The purpose of the present study was to construct and validate a brief screening instrument to support parent(s) and preschool/kindergarten teachers in monitoring and screening for cognitive impairment and/or delay in preschoolers. The target population of interest is all preschoolers <i>at-risk</i> for poor psychosocial and school outcomes due to chronic and acute dysfunction of the central nervous system (CNS). The accessible populations of interest to the present study are pediatric cancer survivors, preschoolers with alcohol related neurodevelopmental disorder (ARND), being preterm low birth weight, and/or diagnosed with various learning disabilities. The past practice of waiting until an <i>at-risk</i> child experienced poor school outcomes before being referred for cognitive assessment toward tailoring an intervention is no longer defensible. For the present study, a 61-item screening instrument (18 memory items, 19 verbal ability items, 15 attention items, and 9 demographic items) was pilot tested with parents, playschool teachers, and kindergarten teachers to rate preschoolers on overt behaviours associated with cognitive functioning. A criterion-referenced framework was used to establish a performance standard and set a cut score based on a sample of 151 normally functioning preschoolers aged 4:0- to 5:11-years. The various empirical and substantive analyses conducted resulted in a revised scale of 28 items (10 memory, 11 verbal ability, and 7 attention) titled, <i>Screening Test for the Early Prediction of School Success</i> (STEPSS). Given the need for a future study to validate the STEPSS with clinical groups of preschoolers, the screening instrument is intended to provide the empirical evidence needed to refer <i>at-risk</i> preschoolers for assessment with more comprehensive cognitive batteries. Constructing and validating the STEPSS is important for two reasons: 1) to fill a gap in the types of instruments available for monitoring and assessing cognitive functioning in <i>at-risk</i> preschool populations; and 2) to alleviate the current delay in targeting interventions for preschoolers because of the practice of depending upon the school system to monitor and identify poor cognitive functioning.

The purpose of the present study was to construct and validate a brief screening instrument to support parent(s) and preschool/kindergarten teachers in monitoring and screening for cognitive impairment and/or delay in preschoolers. The target population of interest is all preschoolers <i>at-risk</i> for poor psychosocial and school outcomes due to chronic and acute dysfunction of the central nervous system (CNS). The accessible populations of interest to the present study are pediatric cancer survivors, preschoolers with alcohol related neurodevelopmental disorder (ARND), being preterm low birth weight, and/or diagnosed with various learning disabilities. The past practice of waiting until an <i>at-risk</i> child experienced poor school outcomes before being referred for cognitive assessment toward tailoring an intervention is no longer defensible. For the present study, a 61-item screening instrument (18 memory items, 19 verbal ability items, 15 attention items, and 9 demographic items) was pilot tested with parents, playschool teachers, and kindergarten teachers to rate preschoolers on overt behaviours associated with cognitive functioning. A criterion-referenced framework was used to establish a performance standard and set a cut score based on a sample of 151 normally functioning preschoolers aged 4:0- to 5:11-years. The various empirical and substantive analyses conducted resulted in a revised scale of 28 items (10 memory, 11 verbal ability, and 7 attention) titled, <i>Screening Test for the Early Prediction of School Success</i> (STEPSS). Given the need for a future study to validate the STEPSS with clinical groups of preschoolers, the screening instrument is intended to provide the empirical evidence needed to refer <i>at-risk</i> preschoolers for assessment with more comprehensive cognitive batteries. Constructing and validating the STEPSS is important for two reasons: 1) to fill a gap in the types of instruments available for monitoring and assessing cognitive functioning in <i>at-risk</i> preschool populations; and 2) to alleviate the current delay in targeting interventions for preschoolers because of the practice of depending upon the school system to monitor and identify poor cognitive functioning.

