Cristian Cechinel

Clustering Learning Objects for Improving Their Recommendation via Collaborative Filtering Algorithms

Collaborative Filtering can be used in the context of e-learning to recommend learning objects to students and teachers involved with the teaching and learning process. Although such technique presents a great potential for e-learning, studies related to this application in this field are still limited, mostly because the inexistence of available datasets for testing and evaluating. The present work evaluates a pre-processsing method through clustering for future use of collaborative filtering algorithms. For that we use a large data set collected from the MERLOT repository. The initial results point out that clustering learning objects before the use of collaborative filtering techniques can improve the recommendations performance.

On the Search for Intrinsic Quality Metrics of Learning Objects

Assessing quality of learning resources is a difficult and complex task that often revolve around multiple and different aspects that must be observed. In order to evaluate quality, it is necessary to consider the particular spectrum of users and the particular set of criteria used by these users to value the resources. Existing approaches for assessing LOs quality are normally based on broadly interpreted dimensions that can be subject of divergence among different evaluators. The present work identifies lower-level and easily quantifiable measures of learning objects that are associated to quality with the aim of providing a common and free from ambiguities ground for LO quality assessment.

Descriptive Analysis of Learning Object Material Types in MERLOT

In the last years, learning object repositories have become a reliable source of information about the current state of art regarding learning resource technologies. As learning objects varies in many ways, it is necessary to understand to which extend the different types of materials stored in these repositories are more suitable for certain subject areas or exert more influence on users’ choices when they are selecting a resource. Evaluative metadata, normally used inside repositories as the basis for ranking and recommending resources, allow the comparison amongst heterogeneous materials and help to identify preferences for usage of the repositories community. The present work describes the different types of materials in MERLOT according to the categories of disciplines and the most important evaluative metadata of the repository: peer-reviewers and users ratings, personal collections, and MERLOT Classics Awards.

Towards Automated Evaluation of Learning Resources Inside Repositories

It is known that current Learning Object Repositories adopt strategies for quality assessment of their resources that rely on the impressions of quality given by the members of the repository community. Although this strategy can be considered effective at some extent, the number of resources inside repositories tends to increase more rapidly than the number of evaluations given by this community, thus leaving several resources of the repository without any quality assessment. The present work describes the results of two experiments to automatically generate quality information about learning resources based on their intrinsic features as well as on evaluative metadata (ratings) available about them in MERLOT repository. Preliminary results point out the feasibility of achieving such goal which suggests that this method can be used as a starting point for the pursuit of automatically generation of internal quality information about resources inside repositories.

Classification of images acquired with colposcopy using artificial neural networks.

Objective To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. Results After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Conclusion Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

Análise computacional para auxílio ao diagnóstico de osteoartrite de coluna lombar baseado em redes neurais artificiais

OBJECTIVE: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar column radiographs in order to aid in the process of diagnosing primary osteoarthritis. METHODS: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographs of the lumbar column, which were provided by a radiology clinic located in the municipality of Criciuma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar column and those with patterns that were difficult to characterize were discarded, thus resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographs for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. RESULTS: After 90 cycles, the validation was carried out on the best results, thereby reaching accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. CONCLUSIONS: Even though the effectiveness shown was moderate, this study is of innovative nature. Hence, the values show that the technique used has a promising future, thus pointing towards further studies covering the image and cycle processing methodology with a larger quantity of radiographs.

An initial review of learning analytics in Latin America

Learning Analytics focuses on improving learning process by studying and analyzing data produced during the process itself. It covers the collection, measurement, analysis, reporting and knowledge discovering on data about students, teachers and institutions. Learning Analytics has been widely developed in Anglo Saxon countries. USA, United Kingdom, Canada and Australia are amongst the main contributors to this domain. Latin America is also starting to measure and optimize teaching and learning processes through Learning Analytics; however, the existing attempts in this direction are very isolated as there is a lack of a regional community to foster the interchange of ideas, methodologies, tools and local results in the field. The present work is a first attempt to identify Learning Analytics initiatives in Latin America by conducting a systematic mapping of papers from Latin American authors, and also by analyzing data about research groups from Latin America (collected through an open survey). In total, we categorized 30 articles published from 2011 until May 2016, and we analyzed data from 28 research groups that answered the open survey.

