Using online to enhance student engagement of design reviews and lessons learned experiences
Bibliographic record
Abstract
This paper is about using the online environment to increase engagement in design and instilling a recognition of the importance of life long learning. The work assesses changes in the delivery of a 4th year nuclear engineering design course and evaluates changes in the delivery of the course as it migrated from face to face to hybrid to online. In particular, the design review process was used to enhance engagement of the student body and a lessons learned exercise was used to enhance reflection on life long learning. The engagement is assessed in terms of class attendance, activity within the learning management system, and direct engagement in the design review process. The design review process requires each student to both present a design and to critique another group’s design. The design work is done as a team but the critique is done as an individual paralleling the industry process currently in use for the nuclear sector. In addition to the technical details, the performance of each student with respect to their soft skills is also assessed. This includes the number of students that actively engage or passively engage during both presentation and critique stages. Following the design review process, the students then engage in a lessons learned activity similar to what is done in the industry but simplified to focus on their experience. The activity also included an opportunity to reflect on themselves and establish a life long learning plan to address their personal findings. Note the paper will not discuss the personal findings specifically but instead will comment on the engagement of the students. Before using online approaches, the students fell into two distinct groups. One set was strongly active in the design review process and the other set was strongly resistive to participation and did the minimum necessary to get through the exercise. It was very clear that many students felt uncomfortable speaking openly in front of others. This changed significantly with the use of online technology. There was a significant increase in the number of students that engaged or at least felt comfortable to speak in the online setting. Some students displayed perhaps too much comfort in working from their personal environment space. This observation was also noted in the lessons learned exercise where the students went from saying the minimum necessary to having a large amount of insightful comments to make. The results suggest that allowing online participation in the experience has encouraged engagement of students that would resist a face to face experience.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".