A MODEL FOR PREDICTING UNDERGRADUATE STUDENTS’ ADOPTION OF E-LEARNING IN SELECTED PUBLIC UNIVERSITIES IN KENYA

Abstract

The teaching and learning of science employ several approaches, each of which attempts to improve the quality of students’ learning. One approach that is gaining prominence in higher educational institutions (HEIs) is e-learning. However, e-learning adoption among students in Kenya’s HEIs is not succeeding the way it is expected because of both institutional and individual shortcomings. In this study, the University Students’ E-learning Adoption Model (USeLAM) was developed with the aim of providing a basis for decision making in the adoption of e-learning among undergraduate students in selected public universities in Kenya. The study adopted a cross-sectional survey design based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The sample consisted of 388 undergraduate university students who answered 24 questions on a 7-point Likert Scale in the Students’ E-learning Adoption Questionnaire (SeLAQ). The SeLAQ was validated by six educational research experts at Egerton University and yielded Cronbach - Alpha reliability coefficient of 0.78. Six hypotheses were tested above the 90% level of confidence (p < .100) by applying the Partial Least Squares, Structural Equation Modelling (PLS-SEM) techniques. The results indicate that there were positive and statistically significant relationships between Performance Expectancy (PE) and Behavioral Intention to adopt e-learning (BI) (β = .15, t = 3.16, p = .002) and; between Effort Expectancy (EE) and BI (β = .18, t = 4.32, p < .001). However, Social Influence (SI) was not a statistically significant predictor of BI (β = .00, t = 0.07, p = .945). On the other hand, BI was found to be a positive and statistically significant predictor of Actual Use Behaviour of e-learning (UB) (β = .08, t = 1.83, p = .067) while Facilitating Conditions (FC) was a negative but statistically significantly predictor of UB (β = -.11, t = 1.79, p = .073). Further analysis of the effect of moderators on the relationship between predictors and outcomes of students’ adoption of e-learning was done using the PLS-Multi Group Analysis (MGA). The results indicate that students’ age (AGE), gender (GND) and internet experience (IXP) significantly moderate students’ e-learning adoption in varying degrees. In the final analysis, the USeLAM accounted for 24% (R2 = .24) of the variance in BI and 15 % (R2 = .15) of the variance in UB. In conclusion, therefore, the study underscores the significant influence of PE and EE on students’ BI as well as that of BI and FC on students’ UB. The implications of this study extend to educational policy makers in general, and public university management, in particular, in improving e-learning adoption in HEIs. Further, it lays the groundwork for future research in predicting e-learning adoption in Kenya’s HEIs and buttressing the multidisciplinary nature of the application of e-learning in HEI’s.