bioRxivScientific Communication & EducationPreprint
Influence of non-content instructor talk on students' motivation-related outcomes in laboratory courses
Abstract
Course-based undergraduate research experiences (CUREs) can expand undergraduates access to research and motivate students to stay in science. Yet, little research has examined how CURE instruction shapes student motivation. We leveraged a motivation-related characterization of non-content talk of 48 CURE and non-CURE instructors to predict the motivation-related outcomes of 462 students. We fit a series of multi-level models (MLM) in which we regressed students post-course scientific self-efficacy, task values, scientific identity, and science-related intentions onto instructors self-efficacy and task values-related talk, controlling for students pre-course levels. We also fit an MLM to explore whether instructors relationship-building talk (immediacy talk) was associated with students rapport with their instructor. Instructors self-efficacy talk did not affect students self-efficacy, and instructors immediacy talk had a marginally positive but non-significant association with students rapport ratings. Instructors task values talk positively influenced students scientific identity and some but not all of their task values. Instructors task values talk also positively influenced students intentions to pursue a science career, but not graduate education or research careers. Collectively, these results suggest that instructors task values talk may underpin some of the motivational effects of CURE instruction, but that task values talk need not be limited to CUREs. HIGHLIGHTWe examine whether instructor talk predicts students motivational outcomes in CURE and non-CURE lab courses. Self-efficacy talk had no effect on student self-efficacy. Task values talk positively affected students science identity and career intentions, and some value beliefs. Immediacy talk was marginally related to student-instructor rapport.
- research focus: benchmarking
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
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