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

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

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 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

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

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

Subgoal-Labeled Instructional Material Improves Performance and Transfer in Learning to Develop Mobile Applications

Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications

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

A Taxonomy to Define Courses that Mix Face-to-Face and Online Learning

The efficacy of courses that mix face-to-face and online instruction, such as blended, hybrid, flipped, and inverted courses, is contested in the literature. Some studies find that they improved learning outcomes and some do not. We argue that these unreliable results are due to inconsistent definitions of these courses. To address this problem, we propose the Mixed Instructional eXperience (MIX) taxonomy to define hybrid, blended, flipped, and inverted based on two dimensions. To test the usefulness of the taxonomy to organize the literature, we reclassified research using the taxonomy. The analysis of the literature after reclassification revealed themes that illuminate how mixing face-to-face and online instruction affects learning. These findings validate the taxonomy as a useful tool for classifying literature and further knowledge in this field

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