Building an asynchronous HTML5-related competency-based guided e-learning system

Abstract

Improving learner behavior and attitude is one method for improving learning quality. However, every student has unique characteristics, which makes the prediction of how learning behavior alters learning effects challenging. Humans obtain knowledge through cognitive learning, which can be effectively taught using asynchronous e-learning systems. However, these systems require self-study and include tedious processes that can affect learner motivation and the overall learning effect. This study implemented an HTML5-related competency-based guided e-learning (CBGE) system with an embedded competency-based guided learning (CBGL) mechanism to implement a personalized learning environment. Moreover, learner behaviors were recorded in a log dataset for data mining. The results revealed that the participants were satisfied with the CBGL mechanism and e-course design. The cognitive ability of the participants significantly improved after the experiment. Moreover, the growth in cognitive ability of the participants who completed the guiding process was significantly higher than that of the participants who did not finish the guiding process. This indicates that cognitive ability was improved through the CBGE system and the completion of the CBGL process provided a significantly more pronounced learning effect. A decision tree technique was also employed to construct a predictive model of the learning effect that could help learners achieve the learning objective. The predictive model revealed that learners who used the CBGE system could not achieve the learning objective without passing the stage tests a minimum of 2.5 times. Thus, the number of times that the stage tests were passed was the critical factor in achieving the learning objective.