Data_Sheet_1_Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach.pdf

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.

人格被视为界定个体行为之内在因素。因此，在在线学习中考虑学习者的个性以提供适应性特征与个性化支持，能够提升其学习体验及成果。在此背景下，众多研究探讨了个性行为差异对在线学习的影响。然而，关于个性行为差异如何影响学习者在学习过程中的行为，所知甚少。为填补这一空白，本研究采用滞后序列分析（LSA）方法，基于学习者的个性，对65名学习者在为期三个月的在线课程中的导航行为模式进行探究。在此情境下，五因素模型（FFM）被用于识别学习者的个性。研究结果表明，具有不同个性的学习者采用不同的学习策略在课程中进行导航。例如，高外向性学习者往往受到外在动机的驱动。因此，他们在浏览课程模块与个人成就之间进行了显著的导航。本研究的发现可以促进适应性学习领域的发展，为不同个性的学习者提供关于何种个性化特征能够助益其学习的洞见。同时，这些发现亦可为个性行为自动建模领域提供关于基于学习者个性的导航行为差异的信息。