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Developmental teaching has a long history starting with Vygotsky’s ideas of teaching reaching into the zone of proximal development, an accomplishment that only are possible with the help of qualified teachers. Developmental teaching is oriented both to children’s acquisition of competence and to their formation as persons acquiring theoretical thinking and motive orientation. Central ideas in developmental teaching are that general knowledge in the form of core relations should come before specific and concrete knowledge, and that children through agentic but also teacher-guided exploration should be able to acknowledge these conceptual relations. These ideas have been extended with Hedegaard’s ideas of the double move in teaching and learning. In this process, teaching is a double and moving back to qualify children’s… knowledge to subject matter knowledge and back to quality children’s concept formation. This is illustrated in a project focusing on the subjects of biology, human geography and history working with oppositions, using children’s everyday knowledge and questions to create their activities and motivation for exploration. The Radical-Local approach extends the double move with inclusion of aspects of children’s community as a process of movement from the local to the general and the general to the local. The chapter also addresses assessment challenges through presenting a questionnaire addressing the child’s social situation of development, to capture the child’s perspective on their participation in school practices.
Dr Viktor Dörfler combines his background in developing and implementing AI with scholarly research on knowledge and cultivating talent to address misconceptions about AI. The Element explains what AI can and cannot do, carefully delineating facts from beliefs or wishful thinking. Filled with examples, this practical Element provokes thinking. The purpose is to help CEOs figure out how to make the best use of AI, suggesting how to extract AI's greatest value through appropriate task allocation between human experts and AI. The author challenges the attribution of characteristics like understanding, thinking, and creativity to AI, supporting his argument with the ideas of the finest AI philosophers. He also discusses in depth one of the most sensitive AI-related topics: ethics. The readers are encouraged to make up their own minds about AI and draw their own conclusions rather than accepting opinions from people with vested interests or an agenda.
This chapter explains the rule against hearsay and its exceptions. First, it sets out what hearsay is and some of the common law cases that contributed to its development. It then explains how hearsay is defined under the Act. The chapter then proceeds to explain the various uses for which evidence may be adduced and the different exceptions available under the Act. It is important to note that facts in issue and facts relevant to facts in issue are critical to understanding the purposes behind tendering hearsay evidence.
Hearsay evidence relies not on direct witness testimony but on another witness’s statement about a ‘previous representation’. The rationale for developing the rule against hearsay at common law was that these out-of-court previous representations were usually made by a person whose evidence was not available to be tested. The main concern at common law was whether such evidence was reliable. The witness giving the hearsay evidence could be cross-examined as to what they perceived, but the credibility of the maker of the statement could not be tested.
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