Sequencing computer-assisted learning of transformations of trigonometric functions
Bibliographic record
Abstract
Studies incorporating technology into the teaching of trigonometry, although sparse, have demonstrated positive effects on student achievement. The optimal sequence for integrating technology with teacher-led mathematics instruction has not been determined. Our research investigated whether technology has a greater impact on student achievement and attitudes if it is implemented before or after whole class teaching. The curriculum context of the study was a set of learning objects (CLIPS: Trig) designed to support student learning of transformations of trigonometric functions. The software includes functional features identified in prior research: it relieves students of the tedium of creating graphs by hand; sliders give students control of the simulations within program parameters; there are easy transitions between algebraic and graphic representations; the environment is dynamic; animation and visualization are included with graphing functions. Twenty Canadian classrooms (N = 489 grade 11–12 students, aged 17–18 years) were randomly assigned to two instructional sequences: CLIPS: Trig followed by whole-class teaching (CLIPS early treatment) and whole-class teaching followed by CLIPS: Trig (CLIPS late treatment). We found that in the pre-test to post-test comparisons, students who experienced CLIPS: Trig after whole-class teaching of core concepts learned more than students who began the unit with technology-supported simulations. However, there were no statistically significant differences in the pre-test to delayed post-comparisons. Beginning the trigonometry unit with CLIPS: Trig enhanced the impact of whole-class teaching, while beginning with whole-class teaching enriched students’ technology experience. The findings suggest that a tight integration of whole-class and technology-assisted instruction is preferable.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".