Search results for "learning."

Oppimateriaalien kehittäminen, hyödyntäminen ja rooli tieto- ja viestintätekniikan opetuksessa

2014

Dynamic Potential of Feedback in Self-Regulated Learning and Motivation of Children with Mathematical Learning Difficulties

2017

The present study was designed to examine the effects of feedback conditions on learning and motivation of children identified with mathematical learning difficulties (MLDs). The performance of 76 fifth grade children on computational math skills and related task motivation was assessed. The groups of children were randomly assigned to one of two treatment conditions: immediate corrective feedback or delayed conventional feedback on two occasions. Results showed that children performed significantly better when they were provided with the immediate corrective feedback than when they were provided with the delayed conventional feedback. The findings suggest that provision of the immediate co…

“I sámifize it...” : Preschool in the Centre of South Sámi Language and Culture Learning in Norway

2022

For an Indigenous population, there is a need for an inclusive educational space from the language and culture srevitalisation perspective. This is especially important during the early years when the basics of the language are formed alongside cultural knowledge. This paper takes a closer look at a South Sami preschool language learning environment through the lenses of teachers. The South Sami (South Saami) is the southernmost Sami population, frequently described as a minority within the minority. The estimation for South Sami speakers in Norway is around 300, making the language severely endangered This paper aims to take a closer look at how early childhood education teachers describe …

STCMS: A Smart Thermal Comfort Monitor For Senior People

2020

Undoubtedly, the steady increase in the number of elderly people is not to be underestimated. These demographic changes call attention to new challenges regarding adequate aging-in-place strategies. Since the majority of the senior population spend up to 90% of their time indoors, appropriate and comfortable housing represents an important foundation for such strategies. In this respect, different types of data gathered from sensors, connected devices, and Internet of Things (IoT) technologies come to play an important role to support services for the elderly population in indoor environments. One of the aspects of concern is thermal comfort. In this paper, we introduce a new deep learning-…

Natural induction: An objective bayesian approach

2009

The statistical analysis of a sample taken from a finite population is a classic problem for which no generally accepted objective Bayesian results seem to exist. Bayesian solutions to this problem may be very sensitive to the choice of the prior, and there is no consensus as to the appropriate prior to use.

Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification?

1998

We present an overview of the major findings of the principal component analysis (pca) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a l…

DAE-GP

2020

Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of t…

Discrete cortical representations and their stability in the presence of synaptic turnover

2015

Population imaging in mouse auditory cortex revealed clustering of neural responses to brief complex sounds: the activity of a local population typically falls close to one out of a small number of observed states [1]. These clusters appear to group sets of auditory stimuli into a discrete set of activity patterns and could thereby form the basis for representations of sound categories. However, to be useful for the brain, such representations should be robust against fluctuations in the underlying circuitry, which are significant even in the absences of any explicit learning paradigm [2]. Here we introduce a novel firing rate based circuit model of mouse auditory cortex to study the emerge…

Scatter Search for the Point-Matching Problem in 3D Image Registration

2008

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation …

Inferring Learning Strategies from Cultural Frequency Data

2015

Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population…