Situating AI (and Big Data) in the Learning Sciences: Moving toward Large-Scale Learning Sciences

Autor/inn/en	McNamara, Danielle S.; Arner, Tracy; Butterfuss, Reese; Mallick, Debshila Basu; Lan, Andrew S.; Roscoe, Rod D.; Roediger, Henry L., III; Baraniuk, Richard G.
Titel	Situating AI (and Big Data) in the Learning Sciences: Moving toward Large-Scale Learning Sciences
Quelle	(2022), (31 Seiten)Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Learning Processes; Learning Motivation; Educational Research; Interdisciplinary Approach; Instructional Design; Affordances; Educational Technology; Causal Models; Natural Language Processing; Computational Linguistics + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Learning process; Lernprozess; Motivation for studies; Lernmotivation; Bildungsforschung; Pädagogische Forschung; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Lesson concept; Lessonplan; Unterrichtsentwurf; Unterrichtsmedien; Kausalanalyse; Natürliche Sprache; Linguistics; Computerlinguistik
Abstract	The learning sciences inherently involve interdisciplinary research with an overarching objective of advancing theories of learning and to inform the design and implementation of effective instructional methods and learning technologies. In these endeavors, learning sciences encompass diverse constructs, measures, processes, and outcomes pertaining to both learning, motivation, and social interactions. These complex goals are further influenced by a large array of factors stemming from the learning context, learning task, and the characteristics of the individual learners. Learning occurs within a multitude of interacting contextual factors spanning variations between schools, teachers, classrooms, peers, and available technologies. These contexts also differ widely in diverse factors such as the social support that students receive, instructor engagement, demographic and ideological diversity, as well as instructional design strategies and affordances offered by educational technologies (Anderson & Dron, 2011). The learners themselves vary across a host of fixed factors such as age, grade level, ethnicity, and cultural background, as well as malleable individual differences such as engagement, interests, learning strategies, reading skills, and prior knowledge (Cantor et al., 2019; Jonassen & Grabowski, 2012; Winne, 1996). [This chapter was published in: "Artificial intelligence in STEM education: The paradigmatic shifts in research, education, and technology," edited by A. Alavi & B. McLaren, CRC Press, 2022.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01