Enhanced Conceptual Learning with Real Time Student-Generated Data and Visualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it