Factors Affecting The Performance Of Senior Two Students In Chemistry In Rutsiro District Of Rwanda
Bibliographic record
Abstract
Performance in chemistry has been observed as an issue in secondary schools based on attitudes of students toward chemistry lesson, how is taught in class and availability of needed infrastructures that help to conceptualize lesson in chemistry. The classroom hands on, mind on activities, change of attitudes of students, and awareness of stakeholders to provide adequate infrastructures are needed to ensure effectiveness of teaching and learning chemistry in schools in order to improve the performance. The present study was carried out to determine the level of senior two students’ performance in chemistry and to find out the important factors affecting senior two chemistry performance in Rutsiro district, of Western province of Rwanda. The deductive research design was considered and the research was conducted in 5 schools. The target population was 130 respondents that included 100 students, 5 teachers, 5 head teachers and 5 deputy head teachers in charge of studies and 15 PTC members. In this study, purposive and simple sampling techniques were used to select students, teachers and administrative staff to take part in this study. Two instruments namely questionnaires and documentary class performance reports were used to collect data on the factors affecting senior two students’ performance in chemistry. The descriptive statistics were used to present the data and the results were analyzed by SPSS and analysis of frequency, percentage, p value and t- value were used in the present study. The data from the documentary class performance report showed that 71.0% of senior two students have poor performance (performance below 50% marks). The data from questionnaires and respondents showed that factors linked with the poor performance are less activity-based technique of teaching and learning, bad attitudes of students toward chemistry and lack of enough students’ resources materials, low back profile of their families, despites the teacher and administrative staff have higher qualifications needed to deliver the quality education. Teachers, parents, school administration have to be mobilized to motivate students in liking chemistry as core subject. Decision makers have to avail equipped laboratory with enough reagents, library with sufficient chemistry books and textbooks, smart classroom and internet connectivity to enhance teaching- learning.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".