Personal Spaces in Public Repositories as a Facilitator for Open Educational Resource Usage
Bibliographic record
Abstract
Learning object repositories are a shared, open and public space; however, the possibility and ability of personal expression in an open, global, public space is crucial. The aim of this study is to explore personal spaces in a big learning object repository as a facilitator for adoption of Open Educational Resources (OERs) into teaching practices and to gain more insight into different types of OER user behaviors by analyzing the users' behaviors in the Bookmark Collection of MERLOT (a personal space, formerly known as Personal Collection), along with other community activities in that repository: writing comments and peer reviews, as well as sharing learning materials, learning exercises, and contents that were built with the content builder. In addition, using a data mining methodology, most active Bookmark Collection contributors (N=507) were classified into clusters of users with the same patterns of activity. Three clusters resulted, which gave insights into different types of contributor behavior. Furthermore, it was found that personal spaces are applicable for a variety of uses with diverse goals. Members create personal spaces for their own use, while allowing others to view and copy; or for other users. Personal space encourages the reuse of learning materials and enables the construction of unique learning processes that suit the learner's needs. They may offer the possibility of personalizing public repositories and promoting the reuse of OERs.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".