Aplica??o do NSGA-II em uma abordagem multiobjetivo na recomenda??o de objetos de aprendizagem em ambientes inteligentes para educa??o

Existem grandes reposit?rios de conte?dos na Internet que podem ser utilizados como fonte de recursos para promo??o do e-learning. Por?m, o volume de materiais dispon?veis ? muito grande. Para lidar com essa quantidade de itens, s?o utilizados os Sistemas de Recomenda??o, que selecionam os materiais mais adequados aos objetivos do estudante. Nesse contexto, o presente trabalho prop?e uma abordagem multiobjetivo do problema de recomenda??o de Objetos de Aprendizagem (OAs) para atender a uma demanda de ensino, desde uma pequena se??o de aprendizagem at? um curso completo. Na abordagem proposta, uma solu??o n?o necessariamente cobre todos os conceitos estabelecidos pelo design instrucional. Na verdade, buscam-se solu??es que tenham o menor custo e a maior quantidade de conceitos cobertos. O custo de cada OA ? diferente para cada aluno, determinado a partir do seu estilo de aprendizagem e de avalia??es feitas por outros estudantes com perfil semelhante daquele aluno. Todavia, existem depend?ncias entre alguns conceitos estabelecidas pelo design instrucional que cada solu??o deve respeitar. A partir do conjunto de solu??es geradas, o estudante escolhe aquela que melhor atende as suas expectativas. Para se obter solu??es foi utilizado o algoritmo NSGA II no framework MOEA, testado em uma inst?ncia de problema gerada artificialmente. Foram criados dois m?todos de inicializa??o da popula??o, que determina para cada indiv?duo valores aleat?rios de cobertura e seleciona Objetos de Aprendizagem tamb?m de forma rand?mica. Os resultados obtidos foram conjuntos de at? 19 solu??es para inst?ncias com 200 conceitos e 10 depend?ncias entre conceitos, bem como de at? 6 solu??es para inst?ncia com 20 conceitos e 2 depend?ncias. Para cursos com 20 depend?ncias, a quantidade de solu??es obtidas foi menor, no m?ximo 10. Mostra-se importante a avalia??o de outros algoritmos al?m do NSGA-II, bem como a necessidade de aprimoramento do algoritmo de gera??o da popula??o inicial para obter mais solu??es inicialmente vi?veis. Um poss?vel trabalho futuro ? aplicar o problema em reposit?rios reais, no qual o custo de cada OA ? obtido a partir de seus pr?prios atributos.Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2020.There are large repositories of content in the Internet that can be used as resources for e-learning. However, there are too much available materials. Recommender Systems are used to deal with these many items, which select the most suitable materials for the student?s goals. The present paper proposes a multi-objective approach to the problem of Learning Objects (LOs) recommendation to meet a teaching demand, from a small learning section to an entire course. In the proposed approach, a solution does not necessarily cover all course subjects, established by the instructional design. In fact, solutions for a course are sought that have the lowest cost and the largest number of subjects covered. The cost of each LO is different for each student, based on his/her learning style, as well as assessments made by other students with a similar profile to that student. However, the instructional design also establishes dependencies between some subjects that each solution must respect. From the set of generated solutions, the student chooses the one that best meets his/her preferences and expectations. An artificial instance of that problem was tested, using the NSGA II algorithm in the MOEA framework. Two methods of population initialization were created, which determine for each individual random values of coverage and select Learning Objects randomly as well. The results obtained were sets of up to 19 solutions for instances of courses with 200 subjects and 10 dependencies, as well as sets up to 6 solutions for instance of courses with 20 subjects and 2 dependencies. For courses with 20 dependencies, the amount of solution obtained was smaller, at most 10. It?s important to evaluate other algorithms in addition to the NSGA-II, as well as the need to improve the initial population generation algorithm to obtain more feasible solutions. A possible future work is to apply the problem in real repositories, in which the cost of each OA is estimated based on its attributes

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Modelos pedagógicos baseados em sistemas de recomendação : um foco em disciplinas da graduação

