43,046 research outputs found
Detec??o de Estilos de Aprendizagem em Ambientes Virtuais de Aprendizagem utilizando Redes Bayesianas
?rea de concentra??o: Educa??o e Tecnologias aplicadas em Institui??es Educacionais.O avan?o da tecnologia possibilitou o surgimento de ferramentas para o acesso a conhecimento
e experi?ncias individuais e coletivas. As Tecnologias da Informa??o e Comunica??o
e a internet criaram o conceito chamado Ciberespa?o, um local virtual onde o somat?rio
de todas as experi?ncias, saberes e culturas de todos os povos que forma a Intelig?ncia
Coletiva. Tal fen?meno contribuiu para o desenvolvimento da Educa??o ? Dist?ncia e os
Sistemas Inteligentes para Educa??o. Um dos maiores problemas em EaD ? aus?ncia de
adaptatividade do ensino ao Estilo de Aprendizagem dos estudantes, que consiste nas
prefer?ncias que cada aluno tem em receber um determinado conte?do. Dessa forma, o
trabalho aborda uma t?cnica de Redes Bayesianas para detectar automaticamente os
Estilos de Aprendizagem dos estudantes para proporcionar uma oferta de material de
ensino adaptado ?s prefer?ncias de aprendizagem nos Ambientes Virtuais de Aprendizagem.
O trabalho se baseia em conceitos e t?cnicas de Intelig?ncia Artificial e Aprendizado de
M?quina para compor um modelo computacional e probabil?stico de uma Rede Bayesiana
para inferir e detectar qual a melhor combina??o de Estilos de Aprendizagem. Para estruturar
os m?todos de detec??o dos Estilos de Aprendizagem, a pesquisa utiliza o Modelo de
Estilo de Aprendizagem Felder-Silverman. Para representar o comportamento do estudante
no Ambiente Virtual Aprendizagem, o trabalho utiliza utiliza um sistema para simular o
desempenho do estudante em um Sistema de Tutoria Inteligente. Os m?todos utilizados
resultam na constru??o de um algoritmo de detec??o autom?tica de Estilos de Aprendizagem
em Ambientes Virtuais de Aprendizagem. Os resultados do algoritmo de Rede
Bayesiana foram comparados aos resultados de outro algoritmo de detec??o de Estilos de
Aprendizagem na literatura. Nos testes, o algoritmo de Rede Bayesiana se mostrou mais
eficiente comparado ao da literatura, diminuindo consideravelmente o n?mero de itera??es
do sistema que no final converge ao Estilo de Aprendizagem do estudante, diminuindo o
tempo de execu??o e aumentando a precis?o dos resultados. O trabalho abre discuss?o
quanto a robustez, efici?ncia e precis?o da aplica??o de Redes Bayesianas para detec??o
de Estilos de Aprendizagem.Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.The advancement of technology has enabled the emergence of tools for access to knowledge
and individual and collective experiences. Information and Communication Technologies
and the Internet have created the concept called Cyberspace, a virtual place where the
sum of all the experiences, knowledge and cultures of all peoples that forms the Collective
Intelligence. This phenomenon contributed to the development of Distance Education
and Intelligent Systems for Education. One of the major problems in EaD is the lack
of adaptability of teaching to students? learning style, which consists of the preferences
each student has in receiving a certain content. Thus, the paper approaches a technique
of Bayesian Networks to automatically detect the Learning Styles of the students to
provide an offer of teaching material adapted to the preferences of learning in the Virtual
Environments of Learning. The work is based on concepts and techniques of Artificial
Intelligence and Machine Learning to compose a computational and probabilistic model
of a Bayesian Network to infer and detect the best combination of Learning Styles. To
structure Learning Styles detection methods, the search uses the Felder-Silverman Learning
Style Template. To represent student behavior in the Virtual Learning Environment, the
work uses uses a system to simulate student performance in an Intelligent Tutoring System.
The methods used result in the construction of an algorithm for automatic detection of
Learning Styles in Virtual Learning Environments. The results of the Bayesian Network
algorithm were compared to the results of another learning style detection algorithm in
the literature. In the tests, the Bayesian Network algorithm proved to be more efficient
compared to the literature, considerably reducing the number of system iterations that in
the end converges to the student?s Learning Style, reducing execution time and increasing
the accuracy of the results. The paper discusses the robustness, efficiency and accuracy of
the application of Bayesian Networks for the detection of Learning Styles
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
A novel algorithm for dynamic student profile adaptation based on learning styles
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method
Computer Analysis of Architecture Using Automatic Image Understanding
In the past few years, computer vision and pattern recognition systems have
been becoming increasingly more powerful, expanding the range of automatic
tasks enabled by machine vision. Here we show that computer analysis of
building images can perform quantitative analysis of architecture, and quantify
similarities between city architectural styles in a quantitative fashion.
Images of buildings from 18 cities and three countries were acquired using
Google StreetView, and were used to train a machine vision system to
automatically identify the location of the imaged building based on the image
visual content. Experimental results show that the automatic computer analysis
can automatically identify the geographical location of the StreetView image.
More importantly, the algorithm was able to group the cities and countries and
provide a phylogeny of the similarities between architectural styles as
captured by StreetView images. These results demonstrate that computer vision
and pattern recognition algorithms can perform the complex cognitive task of
analyzing images of buildings, and can be used to measure and quantify visual
similarities and differences between different styles of architectures. This
experiment provides a new paradigm for studying architecture, based on a
quantitative approach that can enhance the traditional manual observation and
analysis. The source code used for the analysis is open and publicly available
Automatic Palaeographic Exploration of Genizah Manuscripts
The Cairo Genizah is a collection of hand-written documents containing approximately
350,000 fragments of mainly Jewish texts discovered in the late 19th
century. The
fragments are today spread out in some 75 libraries and private collections worldwide,
but there is an ongoing effort to document and catalogue all extant fragments.
Palaeographic information plays a key role in the study of the Genizah collection.
Script style, and–more specifically–handwriting, can be used to identify fragments that
might originate from the same original work. Such matched fragments, commonly
referred to as “joins”, are currently identified manually by experts, and presumably only
a small fraction of existing joins have been discovered to date. In this work, we show
that automatic handwriting matching functions, obtained from non-specific features
using a corpus of writing samples, can perform this task quite reliably. In addition, we
explore the problem of grouping various Genizah documents by script style, without
being provided any prior information about the relevant styles. The automatically
obtained grouping agrees, for the most part, with the palaeographic taxonomy. In cases
where the method fails, it is due to apparent similarities between related scripts
Who is the director of this movie? Automatic style recognition based on shot features
We show how low-level formal features, such as shot duration, meant as length
of camera takes, and shot scale, i.e. the distance between the camera and the
subject, are distinctive of a director's style in art movies. So far such
features were thought of not having enough varieties to become distinctive of
an author. However our investigation on the full filmographies of six different
authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total
number of 120 movies analysed second by second, confirms that these
shot-related features do not appear as random patterns in movies from the same
director. For feature extraction we adopt methods based on both conventional
and deep learning techniques. Our findings suggest that feature sequential
patterns, i.e. how features evolve in time, are at least as important as the
related feature distributions. To the best of our knowledge this is the first
study dealing with automatic attribution of movie authorship, which opens up
interesting lines of cross-disciplinary research on the impact of style on the
aesthetic and emotional effects on the viewers
- …