Closing the Missing Links and Opening the Relationships among the Factors: A Literature Review on the Use of Clicker Technology Using the 3P Model.
Bibliographic record
Abstract
Clicker technology is one of the most widely adopted communication systems in college classroom environments. Previous literature reviews on clicker technology have identified and thoroughly documented the advantages, disadvantages, and implications of the use of this technology; the current review is intended to synthesize those earlier findings and recast them in terms of the interrelationship between the “3 Ps” of the 3P model: Presage, Process, and Product factors. Using this guided framework enables the identification of the most up-to-date trends and issues in clicker studies published in peer-reviewed journals since 2009. The review shows that recent clicker studies have examined the effects of clickers in terms of student presage factors (cognitive, non-cognitive, background factors), instructor presage factors (instructor effects and the level of the course taught), process factors (delivery method, instructional activities, and assessment and feedback), and product factors (cognitive and non-cognitive outcomes). A heat-mapping approach is used to facilitate the interpretation of the results. The findings also discuss missing/unaddressed links and the untapped relationships among instructional factors in these studies. This study concludes that teaching and learning with the use of clicker technology is a complex and relational phenomenon; factors that are currently under-explored should be examined using more rigorous research methods to close gaps in the literature and to enhance understanding of the use of clickers in classroom learning environments.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".