Uma Proposta para Localização Facilitada de Objetos de Aprendizagem

A educacao a distancia tem ganhado significativa atencao tanto na academia quanto nas iniciativas governamentais. Neste contexto, cresce tambem a preocupacao com a avaliacao da qualidade dos diversos aspectos destes cursos mediados pelas tecnologias da informacao e comunicacao. Apesar de muitos trabalhos discutirem diversos aspectos da avaliacao em Ead, a literatura carece de relatos de experiencia que, especialmente, abordem os cursos tecnicos a distancia que possuem especificidades relevantes. Assim, este trabalho relata uma experiencia onde se avalia e adapta uma das propostas existentes na literatura de avaliacao mediada por foruns, ao contexto dos cursos tecnicos a distancia, constituindo-se esta adaptacao e sua discussao as principais contribuicoes do mesmo.Em Educacao a Distância mediada por meio de Ambientes Virtuais de Aprendizagem, foruns de discussao sao um instrumento importante e amplamente utilizado na articulacao de debates e discussoes entre os atores envolvidos no processo de ensino e aprendizagem. Com a ampla utilizacao dos foruns muitas mensagens sao trocadas e isso, por vezes, excede a capacidade de monitoramento por parte dos professores e tutores. O presente trabalho apresenta a concepcao de um classificador de mensagens de foruns que classifica as mensagens em positivas ou negativas, a fim de identificar mensagens que necessitam de maior atencao. Este trabalho aplica conceitos de mineracao de textos, com o algoritmo SVM obtendo taxas de acerto satisfatorias.Este artigo apresenta o framework Contagious, cujo proposito e estabelecer diretrizes que norteiem a construcao de redes sociais online orientadas a Difusao de Inovacoes. Compreendo-se o fenomeno das redes sociais online como consequencia natural do carater social do ser humano, vislumbrou-se esse meio tecnologico de comunicacao e interacao social como potencial ferramenta para a extensao de praticas educativas, com vistas a formacao do carater integral do cidadao. Para isso, foi adotada a teoria de Difusao de Inovacoes, propria das ciencias sociais. As contribuicoes deste trabalho, portanto, compreendem duas vertentes: a) o mapeamento de principios de uma teoria social na forma de recursos computacionais e; b) um enfoque orientado a educacao sobre as redes sociais online.A proposta do trabalho consiste em desenvolver um sistema para ser usado no celular como ferramenta de auxilio para alfabetizacao, utilizando-se de imagens e sons como forma de facilitar o aprendizado. Como metodo de desenvolvimento utiliza-se o processo P@PSEduc (Processo Agil para Software Educativo) e a ferramenta JME (Java Micro Edition).O crescente uso e difusao de tecnologias Web, a ubiquidade de ferramentas educacionais vem proporcionado verdadeiras revolucoes nos ambientes de ensino. Atualmente, sabe-se que nao mais se deve tratar alunos de forma homogenea, como se assim os fossem. Em face disso, este artigo apresenta um sistema adaptativo de apoio a aprendizagem colaborativa, cujo tema e a construcao e representacao do conhecimento por meio de mapas mentais multimidia. Tal sistema, baseia-se na Teoria da Carga Cognitiva, cuja preocupacao primaria e a facilidade com a qual as informacoes sao processadas pelos individuos.

Predição de estudantes com risco de evasão em cursos técnicos a distância

The present paper describes an approach for detecting possible dropout students in technical distance learning courses. The proposed method uses only the count of students interactions inside a Learning Management System (LMS), along with other attributes derived from the counts. Such strategy allows better generalization in different platforms and LMS, as it does not rely on the differences among the interaction types, or use other darta sources besides LMS logs. Predictive models were trained and tested with data from 4 technical distance learning courses in two different scenarios: 1) train and test with data from one course, and 2) train with data combined from 3 courses and test with data of the remaining course. Results point out it is possible to predict dropout students in the first weeks of the courses with average accuracy rates of 75% in most of scenarios, achieving 95% in the best case scenarios. Resumo. O presente trabalho apresenta uma abordagem para a detecção de alunos em risco de evasão em cursos técnicos a distância que utiliza apenas a contagem de interações dos estudantes dentro do AVA, além de atributos derivados dessas contagens. A premissa inicial é de que essa estratégia permite uma maior generalização em diferentes plataformas e AVA, uma vez que não utiliza diferenciações entre os tipos de interações, nem informações de outra ordem encontradas fora do AVA (dados demográficos, exames, questionários, etc). Os modelos de predição foram testados e treinados com dados de 4 diferentes cursos técnicos EAD em dois cenários diferentes: 1) treino e teste com dados de um mesmo curso, e 2) treino com dados de 3 cursos e teste com dados do curso restante. Os resultados apontam a possibilidade de predição de estudantes em risco de evasão já nas primeiras semanas dos cursos com taxas de desempenho próximas a 75% na maioria dos cenários, e chegando a 95% nos melhores casos.

A comparison among approaches for recommending learning objects through collaborative filtering algorithms

Purpose The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration. Design/methodology/approach The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage. Findings Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be. Research limitations The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions. Originality/value This research provides evidence toward new recommendation methods directed toward LO repositories.