Changes in student effort and grade expectation in the course of a term
Bibliographic record
Abstract
Students were surveyed and asked to self-evaluate their performance and time-on-task in six engineering courses (eight sections). Surveys were conducted four times over the course of a 10-week quarter. Students with the highest grade point averages (GPAs) (2.88 mean; 4.00 mode) indicated ‘no change’ while students with lower GPAs (2.67 mean; 1.83 mode) indicated a ‘negative change’ in grade expectations by the end of the quarter, indicating that better performing students are better self-evaluators. Students reduced time-on-task 2–4 hours/week, especially early in the quarter. Students who changed their time-on-task during each survey had a GPA mode of 4.00, indicating that ‘master’ students readily adapt to course demands. This study shows that most students over-predict their grades and their level of commitment to a course and lose confidence in their abilities as the term progresses. It is suggested that instructors obtain student grade predictions and use them to provide timely and appropriate feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".