Use of deep multi-target prediction to identify learning styles

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students105Applied machine learnin

Architecture of artificial neural networks for adaptive learning in e-learning systems

Orientador: Leonardo de Souza MendesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O problema de Aprendizado Adaptativo pode ser descrito como um conjunto de preferências P, do aluno A, que implica na interação com o conjunto de objetos de aprendizagem O, contidos no sistema de e-learning; portanto, P -> O. Os dados desta interação permitem gerar modelos que expliquem o comportamento de tal aluno e, adicionalmente, prever seu comportamento. As redes neurais artificiais desempenham um papel importante para a modelagem de soluções, em diferentes tipos de problemas nos mais variados contextos. Para investigar o problema de Aprendizado Adaptativo, duas arquiteturas de redes neurais artificiais foram testadas; uma para classificação baseada na arquitetura Multi Layer Perceptron e outra para recomendação baseada na arquitetura Deep Auto Enconder. Como resultado, obtiveram-se duas estratégias, sendo a primeira relacionada com a classificação de um aluno no modelo de estilos de aprendizagem de Felder-Silverman e a segunda uma lista de objetos de aprendizagem que possam ser recomendados de acordo com as preferências de aprendizagem. Ambas as arquiteturas se mostraram eficazes permitindo que conteúdos e/ou formatos possam ser entregues, nos sistemas adaptativos de e-learning, de maneira adequada às preferências de seus usuários. Dentre as implicações práticas, pode-se destacar a possibilidade de melhora na experiência de aprendizagem do aluno devido à adaptação de conteúdos e/ou formatosAbstract: The Adaptive Learning problem can be described as a set of objects P, from student A, which implies interaction with the set of learning objects O, in an e-learning system; therefore, P -> O. The data of this interaction allows generate models that explain the student's behavior and, in addition, predict his behavior. The artificial neural networks have played an important role in modeling solutions, in different types of problems in several contexts. To investigate the Adaptive Learning problem, two artificial neural network architectures were tested; a specific classification in the Multi Layer Perceptron architecture and another recommended recommendation based on Deep Auto Encoder architecture. As a result, two strategies were obtain, the first one related to the classification of student in the Felder-Silverman Learning Style Model and the second one related to a list of recommended objects suitable to student¿s learning preferences. Both architectures have proven to be effective, allowing content and/or formats to be delivered, in adaptive e-learning systems, in a manner appropriate to the preferences of its users. Among the practical implications, the possibility of improving the student's learning experience due to the adaptation of contents and/or formatsDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétric

Application of Computational Intelligence to Improve Education in Smart Cities

According to UNESCO, education is a fundamental human right and every nation’s citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student’s knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives’ achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders