Impact of Flipped Classroom on Mathematics Learning Outcome of Senior Secondary School Students in Lagos, Nigeria
Bibliographic record
Abstract
This study examined the impact of the flipped classroom on the learning outcome of secondary school students in mathematics in Lagos, Nigeria. It examined the impact of a flipped classroom package (FCP) on post-test performance (PP) and retention performance (RP) of students in Mathematics; it also sought to determine the influence of gender on PP and RP of students towards learning mathematics in the flipped classroom (FC). This is in response to the search for means to reverse the poor performance of students in mathematics O’level West African Secondary School Certificate Examinations. The flipped classroom, an innovative teaching technique, was introduced as a possible corrective that could produce effective student learning engagement and performance. A Quasi-experimental design was adopted and 275 Senior secondary school (SSS) 2 Students, 147experimental and 128 as control (conventional) intact classes, constituted the purposive sampled population for the study. Three research instruments: Flipped Classroom Package, Lesson Note and Performance Test were validated by expects and used for the study. The instruments were also checked for reliability; and the inter-rater reliability coefficient of a developed FC package was 0.79; lesson note, 0.83; and test instrument 0.85. Four hypotheses were raised and tested after 6 weeks of the experiment. The results of the findings indicated that the flipped classroom encourages good performance in mathematics and should, thus, be encouraged in schools for being a student centred learning approach. The study concluses among others that teachers should be encouraged to attend seminars and workshops on the use of the approach for effective performance of the learners.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".