the divergent thinking and convergent thinking demands of decision making. We provide case-based examples where the use of different recommender metrics, such as novelty and diversity, may provide value to users with different approaches to the decisionmaking process. (As Provided).
Anmerkungen	Academic Journals. e-mail: err@academic.journals.org; e-mail: service@academicjournals.org; Web site: http://academicjournals.org/journal/ERR
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01