From Open Content to Open Course Models: Increasing Access and Enabling Global Participation in Higher Education
Bibliographic record
Abstract
Two of the major challenges to international students’ right of access to higher education are geographical/economic isolation and academic literacy in English (Carey, 1999, Hamel, 2007). The authors propose that adopting open course models in traditional universities, through blended or online delivery, can offer benefits to the institutions and to the open education movement itself, in particular with non-Anglophone students. This paper describes the model and an implementation with undergraduate students in Canada, Mexico, and Russia. The implementation of the model was examined in three studies, which relied on data collected from student interviews, instructor observations and reflections, instructor interviews, course documents, and discussion forum transcripts. The authors note that the main benefit of an open course model is the development of academic literacy for students of English as an Other Language (EOL). Other benefits include 1) international course transfers, 2) breadth of professorial exposure for the students, 3) flexibility in professors’ employment and professional development, and 4) course credits for students. Some of the challenges include 1) varying levels of Internet access, 2) coordination of the participation of the instructors, and 3) different teaching and learning practices. The authors conclude that an open course model might be applied in various contexts, such as in disciplines where global perspectives are important, in applied/professional programs, and in distance or face-to-face courses. Also, the model is useful for students working together on research, case studies, or joint projects, and it could be applied within an institution to enhance inter-disciplinary content and approaches
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".