IMPACT OF ONLINE TEACHING TECHNIQUES ON STUDENT ENGAGEMENT IN ENGINEERING DYNAMICS
Bibliographic record
Abstract
Engineering Dynamics has historically been one of the most challenging courses in the engineeringcurriculum. At this institution, Dynamics is taken by approximately 500 students annually and the failure rate has been between 15-20% for the past 10 years. This rate has serious implications on program length and student retention. In the last few years, comprehensive studies have been conducted by the authors aimed at improving these statistics. Plans to focus further on improvingstudent engagement in Dynamics were made critical in Fall 2020 due to the COVID-19 pandemic and the consequential requirement that it had to be offered completely online. The primary objective when setting up this online offering of Dynamics was to maximize student engagementwhile leveraging the new possibilities of online education. This paper reflects on the impacts of the details of the course structure on student engagement. In addition to student outcomes, student survey results associated with the impacts of online learning are analyzed. Some challenges are identified that require further focus and evaluation. It is concluded that student outcomes inEngineering Dynamics may benefit post-pandemic by implementing some of the online learning techniques adopted in Fall 2020 in a blended course delivery.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".