Characteristics of students assigned to technology‐based instruction
Bibliographic record
Abstract
Abstract Previous research has examined factors influencing teacher decisions to integrate technology using between‐teacher designs. This study used a within‐teacher design to compare students who were assigned multi‐media learning objects for learning fractions with students taught by the same teachers who were not assigned to the technology. There were two conditions: (1) teachers were asked to limit the number of assigned students to 25% of their class (N = 375 grade 7–10 students) and (2) teachers could assign as many students as they wanted (N = 149 grade 7 students). In the constrained decision setting, students assigned to the technology were more likely than students not assigned to score lower on a fractions achievement test, have dysfunctional attitudes towards mathematics learning, have low self‐efficacy, exert low effort, and be male. In the unconstrained decision setting, 70% of students were assigned the technology and the only statistically significant predictor was prior achievement. Teachers' criteria were congruent with research identifying correlates of mathematics achievement and comfort with technology.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".