ANALYZING USER INTERACTION LOGS OF AN EDUCATIONAL VISUALIZATION SYSTEM TO UNDERSTAND HOW STUDENTS GENERATE INSIGHTS

Department of Computer Science and EngineeringVisual analytics systems have been becoming popular in many domains. Recently, a visual analytical tool, VAiRoma is designed in educational domain to support students learn the history class. However, how users are interacting with such systems is still not known enough. In an educational domain, it is important to know how users are gaining insights. It may give us an opportunity to understand the user???s learning style, so that we can design better visualization tools in the future. In this thesis, I will analyze the interaction logs of an educational visualization system, VAiRoma, in order to explore how users generating insights via the system. Based on the results, users tried more explorative interactions at the initial stages of their insight generation path. In the middle of the path, users mostly read some textual information. Toward the end, they attempted to show their understandings from what they learnt by creating an annotation. There is also a cyclic behavior of an insight generation path. In 38% of cases, during the annotation creation process, the users cancelled to ???create an annotation??? and went back to read some textual information.ope

Using lag-sequential analysis for understanding interaction sequences in visualizations

\u3cp\u3eThe investigation of how users make sense of the data provided by information systems is very important for human computer interaction. In this context, understanding the interaction processes of users plays an important role. The analysis of interaction sequences, for example, can provide a deeper understanding about how users solve problems. In this paper we present an analysis of sequences of interactions within a visualization system and compare the results to previous research. We used log file analysis and thinking aloud as methods. There is some indication based on log file analysis that there are interaction patterns which can be generalized. Thinking aloud indicates that some cognitive processes occur together with a higher probability than others.\u3c/p\u3

Examining scientific thinking processes in open-ended serious games through gameplay data

Research on scientific problem-solving emphasizes the importance of problem solving and scientific inquiry as central components of the twenty-first century skills. Research has shown that open-ended serious games can facilitate students’ development of specific skills and improve learning performance through scientific problem-solving. However, understanding how students learn these complex skills in a game environment is a major challenge, as much research depends on typical paper-and-pencil assessments and self-reported surveys or other traditional observational and quantitative methods. The participants of the study were 237 sixth graders from two middle schools in the Southwestern area of the United States. The students used an open-ended serious game called Alien Rescue as their science curriculum for three weeks. The purpose of this study is, first, to identify students’ navigation behavior patterns in cognitive processes between at-risk and non-at-risk students within Alien Rescue. To accomplish this purpose, this study intends to use gameplay data by incorporating the integrated method of lag sequential analysis and sequential pattern mining together with a statistical analysis. The findings confirmed that the integrated method helped to explore students’ latent navigation behaviors as well as discover the differences of problem-solving processes between non-at-risk and at-risk students. The second purpose of this study is to examine the relationship between students’ learning performance and their scientific inquiry behaviors, which emerged as students engaged with Probe Design Center in this serious game. The results showed that the game metrics developed in Probe Design Center improved the predictions of both in-game and after-game performance. The cluster analyses with game metrics confirmed four unique groups regarding students’ scientific inquiry behaviors in Probe Design Center. This study concluded that the integrated methods of serious games analytics enabled researchers to investigate in-depth cognitive processes and scientific inquiry behaviors within a specific cognitive tool, Probe Design Center, and discover unique behavior groups across different school settings. The researcher identified the challenges of at-risk students in their cognitive processes and highlighted the support needs for these students. Consequently, this study proposed an interactive dashboard using the data-driven evidences to provide teachers just-in-time information to support students’ cognitive processes.Curriculum and Instructio