Personalized Learning in iSTART: Past Modifications and Future Design

Autor/inn/en	McCarthy, Kathryn S.; Watanabe, Micah; Dai, Jianmin; McNamara, Danielle S.
Titel	Personalized Learning in iSTART: Past Modifications and Future Design
Quelle	In: Journal of Research on Technology in Education, 52 (2020) 3, S.301-321 (21 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (McCarthy, Kathryn S.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-1523
DOI	10.1080/15391523.2020.1716201
Schlagwörter	Game Based Learning; Reading Comprehension; High School Students; Educational Technology; Technology Uses in Education; Reading Strategies; Reading Programs; Computer Assisted Instruction; Individualized Instruction; Reading Tests; Difficulty Level; Program Effectiveness; Reading Instruction; Reading Material Selection; Student Motivation; Student Attitudes; Tutoring; Gates MacGinitie Reading Tests + Suchen Sie Ihr Suchwort? Leseverstehen; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Reading strategy; Leselernstufe; Lesetechnik; Computer based training; Computerunterstützter Unterricht; Individualisierender Unterricht; Lesetest; Schwierigkeitsgrad; Leseunterricht; Schulische Motivation; Schülerverhalten; Förderkonzept; Nachhilfeunterricht
Abstract	Computer-based learning environments (CBLEs) provide unprecedented opportunities for personalized learning at scale. One such system, iSTART (Interactive Strategy Training for Active Reading and Thinking) is an adaptive, game-based tutoring system for reading comprehension. This paper describes how efforts to increase personalized learning have improved the system. It also provides results of a recent implementation of an adaptive logic that increases or decreases text difficulty based on students' performance rather than presenting texts randomly. High school students who received adaptive text selection showed increased sense of learning. Adaptive text selection also resulted in greater pre-training to post-training comprehension test gains, especially for less-skilled readers. The findings demonstrate that system-driven, just-in-time support consistent with the goals of personalized learning benefit the efficacy of computer-based learning environments. [For the corresponding grantee submission, see ED605677.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01