GEOGEBRA SOFTWARE: A VERITABLE PEDAGOGICAL TOOL FOR IMPROVING STUDENTS’ ACHIEVEMENT IN GEOMETRY
Bibliographic record
Abstract
The study investigated the impact of Geogebra Software on students' achievement in geometry.The study was carried out in Owerri Municipal Council of Imo State.The study was a quasiexperimental research type adopting the pre-test post test non-equivalent control design.A sample of 114 senior secondary two (SS II) students was used for the study.The instrument for data collection was researchers made objective test with reliability coefficient of 0.80 determined using Kuder-Richardson (KR 20 ) formula.The experiment group was taught geometry using Geogebra software while the control group was taught using conventional approach.The data generated was analyzed using mean and standard deviation to answer research questions while the hypotheses were tested using ANCOVA statistical tool at 0.05 level of significance.The result of the study revealed that Geogebra software was effective in improving students' achievement in geometry irrespective of gender.Based on the result, it was recommended that Geogebra software should be introduced in secondary schools to enable teachers use them for teaching 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".