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.
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
Motivation: To develop and validate a quantitative, multiple choice test of computational thinking that can be easily administered, used as both a pre-test and post-test, and used in conjunction with qualitative approaches to gain a holistic understanding of learners’ code-literacy.
Computational Thinking Test (CTt): Román-González first published about the Computational Thinking Test (CTt) in 2015. He started with 40-items that were independent from a programming environment and measured computational thinking (CT) concepts that were identified by a number of people in the field, primarily CSTA & ISTE (2011) and Grover & Pea (2013). After exploring the content validity of the items, CT concepts, and measure overall with 20 experts, he cut the measure down to 28-items on the following concepts: Continue reading
Motivation: To present the history of computational thinking so that researchers do not repeat the mistakes made in the past or resolve problems. To identify potential threats to widespread implementation of CT in K-12 education.
Computational Thinking (CT): Computational thinking (CT) has a long past dating back to nearly the beginning of the computing field. Therefore, the definition of CT has evolved along with computing in general. The current conception of CT is a set of skills related to computing but useful beyond computing. This conception originated from Wing’s (2006) paper that argued CT should be as fundamental to education at reading, writing, and arithmetic. While many people agree with her argument, few agree on what should be included in that set of skills. Continue reading
Motivation: To summarize research that examines providing feedback to students through educational technology and to identify factors that impact its efficacy.
Characteristics of Feedback that Affect Efficacy:
- The most effective type of feedback in general is feedback that explains why an answer is correct or incorrect. If you cannot provide that level of detail, feedback should at least say what the correct answer is rather than only whether the student is right or wrong.
- Feedback is most effective when it’s given throughout a learning task rather than at the end of it, but giving feedback too often can also hurt learning.
- When students are new to a task or working on a particularly hard task, giving feedback through a human avatar can hinder their performance (due to the social facilitation effect).
Motivation: Explore the trade-offs in learning efficacy between completing fewer problems with guidance from a tutored problem solving system compared to seeing more worked problems without guidance.
Tutored problem solving: Computer systems can tutor students who are solving problems by providing them with hints and feedback at each step of the problem solving process. These kinds of systems, such as intelligent tutoring systems, generally improve problem solving performance. Using tutoring systems, however, is time consuming because they require students to attend to each step of the problem in depth, even if the student is not struggling with that step. Continue reading