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

The Curious Case of Loops

Background and Context: Subgoal labeled worked examples are effective for teaching computing concepts, but the research to date has been reported in a piecemeal fashion. This paper aggregates data from three studies, including data that has not been previously reported upon, to examine more holistically 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 and explanatory power for somewhat surprising yet replicable results. We discuss which results generalize across populations, focusing on a stable effect size to be expected when using subgoal labels in programming instruction. Method: We use descriptive and inferential statistics to examine the data for the effect of subgoal labeled worked examples across different student populations and different classroom instructional designs. We specifically concentrate on the potential effect size across samples of the intervention for potential generalization. Findings: Two groups of students learning how to write loops using subgoal labeled instructional materials perform better than the others. The better performing groups were 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, both with medium-large effect sizes. Implications: For educators wishing to improve student learning using subgoal labeled materials should either provide students with subgoal labels while having them practice with a wide range of practice problems or allow students to generate their own subgoal labels and practice problems within similar contexts

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

Replicating experiments from educational psychology to develop insights into computing education: cognitive load as a significant problem in learning programming

Students often find learning to program difficult. This may be because the concepts are inherently difficult due to the fact that the elements of learning to program are highly interconnected. Instructors may be able to lower the complexity of learning to program by designing instructional materials that use educational psychology principles. The overarching goal of this research is to gain more understanding and insight into when cognitive load might be interfering with learning programming. Cognitive load theory (CLT), and its associated effects, describe the role of the learner's memory during the learning process. By minimizing undesirable loads within the instructional materials, the learner's memory can hold more relevant information, thereby improving the effectiveness of the learning process. This research uses cognitive load theory to improve learning in programming. First an instrument for measuring cognitive load components within introductory programming was developed and initially validated. We have explored reducing the cognitive load by changing the modality in which students receive the learning material. This had no effect on novices' retention of knowledge or their ability to transfer knowledge. We then attempted to reduce the cognitive load by adding subgoal labels to the instructional material. This had some effect on the learning gains under some conditions. Students who learned using subgoal labels demonstrated higher learning gains than the other conditions on the programming assessment task. We also explored using a low cognitive load assessment technique (Parsons problem), to measure learning gains. This low cognitive load assessment task proved more sensitive than the open ended programming assessment tasks in capturing student learning. Students who were given subgoal labels regardless of context transfer condition out performed those in the other conditions. In the final study the format and content of the loop programming construct was changed in the material used to teach the students in order to reduce cognitive load. While the changed construct was presumed to be a more natural cognitive fit for students based on previous research, the data indicated that it had no statistical difference in learning performance. Some CS educators have argued that the changed construct might harm learning performance. However, these results suggest that learning performance was not harmed, meaning that either format could be used to teach the programming construct to students without disadvantaging the learner.Ph.D