Addressing the eLearning Contradiction: A Collaborative Approach for Developing a Conceptual Framework Learning Object
Bibliographic record
Abstract
In many universities there seems to be an "eLearning Contradiction" between the expressed need to integrate technology into the teaching-learning process and what is actually occurring in the majority of classrooms.In this paper we describe the collaborative process we used to design an online Conceptual Framework Learning Object (C-FLO).The object can be viewed at http://innovation.dc-uoit.ca/cloe/lo/cf/This account is grounded in practical experiences and supported by the research literature.First, we offer a rationale for the development of C-FLO.We then illustrate how an interdisciplinary collaborative perspective enhanced both the process and learning outcomes.The impact of this learning object from both the learners' and professors' perspectives is detailed.Collaborative projects such as C-FLO, where professors share resources and expertise to improve student learning, could be a first step toward addressing the eLearning Contradiction.
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".