Web-Based Learning as a Tool of Knowledge Continuity
Bibliographic record
Abstract
The outbreak of information in a borderless world has prompted lecturers to move forward together with the technological innovation and erudition of knowledge in performing his/her responsibility to educate the young generations to be able to stand above the crowd at the global scene. Teaching and Learning through web-based learning platform is a complementary method of conventional teaching and learning approaches which has a lot of potential to produce a more meaningful learning experience. In the School of Mathematical Sciences of Universiti Kebangsaan Malaysia, courses such as Basis Accounting and Finance have begun to employ web-based learning website known as Connect provided by the book publisher McGraw-Hill. This paper discusses the importance of cultivating teaching and learning through such formal website as a useful tool of providing learning experiences to students in the process of enhancing students’ knowledge retention thus improve their academic performances. In this paper, students’ performances in the academic session prior to introduction of web-based learning where only traditional and e-learning approaches are used is investigated and compared to performances of students employing web-based learning in addition to the traditional and e-learning methods. Findings of this paper found that students’ performances, specifically students whom are considered as ‘weak’, improve when web-based learning is introduced.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".