Low levels of learner performance in Mathematics in the Senior Phase (Grades 7-9) in South Africa is often attributed to insufficient mathematics content knowledge among teachers. Although this view might be justifiable, it is often incorrect to assume that content knowledge alone will solve the problem of low performance in mathematics. This study, therefore, argues that understanding learner misconceptions and/or errors and their underlying intricacies could provide the basis for instructional decision making, subsequently improved performance in mathematics. The purpose of the study was to explore the implications of teachers’ understanding of learner errors for mathematics learning. The study was guided by qualitative methods using a case study design which involved data collection from two schools, followed by in-depth data analysis. Two theoretical lenses, namely, Cognitively Guided Instruction (CGI) and Constructivist theory were used to explore the main research question: What are the implications of the teachers’ understanding of learner errors on the learning of school mathematics in the Senior Phase (specifically Grade 9)? Data was collected through lesson observations, analysis of learners’ test responses and interviews. The findings revealed that teachers’ understanding of learner errors from written responses differed notably from intricacies of same errors emanating from interviewing the learners as well as the same errors analysed by the researcher. The implications of these findings suggest the likelihood of a mismatch between teachers’ instructional decision making and learner misconception/errors and this may hamper effective learning of mathematics. / Dissertation (MEd)--University of Pretoria, 2020. / Science, Mathematics and Technology Education / MEd / Unrestricted

No description available.

The classification of neurodegenerative disorders is based on the major component of the protein aggregates in the brain. The most common altered proteins associated with neurodegeneration are Hyperphosphorylated tau (HPt), beta amyloid (Aβ), alpha-synclein (αS) and transactive response DNA binding protein 43 (TDP43). In this study we assessed the incidence and the neuroanatomical distribution of proteins associated with neurodegeneration in the brain tissue of cognitively unimpaired subjects. We demonstrated the early involvement of the Locus Coeruleus (LC) with HPt pathology in cognitively unimpaired mid aged subjects, a finding which supports the notion that LC is an initiation site of HPt pathology. This may suggest that development of clinical assessment techniques and radiological investigations reflecting early LC alterations may help in identifying subjects with early stages of neurodegeneration. Furthermore, we studied a large cohort of cognitively unimpaired subjects with age at death ≥50 years and we applied the National Institute on Aging –Alzheimer’s disease (AD) Association (NIA-AA) guidelines for the assessment of AD related neuropathological changes. Interestingly, a considerable percentage of the subjects were classified as having an intermediate level of AD pathology. We also showed that the altered proteins;  HPt , Aβ, αS, and TDP43 are frequently seen in the brain of cognitively unimpaired subjects with age at death ≥50 years, the incidence of these proteins increased significantly with age. This finding suggests that neurodegeneration has to be extensive to cause functional disturbance and clinical symptoms. Moreover, we investigated the correlation between AD related pathology in cortical biopsies, the AD / cerebrospinal fluid (CSF) biomarkers and the Mini Mental State examination (MMSE) scores in a cohort of idiopathic Normal Pressure Hydrocephalus (iNPH) patients. We demonstrated that AD/ CSF biomarkers and MMSE scores reflect AD pathology in the cortical biopsies obtained from iNPH patients.  In conclusion, this study shows that the altered proteins associated with neurodegeneration are frequently seen in the brain tissue of cognitively unimpaired aged subjects. This fact should be considered while developing diagnostic biomarkers for identification of subjects at early stages of the disease, in order to introduce therapeutic intervention prior to the occurrence of significant cognitive impairment.

