Developing an Undergraduate Business Course Using Open Educational Resources
Bibliographic record
Abstract
There are growing concerns about the affordability and accessibility of post-secondary education. This has resulted in increased attention to the inclusion of open educational resources (OERs) as course materials rather than commercial course resources. OERs are mostly cost-neutral for students. In this research, an elective course for business students was developed using only OERs. To assist with the selection of OERs to be included in this course, an OER evaluation tool available online was used. Resources that were considered and were evaluated using the tool included traditional OERs (fully open), those in the public domain (unrestricted by licensing), and resources that are publicly available for educational purposes. An important contribution of this research is the extension of the definition of OERs to include publicly available resources. This paper reports on the results of this process and students’ perceptions about the inclusion of OERs in their course. Recommendations for further research and for practice are shared.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.020 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".