Usability Evaluation of the Student Centered e-Learning Environment
Bibliographic record
Abstract
<p>Student Centered e-Learning Environment (SCeLE) has substantial roles to support learning activities at Faculty of Computer Science, Universitas Indonesia (Fasilkom UI). Although it has been used for about 10 years, the usability aspect of SCeLE as an e-Learning system has not been evaluated. Therefore, the usability aspects of SCeLE Fasilkom UI as a learning support system and what makes SCeLE Fasilkom UI an ideal system are not known yet. Motivated by the mentioned conditions, the researchers found an urge to conduct a usability evaluation in order to propose a set of recommendation for SCeLE usability improvement, based on usability evaluation reflecting both students and lecturers experience as user.</p><p>In this present research, the usability testing was conducted for SCeLE, targeting learning activities underwent by undergraduate students at Fasilkom UI, in the form of blended mode online learning. The data collection stage in the usability testing was performed by distributing questionnaire to students and interviewing several lecturers and students. The collected data was then analyzed and interpreted to obtain usability problems and solution alternatives. The quantitative data was analyzed using central tendency as reference, while the qualitative data was analyzed using theme-based content analysis. Data interpretation was performed by determining how to handle each kind of data based on the theme, and classifying each of the identified usability problem based on its severity rating.</p><p>The recommendations constructed to solve the usability problems were based on solution alternatives from the analyzed data supported by literature study. The present research comes up with seven main recommendations and an extra recommendation. The main recommendations are solutions to tackle the identified usability problems, while the extra recommendation is not directly related to any of identified usability problems, but was considered potential to improve the SCeLE usability.<br /><br /></p>
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".