Kerangka Panduan Efektif Pengajaran Dan Pemudahcaraan (PdPc) Sains Menggunakan Information Communication Technology (ICT) di Sekolah Jenis Kebangsaan Tamil (SJK) (TAML)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effort of the Ministry of Education of Malaysia (KPM) in strengthening Science, Technology, Engineering and Mathematics (STEM) educations in Malaysia is to increase the source of experts and skilled workforce in the field of research and industry. The inspiration of KPM to empower the strengthening of STEM can be implemented with the availability of ICT. ICT can be a comprehensive tool in teaching and facilitation (PdPc) of science that is complete and inclusive. ICT based science learning is an outcome of the combination of principle and strategy within education and technology domains. ICT has a great potential to spur the learning process and extensive thinking skill among the students since they regularly incorporate the use of ICT in their daily life. However, this mentioned potential has not been fully disclosed in Malaysia. Countries like Finland has a ‘road map’, meanwhile Canada has a framework for T&L of science based on ICT. Malaysia still has not developed a ‘road map’ or a framework as the above for science especially in SJK(T). Besides that, poor achievement in science education is becoming a continuous issue in SJK (T). This case is proven to be true when the results for science subject among the SJK (T) UPSR candidates are dropping since the year 2011. Hence, various elements involving the expansion of ICT based teaching and facilitation of science framework guideline should be identified in Malaysia. The framework guideline will be built based on the Malaysian culture which has the potential to spur success and science competency at the international level.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 it