The effective use of a learning management system still promotes student engagement!

Published Conference ProceedingsLearning management systems have the capabilities of creating, fostering, delivering, and facilitating learning at anytime and anywhere. Although these systems have allowed students to engage in online discussions and collaborative activities, many academics believe that this online space has essentially remained a content repository. However, the fact that many academics simply use a learning management system as a content dumping site cannot be generalized across the board for all academics. No, such a blanket statement would prove to be a grave injustice to those few academics that are trying to improve their teaching abilities and promote student engagement and learning, especially through using a variety of tools which have been seamlessly integrated into many of these systems today. The purpose of this paper is to highlight how an academic in electrical engineering is still effectively using an institutional learning management system to promote student engagement through the use of four major features that are currently available in this platform. An ex post facto study is employed along with descriptive statistics involving quantitative analysis of the collected data. Results indicate that both academics and students engaged with all four primary features of the learning management system. However, the predominant features were accessing content followed by completing online assessments. A significant correlation was established between these two features and the final grade marks awarded to students at the end of the course. These results tend to suggest that some academics are widening their horizons and creating interactive experiences for students to enhance their learning. It is hoped that their experience and enthusiasm in using a variety of educational technologies will rub off on fellow colleagues to the greater benefit of students in higher education

COMPARATIVE STUDY: FEATURE SELECTION METHODS IN THE BLENDED LEARNING ENVIRONMENT

Research presented in this paper deals with the unknown behavior pattern of students in the blended learning environment. In order to improve prediction accuracy it was necessary to determine the methodology for students` activities assessments. The Training set was created by combining distributed sources – Moodle database and traditional learning process. The methodology emphasizes data mining preprocessing phase: transformation and features selection. Information gain, Symmetrical Uncert Feature Eval, RelieF, Correlation based Feature Selection, Wrapper Subset Evaluation, Classifier Subset Evaluator features selection methods were implemented to find the most relevant subset. Statistical dependence was determined by calculating mutual information measure. Naïve Bayes, Aggregating One-Dependence Estimators, Decision tree and Support Vector Machines classifiers have been trained for subsets with different cardinality. Models were evaluated with comparative analysis of statistical parameters and time required to build them. We have concluded that the RelieF, Wrapper Subset Evaluation and mutual information present the most convenient features selection methods for blended learning environment. The major contribution of the presented research is selecting the optimal low-cardinal subset of students’ activities and a significant prediction accuracy improvement in blended learning environment

PA2 - Plataforma pedagógica de auto-aprendizagem

Na atualidade a formação online é cada vez mais relevante para a sociedade pela sua facilidade de acesso na Internet, no entanto existem diversas modalidades de ensino à distância que podem não corresponder ou ser as melhores formas de transmitir conhecimento. Com esta Dissertação são analisados os modelos pedagógicos mais relevantes do ensino à distância, as vantagens de cada um, os conceitos e metodologias em que se baseiam, e é desenvolvida uma proposta de modelo pedagógico para uma arquitetura de um curso direcionado à auto-aprendizagem, que pode ser implementado em qualquer plataforma de ensino virtual. Cada aluno terá um percurso de aprendizagem próprio, conduzido por uma sequência de vídeos que é determinada no final de cada vídeo correspondendo a um questionário de avaliação, permitindo assim que os conteúdos do curso se adaptem aos seus conhecimentos. A arquitetura proposta implementada na plataforma Moodle garante que o processo de aprendizagem é totalmente autónomo, não sendo necessário qualquer interação entre o aluno e professor, garantindo que todo o processo de aprendizagem é realizado entre o aluno e a plataforma com os conteúdos programáticos nos vídeos e respetivas avaliações usando quizes

