Increasing Students Motivation to Learn Slope Analysis Using SLOPE/W Software in Geotechnical Engineering Subject with Visual Aid
Bibliographic record
Abstract
This study aims to investigate students' perceptions about the use of visual aids (e.g., video animations, images, films, and projectors) as motivational tools in increasing interest in learning subjects in Geotechnical Engineering. One of the most significant and crucial issues in mining and geotechnical engineering projects is slope stability analysis. This experimental study uses visual aids for the topic of slope stability to investigate whether it has significance for student learning with visual assistance. The quantitative research method approach is used to gather the required data. The instrument used was the Motivation Questionnaire, which comprised of 5 items. The items had a 4-point Likert scale for students to indicate their response on a scale. Therefore, a total of 25 final year students from UTHM Civil Engineering assigned to experimental conditions, with or without assistance, were chosen. According to data analysis, the vast majority of pupils think that using visual aids is a good idea. While it can help students increase their level of knowledge in learning Geotechnical Engineering subjects with interest, using visual aids allows students to interact with their lecturers closely about issue content. This aspect is essential because it helps to produce students' creative and critical thinking skills.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".