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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