Research project / Case study
Comparison of TUXEL and MELSS Models for Evaluating User Experience and Success in E-Learning Systems
01 / Research problem
Digital technology in education has led universities to adopt e-learning as a primary learning medium. In practice, many institutions use these systems without a comprehensive evaluation, and common problems include low user engagement, difficult navigation, non-intuitive interfaces, and features that do not match learning needs. Most prior studies evaluate e-learning with only one model, so there is little direct comparison of a user-experience-focused model against a system-success model in the same context.
02 / Research question
- How does the TUXEL model evaluate user experience in the e-learning system?
- How does the MELSS model assess the success of the e-learning system?
- Which evaluation model is more effective at describing both user experience quality and system success in higher education?
03 / Methodology
The study uses a quantitative approach with a comparative experimental design (comparative evaluation design). A single e-learning system is evaluated using two different models. Respondents are students who actively use the system, selected through purposive sampling. Data are collected through a questionnaire built from the TUXEL and MELSS indicators, then analysed with descriptive statistics and comparative analysis.
04 / Findings and contribution
[IN PROGRESS: This study is in progress. Findings will be reported after data collection and analysis.] The expected contribution is a clearer account of how the two models differ in what they reveal, plus a recommendation on which model better captures user experience and system success for university e-learning.
05 / Reflection and future research
The comparison connects interface evaluation to learning outcomes and contributes to User Experience, Human-Computer Interaction, and digital learning evaluation. It also informs the doctoral direction by testing how evaluation methods can link interface decisions to measurable user and system outcomes.