When teaching grammar, one of the biggest challenges teachers face is how to make their students achieve conceptual understanding. Some scholars have argued that metaconcepts from theoretical linguistics should be used to pedagogically and conceptually enrich traditional L1 grammar teaching, generating more opportunities for conceptual understanding. However, no empirical evidence exists to support this theoretical position. The current study is the first to explore the role of linguistic metaconcepts in the grammatical reasoning of university students of Dutch Language and Literature. Its goal was to gain a better understanding of the characteristics of students’ grammatical conceptual knowledge and reasoning and to investigate whether students’ reasoning benefits from an intervention that related linguistic metaconcepts to concepts from traditional grammar. Results indicate, among other things, that using explicit linguistic metaconcepts and explicit concepts from traditional grammar is a powerful contributor to the quality of students’ grammatical reasoning. Moreover, the intervention significantly improved students’ use of linguistic metaconcepts.
Most higher education alumni work in professional fields, making higher education responsible for the provision of high-quality professionalism throughout society. However, higher education does not yet fulfil its role as a provider of methodologies for the renewal of professionalism. In this lecture, Didi Griffioen outlines the characteristics of professionalism, asserting that current methodologies for the continuous renewal of different elements of professionalism are lacking. By positioning itself as provider of these methodologies, higher education can play a more relevant role in society while also contributing to its own professional renewal and innovativeness.
Many attempts have been made to build an artificial brain. This paper aims to contribute to the conceptualization of an artificial learning system that functionally resembles an organic brain in a number of important neuropsychological aspects. Probably the techniques (algorithms) required are already available in various fields of artificial intelligence. However, the question is how to combine those techniques. The combination of truly autonomous learning, in which "accidental" findings (serendipity) can be used without supervision, with supervised learning from both the surrounding and previous knowledge, is still very challenging. In the event of changed circumstances, network models that can not utilize previously acquired knowledge must be completely reset, while in representation-driven networks, new formation will remain outside the scope, as we will argue. In this paper considerations to make artificial learning functionally similar to organic learning, and the type of algorithm that is necessary in the different hierarchical layers of the brain are discussed. To this end, algorithms are divided into two types: conditional algorithms (CA) and completely unsupervised learning. It is argued that in a conceptualisation of an artificial device that is functional similar to an organic learning system, both conditional learning (by applying CA’s), and non-conditional (supervised) learning must be applied.
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