Flexible Learning Strategies in First through Fourth-Year Courses
Bibliographic record
Abstract
Flexible Learning (FL) is a pedagogical approach allowing for flexibility of time, place, and audience, including but not solely focused on the use of technologies. We describe Flexible Learning as a pedagogical approach in four courses framed by three key themes: 1) objectives and aspects of course design, 2) evaluation and assessment, and 3) challenges and improvements. Examples of strategies include: digital media-based assignments; iClicker and on-line quizzes; a librarian-created tutorial and links to copyright-cleared readings; use of Calibrated Peer Review as formative feedback; TurnItIn for self-review; wiki sites, group blogs and community work through Community-based Action Research (CBAR) conducted through the pedagogy of Community-Based Experiential-Learning (CBEL). We believe that the transferability of our experiences and findings is most relevant to educators seeking to create learning experiences that increase student engagement with complexity and uncertainty. FL approaches can help educators create learning environments that more closely resemble the contexts that students find upon graduation.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".