Student Use Of Textbook Solution Manuals: Student And Faculty Perspectives In A Large Mechanical Engineering Department
Bibliographic record
Abstract
Anecdotal evidence indicates that Mechanical Engineering students have unprecedented access to textbook solutions manuals, and possibly a large percentage of students regularly refer to these manuals when working graded homework assignments. Many faculty voice concerns regarding the ethics of this behavior and its affect on student learning; however, the prevalence of the solutions manual usage and its effects on learning are not well documented. To better understand how students use solutions manuals, a survey was submitted to undergraduate students and faculty of the Mechanical Engineering Department at California Polytechnic State University, San Luis Obispo, as part of a larger study on the effects of solution manual access on student learning. The methodology emulates earlier studies at M.I.T. 1 and Georgia Tech 2 that addressed student perceptions of cheating. This survey was administered in a number of required courses, with multiple sections that are typically offered every quarter at Cal Poly. The goal of this survey was to determine the incidence rate of solution manual use and student perceptions on the ethics and educational value of using the solution manuals when working homework assignments. Faculty perceptions were also tabulated using a similar survey. Quantitative results are presented along with an assessment of interactions between student perceptions and their use of the solution manuals.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".