The ability to organize concepts (e.g., dog, chair) into efficient mental representations, i.e., categories (e.g., animal, furniture) is a fundamental mechanism which allows humans to perceive, organize, and adapt to their world. Much research has been dedicated to the questions of how categories emerge and how they are represented. Experimental evidence suggests that (i) concepts and categories are represented through sets of features (e.g., dogs bark, chairs are made of wood) which are structured into different types (e.g, behavior, material); (ii) categories and their featural representations are learnt jointly and incrementally; and (iii) categories are dynamic and their representations adapt to changing environments. This thesis investigates the mechanisms underlying the incremental and dynamic formation of categories and their featural representations through cognitively motivated Bayesian computational models. Models of category acquisition have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this thesis, we focus on categories acquired from natural language stimuli, using nouns as a stand-in for their reference concepts, and their linguistic contexts as a representation of the concepts’ features. The use of text corpora allows us to (i) develop large-scale unsupervised models thus simulating human learning, and (ii) model child category acquisition, leveraging the linguistic input available to children in the form of transcribed child-directed language. In the first part of this thesis we investigate the incremental process of category acquisition. We present a Bayesian model and an incremental learning algorithm which sequentially integrates newly observed data. We evaluate our model output against gold standard categories (elicited experimentally from human participants), and show that high-quality categories are learnt both from child-directed data and from large, thematically unrestricted text corpora. We find that the model performs well even under constrained memory resources, resembling human cognitive limitations. While lists of representative features for categories emerge from this model, they are neither structured nor jointly optimized with the categories. We address these shortcomings in the second part of the thesis, and present a Bayesian model which jointly learns categories and structured featural representations. We present both batch and incremental learning algorithms, and demonstrate the model’s effectiveness on both encyclopedic and child-directed data. We show that high-quality categories and features emerge in the joint learning process, and that the structured features are intuitively interpretable through human plausibility judgment evaluation. In the third part of the thesis we turn to the dynamic nature of meaning: categories and their featural representations change over time, e.g., children distinguish some types of features (such as size and shade) less clearly than adults, and word meanings adapt to our ever changing environment and its structure. We present a dynamic Bayesian model of meaning change, which infers time-specific concept representations as a set of feature types and their prevalence, and captures their development as a smooth process. We analyze the development of concept representations in their complexity over time from child-directed data, and show that our model captures established patterns of child concept learning. We also apply our model to diachronic change of word meaning, modeling how word senses change internally and in prevalence over centuries. The contributions of this thesis are threefold. Firstly, we show that a variety of experimental results on the acquisition and representation of categories can be captured with computational models within the framework of Bayesian modeling. Secondly, we show that natural language text is an appropriate source of information for modeling categorization-related phenomena suggesting that the environmental structure that drives category formation is encoded in this data. Thirdly, we show that the experimental findings hold on a larger scale. Our models are trained and tested on a larger set of concepts and categories than is common in behavioral experiments and the categories and featural representations they can learn from linguistic text are in principle unrestricted.

Desde a segunda metade do último século, as teorias cognitivas têm trazido algumas visões interessantes em relação ao aprendizado de linguagem. A aplicação destas teorias em modelos computacionais tem duplo benefício: por um lado, implementações computacionais podem ser usaas como uma forma de validação destas teorias; por outro lado, modelos computacionais podem alcançar uma performance melhorada a partir da adoção de estratégias de aprendizado cognitivamente plausíveis. Estruturas sintáticas são ditas fornecer uma pista importante para a aquisição do significado de verbos. Ainda, para um subconjunto particular de verbos muito frequentes e gerais - os assim-chamados light verbs - há uma forte ligação entre as estruturas sintáticas nas quais eles aparecem e seus significados. Neste trabalho, empregamos um modelo computacional para investigar estas propostas, em particular, considerando a tarefa de aquisição como um mapeamento entre um verbo desconhecido e referentes prototípicos para eventos verbais, com base na estrutura sintática na qual o verbo aparece. Os experimentos conduzidos ressaltaram alguns requerimentos para um aprendizado bem-sucedido, em termos de níveis de informação disponível para o aprendiz e da estratégia de aprendizado adotada. / Cognitive theories have been, since the second half of the last century, bringing some interesting views about language learning. The application of these theories on computational models has double benefits: in the one hand, computational implementations can be used as a form of validation of these theories; on the other hand, computational models can earn an improved performance from adopting some cognitively plausible learning strategies. Syntactic structures are said to provide an important cue for the acquisition of verb meaning. Yet, for a particular subset of very frequent and general verbs – the so-called light verbs – there is a strong link between the syntactic structures in which they appear and their meanings. In this work, we used a computational model, to further investigate these proposals, in particular looking at the acquisition task as a mapping between an unknown verb and prototypical referents for verbal events, on the basis of the syntactic structure in which the verb appears. The experiments conducted have highlighted some requirements for a successful learning, both in terms of the levels of information available to the learner and the learning strategies adopted.

Lawrence Bonjour supported coherentism in the early period, but turns to foundationalism in the later period. In this paper I shall focus on two sides in relation to his epistemology. To understand his early and later positions, first, I shall explain his coherentism and foundationalism. Second, I shall consider what objections have been raised to each position. Thus we can evaluate why Bonjour abandoned his coherentism and why his foundationalism succeeds as a plausible theory for empirical justification.

