From Student’s Experience: Does E-learning Course Structure Influenced by learner’s Prior Experience, Background Knowledge, Autonomy, and Dialogue

2021 
Background: E-learning is increasingly becoming a preference in higher education institutions worldwide; this is intended to assist educational institutions in achieving objectives to meet the proportion of individuals with their educational opportunities. Nevertheless, instructors and students frequently have concerns with their capacity to succeed in E-learning environments. Objectives: This study aimed to presents common eLearning challenges in regard to e-learning courses structure and its relations to various factors, for instance; students’ autonomy, prior knowledge and experience, students- students dialogue, and students- instructor dialogue, and proposes solutions to these challenges based on the transactional distance theory. Moreover, this study presents evidence from Malaysian higher institutions based on theoretical models for e-learning course structure and its relations to the factors mentioned above. Methods: Data have been collected from 680 university learners all over Malaysia. Data were then examined using exploratory factor analysis, confirmatory factor analysis, and structural equation modelling employing Smart PLS 3.0 software. Results and conclusion: Research findings indicated that e-learning course structure was affected by all dimensions of overall path analysis findings: student autonomy, students background, student-instructor dialogue, and student-student dialogue. However, the e-learning course structure showed insignificant with students’ prior experience. Implications: Implications for universities are discussed accordingly. Such findings provide vital support to the integrative association among collaborative control (CC) and transactional distance theory (TDT) regarding e-learning environments experience, which might support universities administrators in the higher education industry to implement, plan and evaluate online learning platforms applications in their institutions.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    76
    References
    0
    Citations
    NaN
    KQI
    []