Wide-Scale Automatic Analysis of 20 Years of ITS Research

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount of time. Additionally, major publications which have gained a large number of citations are more likely to be included in a review, with recent or fringe publications receiving less inclusion. We provide thus an automatic methodology for the large-scale analysis of literature within the Intelligent Tutoring Systems (ITS) domain, with the aim of identifying trends and areas of research from a corpus of publications which is significantly larger than is typically presented in conventional literature reviews. We illustrate this by a novel analysis of 20 years of ITS research. The resulting analysis indicates a significant shift of the status quo of research in recent years with the advent of novel neural network architectures and the introduction of MOOCs

Student Modeling and Analysis in Adaptive Instructional Systems

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability

Scalable Intelligence for Scheduling Systems

A personalização é um aspeto chave de uma interação homem-computador efetiva. Numa era em que existe uma abundância de informação e tantas pessoas a interagir com ela, de muitas maneiras, a capacidade de se ajustar aos seus utilizadores é crucial para qualquer sistema moderno. A criação de sistemas adaptáveis é um domínio bastante complexo que necessita de métodos muito específicos para ter sucesso. No entanto, nos dias de hoje ainda não existe um modelo ou arquitetura padrão para usar nos sistemas adaptativos modernos. A principal motivação desta tese é a proposta de uma arquitetura para modelação do utilizador que seja capaz de incorporar diferentes módulos necessários para criar um sistema com inteligência escalável com técnicas de modelação. Os módulos cooperam de forma a analisar os utilizadores e caracterizar o seu comportamento, usando essa informação para fornecer uma experiência de sistema customizada que irá aumentar não só a usabilidade do sistema mas também a produtividade e conhecimento do utilizador. A arquitetura proposta é constituída por três componentes: uma unidade de informação do utilizador, uma estrutura matemática capaz de classificar os utilizadores e a técnica a usar quando se adapta o conteúdo. A unidade de informação do utilizador é responsável por conhecer os vários tipos de indivíduos que podem usar o sistema, por capturar cada detalhe de interações relevantes entre si e os seus utilizadores e também contém a base de dados que guarda essa informação. A estrutura matemática é o classificador de utilizadores, e tem como tarefa a sua análise e classificação num de três perfis: iniciado, intermédio ou avançado. Tanto as redes de Bayes como as neuronais são utilizadas, e uma explicação de como as preparar e treinar para lidar com a informação do utilizador é apresentada. Com o perfil do utilizador definido torna-se necessária uma técnica para adaptar o conteúdo do sistema. Nesta proposta, uma abordagem de iniciativa mista é apresentada tendo como base a liberdade de tanto o utilizador como o sistema controlarem a comunicação entre si. A arquitetura proposta foi desenvolvida como parte integrante do projeto ADSyS - um sistema de escalonamento dinâmico - utilizado para resolver problemas de escalonamento sujeitos a eventos dinâmicos. Possui uma complexidade elevada mesmo para utilizadores frequentes, daí a necessidade de adaptar o seu conteúdo de forma a aumentar a sua usabilidade. Com o objetivo de avaliar as contribuições deste trabalho, um estudo computacional acerca do reconhecimento dos utilizadores foi desenvolvido, tendo por base duas sessões de avaliação de usabilidade com grupos de utilizadores distintos. Foi possível concluir acerca dos benefícios na utilização de técnicas de modelação do utilizador com a arquitetura proposta.Personalization is a key aspect of effective Human-Computer Interaction. The ability to adjust itself to its users is crucial to any modern system, in an era where there is so much information and so many people interacting in so many ways. The creation of adaptable systems is a complex domain that requires very specific methods in order to be successful. However, still today there is no standard model or architecture to use on a modern adaptive system. The main motivation of this dissertation is to propose an architecture for user modelling that is able to incorporate separate modules required to create a scalable intelligence system with user modelling techniques. The modules cooperate in order to analyse users and characterize their behaviour, using that information to provide a customized system experience that will increase not only the usability of the system but also the user’s productivity and knowledge. The proposed architecture is composed by three components: a user information unit, a mathematical structure able to classify users and the technique to use when adapting content. The user information unit is responsible for knowing the several types of individuals that can use the system, for capturing every part of relevant interaction between itself and its users and also contains the database which stores that information. The mathematical structure is the user classifier and is in charge of analysing the users and classifying them into one of three roles: beginner, intermediate or expert. Both Bayesian and Artificial Neural Networks are used, and an explanation on how to prepare and train them to deal with user information is provided. With the user role defined, a proper technique to adapt system’s content is required. In this work, a Mixed-Initiative approach is detailed which is based on allowing both the user and the system to gain control in the communication between them. The proposed architecture was developed as part of the ADSyS project. ADSyS is a Dynamic Scheduling system to solve scheduling problems subject to dynamic events. It has a high complexity even for frequent users, hence the need for the adaptation of its content to increase its usability. In order to evaluate the contribution of this work, a computational study of the user recognition was developed, as well as two usability evaluation sessions with distinct users. It was possible to conclude about the benefits of employing user modelling techniques with the proposed architecture

Some Research Questions and Results of UC3M in the E-Madrid Excellence Network

32 slides.-- Contributed to: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010.-- Presented by C. Delgado Kloos.Proceedings of: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010Universidad Carlos III de Madrid is one of the six main participating institutions in the eMadrid excellence network, as well as its coordinating partner. In this paper, the network is presented together with some of the main research lines carried out by UC3M. The remaining papers in this session present the work carried out by the other five universities in the consortium.The Excellence Network eMadrid, “Investigación y Desarrollo de Tecnologías para el e-Learning en la Comunidad de Madrid” is being funded by the Madrid Regional Government under grant No. S2009/TIC-1650. In addition, we acknowledge funding from the following research projects: iCoper: “Interoperable Content for Performance in a Competency-driven Society” (eContentPlus Best Practice Network No. ECP-2007-EDU-417007), Learn3: Hacia el Aprendizaje en la 3ª Fase (“Plan Nacional de I+D+I” TIN2008-05163/ TSI), Flexo: “Desarrollo de aprendizaje adaptativo y accesible en sistemas de código abierto” (AVANZA I+D, TSI-020301- 2008-19), España Virtual (CDTI, Ingenio 2010, CENIT, Deimos Space), SOLITE (CYTED 508AC0341), and “Integración vertical de servicios telemáticos de apoyo al aprendizaje en entornos residenciales” (Programa de creación y consolidación de grupos de investigación de la Universidad Carlos III de Madrid).Publicad