Desde a segunda metade do último século, as teorias cognitivas têm trazido algumas visões interessantes em relação ao aprendizado de linguagem. A aplicação destas teorias em modelos computacionais tem duplo benefício: por um lado, implementações computacionais podem ser usaas como uma forma de validação destas teorias; por outro lado, modelos computacionais podem alcançar uma performance melhorada a partir da adoção de estratégias de aprendizado cognitivamente plausíveis. Estruturas sintáticas são ditas fornecer uma pista importante para a aquisição do significado de verbos. Ainda, para um subconjunto particular de verbos muito frequentes e gerais - os assim-chamados light verbs - há uma forte ligação entre as estruturas sintáticas nas quais eles aparecem e seus significados. Neste trabalho, empregamos um modelo computacional para investigar estas propostas, em particular, considerando a tarefa de aquisição como um mapeamento entre um verbo desconhecido e referentes prototípicos para eventos verbais, com base na estrutura sintática na qual o verbo aparece. Os experimentos conduzidos ressaltaram alguns requerimentos para um aprendizado bem-sucedido, em termos de níveis de informação disponível para o aprendiz e da estratégia de aprendizado adotada. / Cognitive theories have been, since the second half of the last century, bringing some interesting views about language learning. The application of these theories on computational models has double benefits: in the one hand, computational implementations can be used as a form of validation of these theories; on the other hand, computational models can earn an improved performance from adopting some cognitively plausible learning strategies. Syntactic structures are said to provide an important cue for the acquisition of verb meaning. Yet, for a particular subset of very frequent and general verbs – the so-called light verbs – there is a strong link between the syntactic structures in which they appear and their meanings. In this work, we used a computational model, to further investigate these proposals, in particular looking at the acquisition task as a mapping between an unknown verb and prototypical referents for verbal events, on the basis of the syntactic structure in which the verb appears. The experiments conducted have highlighted some requirements for a successful learning, both in terms of the levels of information available to the learner and the learning strategies adopted.

Desde a segunda metade do último século, as teorias cognitivas têm trazido algumas visões interessantes em relação ao aprendizado de linguagem. A aplicação destas teorias em modelos computacionais tem duplo benefício: por um lado, implementações computacionais podem ser usaas como uma forma de validação destas teorias; por outro lado, modelos computacionais podem alcançar uma performance melhorada a partir da adoção de estratégias de aprendizado cognitivamente plausíveis. Estruturas sintáticas são ditas fornecer uma pista importante para a aquisição do significado de verbos. Ainda, para um subconjunto particular de verbos muito frequentes e gerais - os assim-chamados light verbs - há uma forte ligação entre as estruturas sintáticas nas quais eles aparecem e seus significados. Neste trabalho, empregamos um modelo computacional para investigar estas propostas, em particular, considerando a tarefa de aquisição como um mapeamento entre um verbo desconhecido e referentes prototípicos para eventos verbais, com base na estrutura sintática na qual o verbo aparece. Os experimentos conduzidos ressaltaram alguns requerimentos para um aprendizado bem-sucedido, em termos de níveis de informação disponível para o aprendiz e da estratégia de aprendizado adotada. / Cognitive theories have been, since the second half of the last century, bringing some interesting views about language learning. The application of these theories on computational models has double benefits: in the one hand, computational implementations can be used as a form of validation of these theories; on the other hand, computational models can earn an improved performance from adopting some cognitively plausible learning strategies. Syntactic structures are said to provide an important cue for the acquisition of verb meaning. Yet, for a particular subset of very frequent and general verbs – the so-called light verbs – there is a strong link between the syntactic structures in which they appear and their meanings. In this work, we used a computational model, to further investigate these proposals, in particular looking at the acquisition task as a mapping between an unknown verb and prototypical referents for verbal events, on the basis of the syntactic structure in which the verb appears. The experiments conducted have highlighted some requirements for a successful learning, both in terms of the levels of information available to the learner and the learning strategies adopted.