CRCD/EI: Adaptive Explanatory Visualization for Learning Programming Concepts

[0426021] Peter Brusilovsky匹兹堡大学$220,000“crcd /EI：用于学习编程概念的自适应解释性可视化”该项目由匹兹堡大学和Ramapo学院合作开展，致力于探索自适应解释性可视化作为一种编程概念教学技术。交互式可视化为理解复杂的概念和揭示隐藏过程的动态提供了一种视觉隐喻。可视化研究的一个新兴和活跃的应用领域是计算机和信息科学（CIS）教育中程序执行的可视化，特别是在机器级语言、高级语言、算法和数据结构的背景下。本项目侧重于有前途的可视化方法，称为参与可视化、解释性可视化和适应性可视化，并试图回答以下与可视化相关的研究问题：在程序可视化的背景下，适应性和解释的价值是什么？可视化增强技术会使人们更好地理解编程概念吗？最近在基于模型的生成和集中式学生建模方面的进展能否产生高效和可扩展的自适应解释性可视化？适应性解释性可视化能否帮助教授面向编程的课程中最具挑战性的部分？从概念验证系统开始，研究团队开发了一套基于自适应解释性可视化的实用工具，并检查了它们在两个合作机构的课堂学习中的有效性。

0426021 Peter Brusilovsky University of Pittsburgh $220,000'CRCD/EI: Adaptive Explanatory Visualization for Learning Programming Concepts' This project, involving a partnership between the University of Pittsburgh and Ramapo College, engages in an exploration of adaptive explanatory visualization as a technology to teach programming concepts. Interactive visualization provides a visual metaphor for understanding complicated concepts and uncovering the dynamics of processes that are hidden. An emerging and active application area for visualization research is visualization of program execution in Computer and Information Science (CIS) education, particularly in the context of machine level languages, high-level languages, and algorithms and data structures. This project focuses on promising approaches to useful visualization called engaging visualization, explanatory visualization, and adaptive visualization and attempts to answer the following research questions pertaining to visualization: What is the value of adaptation and explanation in the context of program visualization? Will visualization enhancement technologies produce better understanding of programming concepts? Can recent advancements in model-based generation and centralized student modeling produce efficient and scaleable adaptive explanatory visualization? Can adaptive explanatory visualization help teach the most challenging parts of programming-oriented courses? Starting with a proof-of-concept system the research team develops a set of practical tools based on adaptive explanatory visualization and examines their effectiveness in classroom studies at both partner institutions.