Usability Evaluation in E-Learning Environments

Usability evaluation in e-learning environments examines how effectively and efficiently learners can interact with digital educational systems while experiencing satisfaction and minimal cognitive load. It spans a spectrum of methods, from expert-led heuristic inspections to empirical user testing employing questionnaires, eye-tracking and performance metrics. Central objectives include ensuring intuitive navigation, clear instructional design, accessibility across devices and alignment with diverse learning styles. Modern evaluations also integrate adaptive elements, responding dynamically to individual progress and preferences. Growing emphasis on data analytics has further enabled continuous refinement through real-time feedback and learning analytics. Globally, robust usability practices support equitable access to education, particularly in remote or under-resourced settings, and guide the development of scalable platforms that can accommodate multilingual content and varied pedagogical approaches. By embedding usability considerations throughout design and implementation, institutions and developers can enhance learner engagement, reduce dropout rates and improve overall outcomes in both formal and informal digital learning contexts.

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Usability Evaluation in E-Learning Environments publication trend

The graph below shows the total number of articles in usability evaluation in e-learning environments across all publications each year (not limited to Nature Index journals).

Technical terms

Usability evaluation: The systematic assessment of how effectively, efficiently and satisfactorily users can achieve specified goals within a system.

System Usability Scale (SUS): A ten-item questionnaire yielding a single usability score, widely used for quick, reliable assessment of interactive systems.

Heuristic evaluation: An expert-based inspection method in which usability principles are applied to identify interface problems.

Adaptive e-learning system: A digital learning platform that dynamically adjusts content, difficulty and presentation based on individual learner performance and preferences.

Fuzzy Preference Programming: A decision-making technique that uses fuzzy logic to model uncertainty and derive weighted priorities among evaluation criteria.

References

1. Inverse Trigonometric Fuzzy Preference Programming to Generate Weights with Optimal Solutions Implemented on Evaluation Criteria in E-Learning. Computers (2024).
2. Usability evaluation of personalized adaptive e-learning system using USE questionnaire. Knowledge Management & E-Learning An International Journal (2020).
3. Perceived usability evaluation of learning management systems: Empirical evaluation of the System Usability Scale. The International Review of Research in Open and Distributed Learning (2015).