Learning Process in Mathematics and Statistics Courses towards Engineering Students: E-learning or Traditional Method?
Bibliographic record
Abstract
Engineering courses such as Mathematics and Statistics at an undergraduate level are frequently presented to the students in traditional way. In order to be parallel with young generations in terms of technology, e-learning was introduced to engineering students in FKAB with the hope that e-learning is a way to enhance learning in a more convenience and cost-effective manner. This study examines students’ perception towards the importance and usefulness of modern technologies such as e-learning (WILEY PLUS) in comparison with the more traditional lecture, as knowledge delivery or alternatively, a method of learning process. The objectives of this study are to test whether there is any difference between these two methods and to identify which method is more important and agreeable to the students. A total of 182 students of First Year and 179 of Second Year engineering students at the Faculty of Engineering and Built Environment, UKM who have taken Mathematics and Statistics courses respectively involved in this survey. The descriptive statistics such as mean and standard deviation and inferential statistics as paired t-test was used to compare these two methods. This study reveals that there is a significant difference between WILEY PLUS and lecturing in Mathematics and Statistics courses. Overall, lecturing was significantly of importance and favourable in the learning process for both courses compared to the newly-introduced WILEY PLUS.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".