<p>CAL (Computer Assisted Learning) programs are widespread today in schools and families due to the effectiveness of CAL programs in improving students’ learning and task performance. The flourishing of CAL programs in education has brought large amounts of students’ learning data including log data, performance data, mouse movement data, eye movement data, video data, etc. These data can present students’ learning or problem-solving processes and reflect underlying cognitive processes. These data are valuable resources for educators to comprehend students’ learning and difficulties. However, few data analysis methods can analyze and present CAL data for educators quickly and clearly. Traditional video analysis methods can be time-consuming. Current visualization analysis methods are limited to simple charts or visualizations of a single data type. In this dissertation, I propose a visual learning analytic approach to analyze and present students' problem-solving data from CAL programs. More specifically, a visualization system was developed to present students’ problem-solving data, including eye movement, mouse movement, and performance data, to help educational researchers understand student problem-solving processes and identify students’ problem-solving strategies and difficulties. An evaluation experiment was conducted to compare the visualization system with traditional video analysis methods. Seven educational researchers were recruited to diagnose students’ problem-solving patterns, strategies, and difficulties using either the visualization system or video. The diagnosis task loads and evaluators’ diagnosis processes were measured and the evaluators were interviewed. The results showed that analyzing student problem-solving tasks using the proposed visualization method was significantly quicker than using the video method. In addition, diagnosis using the visualization system can achieve results at least as reliable as the video analysis method. Evaluators’ preferences between the two methods are summarized and illustrated in the dissertation. Finally, the implications of the visual analytic approach in education and data visualization areas are discussed. </p>

Teaching computational thinking has been a focus of recent efforts to broaden the reach of computer science (CS) education for today’s students who live and work in a world that is heavily influenced by computing principles. Computational thinking (CT) essentially means thinking like a computer scientist by using principles and concepts learned in CS as part of our daily lives. Not only is CT essential for the development of computer applications, but it can also be used to support problem solving across all disciplines. Computational thinking involves solving problems by drawing from skills fundamental to CS such as decomposition, pattern recognition, abstraction, and algorithm design. The present study examined how Dr. Scratch, a CT assessment tool, functions as an assessment for computational thinking. This study compared strengths and weaknesses of the CT skills of 360 seventh- and eighth-grade students who were engaged in a Scratch programming environment through the use of Dr. Scratch. The data were collected from a publicly available dataset available on the Scratch website. The Mann-Whitney U analysis revealed that there were specific similarities and differences between the seventh- and eighth-grade CT skills. The findings also highlight affordances and constraints of Dr. Scratch as a CT tool and address the challenges of analyzing Scratch projects from young Scratch learners. Recommendations are offered to researchers and educators about how they might use Scratch data to help improve students’ CT skills.

Nowadays, interactive visualizations have been one of the most used tools in Big Data fields for the purpose of searching for relationships and structured information in large datasets of unstructured information. In this project, these tools are applied to extract structured information from students following Self-Regulated Learning (SRL). By means of an interactive graph, we are able to study the paths that the students follow in the learning materials. Our visualization supports the investigation of patterns of behaviour of the students, which later could be used, for example, to adapt the study program to the student’s needs in a dynamic way or offer guidance if necessary.