‘Open Learning 2.0’? Aligning Student, Teacher and Content for Openness in Education
Bibliographic record
Abstract
The mission of Thompson Rivers University Open Learning (TRU-OL) can be understood in terms of three entities: the student, the faculty member and the curriculum content. Their conjuncture – when a TRU-OL student works with TRU-OL courseware and is supported by a TRU-OL faculty member – is where learning, assessment and, ultimately, credentialing take place. These three elements may form three points in a triangle, with assessment and credentialing in the centre. TRU-OL is currently exploring the results of defining these three elements differently. Instead of designating TRU-OL students, teachers and contents specifically, these elements may serve as placeholders for any students, any instructional personnel or supports, and any open content. These can, in theory, all be shared, opened and disaggregated among various institutions, with assessment and credentialing remaining as the principal service offered locally. The purpose of this article is to explain this model in the context of the open educational movement, to describe its various permutations and implications, and to consider some questions and objections that may arise in relation to it. The result is an updated version of similar triangular models that would interconnect student, teacher and content in pedagogical interrelationship.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".