THE EFFECT OF EXPERIENTIAL ENGAGEMENT WITH VIRTUAL LEARNING ON UNDERGRADUATE STUDENT SUCCESS
Bibliographic record
Abstract
The purpose of this study is to better understand the effect of undergraduate engineering student engagement with an experiential learning opportunity on academic success in a virtual format. Students in a second year Civil Engineering Materials course that was virtual due to the COVID-19 pandemic were given the option to shift a portion of the final exam weight onto an experiential project. The project consisted of the construction and loading of a small bridge, introducing an experiential component to the virtual course. As a reflective question onthe final exam, students were asked to record a brief video testimony related to their motivations and any perceived benefits for participating or not participating in theproject. Of the students who participated in the bridge project, 58% were characterized as having thorough or above average knowledge and understanding of the graduate attribute indicators, relative to 33% of students who did not participate. Engagement with the bridge project through experiential learning therefore aligned with strengthened understanding of the graduate attributes, within the restrictions of the remote environment. In planning for future online courses, this study shows a method of engaging students with an experiential activity virtually, its positive effect on academic achievement, and other associated benefits.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".