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

协作研究：开发和评估命令式编程的子目标标签，以提高学生的学习成果

• 批准号: 1712025
• 负责人: Adrienne Decker
• 金额: $ 3.31万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712025&HistoricalAwards=false
• 关键词: Collaborative; Research; Developing; Assessing; Subgoal

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.

我们的国家设定了一个目标，可以为所有学生提供计算教育，但是大多数公民仍然无法访问计算教育。除了填补软件开发工作（预计将有一百万个就业机会增长）之外，计算在许多其他行业中变得至关重要。准备CS职业的学生首先是计算机科学介绍（CS）课程；但是，其中大约一半将失败，迫使他们重复该课程或离开计算。该项目将结合通过教育心理学研究确定的教学技巧，作为改善学生学习和入门编程中的保留率的有效方法。研究团队将开发出纳入子目标标签的问题的工作示例，这些问题是描述学习者解决问题的步骤功能并突出解决问题的过程的解释。在工作示例中使用子目标标签，该示例在其他STEM领域有效，学生可以看到专家解决问题的解决方案，该问题可以帮助学生学习解决问题的方法，然后才能自己解决问题。此外，将评估学习成果和保留率，以衡量使用次目标标签使用工作问题示例的影响。 该项目的知识和结果有可能通过传播一种不仅增强学生学习，而且潜在保留的教育策略来积极影响计算和计算机科学教育。 专家，包括讲师，教学介绍水平课程通常无法解释他们在解决问题的过程中使用的过程，因为他们已经自动化了许多多年实践的解决问题的过程，因为他们已经自动化了解决问题的过程。该项目的目的是在整个入门计算课程中使用子目标标签，并研究对学生学习和保留的影响。研究团队将开发出介绍性计算机科学课程中每个概念的多个工作示例。这些例子将在多个教室和多个学期中实施，以衡量干预的有效性并不断改善学习材料的开发和交付。使用混合方法设计，结合定性和定量数据收集方法，使用对照组的使用将指导学习影响的研究和测量。在项目的最后一年中，干预和支持学习材料的大规模部署将被传播到各种各样的机构，以进一步调查影响。该项目期间产生的发现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，而且会影响其他STEM学科。