Effects of Computer Animations on Students' Geometrical Mathematics Misconceptions in Secondary Schools, Kitui County, Kenya
Bibliographic record
Abstract
Children come across mathematics before they start schooling. From infancy to secondary, they develop mathematical concept formation skills and hold misconceptions. Learning mathematics concepts is spiral in nature, with one level affecting later learning. Poor performance in mathematics is traceable back to Mathematics Misconceptions held by students at an early age. Animations has been used in Symmetry and Matrices with a remarkable reduction of students’ misconceptions. Their use in photoelectric effect in physics signifipppppcantly reduced students’ misconceptions. This study inquired into the effects of computer animations on geometrical misconceptions. The constructivist theory of learning guided the study where prior knowledge in geometry was used to build geometrical concepts from day-to-day life experiences. The study employed Solomon-Four Group Design with experimental and control categories having two groups each. The four groups for the study were purposively chosen. 112 boys and 95 girls participated in the research. MAT (Mathematics Achievement Test) adopted from past KCSE (Kenya Certificate of Secondary Education) questions were used to find the misconceptions held by students. The instrument was pilot-tested and resulted in a reliability coefficient of 0.8826 using the KR-20 formula. Pre-testing was done to the control and an experimental group before intervention, and all the four groups sat for a Post-test. ANOVA and t-test were applied in the testing of the hypothesis at a 0.05 level of confidence. With the use of Animation, a reduction of students’ mathematical misconceptions was observed. The performance of boys and girls after exposure to animations were noted to be significantly the same. The findings may help stakeholders in Mathematics Education.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".