Engaging Engineering Students in Lectures Using Anecdotes, Activities, and Games
Bibliographic record
Abstract
Abstract Students being engaged in lectures plays a big role in their learning process. Students come to lectures sometimes tired, bored, or just have lots of things going on in their mind, either personal, or course/program related, etc. As such it is important to set their mind clear to be ready to digest the new material they are going to learn in the course. It is also important to excite them enough to come to early morning classes and keep their attention to stay in the late afternoon classes while staying focused. This paper discusses the use of different methods to increase engagement, attention and attendance in class and the students’ reflection on these methods. Some of these engagement pieces are directly course related and some are just general engagement information. Two instructors used these methods in second and third year engineering courses. The engagement pieces included: mini-games at the beginning of the lecture, unrelated anecdotes in the middle of the lecture, and semi-related special information pieces. All of these are being part of mechanics courses taught in civil, mechanical and mechatronics engineering programs. Examples of these mini-games include: centroid-balance games, where student participate in groups reinforcing their group dynamics, or “guess the unit games” where students participate individually using Kahoot! website. The instructors also used anecdotes such as the etymology of Greek letters and the effect of climate change. In the other attempts, instructors showed short videos of special mechanisms/machines to emphasize a broader application of the topic that they are learning. The students were enthusiastic about these engagement pieces (EP) and they mentioned the positive effect it had on their learning. They were looking forward for these EPs, and were asking that they should be used in other courses as well. The use of these EPs also improved instructors’ course evaluations.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".