Evaluation of Open Educational Resources for an Introductory Exercise Science Course
Bibliographic record
Abstract
While open educational resources (OER) have gained popularity, nearly three quarters of faculty are not aware they are available for use. However, when used, they are well received and do not negatively impact quality of learning. OER can be used within a variety of platforms, including software that aims to be more interactive and engage students in active learning and assessment. One such platform is Top Hat, which was used by the authors of this study to develop a textbook for an introductory exercise science course. We assessed student’s perceptions of Top Hat and barriers to use for reading their textbook and for class assessments over the course of two years. A total of 486 students were registered for this course. Although two thirds of students had previous experience with Top Hat and half of those used the textbook feature, students (n = 39, 38%) were apprehensive about reading their textbook online via Top Hat. However, these feelings resolved as students became comfortable with the platform’s features. Nearly 80% of students have sometimes or never acquired their textbooks before the start of the semester, despite 96% who expressed the importance of having their materials accessible online and available on or before the first day of the course. This indicated that students understood the importance of having their materials for the start of the semester, however they perceived the barriers of purchasing books to be greater. Therefore, using OER and Top Hat removed student learning barriers and had potential to increase course participation and success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".