Adaptive E-learning Content Design and Delivery Based on Learning Styles and Knowledge Level

ACM Computing Classification System (1998): K.3.1, K.3.2.

Integrating Moodle with University Information Systems

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Effects of Cooperative and Individual Integrated Learning System on Attitudes and Achievement in Mathematics

The purpose of this study was to determine the effects of a computer-based Integrated Learning Systems (ILS) model used with adult high school students engaging mathematics activities. This study examined achievement, attitudinal and behavior differences between students completing ILS activities in a traditional, individualized format compared to cooperative learning groups.

Ontologies derived from Wikipedia : a framework for comparison

Since its debut in 2001 Wikipedia has attracted the attention of many researchers in different fields. In recent years researchers in the area of ontology learning have realised the huge potential of Wikipedia as a source of semi-structured knowledge and several systems have used it as their main source of knowledge. However, the techniques used to extract semantic information vary greatly, as do the resulting ontologies. This paper introduces a framework to compare ontology learning systems that use Wikipedia as their main source of knowledge. Six prominent systems are compared and contrasted using the framework.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

What makes an LMS effective : a synthesis of current literature

There is a growing number of organizations and universities now utilising e-learning practices in their teaching and learning programs. These systems have allowed for knowledge sharing and provide opportunities for users to have access to learning materials regardless of time and place. However, while the uptake of these systems is quite high, there is little research into the effectiveness of such systems, particularly in higher education. This paper investigates the methods that are used to study the effectiveness of e-learning systems and the factors that are critical for the success of a learning management system (LMS). Five major success categories are identified in this study and explained in depth. These are the teacher, student, LMS design, learning materials and external support.

Motion planning and stochastic control with experimental validation on a planetary rover

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety.

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision.

Towards multilingual domain module acquisition

Máster y Doctorado en Sistemas Informáticos Avanzados, Informatika Fakultatea - Facultad de Informática

Automatic detection of learner-style for adaptive eLearning

The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Dinâmicas de MOODLiz@ção num agrupamento de escolas de Matosinhos: o caso do agrupamento vertical de escolas de Leça da Palmeira/Santa Cruz do Bispo

O advento da Internet e da Web, na década de 1990, a par da introdução e desenvolvimento das novas TIC e, por consequência, a emergência da Sociedade da Informação e do Conhecimento, implicaram uma profunda alteração na forma de análise dos processos de ensino-aprendizagem, já não apenas segundo um prisma cognitivista, mas, agora, também social, isto é, segundo a(s) perspetiva(s) construtivista(s). Simultaneamente, torna-se imperativo que, para que possam transformar-se em futuros trabalhadores de sucesso, isto é, trabalhadores de conhecimento (Gates, 1999), os sujeitos aprendentes passem a ser efetivamente educados/preparados para a Sociedade da Informação e do Conhecimento e, tanto quanto possível, através da educação/formação ao longo da vida (Moore e Thompson, 1997; Chute, Thompson e Hancock, 1999). Todavia, de acordo com Jorge Reis Lima e Zélia Capitão, não se deve considerar esta mudança de paradigma como uma revolução mas, antes, uma evolução, ou, mais concretamente ainda, uma “conciliação de perspectivas cognitivas e sociais” (Reis Lima e Capitão, 2003:53). Assim, às instituições de ensino/formação cumprirá a tarefa de preparar os alunos para as novas competências da era digital, promovendo “a aprendizagem dos pilares do conhecimento que sustentarão a sua aprendizagem ao longo da vida” (Reis Lima e Capitão, Ibidem:54), isto é, “aprender a conhecer”, “aprender a fazer”, “aprender a viver em comum”, e “aprender a ser” (Equipa de Missão para a Sociedade da Informação, 1997:39; negritos e sublinhados no original). Para outros, a Internet, ao afirmar-se como uma tecnologia ubíqua, cada vez mais acessível, e de elevado potencial, “vem revolucionando a gestão da informação, o funcionamento do mercado de capitais, as cadeias e redes de valor, o comércio mundial, a relação entre governos e cidadãos, os modos de trabalhar e de comunicar, o entretenimento, o contacto intercultural, os estilos de vida, as noções de tempo e de distância. A grande interrogação actual reside em saber se a Internet poderá também provocar alterações fundamentais nos modos de aprender e de ensinar” (Carneiro, 2002:17-18; destaques no original). Trata-se, portanto, como argumenta Armando Rocha Trindade (2004:10), de reconhecer que “Os requisitos obrigatórios para a eficácia da aprendizagem a ser assim assegurada são: a prévia disponibilidade de materiais educativos ou de formação de alta qualidade pedagógica e didáctica, tanto quanto possível auto-suficientes em termos de conteúdos teóricos e aplicados, bem como a previsão de mecanismos capazes de assegurar, permanentemente, um mínimo de interactividade entre docentes e aprendentes, sempre que quaisquer dificuldades destes possam manifestarse”. Esta questão é também equacionada pelo Eng.º Arnaldo Santos, da PT Inovação, quando considera que, à semelhança da “maioria dos países, a formação a distância em ambientes Internet e Intranet, vulgo e-Learning, apresenta-se como uma alternativa pedagógica em franca expansão. Portugal está a despertar para esta nova realidade. São várias as instituições nacionais do sector público e privado que utilizam o e-Learning como ferramenta ou meio para formar as suas pessoas” (Santos, 2002:26). Fernando Ramos acrescenta também que os sistemas de educação/formação que contemplam componentes não presenciais, “isto é que potenciam a flexibilidade espacial, têm vindo a recorrer às mais variadas tecnologias de comunicação para permitir a interacção entre os intervenientes, nomeadamente entre os professores e os estudantes. Um pouco por todo o mundo, e também em Portugal, se têm implantado sistemas (habitualmente designados como sistemas de ensino a distância), recorrendo às mais diversas tecnologias de telecomunicações, de que os sistemas de educação através de televisão ou os sistemas de tutoria por rádio ou telefone são exemplos bem conhecidos” (Ramos, 2002b:138-139). Ora, o nosso estudo entronca precisamente na análise de um sistema ou plataforma tecnológica de gestão de aprendizagens (Learning Management System - LMS), o MOODLE, procurando-se, deste modo, dar resposta ao reconhecimento de que “urge investigar sobre a utilização real e pedagógica da plataforma” (Carvalho, 2007:27). Por outro lado, não descurando o rol de interrogações de outros investigadores em torno da utilização do MOODLE, nem enveredando pelas visões mais céticas que inclusive pressagiam a sua “morte” (Fernandes, 2008b:134), também nós nos questionamos se esta ferramenta nem sequer vai conseguir transpor “a fase de final de entusiasmo, e tornar-se uma ferramenta de minorias e de usos ocasionais?” (Fernandes, Op. cit.:133).