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Research has identified 10 high-leverage teaching practices (HLTPs) that can impact student learning of a foreign language. While acknowledging the importance of this work, more research is needed to inform the preparation of novice teachers to enact these practices. In response, the researchers conducted a case study involving two foreign language teacher preparation programs in the United States and Germany, to better understand how the two very different programs prepare their candidates to implement HLTPs, which HLTPs are emphasized, and how successful they are at preparing their aspiring teachers to implement one practice that has been identified in the research as particularly important (facilitating target language comprehensibility). Survey, teaching observation, and interview data collected from teacher candidates and their instructors suggested the critical nature of select HLTPs, that some of the subcomponents of one of these practices may be more challenging for novice teachers to master than others, and that there may be multiple approaches to preparing foreign language teachers to implement HLTPs.
This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.
The notion of “bounded rationality” was introduced by Simon as an appropriate framework for explaining how agents reason and make decisions in accordance with their computational limitations and the characteristics of the environments in which they exist (seen metaphorically as two complementary scissor blades).We elaborate on how bounded rationality is usually conceived in psychology and on its relationship with logic. We focus on the relationship between heuristics and some non-monotonic logical systems. These two categories of cognitive tools share fundamental features. As a step further, we show that in some cases heuristics themselves can be formalized from this logic perspective. We have therefore two main aims: on the one hand, to demonstrate the relationship between the bounded rationality programme and logic, understood in a broad sense; on the other hand, to provide logical tools of analysis of already known heuristics. This may lead to results such as the characterization of fast and frugal binary trees in terms of their associated logic program here provided.