A Predictive Study on Instructional Design Quality, Learner Satisfaction and Continuance Learning Intention with E-learning Courses: Data Screening and Preliminary Analysis

Authors

  • Bashir Kishabale International Islamic University Malaysia
  • Sharifah Sariah Hassan International Islamic University Malaysia

DOI:

https://doi.org/10.53449/ije.v1i2.59

Keywords:

instructional design quality, CISCO E-learning in Uganda, learner satisfaction, continuance learning intention, data screening and preliminary analysis

Abstract

As E-learning initiatives are increasingly being deployed in educational and corporate training settings to revamp work-place productivity through life-long learning, concerns related to instructional design quality among stakeholders are equally growing. Thus, the overriding objective of the study was to carry out initial screening and preliminary analysis of the data related to the causal influence of instructional design quality on learner satisfaction and continuance learning intention. Based on the survey design, the quantitative data were collected from 837 students across ten CISCO Networking academies in Uganda. Descriptive statistics, multiple regression and factor analysis techniques were employed to address the purpose of the study. Primary attention was paid to the assumptions of response rate, missing data, outliers, data normality, multicollinearity, homoscedasticity and common method bias. The results of the initial screening and preliminary data analysis revealed non violation of prerequisite multivariate assumptions. The findings have provided empirical evidence on the psychometric study of which the instrument can be further used for future research. The steps taken for the analysis have provided a benchmark of audit trail in the methodology and statistical analysis for the replication of the study.

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Published

2018-12-26

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Articles

How to Cite

Kishabale, B., & Hassan, S. (2018). A Predictive Study on Instructional Design Quality, Learner Satisfaction and Continuance Learning Intention with E-learning Courses: Data Screening and Preliminary Analysis. Interdisciplinary Journal of Education, 1(2), 122-137. https://doi.org/10.53449/ije.v1i2.59

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