Motivation

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.”

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Motivation

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 →

Motivation 

Discuss the benefits and challenges of interdisciplinary research, which requires navigating differences in theoretical and methodological approaches, and recommend strategies for conducting this work.

DBER

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 →

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Motivation 

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 →

Motivation: To conduct a meta-analysis on the within-subjects effects (i.e., learning gains) of computer-based scaffolding.

Scaffolding: Scaffolding in construction is a temporary structure that allows workers to build the upper portion of a building before it can support itself. Scaffolding in learning is very similar. It is temporary support, provided by instructors or instructional materials, that allows learners to build advanced knowledge and skills until the extra support is no longer needed. Belland et al. discuss three key attributes of scaffolding: Continue reading →

Motivation: To make recommendations for effective online professional development (PD) for computer science (CS) teachers based on individual differences in computing knowledge and prior computing teaching experience.

Three theoretical perspectives: The research balances three complex components:

1. Knowledge required to be a CS teacher, both in terms of content (computing) knowledge and pedagogical content knowledge (PCK) — this component is complex because the teachers come to PD with vastly varied prior experience in both computing and teaching computing. Qian et al. point out that there is not yet a comprehensive framework for knowledge that a CS teacher needs, but their PD does include both the CS content knowledge and PCK.
2. Framework for PD — this component is complex because countless frameworks have been developed for PD based on a myriad of features. Selecting one framework that is general enough for the current program yet specific enough to be useful is a tough balance to strike. Qian et al. selected Desimone’s (2009) framework, which abstracted from many PD programs five features of effective PD.
3. Design for motivating and engaging teachers in an online learning environment — this component is complex because online learning environments lack many of the social aspects of in-person learning, particularly social benefits of having a community of peers and the social pressure to stay on task and keep coming back. For this reason and others, motivating students in online learning is quite different than in face-to-face classrooms. Qian et al. selected Keller’s (1999) ARCS model for motivation in online learning environments.

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Motivation: To review selected microinstructional methods that instructional designers can use to improve learning and develop knowledge structures.

Microinstructional methods: Andre starts the chapter by admitting that learning does not happen in small, isolated sessions. Instead learning happens every day, across different domains, and through many methods. For this chapter, though, he focuses on instructional episodes, which are defined as dynamic interactions between a learner and the environment (including teachers, peers, instructional materials, and culture). Instructional episodes have three components: activation phase, instruction phase, and feedback phase. These components align with Merrill’s (2002) phases except that Merrill adds the integration phase. Andre recommends microinstructional methods for each of these components within the cognitive information-processing model (Atkinson & Shiffrin, 1968) and constructivism theory of learning.

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Banning laptops in classrooms is a mistake, despite the intuitively compelling and research-supported reasons for doing so. These reasons highlight the ways that laptop misuse hurts learning, suggesting that instructors must ban laptops if they care about student learning. Banning laptops treats the symptom (the misuse of laptops) and not the problem (ineffective learning techniques). If we care about students learning professional and social skills as well as academic ones, we must allow students to use their laptops in the classroom and train them how to do so effectively.

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Motivation: To contribute to the direct instruction vs. discovery learning debate with a meta-analysis that explores the nuances of the literature.

Discovery-based learning: Discovery(-based) learning is one of those terms that is better defined by what it is not (i.e., direct or explicit instruction) than what it is. I found Alfieri et al.’s general definition very helpful, though. They state that “discovery learning occurs whenever the learner is not provided with the target information or conceptual understanding and must find it independently and with only the provided materials,” (p. 2). Others would argue that the definition should be extended to include collaborative learning, especially because it is already pretty broad. Alfieri et al. go on to distinguish between unguided and enhanced discovery learning. They further break down enhanced discovery learning into three subcategories: Continue reading →