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The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). Programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like Computer Science Principles and non-profit organizations like Code.org. Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in Code.org's Computer Science Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least one-third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed. 

This work extends previous research on subgoal labeled instructions by examining their effect across a semester-long, Java-based CS1 course. Across four quizzes, students were asked to explain in plain English the process that they would use to solve a programming problem. In this mixed methods study, we used the SOLO taxonomy to categorize student responses about problem-solving processes and compare students who learned with subgoal labels to those who did not. The use of the SOLO taxonomy classification allows us to look deeper than the mere correctness of answers to focus on the quality of the answers produced in terms of completeness of relevant concepts and explanation of relationships among concepts. Students who learned with subgoals produced higher-rated answers in terms of complexity and quality on three of four quizzes. Also, they were three times more likely to discuss issues of data type on a question about assignments and expressions than students who did not learn with subgoal labeling. This suggests that the use of subgoal labeling enabled students to gain a deeper and more complex understanding of the material presented in the course. 

Subgoal learning has improved student problem-solving performance in programming, but it has been tested for only one-to-two hours of instruction at a time. Our work pioneers implementing subgoal learning throughout an entire introductory programming course. In this paper we discuss the protocol that we used to identify subgoals for core programming procedures, present the subgoal labels created for the course, and outline the subgoal-labeled instructional materials that were designed for a Java-based course. To examine the effect of subgoal labeled materials on student performance in the course, we compared quiz and exam grades between students who learned using subgoal labels and those who learned using conventional materials. Initial results indicate that learning with subgoals improves performance on early applications of concepts. Moreover, variance in performance was lower and persistence in the course was higher for students who learned with subgoals compared to those who learned with conventional materials, suggesting that learning with subgoal labels may uniquely benefit students who would normally receive low grades or dropout of the course. 

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in disciplines both in and out of computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, #1712231), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes). This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include over 100 worked examples and practice problem pairs that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels. 

This NSF IUSE project incorporates instructional materials and techniques into introductory programming identified through educational psychology research as effective ways to improve student learning and retention. The research team has developed 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 shown effective in other STEM fields, is intended to break down problem solving procedures into pieces that are small enough for novices to grasp. Experts, including instructors, teaching introductory level courses are often unable to explain the subgoal-level processes that they use in problem solving because they have automated much of the problem solving processes after many years of practice. This intervention had been tested in programming for a few hours of instruction and found effective. The current project expands upon that work. 

Subgoal labels are function-based instructional explanations that describe the problem-solving steps to the learner, highlighting the solution process. There is strong evidence that the use of subgoal labels within worked examples improves student learning in other STEM fields. Initial research shows that using subgoal labels within computer science improves student learning, but this has only been tested using a single programming concept (indefinite loops) with text-based programming languages. The proposers are currently expanding subgoal labels to the main programming concepts taught in an introductory programming course using an imperative programming language. In this BOF we seek to uncover tacit knowledge that programming instructors have in order to develop instructional materials that bridge the gap between students, who are CS novices, and instructors, who are CS experts, to improve learning for students who are under-prepared for or struggle in CS1. We will be seeking feedback on the selection of programming topics to be covered, the defined subgoals for those topics and the worked examples created for instructional purposes. 

A recent study about the effectiveness of subgoal labeling in an introductory computer science programming course both supported previous research and produced some puzzling results. In this study, we replicate the experiment with a different student population to determine if the results are repeatable. We also gave the experimental task to students in a follow-on course to explore if they had indeed mastered the programming concept. We found that the previous puzzling results were repeated. In addition, for the novice programmers, we found a statistically significant difference in performance based on whether the student had previous programming courses in high school. However, this performance difference disappears in a follow-on course after all students have taken an introductory computer science programming course. The results of this study have implications for how quickly students are evaluated for mastery of knowledge and how we group students in introductory programming courses.