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Using Visualization to Understand the Problem-Solving Processes of Elementary Students in a Computer-Assisted Math Learning ProgramShuang Wei (8809922) 08 May 2020 (has links)
<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>
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