Enhancing Spatial Literacy through ClassPoint-Integrated Geometry Learning: A Quasi-Experimental Study Type
Bibliographic record
Abstract
This study evaluates the efficiency of ClassPoint learning media in improving students' spatial literacy in geometry education. Spatial literacy, which includes imagery, reasoning, and communication skills, is crucial for understanding and solving complex geometric problems. The research employed a quasi-experimental methodology, dividing 52 eleventh-grade students into two groups: one utilizing ClassPoint for the experiment and the other employing conventional methods as the control group. We collected data through pretests and post-tests with instruments devised particularly to assess three characteristics of spatial literacy. The data analysis included t-tests and N-Gain to evaluate the intervention's effectiveness. The results demonstrated that students in the experimental group showed a significant improvement in spatial literacy compared to the control group. The mean score of the experimental group increased from 66.38 to 85.23 following the intervention, with N-Gain scores for vision at 0.84 (high category), reasoning at 0.81 (high category), and communication at 0.70 (medium category). The interactive features of ClassPoint, such as quizzes, polls, and live annotations, improved the learning experience and aided students' understanding of concepts. This study contributes to educational literature by illustrating the effectiveness of the ClassPoint learning medium in improving spatial literacy in geometry education. This study's practical implications suggest utilizing learning tools like ClassPoint in mathematics lessons to create a more innovative, engaging, and relevant educational environment. We expect the results of this study to guide educators and policymakers in developing learning strategies that enhance students' spatial skills development
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".