Subgoal Labeled Worked Examples Improve K-12 Teacher Performance in Computer Programming Training

Technology has become integrated into many facets of our lives. Due to the rapid onset of this integration, many current K-12 teachers do not have the skills required to supply the sudden demand for technical training. This deficit, in turn, has created a demand for professional development programs that allow working teachers to learn computer science so that they might become qualified to teach this increasingly important field. Subgoal labeled worked examples have been found to improve the performance of learners in highly procedural domains. The present study tested subgoal labeled worked examples in an online learning program for teachers. Teachers who received the subgoal labels solved novel problems more accurately than teachers who received the same worked examples without the subgoal labels. These findings have implications for the use of subgoal labels in professional development, other types of lifelong learning, and online learning

Employing Subgoals in Computer Programming Education

The rapid integration of technology into our professional and personal lives has left many education systems ill-equipped to deal with the influx of people seeking computing education. To improve computing education, we are applying techniques that have been developed for other procedural fields. The present study applied such a technique, subgoal labeled worked examples, to explore whether it would improve programming instruction. The first two experiments, conducted in a laboratory, suggest that the intervention improves undergraduate learners’ problem solving performance and affects how learners approach problem solving. A third experiment demonstrates that the intervention has similar, and perhaps stronger, effects in an online learning environment with in-service K-12 teachers who want to become qualified to teach computing courses. By implementing this subgoal intervention as a tool for educators to teach themselves and their students, education systems could improve computing education and better prepare learners for an increasingly technical world

The curious case of loops

Background and Context Subgoal labeled worked examples have been extensively researched, but the research has been reported piecemeal. This paper aggregates data from three studies, including data previously unreported, to holistically examine the effect of subgoal labeled worked examples across three student populations and across different instructional designs. Objective By aggregating the data, we provide more statistical power for somewhat surprising yet replicable results. We discuss which results generalize across populations, focusing on a stable effect size for subgoal labels in programming instruction. Method We use descriptive and inferential statistics to examine the data collected from different student populations and different classroom instructional designs. We concentrate on the effect size across samples of the intervention for generalization. Findings Students using two variations of subgoal labeled instructional materials perform better than the others: the group that was given the subgoal labels with farther transfer between worked examples and practice problems and the group that constructed their own subgoal labels with nearer transfer between worked examples and practice problems

Varying effects of subgoal labeled expository text in programming, chemistry, and statistics

Originally intended as a replication study, this study discusses differences in problem solving performance among different domains caused by the same instructional intervention. The learning sciences acknowledges similarities in the learners’ cognitive architecture that allow interventions to apply across domains, but it also argues that each domain has characteristics that might affect how interventions impact learning. The present study uses an instructional design technique that had previously improved learners’ problem solving performance in programming: subgoal labeled expository text and subgoal labeled worked examples. It intended to replicate this effect for solving problems in statistics and chemistry. However, each of the experiments in the three domains had a different pattern of results for problem solving performance. While the subgoal labeled worked example consistently improved performance, the subgoal labeled expository text, which interacted with subgoal labeled worked examples in programming, had an additive effect with subgoal labeled worked examples in chemistry and no effect in statistics. Differences in patterns of results are believed to be due to complexity of the content to be learned, especially in terms of mapping problem solving procedures to solving problems, and the familiarity of tools used to solve problems in the domain. Subgoal labeled expository text was effective only when students learned more complex content and used unfamiliar problem solving tools

Effect of Implementing Subgoals in Code.org\u27s Intro to Programming Unit in Computer Science Principles

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\u27s 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

Effect of Implementing Subgoals in Code.org’s Intro to Programming unit in Computer Science Principles

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 a 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

Finding the Best Types of Guidance for Constructing Self-Explanations of Subgoals in Programming

Subgoal learning, a technique used to break down problem solving into manageable pieces, has been used to promote retention and transfer in procedural domains, such as programming. The primary method of learning subgoals has been passive, and passive learning methods are typically less effective than constructive methods. To promote constructive methods of learning subgoals, learners were prompted to self-explain the subgoals of a problem-solving procedure. Self-explanation asks learners to make sense of new information based on prior knowledge and logical reasoning. Self-explanation by novices is typically more effective when they receive guidance, because it helps them to focus on relevant information. In the present experimental study, the types of guidance that students received while self-explaining determined whether the constructive learning method was more effective than the passive method. Participants assigned to the constructive learning method performed best when they either received hints about the subgoals or received correct explanations as feedback, but not when they received both. These findings suggest that constructive learning of subgoals can further improve the benefits of subgoal learning when students receive only guidance that complements their construction of knowledge. This nuance is important for educators who engage their students in constructive learning and self-explanation

Interaction of Instructional Material Order and Subgoal Labels on Learning in Programming

Subgoal labeled expository instructions and worked examples have been shown to positively impact student learning and performance in computer science education. This study examined whether problem solving performance differed based on the order of expository instructions and worked examples and the presence of subgoal labels within the instructions. Participants were 132 undergraduate college students. A significant interaction showed that when learners were presented with the worked example followed by the expository instructions containing subgoal labels, the learner was better at outlining the procedure for creating an application. However, the manipulations did not affect novel problem solving performance or explanations of solutions,. These results suggest that the order instructional materials are presented have has little impact on problem solving, although some benefit can be gained from presenting the worked example before the expository instructions when subgoal labels are included

Learning Sciences for Computing Education

his chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them