Understanding Barriers to Student Success: What Students Have to Say
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on feedback received from a set of qualitative questions that were administered to undergraduate students in the College of Engineering at the University of Saskatchewan, as part of a larger mixed methods study. The larger study aims to identify what characteristics, if any, can predict or are related to student success; The “start-stop-continue” method was utilized to assess student perceptions about their success in the college as a whole. The students were asked: Are there any specific things that you can think of that act/acted as barriers to your success in engineering (stop)? What could the college do/change to make first year more successful for engineering students (start)? Is there anything in your engineering degree so far that you feel is done well and helps students succeed (continue)? Students identified the quality of instruction early in their program as well as adjustment to college workloads and self-directed learning as the most significant barriers tostudent success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it