Enhanced Conceptual Learning with Real Time Student-Generated Data and Visualization
Bibliographic record
Abstract
Interactive student response systems (SRS, clickers) are used in post-secondary classrooms to enhance student engagement and learning. Their use, however, is most often limited to reviewing material with multiple choice questions. The present study examined student responses to a strategy for technology-enhanced learning within an introductory understanding research course to improve student experiences. SMART Technologies Interactive Response System™ was used to collect anonymous student data during classes, with raw data exportation into a Microsoft Excel™ spreadsheet coupled with Tableau Data Visualization software. Students engaged with statistical concepts through their own real-time data generation and immediate visualization, as well as participated in discussions of concepts with their peers and instructor. Students gave positive feedback on the use of clickers in this novel application. The unique combination of technologies provided a fast and powerful means of illustrating student-generated data and encouraged critical thinking and student engagement and enjoyment. Such implementations, which appear to be both enjoyable and beneficial to learning, should be further designed as low to no cost options. Further, with increased engagement and enjoyment, challenges such as mathematics and statistics anxiety could be investigated and potentially managed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".