MODEL NEURONSKIH MREŽA ZA PREDVIĐANJE MATEMATIČKE DAROVITOSTI U DJECE

The aim of this paper was to model a neural network capable of detecting mathematically gifted fourth-grade elementary school pupils. The input space consisted of variables describing the five basic components of a child\u27s mathematical gift identified in the body of previous research. The scientifically confirmed psychological evaluation of gift based on Raven\u27s standard progressive matrices was used at the output. Three neural network models were tested on a Croatian dataset: multilayer perceptron, radial basis, and probabilistic network. The models\u27 performances were measuredaccording to the average hit rate obtained on the test sample. According to the results, the highest accuracy is produced by the radial basis neural network, which correctly recognizes all gifted children. Such high classification accuracy shows that neural networks have the potential to serve as an effective intelligent decision support tool able to assist teachers in detecting mathematically gifted children. This can be particularly useful in schools in which there is a shortage of psychologistsCilj ovoga rada bio je modeliranje neuronske mreže kojom bi se mogla otkriti matematička darovitost u učenika četvrtih razreda osnovnih škola. Ulaz se sastojao od varijabli izvedenih za opis pet osnovnih komponenata matematičke darovitosti u djece, a koje su ustanovljene u prethodnim istraživanjima. Kao izlazni rezultat upotrijebljena je znanstveno potvrđena psihološka evaluacija darovitosti utemeljena u Ravenovim progresivnim matricama. Tri modela neuronskih mreža testirana su na hrvatskim podatcima: višeslojni perceptron, mreža s radijalno zasnovanom funkcijom i probabilistička (vjerojatnosna) mreža. Rad mreža mjeren je u odnosu na prosječnu stopu pogodaka prikupljenih na testnom uzorku. Analiza je pokazala da je najvišu točnost postigla neuronska mreža s radijalno zasnovanom funkcijom, kojom se mogu točno prepoznati sva darovita djeca. Tako visoka točnost u klasifikaciji pokazuje da neuronske mreže imaju potencijal služiti kao efektivan alat inteligentne odluke pomoću kojega bi učitelji mogli otkriti djecu s darovitošću za matematiku. To može biti osobito korisno u školama s manjkom psihologa

DeepEval: An Integrated Framework for the Evaluation of Student Responses in Dialogue Based Intelligent Tutoring Systems

The automatic assessment of student answers is one of the critical components of an Intelligent Tutoring System (ITS) because accurate assessment of student input is needed in order to provide effective feedback that leads to learning. But this is a very challenging task because it requires natural language understanding capabilities. The process requires various components, concepts identification, co-reference resolution, ellipsis handling etc. As part of this thesis, we thoroughly analyzed a set of student responses obtained from an experiment with the intelligent tutoring system DeepTutor in which college students interacted with the tutor to solve conceptual physics problems, designed an automatic answer assessment framework (DeepEval), and evaluated the framework after implementing several important components. To evaluate our system, we annotated 618 responses from 41 students for correctness. Our system performs better as compared to the typical similarity calculation method. We also discuss various issues in automatic answer evaluation

The possible role of intuition in the child's epistemic beliefs in the Piagetian data set

U.S. schools teach predominately to the analytical, left-brain, which has foundations in behaviorism, and uses a mechanistic paradigm that influences epistemic beliefs of how learning takes place. This result is that learning is impeded. Using discourse analysis of a set of Piagetian children, this study re-analyzed Piaget’s work. This study found that, although the participating children answered from both an intuitive and an analytical perspective, Piaget's analysis of the interviews ignored the value in the intuitive, right-brain answers; Piaget essentially stated that the children were only doing valuable thinking when they were analytical and logical. Using other comparable re- analysis as the yardstick, this study extended Piaget's original interpretations. Implications for teaching and learning are also described. This study also extends a call for research into a pedagogical balance between analytic and intuitive teaching

Perspectives in Gifted Education: Twice-Exceptional Children

This is the second in a series of monographs funded by the Lynde and Harry Bradley Foundation through the Institute for the Development of Gifted Education at the University of Denver. The first monograph contained different perspectives on the growth and development of young gifted children. This monograph addresses the characteristics and needs of twice-exceptional students. These are students who are both gifted and have some type of disabling condition. These students constitute a major group of underserved gifted children as their gifts often mask their disabilities, or their disabilities mask their gifts.https://digitalcommons.du.edu/perspectivesingifteded/1001/thumbnail.jp

Volume IV, No. 2

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