Collaborative Research: Developing and Assessing Subgoal Labels for Imperative Programming to Improve Student Learning Outcomes

我们国家已经制定了向所有学生提供计算机教育的目标，但大多数公民仍然无法获得计算机教育。除了填补预计将增加100万个工作岗位的软件开发工作之外，计算在许多其他行业中正变得至关重要。为CS职业生涯做准备的学生从计算机科学(CS)入门课程开始；然而，大约一半的学生会失败，迫使他们要么重修这门课程，要么离开计算机。该项目将纳入通过教育心理学研究确定的教学技术，作为提高学生在入门编程中学习和保持记忆的有效方法。研究团队将开发包含子目标标签的问题实例，子目标标签是向学习者描述问题解决步骤的功能并突出问题解决过程的解释。在工作示例中使用子目标标签，这在其他STEM领域已经很有效，学生能够看到专家对问题的解决方案，帮助学生在自己解决问题之前学习解决问题的方法。此外，将评估学习结果和保持能力，以衡量使用带有子目标标签的工作问题示例的影响。该项目的知识和成果有可能对计算和计算机科学教育产生积极影响，因为它传播了一种教育战略，不仅提高了学生的学习能力，而且潜在地提高了学生的学习能力。教授入门级课程的专家，包括讲师，往往无法解释他们在解决问题时使用的过程，而这一水平是学习者可以掌握的，因为他们已经在多年的实践中实现了大部分问题解决过程的自动化。这个项目的目标是在计算机入门课程中使用子目标标签，并调查其对学生学习和保持的影响。研究小组将为计算机科学入门课程中的每个概念开发多个带有子目标标签的实例。然后，将在多个教室和多个学期实施这些例子，以衡量干预措施的有效性，并不断改进学习材料的编写和交付。使用混合方法设计，结合定性和定量数据收集方法，并使用对照组，将指导学习影响的调查和衡量。在该项目的最后一年，将向不同的机构分发大规模部署的干预措施和辅助学习材料，以进一步调查影响。在这个项目中产生的发现和教学材料不仅有可能对计算机和计算机科学教育产生积极影响，而且在更广泛的范围内对其他STEM学科也有积极影响。

Our nation has set a goal to provide computing education to all students, but computing education remains inaccessible to most citizens. Outside of filling software development jobs, for which there is a projected growth of one million jobs, computing is becoming critical in many other industries. Students preparing for CS careers start with an introduction to computer science (CS) course; however, approximately half of them will fail, forcing them to either repeat the course or leave computing. This project will incorporate instructional techniques identified through educational psychology research as effective ways to improve student learning and retention in introductory programming. The research team will develop worked examples of problems that incorporate subgoal labels, which are explanations that describe the function of steps in the problem solution to the learner and highlight the problem-solving process. Using subgoal labels within worked examples, which has been effective in other STEM fields, students are able to see an expert's solution to a problem which helps students learn an approach to solving problems before they can solve problem themselves. Further, learning outcomes and retention will be assessed to measure the impact of using worked problem examples with subgoal labels. The knowledge and outcomes of this project have the potential to positively influence computing and computer science education by disseminating an educational strategy that not only enhances student learning but potentially also retention. Experts, including instructors, teaching introductory level courses are often unable to explain the process they use in problem solving at a level that learners can grasp because they have automated much of the problem-solving processes given the many years of practice. The goal of this project is to use subgoal labels throughout introductory computing courses and investigate the impact on student learning and retention. Multiple worked examples with subgoal labels for each concept in an introductory computer science course will be developed by the research team. These examples will then be implemented in multiple classrooms and across multiple semesters in order to measure the effectiveness of the intervention and to continuously improve the development and delivery of the learning materials. The use of a mixed-methods design, incorporating qualitative and quantitative data collection methods, with use of control groups will guide the investigation and measurement of learning impact. In the final year of the project, a large-scale deployment of the intervention and supporting learning materials will be disseminated to a diverse set of institutions to further investigate impacts. The findings and instructional materials generated during this project have the potential to positively impact not only computing and computer science education, but more broadly other STEM disciplines.