Some factors influencing the academic performance of junior high school pupils in English Language: The case of Assin North Municipality, Ghana
Bibliographic record
Abstract
Academic achievements, like any attainable goal in life, do not come by chance. Men, money and materials (resources) must be combined in an ideal manner to ensure its success. A total number of thirty (30) pupils; eighteen (18) boys and twelve (12) girls, with an average age of 12 years, sampled from the Junior High School two class of Amoakrom, Nyame-bekyere and Ningo basic schools, and eight (8) teachers from Basofi-Ningo circuit in the Assin North Municipality were used for the study. Questionnaires of thirty (30) items each were administered to both teachers and pupils. The study reviewed sex, age and form (class) for the pupils, and sex and age for the teachers. For the pupils, 60% were males; the majority (43.4%), were aged between 11 and 14 with 36.7% recording sibling size of between 7 and 9. Over seventy percent (73.3%) of the pupils responded yes to the availability of textbooks whereas more than half (60%) of the respondents answered yes to the speaking of Pidgin English with peers. Among the teachers, 50% were aged between 20 and 36 years and a minority 12.5% were aged between 37 and 40 years. 62.5% of the teachers answered no to the use of Teaching Learning Materials. 37.5% of the teachers held Senior Secondary School Certificate and Teachers’ certificate ‘A’ respectively while just about one quarter (25%) held a bachelor’s degree. The results revealed that two-thirds (75%) of the teachers found it difficult to understand and teach some concepts in English Language. Key words: Academic, certificate, municipality, Pidgin English, pupils, resources, sex ratio, teachers, textbooks, questionnaire.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".