The main aim of this study was to determine the agreement in classification between the modified KörperKoordinations Test für Kinder (KTK3+) and the Athletic Skills Track (AST) for measuring fundamental movement skill levels (FMS) in 6- to 12-year old children. 3,107 Dutch children (of which 1,625 are girls) between 6 and 12 years of age (9.1 ± 1.8 years) were tested with the KTK3+ and the AST. The KTK3+ consists of three items from the KTK and the Faber hand-eye coordination test. Raw scores from each subtest were transformed into percentile scores based on all the data of each grade. The AST is an obstacle course consisting of 5 (grades 3 till 5, 6–9 years) or 7 (grades 6 till 8, 9–12 years) concatenated FMS that should be performed as quickly as possible. The outcome measure is the time needed to complete the track. A significant bivariate Pearson correlation coefficient of 0.51 was found between the percentile sum score of the KTK3+ and the time to complete the AST, indicating that both tests measure a similar construct to some extent. Based on their scores, children were classified into one of five categories: <5, 5–15, 16–85, 86–95 or >95%. Cross tabs revealed an agreement of 58.8% with a Kappa value of 0.15 between both tests. Less than 1% of the children were classified more than two categories higher or lower. The moderate correlation between the KTK3+ and the AST and the low classification agreement into five categories of FMS stress the importance to further investigate the test choice and the measurement properties (i.e., validity and reliability) of both tools. PE teachers needs to be aware of the context in which the test will be conducted, know which construct of motor competence they want to measure and know what the purpose of testing is (e.g., screening or monitoring). Based on these considerations, the most appropriate assessment tool can be chosen.
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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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Introduction: Self-regulated learning (SRL) has traditionally been associated with study success in higher education. In contrast, study success is still rarely associated with executive functions (EF), while it is known from neuropsychological practice that EF can influence overall functioning and performance. However some studies have shown relationships between EF and study success, but this has mainly been investigated in school children and adolescents. EF refer to higher-order cognitive processes to regulate cognition, behavior, and emotion in service of adaptive and goal-directed behaviors. SRL is a dynamic process in which learners activate and maintain cognitions, affects, and behaviors to achieve personal learning goals. This study explores the added value of including EF and SRL to predict study success (i.e., the obtained credits). Methods: In this study, we collected data from 315 first-year psychology students of a University of Applied Sciences in the Netherlands who completed questionnaires related to both EF (BRIEF) and SRL (MSLQ) two months after the start of the academic year. Credit points were obtained at the end of that first academic year. We used Structural Equation Modeling to test whether EF and SRL together explain more variance in study success than either concept alone. Results: EF explains 19.8% of the variance, SRL 22.9%, and in line with our hypothesis, EF and SRL combined explain 39.8% of the variance in obtained credits. Discussion: These results indicate that focusing on EF and SRL could lead to a better understanding of how higher education students learn successfully. This might be the objective of further investigation.
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