I-Think Mind Mapping: An Approach to Improve Students’ Interest and Motivation in Malaysian Nationhood Course at Universiti Utara Malaysia
Bibliographic record
Abstract
Malaysian Nationhood course is an introduction to the history of nation building that covers the period from pre-independence until the establishment of post-independence government policies. In addition, the course discusses the fundamental factors that have taken place in the development of Malaysian history and lists the factors that have been the turning point in the country's history. This course is very important in building a new generation of Malaysians using history as the foundation of nation building. But the students' reactions gave the negative perception of the Malaysian Nationhood course by stating that it was a heavy subject, uninteresting, too many facts and boring lectures. As a result, they have reacted negatively when they were in the classroom. These include coming late to the class, skip class, talking during lectures, reading other course notes, looking at cell phones and often falling asleep. To overcome this problem, the I-Think mind mapping method is used in learning activities. Qualitative methods are applied through observation, activities using Mind Map and Focus Group Discussions. The results show that activities carried out through I-Think mind mapping have had a positive impact on increasing students' interest and motivation in this course. However, in this large-scale classroom, lecturers will need to diversify their teaching techniques and other teaching aids to keep students engaged in the lectures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".