The Impact of Hands-On-Approach on Student Academic Performance in Basic Science and Mathematics
Bibliographic record
Abstract
Children can learn mathematics and sciences effectively even before being exposed to formal school curriculum if basic Mathematics and Sciences concepts are communicated to them early using activity oriented (Hands-on) method of teaching. Mathematics and Science are practical and activity oriented and can best be learnt through inquiry (Okebukola in Mandor, 2002) and through intelligent manipulation of objects and symbols (Ekwueme, 2007). The study tries to ascertain the impact of Hands-on-approach on the students’ academic performance and the students’ opinion about this activity-based methodology. The general objective is to assess the impact and provide another platform for students to display their understanding of what they have learnt other than the usual written tests with memorized formulae. The activity focuses on Mensuration and Geometry (with 25% questions in each area) and separation of mixtures (pure and impure substances). This paper includes the analysis of the feedback of the pre-test and post-test scores of the students before and after the Hands-on-approach was given as well as students’ interview responses. The study showed positive improvement on both the students’ performance and participation on mathematics and basic science activities and willingness on the part of the teachers to use Hands-on-approach in communicating mathematical and scientific concepts to their students.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".