‘Open Learning 2.0’? Aligning Student, Teacher and Content for Openness in Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it