Malay Epic and Historiography Literature Students’ Perception Towards Interface Design Elements
Bibliographic record
Abstract
The interface is an important element in delivering information. This is because the interface becomes an early attraction to the user's perception before going to the next level. A good interface design will emphasize easy and user-friendly elements. Unfriendly interface design may impact the activity provided. This study aims to identify, parse and analyze user ratings on interface design. The respondents of the study involved 87 BBK 3311 Malay Epic and Historiography Literature at Universiti Putra Malaysia. The emphasis of this study will focus on nine interface design elements. The quantitative approach is used to carry out this study. A questionnaire instrument specifically designed to assess respondents' assessment of the interface design. This survey is available online using the Survey Monkey website and analyzed based on the Technology Acceptance Model (TAM) approach. This is because this approach is specifically designed to analyze the acceptance of the technology. The results of the study showed the level of tendency of students to be interested in the interface design. The results of the study can also help in giving an overview to the online course builder on the interface design that affects students' interest. Furthermore, it is hoped that the results of this study will be a guideline in building interface designs in the website.
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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 | 0.002 | 0.002 |
| 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) | 0.013 | 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".