Математическое и информационное моделирование: сборник научных трудов. C. 139-149. Использование методов интеллектуального анализа данных для получения знаний об образовательном процессе

Отражена важность применения методов интеллектуального анализа данных, возникающих в рамках образовательного процесса

ESTUDO DO DESENVOLVIMENTO COGNITIVO INDIVIDUAL E DE GRUPOS ATRAVÉS DA ANÁLISE AUTOMÁTICA

Este artigo propõe o uso de métodos automáticos de análise de dados para possibilitar a integração das teorias de ensino-aprendizagem aos ambientes virtuais de aprendizagem. Em domínios onde o desenvolvimento cognitivo passa pela resolução de problemas, a análise do processo de aprendizagem depende do raciocínio desenvolvido pelo aluno para chegar à solução e do devido acompanhamento do professor. O acompanhamento deste processo permite reconhecer padrões cognitivos que podem desencadear o sucesso ou o fracasso na aprendizagem, auxiliando o professor nas suas tarefas. Com esta finalidade, este artigo propõe o uso de algoritmos de clustering para auxiliar a análise do desenvolvimento cognitivo individual e de grupos através de um experimento na aprendizagem da lógica de programação

Méthodologie d'enrichissement semi-automatique d'un profil-apprenant basée sur une ontologie

National audienceLe contexte de la formation continue amène des adultes-apprenants de profils hétérogènes à s’inscrire à une même formation. La principale difficulté est de concilier les besoins de formation individuels au sein d’un groupe de formation en présentiel. Notre proposition consiste à représenter et à enrichir le profil initial de l’adulte-apprenant en utilisant une formalisation ontologique.Cet article de recherche présente une méthodologie pour clarifier et analyser les besoins de formation des adultes-apprenants instrumentés à partir des traces recueillies par un EPA (Environnement Personnel d’Apprentissage) communautaire.Dans la continuité de cette analyse, il s’agit de composer par la suite des groupes de formation homogènes et raisonnés selon des critères de similarités pré-définis

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

Aplicación de métodos de diseño centrado en el usuario y minería de datos para definir recomendaciones que promuevan el uso del foro en una experiencia virtual de aprendizaje

The use of recommendation systems in learning virtual environments is increasingly becoming a feasible approach for providing the adaptive support required to attend students’ learning needs. With the interaction data obtained from these virtual environments it is possible to find indicators where data mining and machine learning techniques can be applied to identify relevant information that allows for the definition of recommendations. In this research we have applied unsupervised learning techniques to identify common interaction patterns with available forums in a course on the OpenACS/dotLRN platform. This will allow recommendations to be defined that help improve the students’ learning experience.La adopción de sistemas recomendadores en ambientes virtuales de aprendizaje se está convirtiendo en una alternativa; para lograr la adaptación automática requerida, para atender las necesidades de aprendizaje de los estudiantes. Con los datos de interacción, que proveen estos ambientes es posible encontrar indicadores que con la aplicación de técnicas de minería de datos y aprendizaje automático se pueda identificar información relevante, para la definición de recomendaciones. En esta investigación, hemos aplicado técnicas de aprendizaje no supervisado, para la identificación de patrones comunes de interacción con los foros disponibles en un curso de la plataforma OpenACS/dotLRN. Esto facilitará la definición de recomendaciones que ayuden a mejorar la experiencia de aprendizaje de los estudiantes

Analysing, visualising and supporting collaborative learning using interactive tabletops

The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups