Teachers’ Knowledge and Readiness towards Implementation of School Based Assessment in Secondary Schools
Bibliographic record
Abstract
School-Based Assessment (SBA) was implemented in Malaysian secondary schools in 2012. Since its implementation, teachers have faced several challenges to meet the aims and objectives of the School-Based Assessment. Based on these challenges this study aims to find the level of teachers’ knowledge and readiness towards the implementation of school-based assessment (SBA). The study was conducted in 15 daily secondary schools in the state of Kedah, which is situated in the northern part of Malaysia, bordering Thailand. 155 teachers were randomly selected from a total of 260 teachers. This study used 2 questionnaires to assess teachers’ knowledge and readiness to implement SBA. The questionnaire was adapted from Alabah (2012) which was designed to assess the teachers’ knowledge (30 items) and readiness (35 items) on Nigerian teachers’ perception of SBA. This questionnaire used a 4-point Likert-type scale with strongly disagree e to strongly agree. The findings provide evidence that the knowledge of the teachers in terms of 5 dimensions, that is, conducting SBA, bands in SBA, knowledge of evaluating SBA, SBA procedural knowledge and knowledge of implementation of SBA. The overall mean (3.27) for the level of teachers’ knowledge towards SBA shows that all the teachers agree that they have the knowledge about SBA. In terms of readiness, the mean (3.09) shows that all the teachers agree that they are ready to implement SBA. The comparison of the two means suggest that teachers have relatively more knowledge but are less ready to implement the SBA. The implication here is that teachers feel that their level of knowledge is not complete and more initiatives need to be taken by the educational authorities so that teachers are more confident of their level of readiness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".