54,216 research outputs found
High level cognitive information processing in neural networks
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field
t-Exponential Memory Networks for Question-Answering Machines
Recent advances in deep learning have brought to the fore models that can
make multiple computational steps in the service of completing a task; these
are capable of describ- ing long-term dependencies in sequential data. Novel
recurrent attention models over possibly large external memory modules
constitute the core mechanisms that enable these capabilities. Our work
addresses learning subtler and more complex underlying temporal dynamics in
language modeling tasks that deal with sparse sequential data. To this end, we
improve upon these recent advances, by adopting concepts from the field of
Bayesian statistics, namely variational inference. Our proposed approach
consists in treating the network parameters as latent variables with a prior
distribution imposed over them. Our statistical assumptions go beyond the
standard practice of postulating Gaussian priors. Indeed, to allow for handling
outliers, which are prevalent in long observed sequences of multivariate data,
multivariate t-exponential distributions are imposed. On this basis, we proceed
to infer corresponding posteriors; these can be used for inference and
prediction at test time, in a way that accounts for the uncertainty in the
available sparse training data. Specifically, to allow for our approach to best
exploit the merits of the t-exponential family, our method considers a new
t-divergence measure, which generalizes the concept of the Kullback-Leibler
divergence. We perform an extensive experimental evaluation of our approach,
using challenging language modeling benchmarks, and illustrate its superiority
over existing state-of-the-art techniques
Developing algebraic and didactical knowledge in pre-service primary teacher education
This study analyzes the contribution of a teaching experiment for the development of
prospective primary teachers regarding knowledge of algebra and of algebra teaching
as well as their professional identity. The case study of a prospective teachersuggests
that an exploratory approach combining content and pedagogy supports this
development, especially in the need to propose challenging tasks, to provide
opportunity for students’ autonomous work and collective discussions and to be attentive to children’s representations and strategies in order to promote algebraic
thinking
Simulation modelling: Educational development roles for learning technologists
Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, ‘adaptive and productive’ media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies ‐ one that realizes their creative‐transformative potential
Improving Student’s Emotional Intelligence By Mathematics Learning
This paper aims to provide a description of realistic mathematics education in improving emotional intelligence. Mathematics is a tool that can be developed to foster thinking (reasoning) and attitudes (emotions) (Nelissen, 2007). Emotional intelligence is the ability to feel, understand and effectively apply the power and emotional sensitivity as an energy source and as a motivator. The models that emerged from students' mathematical activity can promote interaction in class, thus leading to the level of mathematical thinking and higher learning meaningful democracy. Thus, learning mathematics is a realistic learning actively involves students both physically and mentally (student centered learning), and be democratic, so as to have a better profile in the critical thinking skills and emotional intelligence of students.
Keywords: emotional intelligence, learning, mathematics, constructive, interactive, reflective, realisti
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