Os Sistemas de Recomendação (SR) utilizam diferentes técnicas de filtragem para selecionar informações úteis ao usuário, de acordo com as suas necessidades. Portanto, considerando o crescente aumento no volume de dados disponíveis na rede, os SR podem colaborar com a redução do tempo despendido pelo sujeito na busca por materiais relevantes. Na educação esses sistemas são capazes de apoiar o processo de aprendizagem, personalizando os ambientes de ensino. Logo, a presente tese tem como objetivo construir um Modelo Pedagógico (MP_SRecEdu) para disciplinas da graduação, com base em Sistemas de Recomendação Educacionais (SRE). Assim, o professor poderá planejar uma atividade de ensino utilizando um SRE, que irá indicar um ou mais elementos do modelo, de acordo com o perfil dos estudantes, resultando em um espaço de aprendizagem adaptativo. Para isso, o texto aprofunda o estudo sobre os conceitos de SR, Modelos Pedagógicos (MP) e Recomendação Pedagógica (RecPed). A pesquisa caracteriza-se como exploratória, de abordagem qualitativa, com a análise de estudos de caso múltiplos, e está organizada em cinco etapas metodológicas. A primeira etapa trata da elaboração de um referencial teórico sobre Modelos Pedagógicos e Sistemas de Recomendação. Na segunda etapa foram construídos e avaliados, por meio de aplicações em disciplinas da graduação, três Modelos Pedagógicos, que utilizam ferramentas de recomendação diferentes, a saber: 1. Recomendador de Objetos de Aprendizagem baseado em Competências (RecOAComp), 2. Recomendador de conteúdo do Editor de Texto Coletivo (RecETC) e o 3. Recomendador de estratégias pedagógicas, do Mapa Social no Ambiente Virtual de Aprendizagem ROODA. Na etapa três verificou-se modificações nos modelos, para uma nova aplicação e análise. Desse modo, na etapa quatro foram realizados três novos estudos de caso, e então, na etapa cinco, com base nas aplicações e avaliações dos mesmos, foi possível construir um MP baseado em SRE. O MP_SRecEdu tem como finalidade apoiar os professores na organização e planejamento de atividades de ensino, que utilizam os SRE em disciplinas de graduação, criando espaços de aprendizagem dinâmicos e personalizados segundo as necessidades do perfil de cada estudante.Recommendation Systems (SR) use different filtering techniques to select useful information for the user, according to their needs. Therefore, considering the growing increase in the volume of data available on the network, the SR can collaborate with the reduction of the time spent by the user in the search for relevant materials. In education, these systems are able to support the learning process, personalizing the teaching environments. As a result, this thesis aims to build a Pedagogical Model (MP_SRecEdu) for undergraduate courses, based on Educational Recommendation Systems (ERS). Thus, the professor will be able to plan a teaching activity using an ERS, which will indicate one or more elements of the model, according to the profile of the students, resulting in an adaptive learning space. In order to do that, the text deepens the study on the concepts of SR, Pedagogical Models (MP) and Pedagogical Recommendation (RecPed). The research is characterized as exploratory, with a qualitative approach, with the analysis of multiple case studies, and is organized in five methodological stages. The first stage deals with the development of a theoretical framework on Pedagogical Models and Recommendation Systems. In the second stage, three Pedagogical Models were built and evaluated, using applications in undergraduate disciplines, using different recommendation tools, namely: 1. Competency Based Learning Object Recommender (RecOAComp), 2. Collective Text Editor Content Recommender (RecETC) and 3. The Social Map Pedagogical Strategies Recommender in the Virtual Learning Environment ROODA. In step three, modifications in the models were verified, for a new application and analysis. Thus, in step four, three new case studies were carried out, and then, in step five, based on their applications and evaluations, it was possible to build a PM based on SRE. MP_SRecEdu aims to support professors in the organization and planning of teaching activities, which use ERS in undergraduate courses, creating dynamic and personalized learning spaces according to the needs of each student's profil