*this summary is for an article featured in a Voice of America article that I did an interview for based on my Case for Laptops in the Classroom blog post.
To measure the effect of access to laptops, cell phones, and tablets on student performance in lecture-based classes. Glass and Kang predicted that access to devices would allow students to divide their attention during lecture and hurt their performance on assessments. (For those of you who don’t care about lecture-based classes, stick with it, I’ll connect it back to you at the end).
Students in sections of a cognitive psychology, lecture-based course were allowed to bring their devices to some class periods but not to others. This is a great design because students are being compared to themselves, not to other students who might have other technology habits. In addition, the researchers weren’t forcing students to use devices, they were allowing them to use devices as they normally would. The other clever aspect of the study design was that they measured performance at two time points, during class on just-presented information and a few weeks later as an exam. Continue reading
My original intention was to replicate the effects of subgoal labeled worked examples and expository text across different disciplines, but it didn’t really work out like that.
Subgoal learning in expository text
The subgoal learning framework is typically used to break down worked examples into functional pieces that are small enough for novices to grasp (and so small that experts often have a hard time verbalizing because the have become so automatic, further explained here). Subgoals have been used in many fields that focus on procedural problem solving since the 1970s, and most of my work has been applying the framework to programming education. In this work, I explored adding subgoal labels to expository text (i.e., the text that abstractly describe the problem solving procedure) in addition to worked examples (i.e., a concrete problem with the worked out solution that learners can use as a model). I found the combination of both subgoal labeled text and examples to further improve performance in programming over subgoal labeled worked examples alone (Margulieux & Catrambone, 2016). I argued that because students tend to struggle to translate between abstract descriptions of procedures and concrete examples of procedures, having the same subgoal labels in both types of instruction helps them to make connections between the two.
To explore the relationships between communities in which learning occurs and the situated nature of learning, remembering, and understanding. This sociocultural perspective was in contrast to the cognitive perspectives of learning that were popular at the time (i.e., that studied learning as a change in the brain and focused on individuals in isolation from the learning context).
Legitimate Peripheral Participation
Legitimate peripheral participation evolved from observations about cognitive apprenticeship and situated learning in communities of practice. A community of practice is a learning environment that includes a spectrum of participants from inexperienced members who are joining the community (or apprentices) to experienced members who have a lot of knowledge about practicing an occupation (or masters). Legitimate peripheral participation describes how apprentices learn from each other and masters to engage in the community and develop skills. An important feature of legitimate peripheral participation as a sociocultural theory (rather than a cognitive theory) is that it seeks to explain social practice within a community, and learning is only one characteristic of that practice. As such, Lave and Wenger say that there is likely no “illegitimate peripheral participation,” “legitimate central participation” (because there is no one center to a community), or “legitimate peripheral nonparticipation.”
diSessa’s motivation was to understand students’ intuitions of mechanisms in physics and how those intuitions affect formal learning of physics (the actual title of this paper is “Toward an Epistemology of Physics”). The Knowledge in Pieces framework used in this paper, and which will be the focus of this summary, has since been used across many topics, such as learning recursion in computing (Chao et al., 2018). The framework is based on empirical work, but as the paper is 126 pages long, those parts of the paper, and many others, are excluded from this summary.
Knowledge in Pieces (KiP)
The KiP framework is intended to describe learning of complex topics in a way that accounts for learners preconceptions (i.e., intuitions and informal self-explanations) of the topic. In addition, KiP explores the misconceptions that arise as knowledge develops. diSessa discusses four issues related to “theory building about any knowledge system” (p. 111) and how they relate to phenomenological primitives (p-prims), the building blocks of KiP: Continue reading
Discuss the benefits and challenges of interdisciplinary research, which requires navigating differences in theoretical and methodological approaches, and recommend strategies for conducting this work.
Discipline-based education research (DBER) focuses on understanding learning within a particular domain, such as biology or computer science. It requires, at minimum, four types of expertise: expertise in the domain, expertise in learning within the domain, expertise in learning more generally (i.e., cognition, motivation, etc.), and expertise in social science research methodology. Many DBERers do not attempt to develop all of these areas of expertise by themselves (though there are some superheroes who can magically stay up-to-date in four fields simultaneously). Instead, they opt to work with other researchers with complementary areas of expertise. Within these teams, each researcher likely knows at least a little bit about each area, but does not need to be an expert in all of them. Popular team compositions include a discipline-based education researcher (i.e., a domain expert who focuses on education within that domain) and a learning scientist. Continue reading
100 free eprints are available at this link
To explore the efficacy different types of guidance during a constructive learning activity (self-explanation of the subgoals of a programming problem-solving procedure) in an online learning environment.
Self-explanation of subgoals
Self-explanation is exactly what it sounds like. It is the process of a learner explaining to themselves (i.e., using their prior knowledge, new information, and logic) something that they are learning. It has been consistently effective for learning, except in fields that do not follow logical rules (e.g., learning how to pronounce English words; Wylie & Chi, 2014). In this study, we explored whether learners could successfully self-explain the subgoals of a programming procedure. The fundamentals of subgoal learning are explained in this post about our 2016 Computer Science Education paper. Continue reading
Motivation: To explore the effect of different degrees of comparison on learning through analogical encoding.
Analogies in education: Analogical learning compares two similar concepts, such as water flow and electricity, to bootstrap learning. Bootstrapping means use existing resources – in this case, prior knowledge – to solve a problem. Therefore, analogies bootstrap learning by transferring knowledge about a well-known concept to a new concept (Pirolli & Anderson, 1985). For example, learning about electricity is difficult because electricity is invisible and all visible effects of electricity, such as a light turning on, do not demonstrate how electricity itself works. Instead of purely describing electricity through abstract terms, instructors can make an analogy to flowing water, which is something that learners have prior knowledge of and can visualize. This particular analogy is so prevalent that it is commonly used in college-level classes in engineering and physics. Continue reading