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

我们的国家已经制定了一个目标，为所有学生提供计算机教育，但计算机教育仍然无法进入大多数公民。除了填补软件开发工作岗位之外（预计将增加100万个工作岗位），计算在许多其他行业中也变得至关重要。为CS职业做准备的学生从计算机科学（CS）入门课程开始;然而，大约一半的人会失败，迫使他们要么重复课程，要么离开计算。这个项目将把通过教育心理学研究确定的教学技术作为提高学生学习和保留入门编程的有效方法。研究团队将开发包含子目标标签的问题的工作示例，子目标标签是向学习者描述问题解决方案中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领域很有效，学生能够看到专家对问题的解决方案，这有助于学生在自己解决问题之前学习解决问题的方法。此外，将评估学习成果和保留率，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通过传播一种不仅能提高学生学习而且能提高学生保留率的教育策略，对计算和计算机科学教育产生积极影响。 教授入门级课程的专家，包括教师，往往无法解释他们在学习者可以掌握的水平上解决问题的过程，因为他们已经自动化了多年的实践。这个项目的目标是在整个介绍计算课程中使用子目标标签，并调查对学生学习和保留的影响。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子目标标签的工作示例。然后，这些例子将在多个教室和多个学期实施，以衡量干预措施的有效性，并不断改进学习材料的开发和交付。采用混合方法设计，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。在项目的最后一年，将向各种机构大规模部署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研究结果和教学材料不仅有可能对计算和计算机科学教育产生积极影响，而且还可能对其他STEM学科产生更广泛的影响。

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.