Designing Inclusive Computational Thinking Metrics to Broaden Participation in Computer Science

扩大对计算机科学的参与需要帮助学生了解如何以他们认为有价值的方式使用计算机科学。这包括学生通过设计自己的游戏来学习。游戏设计的经验在帮助他们以有意义的方式学习算法、计算思维和编程方面被证明是有效的。像Scratch编程这样的工具使学生能够从事这样的游戏设计学习。然而，在这些环境中解释和评估学生对计算机科学的理解是具有挑战性的。评估学生创造的产品的自动化方法可能会有偏见，或者可能没有考虑到游戏设计的不同方法。自动评估有可能实时向学生提供关于他们工作的反馈，并帮助他们有效地学习计算机科学。这个项目旨在了解如何创建公平、包容和多样化的方法来自动分析学生的计算思维。这项工作解决了计算机科学教育中的三个关键需求：(1)改进计算思维指标，以支持计算思维的自动评估；(2)为学生提供实时反馈，帮助他们监控他们在计算思维和编程方面的进展，以建立对他们技能的信心；以及(3)创建上下文敏感的评估和反馈工具，促进包容性，并鼓励学生学习计算机科学、计算思维和编程。这个项目的关键研究问题是：我们如何处理现有基于度量的自动化计算思维评估中的包容性和多样性，以帮助扩大对计算机科学的参与？该项目将使用从八年级学生那里收集的评估数据，这些学生使用Scratch创建了与科学概念相关的游戏。对学生产品的分析将记录学生的计算思维发展、设计实践和编程例程目前是如何评估的。后续工作将创建更具包容性和公平性的指标。该项目将使用机器学习技术来分析数据(例如，识别学生产品中的模式)。该项目还将使用计算机科学中的自我效能感来了解学生的产品设计。在项目的后期阶段，将使用参与式设计过程来重新设计度量标准，以通过Scratch的游戏设计来提高计算思维评估的包容性。该项目由CS for All：Research and RPPS计划资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Broadening participation in computer science requires helping students understand how they can use computer science in ways that they see as valuable. This includes students’ learning by designing their own games. The experience of game design has proven effective at helping them learn about algorithms, computational thinking, and programming in meaningful ways. Tools such as Scratch programming enable students to engage in such game design learning. However, interpreting and assessing students’ understanding of computer science in these environments is challenging. Automated approaches to assessing the products students create may have biases or may not account for diverse approaches to game design. Automated assessments have the potential to provide feedback on their work to students in real time and help them learn about computer science effectively. This project seeks to understand how to create methods for automatically analyzing students’ computational thinking that are equitable, inclusive, and diverse. The work addresses three critical needs in computer science education: (1) improving computational thinking metrics to support automated assessment of computational thinking; (2) providing students with real-time feedback that helps them monitor their progress in computational thinking and programming to build confidence in their skills; and (3) creating context-sensitive assessment and feedback tools that promote inclusivity and encourage students to learn about computer science, computational thinking, and programming. The key research question in this project is: How do we address inclusivity and diversity in existing metrics-based automated computational thinking assessments to help broaden participation in computer science? The project will use assessment data gathered from eighth-grade students using Scratch who created games connected to science concepts. The analysis of students’ products will document how students’ computational thinking development, design practices, and programming routines are assessed currently. Subsequent work will create metrics that are more inclusive and equitable. The project will use machine learning techniques to analyze data (e.g., to identify patterns in the students’ products). The project will also use measures of self-efficacy in computer science to understand students’ product design. In later phases of the project, a participatory design process will be used to redesign the metrics to enhance inclusivity in the assessment of computational thinking via game design in Scratch.This project is funded through the CS for All: Research and RPPs programThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.