Effects of Gender and Collaborative Learning Approach on Students’ Conceptual understanding of Electromagnetic Induction
Bibliographic record
Abstract
The study investigates the effect of gender and collaborative learning approach on students’ conceptual understandingof electromagnetic induction in Secondary Schools in Nigeria. Three research questions and 2 hypotheses wereformulated to guide the research. The research design adopted for this study is the quasi-experimental design. Inparticular, the design is the non-randomized, pretest-posttest, control group design. The population of the study is madeup of the 323 Senior Secondary III physics students in all 6 public co-educational Senior Secondary schools in PortHarcourt local Government area. A sample of 90 students, comprising of 60 male and 30 females were selected for thestudy. The research instrument developed and used for this study is the Test on Electromagnetic Induction (TOEI). Theinstrument is composed of 50 questions covering the content area and testing the various levels of understanding.Simple means, standard deviation and variance were used to answer research questions while inferential statistics suchas t-test, Analysis of variance (ANOVA) and 2x2 factorial analysis of variance were utilized for the testing of thehypotheses. The results show that gender does not significantly affect the understanding of students in electromagneticinduction when taught with collaborative teaching approach. The study also showed that gender and teachingapproaches do not jointly affect students’ conceptual understanding of electromagnetic induction at the secondaryschool level.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".