Using Virtual Environments to Motivate Students to Pursue STEM Careers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this chapter is to bring a rigorous and well-studied theoretical framework of motivation to the study and design of virtual learning environments. The authors outline the key motivation constructs that compose Eccles and Wigfield’s Expectancy-Value Theory (e.g., Eccles, et al., 1989; Wigfield & Eccles, 1992, 2000), and how it can be used in the creation of a virtual learning environment designed to promote students’ interest in and motivation to pursue Science, Technology, Engineering, and Mathematics (STEM) careers. In addition, using Brophy’s (1999) model of the motivated learner, the authors outline how this type of motivational virtual environment can be incorporated in classroom instruction to further bolster adolescents’ motivation and competence in mathematics. Finally, they describe a NSF-funded project underway at Harvard’s Graduate School of Education that seeks to develop a 4-day mathematics intervention, merging innovative technologies with regular classroom instruction to spark students’ interest in STEM careers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it