Monitoring Student Engagement in First Year Engineering
Bibliographic record
Abstract
In a 2006 study to assess student engagement in the first year, engineering students were the least likely to express positive views about that experience and reported low levels of academic engagement. Initiatives to address this situation in engineering were in progress, including the development of two first year courses integrated engineering design with required writing courses. To monitor engagement and satisfaction, and assess the impact of the course interventions, the same research methodology as the 2006 study was used in 2010, 2011, and 2012 following the implementation of these courses. Participants in focus groups discussed questions related to engagement and identified factors that had led to their satisfaction or dissatisfaction. Themes identified for the three different time periods were compared to each other and to those that emerged in the original 2006 study. Significant among the themes was the experience in the design/communications courses. Themes of dissatisfaction and alienation were highly consistent with the 2006 study but became moderated. This process reinforced the need for authentic engineering course experiences in the first year and is consistent with previous research.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".