Bibliographic record
Abstract
The importance of the time factor in online learning is starting to be recognized as one of the main factors in the learners’ achievements and drop outs (Barbera, Gros, & Kirshner, 2012; Park & Choi, 2009; Romero, 2010). Despite the recognition of the time factor importance, there is still the need for theorizing temporality in the context of online education. In this chapter, the authors contribute to the advancement of the evaluation of time factors in online learning by adapting the theoretical framework of the Academic Learning Times (Caldwell, Huitt, & Graeber, 1982; Berliner, 1984) for evaluating the online learners’ time regulation. For this purpose, they compare two case studies based on the Academic Learning Times framework. The case studies characterize online learner regulation based on an analysis of online learners at the Universitat Oberta de Catalunya (UOC), Spain, and the initiatives taken by the instructional team of the Virtual Campus at the University of Limoges (CVTIC) to support online learner time regulation on this virtual campus in France. After comparing the two case studies, the chapter provides guidelines for improving online learners’ individual and collaborative time regulation and reflects about the need to advance in the theorization of the time factor frameworks in online education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".