Are online behavioural characteristics effective predictors of intrinsic motivation and user engagement in the online learning environment?
Year: 2025
Type: article
Study design: Observational Study
Abstract: This study aimed to investigate the predicted relationship among online behavioural characteristics, intrinsic motivation and user engagement. An online learning platform was used to collect data on the online reading time and the number of test attempts of 161 graduate students, as well as their post-learning motivation and user engagement levels. The data were processed based on the e-learning motivation and user engagement scales. The data structure was validated using structural equation modelling. The findings showed that online reading time positively predicts anxiety and negatively affects focused attention. A higher number of test attempts negatively affects effort expectancy, perceived usability, novelty, felt involvement and endurability, leading to reduced user interaction quality. The findings suggest designing online courses with multiple smaller units, each with controlled learning time. Implications for practice or policy: Educators can integrate motivation and engagement measures with learning logs to better align instructional support with learners’ psychological and behavioural patterns. Instructional designers can apply learning analytics evidence to optimise platform features that strengthen learner motivation and sustained engagement. Course designers should limit excessive online text reading and adopt multimodal materials to reduce anxiety and attention loss. Assessment designers may need to limit repeated test attempts, as frequent retries are linked to lower perceived ease of use and adaptability.
DOI: 10.14742/ajet.10526
Institution: National Yang Ming Chiao Tung University
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7127110579