HELPING PROFESSORS CRAFT STRATEGIES TO IMPROVE FIRST-YEAR ENGINEERING STUDENT SUCCESS USING WEEKLY FEEDBACK FORMS
Bibliographic record
Abstract
Students’ success in their first year of an engineering program is of great attention and importance to engineering schools and students alike. While the influencing factors are complex and multifaceted, workload and wellbeing play an integral role in students’ success. Since professors can impact both of these factors through tailoring their delivery, in-class interaction, and workload expectation, the need for effective and continuous two-way communication pathways among students and professors focused on instruction, workload, and wellbeing aspects is evident. In this study, using weekly surveys covering the above-mentioned aspects and tailored to each week’s course activities, data was collected and analyzed to allow professors to understand the students’ perspectives on the assigned workload and effectiveness of the lecture delivery, along with additional information pertaining to the instructional environment. The aim of this work is to enable students to obtain a greater degree of ownership of their learning using clear and continuous communication channels with the instructional team, as well as realize the value in student-centric feedback forms. The study has concluded with valuable insights generated, and improvement in student participation and survey implementation over multiple revisions to the protocol. Although the COVID-19 pandemic led to the premature termination of the pilot program, key insights will be carried forth towards the following school year, with an emphasis on tailoring surveys for online learning, improving implementation, and more defined recommendations to improve the course.
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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 | 0.044 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".