What do students do on-line? Modeling students' interactions to improve their learning experience. (November 2016)

• Record Type: Journal Article
• Authors: Paiva, Ranilson; Bittencourt, Ig Ibert; Tenório, Thyago; Jaques, Patricia; Isotani, Seiji
• Is Part Of: Computers in human behavior. Volume 64(2016)
• Page Start: 769
• Page End: 781
• Publication Date: 2016-11
• Subjects: User modeling -- Gamified learning environments -- Interactional characteristics -- Pedagogical decision-making -- Data-informed decision-making; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2016.07.048
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: In this work, we present an approach to model and analyze students interactions, within a gamified on-line learning environment, in order to assist teachers and tutors (education professionals) decision-making, regarding their students learning experience. We noticed that asking students this information might not bring precise and dynamic results for all students. This way, we characterize the educational resources available in the studied environment, and collected data from students interactions with these resources. Our objective was to generate the students' interactional profile (a model of their interactions). The information, then, is presented to teachers and tutors, who should use it to guide their pedagogical decision-making process. In this study, the types of interactions were used to personalize gamification elements named missions. We experimented the approach with two groups of users from the studied environment (MeuTutor). The data analysis showed differences in the way these groups were performing, where group B was considerably above group A. We sent the personalized missions (following our approach) to every student from group A, and waited some time for them to interact with it. In the end we checked the effect of this treatment, which, according to the results, promoted relevant improvement in group A interactions. Highlights: Model students' interactions with the educational resources. Use data produced by the learning environments to create these models. Use data to visualize students' interactions to assist pedagogical decisionmaking. Turn pedagogical decision-making into personalized recommendations. Monitor and evaluate the efficacy of the recommendations.