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Title: Exploring Predictors of Achievement-Goal Profile Stability during Mathematics Learning in an Intelligent Tutoring System
Using archived student data for middle and high school students’ mathematics-focused intelligent tutoring system (ITS) learning collected across a school year, this study explores situational, achievement-goal latent profile membership and the stability of these profiles with respect to student demographics and dispositional achievement goal scores. Over 65% of students changed situational profile membership at some time during the school year. Start-of-year dispositional motivation scores were not related to whether students remained in the same profile across all unit-level measurements. Grade level was predictive of profile stability. Findings from the present study should shed light on how in-the-moment student motivation fluctuates while students are engaged in ITS math learning. Present findings have potential to inform motivation interventions designed for ITS math learning.
Authors:
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Award ID(s):
1934745
Publication Date:
NSF-PAR ID:
10353232
Journal Name:
Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
Sponsoring Org:
National Science Foundation
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