Integrating Graphing Calculators and Probeware Into Science Methods Courses: Impact on Preservice Elementary Teachers’ Confidence and Perspectives on Technology for Learning and Teaching
Bibliographic record
Abstract
Graphing calculators and data collection technology was in-tegrated in an undergraduate science methods classes for pre-service elementary school teachers. The study investigated whether preservice teachers’ knowledge and comfort with the use of graphing calculator technology and their perspectives in the use of graphing calculator technology were infl uenced by integration of this technology in the methods course over a period of one semester. Towards this end a survey was ad-ministered to all class participants at the beginning and at the end of the semester. The survey addressed preservice teach-ers’ confi dence level with the calculator-based technology, at-titudes towards using this technology for their own learning, and the attitudes towards using it as a teaching tool. In addi-tion, preservice teachers completed several open-ended refl ec-tions throughout the semester that provided qualitative data. The results of this study suggest that the preservice teachers view the graphing calculator as a productivity tool while they view data collection devices as a teaching tool. More expo-sure to technology produces more infl uence on preservice teachers’ perspectives and attitudes, but not necessarily their
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".