Malay Epic and Historiography Literature Students’ Perception Towards Interactive Online Interaction
Bibliographic record
Abstract
Technology is a need among modern community that ensures new knowledge and can be shared and utilized together. In teaching and learning, technology usage can be considered as a tool that can improve students’ cognitive, psychomotor and effective, also can interconnect with each other. Without technology, the learning world can be classified as a backward era. Therefore, through the interactive connection, learning can be more interesting and effective. Thus, the effectiveness of students’ needs will be measured either interactive connection that attracts students’ interest or vice-versa. Therefore, the objectives of the study are to determine the response towards online teaching interactive, also to analize and summarize UPM students online interactive connection by using the Technology Acceptance Model (TAM). This research applied online questionnaire by using the Monkey Survey website. About 94 Univeriti Putra Malaysia students from BBK 3311 Malay Epic and Historiography Literature course were choosen as research samples. The results of the study showed that majority of the samples are satisfied with the technology interactive method that had been introduced in this subject through Putramooc Malay Art platform. It is hoped that this research can help to identify the technology interactive connection through the most dominant multimedia and can be applied by the parties involved.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".