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Danielle S. McNamara; Tracy Arner; Reese Butterfuss; Debshila Basu Mallick; Andrew S. Lan; Rod D. Roscoe; Henry L. Roediger; Richard G. Baraniuk – Grantee Submission, 2022
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…
Descriptors: Artificial Intelligence, Learning Processes, Learning Motivation, Educational Research
Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics