Utilizing active learning to engage engineering students in a freshman physics service course taught in an EFL environment
Bibliographic record
Abstract
Abstract Engaging Electrical Engineering (EE) students in a freshman general physics service course is challenging as they see little relationship between the topics and their major. Non-native speakers enrolled in a course taught in English face an additional challenge beyond understanding the basic physics, that is, understanding specialized English. These challenges are especially serious at second tier universities in Taiwan where the students’ knowledge of physics coming out of high school is based on memorization rather than understanding and whose comprehension of the English language is less than ideal. As a result, they easily tire, stop listening, and stop attending classes. We show that an active learning based approach increases both students’ enjoyment of physics and understanding of new physics concepts and English. Bilingual guided discovery worksheets (GD) for PhET interactive simulations, smartphone-based games, small group flashcard responses, and a website summarizing the ‘big idea’ to be presented in each time slot were developed. The effect of this teaching strategy was measured both quantitatively (grades) and qualitatively (student survey). While students agreed that games were most enjoyable, there was no consensus on which activities were most helpful. Strong attendance (relative to lecture based courses) up to the end of the course suggests that students found class time interesting and useful. GD were most effective for topics in which students had little prior knowledge. The subsequent addition of smartphone-based games increased attendance and reported enjoyment, but did not significantly modify the final grade.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".