Innovative Use of Media to Increase Student Engagement for a Large Second-year Core Course: “Engineering Economics”
Bibliographic record
Abstract
Abstract – As most practitioners are aware, student engagement in large first- or second-year engineering classes is difficult. In a traditional lecture-style presentation instructors are given the challenging task of explaining difficult technical material to several hundred students in such a way that they are not distracted by their friends, cellphones, or the lecture hall atmosphere. In the literature, various solutions to student engagement are suggested: flipped classrooms, design projects, brainstorming sessions, paraphrasing exercises, and selfrating exercises [1]. The author attempted to implement various of these interventions with little anecdotal success. However, a modification of the “think-pairshare” idea as described by Karl Smith, from a subjective point of view, seemed to capture the class more than the default lecture/powerpoint method. Enumeration of student comments about the intervention and a comparison of means from student self reports of “stimulation of learning” suggests that the intervention was successful. Future work is planned to further refine the lectures in terms of student engagement in the lecture theatre and the tutorial classroom.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".