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

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.