Student preference between single-box and multi-box homework problem answers using WeBWorK, an online homework system
Bibliographic record
Abstract
WeBWorK is an open-source online homework platform used in mathematics as well as engineering, where students can be assigned calculated answer engineering science problems. Problems with staged answers (multi-box) problems are possible on this system, and could offer feedback for the answers at each intermediate step of the solution. This would allow students to determine the step where they had an error (or deficit in understanding), similar to providing a hint on what their specific error was in adaptive feedback systems.Second-year students in a mechanical engineering program were exposed to both single- and multi-box questions in WeBWorK and were asked to give feedback about their preferences. The vast majority of students reported that they believed that the multi-box questions provided them good feedback on which step or calculation had error(s). They also pointed out the multi-box problems sped up finding errors in their solutions. However, a large minority indicated concern that multi-box problems constrained the solution to a particular path.Based on these results, providing some multi-box problems may assist students in finding their errors through more detailed feedback on their solution. This may be more effective earlier in a particular topic or in the first problems at any given complexity.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".