A Quantitative Characterization of Audience Response System Research
Bibliographic record
Abstract
Audience Response Systems (ARS) can be used to increase students’ commitment and engagement. ARS are becoming popular at lectures, complementing traditional masterclasses and shedding light to a more profitability of the time. Several researchers studied the impact of ARS in the classroom. However, there is a lack of information about the current research landscape to identify paths towards the development of scientific research and projects in ARS field. This bibliometric study discusses a collection of bibliometric parameters on ARS literature that were calculated from data downloaded in Scopus database. A total of 2,015 publications were considered from Scopus database. Results showed that the number of publications is stable since 2010 with a noticeable decrease in 2019. The United States and the United Kingdom are the most productive countries with a total of 898 papers in the US and 179 in the UK. The most prolific author was Daniel Zingaro from the University of Toronto with a total of 10 manuscripts published. This study provides researchers who are interested in conducting research on ARS with insights on potential venues for publications and collaboration with research institutions and researchers that are more prolific in the field.
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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 | 0.064 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".