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Session Type: Symposium
The objective of this session is to develop a conversation about the benefits and limits of data-intensive approaches to studying engagement, a construct that cuts across educational research and is of interest for many educators. This session brings together experts in educational psychology and educational data mining to spark a conversation about how different approaches to collecting and analyzing intensive data can be used to understand learning. Presenters demonstrate their approach in the context of an empirical study and will emphasize the opportunities and challenges for their particular approach and the discussant, a renowned expert in data-intensive approaches to the study of engagement, will highlight commonalities and divergences across approaches, and key directions for future research.
A Clearer Picture: Integration of Multiple Data Sources in MOOC Research - Eric N. Wiebe, North Carolina State University; James H Creager, North Carolina State University
Science Is Too Hard: Daily Perceived Difficulty and the Role of Teacher Autonomy Support - Sophia Hooper; Erika Alisha Patall, University of Southern California; Ariana Christine Vasquez, University of Pittsburgh; Keenan A. Pituch, The University of Texas - Austin; Rebecca Rose Steingut, na
How Engagement During Out-of-School-Time STEM Programs Predicts Changes in Motivation in STEM - Joshua Michael Rosenberg, The University of Tennessee - Knoxville; Patrick Neil Beymer, Michigan State University; Jennifer A. Schmidt, Michigan State University
Assessing the Disengaged Behaviors of Student Teachers With Network-Based Tutors - Eric G. Poitras, University of Utah; Tenzin Doleck, McGill University; Lingyun Huang, McGill University; Shan Li, McGill University; Susanne P. Lajoie, McGill University