Bibliographic record
Abstract
Abstract Learning management systems (LMS) have been used extensively and effectively in educational settings for over a decade. By facilitating interaction within and outside of classrooms using an integrated set of online tools, LMSs have opened up the scope and reach of education in both online and traditional class settings. However, it should be noted that the information architecture of traditional LMSs embody a power dynamic that leaves administrators and instructors firmly in control over the design and maintenance of online educational environments. The architecture of control (Lockton, 2008) of traditional LMSs operates in distinct opposition to the ethos of Web 2.0 technologies, which enable and encourage robust user input and control. These hardwired power relations of the traditional LMS pose significant challenges in creating truly collaborative learning experiences, especially at a time when emerging Web 2.0 technologies enable a more democratic and user-generated educational experience. This chapter suggests that the power differentials inherent in traditional LMSs can be radically redefined by adopting wiki technology as a complement to or even replacement of the traditional LMS. The chapter is based on observations and lessons learned from the authors’ use of wikis as an alternative to their institutionally supported LMSs, and concludes with an analysis of potential future trends in co-creation as a metaphor for class and curriculum management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".