Getting More Out of Midterm Assessments
Bibliographic record
Abstract
An important reason for providing midterm assessments is to give students early feedback on their progress. Ideally, students will carefully analyze their performance and use the feedback provided to adjust their study strategies or approaches to engaging with the course materials. In our experience, however, only a small fraction of students seek advice from instructors or advisors after an unsatisfactory performance. Research has shown that many students have negative perceptions of office hours, and some students find them inconvenient or have misconceptions about the purpose of office hours. In addition, it is difficult for instructors to provide detailed and individualized advice to a large number of students in a weekly office hour. To address this challenge, we automatically provide additional grades that inform students on their performance in four basic question categories that are related to levels in Bloom’s taxonomy. We also provide a table with specific recommendations for how to improve in each of these categories. These recommendations are based on experience: from conversations with students, we have learned that unsatisfying performance can often be traced back to a lack of effective exam preparation. Many students study by reading solutions to in-class activities or homework rather than reworking problems. We also noticed that struggling students often fail at a fundamental level: they tend to read definitions superficially as symbols instead of interpreting them and exploring their meaning.
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 | 0.018 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.029 |
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".