Educational data mining e learning analytics na melhoria do ensino online

Dissertação de Mestrado em Estatística, Matemática e Computação apresentada à Universidade AbertaEducação é um dos temas mais importantes e discutidos em todo o mundo. Sendo um processo de aquisição de conhecimento e/ou aptidões, tem sofrido grandes alterações ao longo dos tempos. Na última década, os avanços das tecnologias de informação e computação têm permitido às pessoas interagirem e aprenderem de uma nova forma. Com as inovações tecnológicas, as escolas e universidades estão a alterar a forma como transmitem e partilham conhecimentos. Ao passo que, até ao ensino secundário, as Escolas disponibilizam uma plataforma Moodle, onde os professores divulgam e partilham alguns documentos e tarefas que servem de apoio às suas práticas letivas; já no ensino superior as alterações são mais significativas. As Universidades chegam mesmo a alterar a metodologia dos seus cursos. Para além do ensino tradicional optam por outras modalidades de ensino: b-learning (ensino simultaneamente presencial e à distância) e/ou e-learning (ensino à distância). Os modelos de ensino/aprendizagem assentes em ambientes online permitem aos alunos terem acesso ao conhecimento a qualquer hora e em qualquer lugar. No entanto, também têm os seus desafios, devido à ausência de contacto humano direto e às insuficiências que isso pode gerar. Contudo, os propugnadores do e-learning defendem que a criação de comunidades virtuais que interagem através de chats, fóruns, emails, etc, compensam essa carência, enriquecendo o processo relacional entre pessoas com o mesmo interesse, mas com diferentes visões e localizadas em regiões e países distintos. Com o aumento do uso de ambientes online e outras tecnologias para apoio ao processo de ensino e aprendizagem, um grande volume de dados tem sido gerado a partir das diferentes 3 interações no sistema, envolvendo estudantes e professores. Com a análise destes dados, podemos obter uma quantidade de informação e conhecimento pertinente e essencial para a melhoria da qualidade do ensino. Nomeadamente, no combate ao insucesso e ao abandono escolar. Diversos estudos têm sido promovidos e desenvolvidos de modo a identificarem e analisarem as causas do insucesso escolar. Como consequência têm sido desenvolvidos programas e medidas que visam a promoção do sucesso. Uma das medidas consiste no acompanhamento adequado e personalizado dos alunos ao longo do seu percurso académico. Neste trabalho é proposto um modelo de análise de dados, com base em cartas de controlo, regressão logística e análise de clusters, com vista à extração de conhecimento, relevante na previsão do desempenho escolar, no ensino online.Education is one of the most important and widely discussed subjects all over the world. Provided that it is a process of knowledge and/or skills acquisition, education has undergone many changes over time. Over the last decade, improvements in information and computer technologies have enabled people to interact and learn in a different way. Due to technological advance, schools and universities have been changing the way they transmit and share knowledge. Whereas up to high school education schools provide a Moodle platform, in which teachers spread and share some documents and tasks that support their classroom practices, in higher education the technological changes are more significant. Universities even change the methodology of their courses. Besides the traditional way of teaching, they choose other types of education: b-learning (both classroom and online learning) and/or e-learning (online learning). The teaching/learning models based on online environments allow students to have access to knowledge at anytime and anywhere. However, it also has its challenges due to the absence of direct human contact and the insufficiencies this might create. Nevertheless, the proponents of e-learning argue that the creation of virtual communities that interact through chat rooms, forums, email, etc, surpasses that absence as they enrich the relational process among people who share the same interest but have different views and may be located in different regions and countries. With the increasing use of online environments and other technologies supporting the educational process, a large amount of data has been generated from different interactions in the system involving students and teachers. From the analysis of these data it is possible to get 5 a considerable and relevant amount of information and knowledge which are essential for improving the quality of teaching, particularly as regards the prevention of school failure and dropout. Several studies have been promoted and developed in order to identify and analyse the causes of school failure. Consequently, some programs and measures aimed at reaching school success have been developed. One of them consists of appropriate and personalized support for students throughout their academic career path. In this work it is proposed a model of data analysis based on control charts, logistic regression, and cluster analysis in order to extract relevant knowledge for the prediction of school performance on the online teaching

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing