Revealing Significant Learning Moments with Interactive Whiteboards in Mathematics
Bibliographic record
Abstract
The aim of this study was to identify when and how the interactive whiteboard (IWB) functioned as a productive tool that impacted student learning in mathematics. Using video data, field notes, and interview transcripts from 1 school year in two optimal case study classrooms, we were able to examine the unique opportunities afforded by the size of the IWB screen, the manipulation of virtual objects onscreen, and related communication using gestures. We: (i) established criteria for defining “significant learning moments”; (ii) assessed these significant learning moments to determine how the interactive whiteboard was supporting the learning; and (iii) isolated the use of gesture during IWB use to magnify the grain size of our analysis and understanding. The data fell into three types of IWB use: productive (89%), reproductive (2%), and problematic (9%). The study recommends that in order to best support student learning, professional development for teachers should emphasize direct and active student use of the IWB to engage students in inquiry of mathematics.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".