Mixing in-class and online learning: Content meta-analysis of outcomes for hybrid, blended, and flipped courses

Over the past 15 years, courses that mix face-to-face and online instructional methods, such as blended, hybrid, and flipped courses, have gained both supporters and skeptics in higher education. Studies that compare mixed courses to face-to-face or online courses have conflicting results: some find improved learning outcomes and some find no significant differences. We contend that these conflicting results are due to inconsistent or vague definitions of hybrid, blended, and flipped. To address this problem, we use the definitions from a recently proposed taxonomy to reclassify studies in the literature. After reclassification, analysis of this literature reveals two main themes that illuminate how mixed instructional methods affect learning outcomes. Courses that use mixed methods can either reduce time in class and maintain learning outcomes or maintain time in class and improve learning outcomes

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

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

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

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

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

Subgoals, Context, and Worked Examples in Learning Computing Problem Solving

Recent empirical results suggest that the instructional material used to teach computing may actually overload students\u27 cognitive abilities. Better designed materials may enhance learning by reducing unnecessary load. Subgoal labels have been shown to be effective at reducing the cognitive load during problem solving in both mathematics and science. Until now, subgoal labels have been given to students to learn passively. We report on a study to determine if giving learners subgoal labels is more or less effective than asking learners to generate subgoal labels within an introductory CS programming task. The answers are mixed and depend on other features of the instructional materials. We found that student performance gains did not replicate as expected in the introductory CS task for those who were given subgoal labels. Computer science may require different kinds of problem-solving or may generate different cognitive demands than mathematics or science

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

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