Perceived usability evaluation of learning management systems: Empirical evaluation of the System Usability Scale
Bibliographic record
Abstract
Perceived usability affects greatly student’s learning effectiveness and overall learning experience, and thus is an important requirement of educational software. The System Usability Scale (SUS) is a well-researched and widely used questionnaire for perceived usability evaluation. However, surprisingly few studies have used SUS to evaluate the perceived usability of learning management systems (LMSs). This paper presents an empirical evaluation of the SUS questionnaire in the context of LMSs’ perceived usability evaluation. Eleven studies involving 769 students were conducted, in which participants evaluated the usability of two LMSs (eClass and Moodle) used within courses of their curriculum. It was found that the perceived usability of the evaluated LMSs is at a satisfactory level (mean SUS score 76.27). Analysis of the results also demonstrated the validity and reliability of SUS for LMSs’ evaluation, and that it remains robust even for small sample sizes. Moreover, the following SUS attributes were investigated in the context of LMSs evaluation: gender, age, prior experience with the LMS, Internet self-efficacy, attitude towards the Internet and usage frequency of the LMS.
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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 | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".