The Future of Adaptive Learning: Does the Crowd Hold the Key?

Autor/inn/en	Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay
Titel	The Future of Adaptive Learning: Does the Crowd Hold the Key?
Quelle	In: International Journal of Artificial Intelligence in Education, 26 (2016) 2, S.615-644 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-016-0094-z
Schlagwörter	Artificial Intelligence; Educational Technology; Student Needs; Electronic Publishing; Collaborative Writing; Feedback (Response); Electronic Learning; Randomized Controlled Trials; Man Machine Systems; Educational Trends; Trend Analysis; Learning Processes; Video Technology; Online Courses; Interaction; Mathematics; Intelligent Tutoring Systems + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Unterrichtsmedien; Elektronisches Publizieren; Mensch-Maschine-System; Bildungsentwicklung; Trendanalyse; Learning process; Lernprozess; Online course; Online-Kurs; Interaktion; Mathematik; Intelligentes Tutorsystem
Abstract	Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students--explanations, feedback, and other pedagogical interactions. Considering the context of ASSISTments, an online learning platform, we explain how interactive mathematics exercises can provide the workflow necessary for eliciting feedback contributions and evaluating those contributions, by simply tapping into the everyday system usage of teachers and students. We discuss a series of randomized controlled experiments that are currently running within ASSISTments, with the goal of establishing proof of concept that students and teachers can serve as valuable resources for the perpetual improvement of adaptive learning technologies. We also consider how teachers and students can be motivated to provide such contributions, and discuss the plans surrounding PeerASSIST, an infrastructure that will help ASSISTments to harness the power of the crowd. Algorithms from machine learning (i.e., multi-armed bandits) will ideally provide a mechanism for managerial control, allowing for the automatic evaluation of contributions and the personalized provision of the highest quality content. In many ways, the next 25 years of adaptive learning technologies will be driven by the crowd, and this article serves as the road map that ASSISTments has chosen to follow. [Federal funding was received from the Graduate Assistance in Areas of National Need (GAANN) program, Student Service, Higher Education Programs, Office of Postsecondary Education. For the corresponding grantee submission, see ED616519.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11