The Factors Affecting the Impacts of Using the School Improvement Plan (SIP) on Students' Academic Achievement in Malaysia
Bibliographic record
Abstract
While professional learning communities are hailed as the solution to raising teacher quality in Malaysia, other nations, such as the UK and Canada, have started to deprivatize teaching. This theory is predicated on the notion that teaching is a collaborative endeavor in which educators grow as individuals and professionals. Rather, it is a cooperative process in which teacher learning enhances student learning. Furthermore, the traditional classroom is rapidly changing to accommodate the needs of students in the twenty-first century while also encouraging the deprivatizing teaching approach. Consequently, this study presents a curriculum-change implementation strategy one school uses to meet Malaysia's evolving understanding of learning in and for the twenty-first century. Narrating a series of anticipated strategic events clarifies the importance of using distributive leadership as an essential tool for supporting educational enhancement and highlights the benefits of consultative techniques. This study aims to determine the variables that influence how the School Improvement Plan (SIP) affects Malaysian students' academic performance. Using a Google Form approach, this study used a quantitative research design, an effective and systematic way to collect quantifiable data. According to this survey, teacher leadership factors have the most significant impact on students' academic success compared to the other two factors. The results of this study suggested a theoretical framework that forecasts the related factors—principal instructional leadership, teacher leadership, and conducive school environment—that influence the effects of implementing the School Improvement Plan (SIP) on students' academic achievement in Malaysia.
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 | 0.005 | 0.001 |
| 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 | 0.001 | 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".