Using Subgoal Learning and Self-Explanation to Improve Programming Education

The present study explored passive, active, and constructive methods of learning problem solving procedures. Using subgoal learning, which has promoted retention and transfer in procedural domains, the study compared the efficacy of different methods for learning a programming procedure. The results suggest that constructive methods produced better problem solving performance than passive or active methods. The amount of instructional support that learners received in the three different constructive interventions also affected performance. Learners performed best when they either received hints about the subgoals of the procedure or received feedback on the subgoal labels that they constructed, but not when they received both. These findings suggest that in some cases constructing subgoal labels is better than passively or actively engaging with subgoal labels. There is an optimal level of instructional support for students engaging in constructive learning and that providing too much support can be equally as detrimental as providing too little support

Improving Problem Solving with Subgoal Labels in Expository Text and Worked Examples

In highly procedural problem solving, procedures are typically taught with context-independent expository text that conceptually describes a procedure and context-dependent worked examples that concretely demonstrate a procedure. Subgoal labels have been used in worked examples to improve problem solving performance. The effect of subgoal labels in expository text, however, has not been explored. The present study examined the efficacy of subgoal labeled expository text and worked examples for programming education. The results show that learners who received subgoal labels in both the text and example are able to solve novel problems better than those who did not. In addition, subgoal labels in the text appear to have a different, rather than an additive, effect on learners compared to subgoal labels in the example. Specifically, subgoal labels in the text appear to help the learner articulate the procedure, and subgoal labels in the example appear to help the learner apply the procedure

Scaffolding Problem Solving with Learners’ Own Self Explanations of Subgoals

Procedural problem solving is an important skill in most technical domains, like programming, but many students reach problem solving impasses and flounder. In most formal learning environments, instructors help students to overcome problem solving impasses by scaffolding initial problem solving. Relying on this type of personalized interaction, however, limits the scale of formal instruction in technical domains, or it limits the efficacy of learning environments without it, like many scalable online learning environments. The present experimental study explored whether learners’ self-explanations of worked examples could be used to provide personalized but non-adaptive scaffolding during initial problem solving to improve later performance. Participants who received their own self-explanations as scaffolding for practice problems performed better on a later problem-solving test than participants who did not receive scaffolding or who received expert’s explanations as scaffolding. These instructional materials were not adaptive, making them easy to distribute at scale, but the use of the learner’s own explanations as scaffolding made them effective

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

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

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

Improving Programming Instruction with Subgoal Labeled Instructional Text

In science, technology, engineering, and mathematics (STEM) education, problem solving tends to be highly procedural, and these procedures are typically taught with general instructional text and specific worked examples. Subgoal labels have been used in worked examples to help learners understand the procedure being demonstrated and improve problem solving performance. The effect of subgoal labels in instructional text, however, has not been explored. The present study examined the efficacy of subgoal labeled instructional text and worked examples for programming education. The results show that learners who received subgoal labels in both the text and example are able to solve novel problems better than those who do not. Subgoal labels in the text appear to have a different effect, rather than an additive effect, on learners than subgoal labels in the example. Specifically, subgoal labels in text appear to help the learner articulate the procedure, and subgoal labels in the example appear to help the learner apply the procedure. Furthermore, having subgoal labels in both types of instruction might help learners integrate the information from those sources better

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

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

Learnersourcing Subgoal Labels for How-to Videos

Websites like YouTube host millions of how-to videos, but the interfaces are not optimized for learning. Previous research suggests that users learn more from how-to videos when the information from the video is presented in outline form, with individual steps and labels for groups of steps (subgoals) shown. We envision an alternative video player where the steps and subgoals are displayed alongside the video. To generate this information for existing videos, we propose a learnersourcing approach, where people actively learning from a video provide such information. To demonstrate this method, we created a workflow where learners contribute and refine subgoal labels for how-to videos. We deployed a live website with our workflow implemented on a set of introductory web programming videos. For the four videos with the highest participation, we found that a majority of learner-generated subgoals were comparable in quality to expert-generated ones. Learners commented that the system helped them grasp the material, suggesting that our workflow did not detract from the learning experience.Massachusetts Institute of Technology. Undergraduate Research Opportunities ProgramCisco Systems, Inc.Quanta Computer (Firm) (Qmulus Project)National Science Foundation (U.S.) (Award SOCS-1111124)Alfred P. Sloan Foundation (Sloan Research Fellowship)Samsung (Firm) (Fellowship