How Selective True-False Questions Reward Student Recognition
Bibliographic record
Abstract
True and False (T/F) questions seldom undergo research in computer science education; hence, little exists to document improvements to T/F questions in CSE. We analyze the effects of Selective True and False (STF) questions. In STF, the student answers a subset of the question pool, rather than answering all of it. Students choose questions they believe they can answer, and avoid questions which they deem difficult. Therefore, an intelligent selection of questions leads to a lower probability ofguessing. This rewards students with knowledge (as it allows them to recognize easier/harder problems) while remaining indifferent to students with total ignorance. In other words, STF lessens the probability of a weaker student achieving a higher grade than a stronger student via guessing. Our data suggests that stronger students are able to recognize and answer questions with success (achieve a score in the top half of the class). Explicitly, the number of times a question is selected versus student success among students which selected that question has a correlation of 0.73. Furthermore, examining the success of the student's particular set of selected questions rather than the success of the student themselves, one finds an even stronger correlation versus the number of times a question is selected, i.e.\ a correlation of 0.99.
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 | 0.021 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".