Attitude of Secondary Students towards the Use of GeoGebra in Learning Loci in Two Dimensions
Bibliographic record
Abstract
The use of computer software has good chance to form an efficient and powerful learning among the students. On the other hand, using open source software to teach mathematics in the school system of Malaysia, particularly in secondary school is still an uncertain issue. As a result, in an attempt to bring in a freeware, GeoGebra, this paper studies the attitude of form two students towards the utilization of GeoGebra in learning Loci in Two Dimensions. This study was conducted with 30 form two students from a secondary school in Johor Bharu district. In the beginning, GeoGebra was used to teach Loci in Two Dimensions and then followed by a survey. Questionnaires were provided to investigate the attitude of the students towards GeoGebra. A research model which was modified from the Technology Acceptance Model (TAM) was used to develop the questionnaires in order to study the students’ attitude. Later on, the data were analyzed by using Statistical Packages for Social Sciences 19.0 (SPSS) software to find the correlation coefficient and regression results. The result revealed that the students showed positive attitudes towards the use of GeoGebra in learning Loci in Two Dimensions. At the same time, there was a significant relationship between perceived ease of use, perceived usefulness and attitude of students towards GeoGebra. This positive attitude of students will bring to positive behavioral intention to use GeoGebra in the future. At last, the implication of the research and recommendations for the future research also are discussed in this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".