Impact of personality traits on learners' navigational behavior patterns in an online course: a lag sequential analysis approach.

Personality is considered as the internal factor that defines a person's behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners' personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners' behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners' navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners' personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners' personalities.