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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.
A crucial aspect of learning about (linear) functions is being able to change between graph and equation. Common German and Slovak textbooks propose different procedures for these representational changes. Within a sample of 49 German and 56 Slovak teachers, we analyzed if these different procedures can also be observed in the teachers’ corresponding knowledge of content and students, i. e. if the teachers expected different student strategies and errors. The results confirm this assumption and emphasize the importance to consider this teacher's knowledge in a country-specific way and being careful when comparing such knowledge of teachers from different countries.
The different facets of professional performance of teachers are being debated in the current educational
research discussion. Both qualitative and quantitative test constructions exist for vignette-based measurement of
competence. For economic reasons, a procedure with closed-ended items is often favored, a reference is required for
determining and comparing alternative responses. This paper sketches out the procedure for identifying adequate
reactions to responses that we call a technical education expert norm (PCK-T). This expert norm is generated from
a multi-step expert survey and, in addition, reveals possible validation steps that can be derived for developing
teaching situations. After a content validation (N1 = 8) a multi-step quantitative survey with specialised subject
experts at schools and universities, departmental heads at public colleges for education and teacher training, as well
as experienced teachers of technology was carried out (N2 = 79; N3 = 76). In order to assess teaching competencies
of pre-service teachers using a vignette-based test procedure, the generated technical education expert norm (PCKT) allows adequate responses to be differentiated from (rather) inadequate responses in the teaching situations