Assessment of the Psychometric Properties of E-learning Instructional Design Quality

• Main Authors: kishabale, bashir, Syed Hassan, Sharifah Sariah
• Format: Article
• Language: English
• Published: University of Eldoret, Kenya 2018
• Subjects: HA Statistics
• Online Access: http://irep.iium.edu.my/69334/1/bashir2.pdf; http://irep.iium.edu.my/69334/; http://www.ajest.info/index.php/ajest

With the ever-growing adoption of E-learning as an alternative mode for instructional delivery, and indeed as part of the strategic plan by higher learning institutions to foster open and distance learning, the development of empirically tested guidelines to evaluate E-learning instructional quality is timely. The purpose of the study was three-fold, that is to, explore the underlying structure of the E-learning instructional design quality construct, test the adequacy of its psychometric properties in terms of common method bias, reliability, convergent and discriminant validity, and cross validate the consistency of the measurement model across samples. The quantitative data was collected from a stratified random sample of 837 students undertaking CISCO E-learning courses at ten different institutions of higher learning in Uganda. A 38-item self-reported questionnaire to measure E-learners’ perceptions on E-learning instructional design quality served as the research instrument. The collected data were analysed using Exploratory Factor Analysis and Confirmatory Factor Analysis, with SPSS version 20.0 and AMOS version 22.0 softwares. The study results revealed that E-learning instructional design quality is a multidimensional construct with the sub dimensions of content quality, interface design quality, instructional strategies, content interactivity and E-learning feedback. Moreover, the measurement model was found to be free from common method bias and demonstrated adequacy in its validity and reliability. However, the results of cross validation indicated that the measurement model was not consistent across the three samples as shown by the variations in the model fit indices. The results are valuable to enable E-learning stakeholders to take strategic and evidence-based decisions regarding the integration of E-learning interventions for quality learning outcomes and enhanced future research in the domain of E-learning instructional design quality. Specifically, this study has successfully validated an E-learning instructional design quality questionnaire that educationists can use in evaluating E-learning courses regarding instructional design soundness.