Algorithm Visualizations (AVs) have been used for years as an interactive method to convey data structures and algorithms concepts. However, AVs have traditionally focused on illustrating the mechanics of how an algorithm works. We have developed visualizations that we name Algorithm Analysis Visualizations (AAVs), that focus on conveying algorithm analysis concepts. We present our findings from an initial evaluation study of the effectiveness of AAVs when applied to a semester long Data Structures course. AAVs were evaluated in terms of student engagement, student satisfaction, and student performance. Results indicate that the intervention group students spent significantly more time with the AAVs than did the control group students who used primarily textual content. Students gave positive feedback regarding the usefulness of the AAVs in illustrating algorithm analysis concepts. Students from the intervention group had better performance on the algorithm analysis part of the final exam than did control group students.
We present lessons learned related to data collection and analysis from 5 years of experience with the eTextbook system OpenDSA. The use of such cyberlearning systems is expanding rapidly in both formal and informal educational settings. Although the precise issues related to any such project are idiosyncratic based on the data collection technology and goals of the project, certain types of data collection problems will be common. We begin by describing the nature of the data transmitted between the student’s client machine and the database server, and our initial database schema for storing interaction log data. We describe many problems that we encountered, with the nature of the problems categorized as syntactic-level data collection issues, issues with relating events to users, or issues with tracking users over time. Relating events to users and tracking the time spent on tasks are both prerequisites to converting syntactic-level interaction streams to semantic-level behavior needed for higher-order analysis of the data. Finally, we describe changes made to our database schema that helped to resolve many of the issues that we had encountered. These changes help advance our ultimate goal of encouraging a change from ineffective learning behavior by students to more productive behavior.
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