The Learning Object Economy: Implications For Developing Faculty Expertise
Bibliographic record
Abstract
The evolving use of learning technologies and systems, such as learning object systems, to support more social learning environments in which learners have more agency than ever before to construct their own learning experiences is an innovation that involves both faculty and learners in a process of difficult sociocultural change. Programs of faculty support that acknowledge that faculty’s learning needs extend beyond the development of technical skills to the development of new pedagogical skills are indicated. This paper argues that the evolving concept of learning objects systems, and the "economy" that is emerging around the idea of sharable, reusable learning objects managed by repositories, presents new challenges and opportunities for our community. Faculty working with these systems may need to be supported through a personal process of reconceptualizing the nature of teaching and learning within these environments. This process of personal transformation has the potential for change in institutional policy and practice, the institutional cultural change of which Tony Bates (2000) and others speak (cf. Advisory Committee for Online Learning, 2000). The Collaboration for Online Higher Education Research (COHERE) is an alliance of eight research-intensive Canadian universities that is examining these challenges through a multi-pronged research program, one focus of which is supporting faculty as they research their own practice related to technology-enhanced teaching innovations. More specifically, this paper is itself a collaboration among the COHERE partners to share our collective belief about the potential for faculty and institutional transformation through participation in these "e-learning evolutions".
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.
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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".