A Novel Video Recommendation System for Algebra: An Effectiveness Evaluation Study [Konferenzbericht] Paper presented at the International Learning Analytics and Knowledge Conference (LAK22) (12th, Online, Mar 12-15, 2022).

Autor/inn/en	Leite, Walter L.; Roy, Samrat; Chakraborty, Nilanjana; Michailidis, George; Huggins-Manley, A. Corinne; D'Mello, Sidney K.; Faradonbeh, Mohamad Kazem Shirani; Jensen, Emily; Kuang, Huan; Jing, Zeyuan
Titel	A Novel Video Recommendation System for Algebra: An Effectiveness Evaluation Study [Konferenzbericht] Paper presented at the International Learning Analytics and Knowledge Conference (LAK22) (12th, Online, Mar 12-15, 2022).
Quelle	(2022), (13 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Leite, Walter L.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Video Technology; Technology Uses in Education; Program Effectiveness; Algebra; Electronic Learning; Item Response Theory; Intelligent Tutoring Systems; Mathematics; Program Evaluation; School Closing; COVID-19; Pandemics; Secondary School Students; Educational Technology + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Item-Response-Theorie; Intelligentes Tutorsystem; Mathematik; Programme evaluation; Programmevaluation; School closings; Schule; Schließung; Schließung (von Schulen); Sekundarschüler; Unterrichtsmedien
Abstract	This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we developed a system of five categories of video recommendations: (1) Watch new video; (2) Review current topic video with a new tutor; (3) Review segment of current video with current tutor; (4) Review segment of current video with a new tutor; and (5) Watch next video in curriculum sequence. The category of recommendation was determined by student scores on a quiz and a sensor-free engagement detection model. New video recommendations (i.e., category 1) were selected based on a novel reinforcement learning algorithm that takes input from an item response theory model. The recommendation system was evaluated in a large field experiment, both before and after school closures due to the COVID-19 pandemic. The results show evidence of effectiveness of the video recommendation algorithm during the period of normal school operations, but the effect disappears after school closures. Implications for teacher orchestration of technology for normal classroom use and periods of school closure are discussed. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11