Continuous Assessment, Mock Results and Gender as Predictors of Academic Performance of Chemistry Students in WASSCE and NECO Examinations in Ekiti State
Bibliographic record
Abstract
Education is one of the fundamental and critical indices of development in a nation or an individual. The challenges post by our fast changing and dynamic world informed the need to constantly access the educational process so as to guarantee quality of educational assessment; several factors have been identified to be responsible for students’ poor performance in chemistry at the secondary school level. One of these factors could be modes of assessment. This paper, therefore investigated Continuous Assessment, Mock results and gender as predictors of academic performance in WASSCE and NECO Examinations. The study adopted a survey research design of the ex-post facto type. The sample comprised 892 senior secondary school III drawn from the secondary schools in Ikere local government area of Ekiti state. Three research questions were answered in the study. Data were analysed using multiple regression statistic. The results showed that 41.3% of the total variance in chemistry students, performance in WASCE results is accounted for by mock results, continuous assessment and gender (adjusted R2 = 0.413). And 0.7% of the total variance in chemistry students, performance in NECO examination results is accounted for by three factors (continuous assessment, mock results and gender) (R2 =0.007%).the three factors made significance relative contribution to chemistry students’ academic performance in WASCE; continuous assessment (?= 0.282, p= 0.05<), mock results (?=0.396, p=<0.05) and gender (?=0.078, P=<0.05) while the contribution to performance in NECO examination is not significance. Mock result made the greatest contributions to chemistry students academics performance in NECO (?=0.070, p=<0.05) followed by continuous assessment (?=0.066, P=<0.05) and gender (?= 0.061, p=<0.05). Gender has significant influence on chemistry students’ academic performance in WASSCE (?=0.078, p<0.05) while it has little or no influence on chemistry students’ performance in NECO (?=0.061, p<0.05). That is, the three variables could be used to predict chemistry students’ academic performance. It is recommended among others that school authority should ensure that only students whose performance in continuous assessment and mock examination is encouraging are allowed to register for either WASSCE or NECO examinations. Also, both internal and external assessment should be done without gender bias.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".