Practical Approaches To Project Based Learning Incorporating Peer Feedback In Order To Enhance Creativity In Engineering Courses
Bibliographic record
Abstract
We report on innovative approaches to integrating student feedback into teaching engineering physics courses.Project-based learning, presentations, and peer-feedback contributed to an enhanced class experience.This interactive method was applied in Optics and Engineering Measurements courses.The Optics course was mainly focused on geometrical optics with a survey of wave optics.In order to compensate for the lack of laboratory work, an optics project was introduced alongside class demos.Students browsed for possible topics for a couple of weeks and then proposed one based on instructor's feedback.The project concluded with a short presentation of the work in front of the class and a brief written report.In order to increase class interest in the project, the presentation took the form of a competition and the winner(s) were chosen by the class, who judged the presentations according to preset criteria.Student feedback was recorded and quantized, and the peer evaluation and feedback were returned to the presenters.The winners received small prizes in recognition of their performance.Interesting project ideas were formulated and some were implemented, although not always with the expected outcomes.Students enjoyed the peer feedback system, which exposed them to a different perspective on and evaluation of their work.For the Engineering Measurements course, students did small group projects on topics of common interest to group members.Group oral presentations and individual written reports replaced the traditional final exam.Subjects included topics such as magnetooptics, urban astronomy, acoustics, electro-mechanics, solar power, stress-strain measurements, laser beam divergence, and Brewster angle for different materials.Faculty attended presentations and participated with the students in the evaluation of the presentations using evaluation sheets provided in advance.Students preferred this type of examination to the stress of the final exam, despite devoting at least as much time and effort to their project and presentation as they would to traditional final exam preparation.Peer and faculty feedback during the term was particularly effective in enhanced collaboration, negotiations, and task prioritizing for successful project completions.In both teaching approaches, the project presentations involving peer feedback and student competition created an effervescent atmosphere and debates, and maintained student interest and participation.In a collaborative yet competitive environment, students learned to use laboratory equipment as well as their own resources.We report on the enhanced class experience, successes, and shortcomings of the project-based peerevaluation method used in the classroom.The effectiveness shown in the Optics and Measurements classes indicates that this teaching approach is more generally applicable to other project-based courses.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".