Examining the technological and pedagogical elements of select open courseware
Bibliographic record
Abstract
Openness in open courseware (OCW) and open educational resources (OER) requires an open licence, such as Creative Commons licenses, but is affected by several factors both technological and pedagogical. This pilot study examines different factors impacting openness by looking at a very small random sample of 10 relatively recent open courseware offerings from TU Delft and MIT. This paper has two primary objectives: 1) to determine how open the sampled OCW are across eight factors of analysis; and, 2) to determine if the sampled OCW are suitable for educator reuse. The authors evaluated the sampled courses using an existing framework to conceptualize openness. The level of openness was evaluated across eight-factors: copyright/open licensing, accessibility/usability, language, support costs, assessment, digital distribution, file format, and cultural considerations. The framework describes each factor across three dimensions of openness — closed, mixed, and most open — and each author coded the sampled OCW accordingly. This content analysis provided several insights into where sampled OCW succeeded and failed in terms of openness. Courses tended to be relatively open in terms of copyright, assessment, and digital distribution, but closed in terms of language, support costs, and file format. Factors such as accessibility and cultural considerations were more mixed; discipline and course content play a factor in a course’s openness and reuse. This paper also serves a secondary purpose, on the effectiveness of the framework for assessing openness. Openness is a spectrum, with an interplay between factors that determine openness. Greater attention needs to be shown toward pedagogical considerations, rather than technical, when developing open content.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".