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Journal Article

Factors Predicting Integration of E‑Learning by Preservice Science Teachers: Structural Model Development and Testing  pp421-435

Cecilia Temilola Olugbara, Moeketsi Letseka

© Oct 2020 Volume 18 Issue 5, Editor: Rikke ├śrngreen, Mie Buhl and Bente Meyer, pp373 - 459

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This study investigated the possible factors that predict e‑learning integration into the teaching and learning of science subjects among preservice science teachers. A unified e‑learning integration model was developed in which factors such as attitude, intention, skill and flow experience served as precursors of e‑learning integration. This was done to help close the gap that no previous studies have developed a structural model to statistically explain the interactions among the most influential factors in various technology integration models. The survey method was used to gather data from 100 preservice science teachers and partial least square structural equation modelling technique was applied for structural path analysis and testing of the developed model. Results revealed a good model fit and hypotheses formulated in this study were faithfully supported. The results also revealed that all factors investigated were found to be significant predictors of e‑learning integration with skill standing out as the most significant and strongest factor that predicts the integration of e‑learning by preservice science teachers.


Keywords: E-learning Integration, Least Square, Planned Behaviour, Preservice Teachers, Structural Model, Technology Adoption


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