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:
- 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.
- 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.
- 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.
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.
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.
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
Motivation: To explore the effect of different levels of guidance on the impact of inquiry-based learning. Lazonder and Harmsen offer a definition of inquiry-based learning, though they stipulate that there is little consensus on what factors define it. They define it as a method “in which students conduct experiments, make observations, or collect information in order to infer the principles underlying a topic or domain” (pp. 682). They emphasize that students act as scientists to achieve these goals.
Inquiry-based learning: Lazonder and Harmsen offer a definition of inquiry-based learning, though they stipulate that there is little consensus on what factors define it. They define it as a method “in which students conduct experiments, make observations, or collect information in order to infer the principles underlying a topic or domain” (pp. 682). They emphasize that students act as scientists to achieve these goals. The article offers a comprehensive review of the seminal and recent work done on inquiry-based learning. Continue reading