Educator Role Transforms from Sage to Ghost: Implementation of Discovery-Based Learning Online with Large Student Enrollment
Bibliographic record
Abstract
“I hear and I forget. I see and I remember. I do and I understand” (Bransford, 2012, p. 1). Discovery-based learning, derived from constructivist learning theory (Legg et al., 2009), is inquiry-based and requires the student to draw on his or her own past experience and existing knowledge to discover facts and inferences to apply to new situations (Alfieri et al., 2010). An experiential example will be presented using an adapted version of the Challenge-Based Learning TM (CBL) method (Bransford) to guide the design of online courses to implement discovery-based learning. This presentation will focus on the discussion and application of quality online practices in relation to encouraging critical thinking, student engagement, interactivity and clear expectations in courses that have high student enrollment numbers (> 80). Course design considers the role of rich media presentations, student led research leaders and asynchronous threaded discussion peer moderators to promote discovery-based learning. The role of the online educator transforms from being a sage on the stage, providing the information to be learned and moderating student discussions, to becoming a ghost in the wings, facilitating discovery-based learning and monitoring, rather than dictating and directing weekly student discussions. Interaction, in student groups and one-on-one between the student and the educator, are operationalized within the course design. A detailed grading matrix is discussed as part of the course design and focuses on the quality, as well as the quantity, of student participation, course engagement and critical thinking
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".