EXP: Understanding Computational Thinking Process and Practices in Open-Ended Programming Environments

网络学习和未来学习技术计划资助的工作将有助于设想下一代学习技术，并推进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索（EXP）项目通过设计和构建新型学习技术并研究其促进学习的可能性和有效使用它们的挑战来探索新型学习技术的可行性。该项目结合了两种方法来帮助K-12学生学习编程和计算机科学：开放式学习环境和基于计算机的学习分析，以帮助创建一个环境，使青少年可以获得针对他们所知道的编程知识的帮助和脚手架，而不必参加考试或参加严格的教科书练习，以便系统了解他们所知道的内容。该项目建议使用来自教育数据挖掘和学习分析的技术来处理Alice编程环境中的学生数据。基于循证设计（Evidence-Centered design）的评估设计模型，学生日志数据将用于构建学生个体计算思维实践的模型，与NGSS等新兴标准和计算思维评估研究保持一致。最初，该系统将基于来自大约600名学生的结对编程日志数据的现有语料库开发，并通过游戏设计练习与手动编码的编程性能评估进行三角测量。在第二阶段的工作中，课程和专业发展将被创建，以使该系统能够在斯坦福大学CS暑期研讨会上被低估的女孩以及来自不同高中的学生中进行“探索计算机科学”课程的测试。直接观察和访谈将用于改进模型。研究将涉及学习者如何在构建计算工件时实施计算思维实践，哪些行为模式可以作为学习CT实践的证据，以及如何更好地设计构建主义编程环境，以便提供个性化的学习者脚手架。通过结合流行的编程环境（Alice）和广泛使用的计算机科学课程（探索计算机科学），该项目可以对计算机科学教育产生广泛的影响；开发的软件将在bsd风格的许可证下发布，以便其他人可以在其上构建。

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project brings together two approaches to help K-12 students learn programming and computer science: open-ended learning environments, and computer-based learning analytics, to help create a setting where youth can get help and scaffolding tailored to what they know about programming without having to take tests or participate in rigid textbook exercises for the system to know what they know. The project proposes to use techniques from educational data mining and learning analytics to process student data in the Alice programming environment. Building on the assessment design model of Evidence-Centered Design, student log data will be used to construct a model of individual students' computational thinking practices, aligned with emerging standards including NGSS and research on assessment of computational thinking. Initially, the system will be developed based on an existing corpus of pair-programming log data from approximately 600 students, triangulating with manually-coded performance assessments of programming through game design exercises. In the second phase of the work, curricula and professional development will be created to allow the system to be tested with underrepresented girls at Stanford's CS summer workshops and with students from diverse high schools implementing the Exploring Computer Science curriculum. Direct observation and interviews will be used to improve the model. Research will address how learners enact computational thinking practices in building computational artifacts, what patters of behavior serve as evidence of learning CT practices, and how to better design constructionist programming environments so that personalized learner scaffolding can be provided. By aligning with a popular programming environment (Alice) and a widely-used computer science curriculum (Exploring Computer Science), the project can have broad impact on computer science education; software developed will be released under a BSD-style license so others can build on it.