Effectiveness of OER Use in First-Year Higher Education Students’ Mathematical Course Performance
Bibliographic record
Abstract
This article examines the effect of two Open Educational Resources (OER) - Khan Academy Collection and a teacher-authored open textbook - on mathematical course performance and attendance amongst first-year higher education Chilean students. It also aims to find out about teachers’ and students’ views on the use of OER in order to understand how these resources are used and valued. To this end, quantitative and qualitative methods were employed. Findings indicate that students in face-to-face classes who used Khan Academy resources obtained better examination grades than students who used the open textbook or relied on traditional proprietary textbooks. Moreover, it was also found that students who used both types of OER had significantly lower attendance levels than students who relied on traditional proprietary textbooks. Finally, it was observed that teachers and students had very positive opinions on the use of both the Khan Academy Collection and open textbook resources.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".