HOW DO ENGINEERING STUDENTS REACT TO MEMORIZATION VS. PROBLEM ANALYSIS QUESTIONS ON EXAMS?
Bibliographic record
Abstract
In engineering, problem analysis skill development is an important aspect of student learning. This skill development may be hindered by the use of surface learning approaches to obtain adequate performance on assessments. In this study, two focus groups were used to investigate reactions to memorization and problem analysis questions on engineering exams based on the nature of the course. Students are primarily motivated by grades and adopt a study approach that will allow them to achieve a high grade on a midterm exam based on the context of the course and contributing factors. When students are presented with memorization questions on an exam, they are more concerned with remembering the answer instead of using their knowledge base to solve the problem. When students perceive an exam will assess their problem analysis skills, they identified questions they have already seen before to be an unfair way to assess those skills. These results suggest that students employ different study approaches depending on the nature of the course and associated assessments. Therefore, exams should be designed with intent based on whether the instructor wants to assess their knowledge base or problem analysis skills.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".