Effects of Behavioural Objectives-based Instructional Strategy on Senior School Students’ Academic Performance in Mathematics in Omu-Aran, Nigeria
Bibliographic record
Abstract
This study sought for the effect of the use of behavioural objectives on Senior Secondary students’ academicperformance in Mathematics in Omu-Aran, Kwara South Senatorial District Area of Kwara State, Nigeria. Thetarget population for the study comprised Senior Secondary Two (SS II) students in Omu-Aran town. Purposivesampling technique was employed to select 179 students for the study. A quasi-experimental, non-randomized,non-equivalent, pre-test, post-test control group involving a 2 x 3 factorial design was employed as researchdesign. The dependent variable was the Mathematics Academic Performance Test (MAPT) administered. Theindependent variables were the instructional strategy and the scoring levels. The test scores were analyzed usingmean scores, standard deviations, t-test and Analysis of Covariance on the two null hypotheses formulated. Analpha level of 0.05 was used to determine the significant level. Findings from the study showed that theexperimental group significantly performed better in Mathematics Academic Performance Test than the controlgroup. Based on this finding, it was recommended among others that teachers of Mathematics should alwayspresent the set behavioural objectives to the students prior to the lesson in order to enhance students’ fullparticipation in the lesson. Mathematics students should also be provided with academic counselling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".