The Role of Task Value and Technology Satisfaction in Student Performance in Graduate-Level Online Courses

Abstract

Though it is well-accepted that Task-Technology Fit theory is a useful framework for examining university student success in online courses, the effectiveness of the theory has rarely been studied with graduate-level students or with a sample representing more than a few universities or programs. The current study investigated learners’ perceived performance in a national sample of students in over 400 graduate-level online learning environments through a path analysis involving five theoretically important constructs in the context of Task-Technology Fit theory. The results demonstrate that learners’ perceived performance will by and large be most greatly influenced by the value of the task. Task value was the strongest predictor after controlling for other variables in the model, followed by quality of content. Learner performance depends on how they perceive both the quality of the content as well as the design of the course itself. The Ease of use, relationships between users, and technology satisfaction had weaker relationships with performance, all leading to the understanding that online education organizations and information systems companies worldwide should emphasize usability when designing LMSs if the goal is to boost learners’ performance and satisfaction.