The Role of Symbolab Calculator Usage to Enhance Pre-Service Primary Teachers’ Conceptual Understanding in Trigonometry Through Community of Inquiry
Bibliographic record
Abstract
This study investigated the role of Symbolab Calculator software usage in enhancing pre-service Science and Mathematics Education (SME) primary teachers' conceptual understanding of trigonometry through community of inquiry.The research used a quasi-experimental design with control and experimental groups.Thus, purposive sampling techniques was used to select pre-service primary teachers in the option of Science and Mathematics Education (SME) taken from two schools randomly selected from Southern Province of Rwanda.Thus, the selected sample was 99 preservice primary teachers from two classes of Year One.Besides, the interview guide was used with seven pre-service primary teachers to explore their perception towards Symbolab Calculator software.Qualitative data was analyzed analytically and thematically.To analyze quantitative data, a t-test statistical analysis tool was used to compare means of the two groups.Although the results from a t-test revealed that there was no statistically significant difference between the control and experimental groups (p>0.5), the treatment group made a significant increase in scores compared to the control group.In addition, the results from pre-service primary teachers' conceptual understanding solutions analysis and those from pre-service primary teachers' interviews, showed that using Symbolab Calculator helped to develop a collaborative skill and more understanding of trigonometry.It recommended the tutors to integrate a Symbolab Calculator software in their instructions for an effective teaching and learning 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".