评估算法分析可视化的有效性

Mohammed F. Farghally, Kyu Han Koh, Hossameldin Shahin, C. Shaffer
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引用次数: 16

摘要

算法可视化(AVs)作为传递数据结构和算法概念的交互方法已经使用多年。然而,自动驾驶汽车传统上专注于说明算法如何工作的机制。我们已经开发了可视化,我们将其命名为算法分析可视化(aav),其重点是传达算法分析概念。我们从应用于一个学期的数据结构课程的aav有效性的初步评估研究中提出了我们的发现。从学生参与、学生满意度和学生表现三个方面对aav进行评估。结果表明,干预组学生花在aav上的时间明显多于主要使用文本内容的对照组学生。学生对aav在说明算法分析概念方面的有用性给予了积极的反馈。干预组学生在期末考试算法分析部分的表现优于对照组学生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the Effectiveness of Algorithm Analysis Visualizations
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.
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