EXPANDED CROSS-DISCIPLINE IMPLEMENTATION STRATEGY OF DISCOVERY: A BIOMEDICAL ENGINEERING-THEMED EDUCATION PROGRAM BRIDGING SECONDARY AND POST-SECONDARY LEARNING
Bibliographic record
Abstract
High school science, technology, engineering, and math (STEM) curricula are generally knowledge-based in methodology and focus on content delivery in preparation for post-secondary study. However, the rapid technological change at the cutting edge and the rate of global integration in STEM highlight the importance in developing a holistic critical thinking framework for student learning. In 2016, graduate students at the Institute of Biomaterials & Biomedical Engineering created Discovery, a collaborative high school educational program focused on critical thinking skill development through inquiry in the context of biomedical engineering (BME) [1]. Aligning with demonstrated evidence that inquiry-based active learning approaches are more effective in enhancing student learning than traditional teaching methods [2], evaluation in Discovery reinforces the value of a differential learning environment for high school STEM students who struggle in a knowledge-focused classroom [3,4]. In addition, the Discovery model is shown to enhance student attitudes towards STEM and post-secondary education, meanwhile providing robust opportunity for graduate trainees to develop and apply pedagogical skills through development of curriculum appropriate for university-preparatory students. Program impact provides opportunities to discuss this unique learning framework, collaborative delivery strategy, and implementation strategy of Discovery as a resource for translation to disciplines beyond BME, and institutions beyond the University of Toronto.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".