Student-generated Multiple-Choice Questions: A Java and Web-Based Tool for Students to Create Multiple Choice Tests
Bibliographic record
Abstract
Student-generated questions can be an effective study technique to improve active learning, metacognitive skills, and performance on examinations. Students have shown greater success when assessed using peer-made study questions than when studying without questions. In three semesters of a kinesiology research methods course students were taught how to write high-quality multiple-choice questions that addressed course objectives and Bloom’s Taxonomy. Students were given a graded assignment to write three multiple-choice questions using Makequiz, a Java and web-based tool for helping students generate multiple choice questions. Student- generated questions that were rated as good quality (n = 169-245) were provided to the students as a study resource prior to the final exam. Of those study questions, 40 were selected each semester to be on the final exam. Students performed significantly better on student-written questions than instructor-written questions on the final exam in Class A (p < .05) and in Class C (p < .05). A majority of students felt this assignment was a worthwhile component of the course, voting to keep Makequiz in the curriculum (Class A: 52.6%, Class B: 62.3%, Class C: 58.3%) or to modify Makequiz (Class A: 25.3%, Class B: 14.5%, Class C: 18.1%). Many of the students stated it was the most valuable assignment of the course (32% Class B and Class C). Makequiz is, therefore, a recommended study tool for students. Comparisons are also made with PeerWise, an online platform for creating and sharing MCQ and feedback. Further investigation is required to measure the impact Makequiz has on learning, metacognitive skills, and anxiety levels before test-taking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".