REDESIGNING THE LEARNING EXPERIENCE: ONE PROFESSOR’S EFFO2 RTS TO DEVELOP AN ACTIVE AND ENGAGING FIRST YEAR THERMODYNAMICS COURSE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Active learning is a pedagogicalmethodology that research has shown both engages andmotivates students. This paper reports on one professor’swork to infuse active learning into his first yearthermodynamics course. Based on the results of a pilotstudy aimed at exploring the use of active learning in afirst-year thermodynamics course to engage students andimprove their learning, a problem-solving learningapproach was designed for a subsequent offering of thecourse. Mini-lectures were interspersed with tutorials,and active learning and pedagogical tools and strategieswere employed with the intent to increase studentengagement and enhance learning. At the conclusion ofthis course, a student exit survey and a student focusgroup were conducted, and students’ course marks werecompared to their cumulative grade point averages toexamine their course performance. Findings showed thatstudents were engaged by the active learning design andevidence of learning was found. This is the second phaseof a practical action research study to turn a traditional,lecture-based course into an active learning arena forfirst year engineering students at the University ofManitoba.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it