Bibliographic record
Abstract
Student Response Systems (Clickers) have been adopted by a number of instructors to increase interactions, student engagement and/or formative assessment and feedback especially in large group sessions. Clickers are well known as tools for active learning strategy particularly in formative assessments. However, they are rarely used in assessments that count. With the advent of clickers with a display screen, attempts are being made to use clickers in summative exams. We examined the feasibility of their use in high stakes summative assessment by piloting such an assessment in a simulated setting. Utilizing the lessons learned in the pilot study, clickers were used in formative and summative assessments in various iterations of a computer course taught by one of the authors (CC). At the end of the course, perceptions of students on the use of clickers for high-stake examinations were obtained using an online survey. The instructor was interviewed to identify factors that facilitated clicker use and the challenges faced. In general, students were accepting of the use of this technology in high stakes exams and found it engaging and satisfying, primarily because of instant feedback. The instructor found the process less time consuming and efficient, and more secure compared to scan sheets. Clickers are best used for examinations of short duration, with multiple-choice questions or questions with minimal text or mathematical entry.
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 | 0.045 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".