Literasi Digital dan Pemanfaatan Media Sosial dalam Pembelajaran: Program Pendampingan Guru di SMK Permas Jaya 2 dan Sekolah Indonesia Johor Bahru, Malaysia
Bibliographic record
Abstract
Improving the level of training and ability of educators to create technology-integrated instruction has become important in the current digital age, especially at Sekolah Indonesia Johor Bahru (SIJB) and Sekolah Menengah Kebangsaan Permas Jaya 2 (SMK Permas Jaya 2) in Malaysia. Making the best use of social media and digital apps in their teaching and learning processes presents difficulties for both institutions. Thirteen teachers from SMK Permas Jaya 2 and ten from SIJB participated in this community service program, which aimed to enhance their knowledge and practical abilities in using digital and artificial intelligence (AI) programs such Canva, ChatGPT, Perplexity, Grammarly, and Book Creator. Pretest and post test assessments were used in conjunction with lessons, discussions, demonstrations, and practical exercises. According to pretest results, the majority of teachers understood digital literacy at a moderate level, but they still lacked the practical abilities to use digital apps. The majority of teachers advanced to the "good" and "excellent" categories in terms of their proficiency with these programs and their ability to produce interactive digital learning resources after the mentoring sessions, indicating a notable improvement. Teachers' successful integration of digital resources into their classroom instruction is proof of the program's tangible impact, as demonstrated by improved practical skills. Direct mentoring helped solve a number of technical challenges that arose during practice. All things considered, this digital literacy mentoring program successfully increased the ability of teachers at both schools, especially when it came to using technology to produce more engaging and creative learning environments. It is advised that similar initiatives be maintained, emphasizing more advanced instruction and better technology infrastructure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".