As Student Response Systems Expand Features and Question Types, Multiple Choice Continues to be the Gold Standard for Calculations from both Student and Instructor Perspectives
Bibliographic record
Abstract
Student response systems (SRS) continue to evolve as bring-your-own-device (BYOD) systems allow more question and answer types to be utilized. While users were once limited to a button press on a clicker selecting from a list of predetermined responses, students can now generate text and numerical responses on their personal devices. Question and response types are now limited only by software, and new features can be added without requiring an overhaul of the existing system. Using two successive course offerings of a biomedical lab techniques class, the effect of question type was evaluated, using a crossover experimental design, and applied to novel discipline-specific calculations. Students used the Top Hat student response system (tophat.com) to answer either multiple choice questions (MCQ) or numerical response questions (NRQ) in class. Student responses were tracked for elapsed time to completion, performance, and subsequent test performance. Additionally, students were surveyed about their question-type preference. Analysis shows that on formative assessments, students take less time on multiple choice questions, are successful more often, and show a clear preference for this type. When students used those calculations on summative exams, they performed similarly regardless of whether they initially used MCQ or NRQ. Students also expressed clear preference for MCQ. The use of NRQ is still recommended to be used strategically as it increases question difficulty and diversity. The findings from this study may assist STEM instructors looking to formulate their own evidence-based best practices when incorporating SRSs intotheir pedagogy.
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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 | 0.142 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.026 |
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".