Data Mining Practical Machine Learning Tools and Techniques

User modeling and user adapted interaction

In this paper, we describe our experimentations in answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has different precision/recall requirements. We present our experiments for both purposes and argue that formulations of better linguistic-quality are beneficial for both answer extraction and HCI.

Advances in Artificial Intelligence

MoodleREC: A recommendation system for creating courses using the moodle e-learning platform

The field of adaptive e-learning is continuously developing. More research is being conducted in this area as adaptive e-learning aims to provide learners with adaptive learning paths and content, according to their individual characteristics and needs, which makes e-learning more efficient and effective. The learner model, which is a representation of different learner’s characteristics, plays a key role in this adaptation. This paper presents a systematic literature review about learner modelling during the last 5 years, describing the different modelled characteristics and the adopted modelling techniques and modeling types: automatic modeling and collaborative modeling. 107 publications were selected and analyzed, and six categories of the modelled characteristics were identified. This literature review contributes to the identification of the learners’ individual traits and presents the most used modelling techniques for each of them. It also identifies the latest research trends of Learner Modeling and generates future research directions in this field.

Learner modelling: systematic review of the literature from the last 5 years

Multimedia capture and context awareness are two relevant subjects in ubiquitous computing research. While the former results in large amounts of digital media (e.g., video, audio, slides, text comments) being automatically produced, the latter offers the proper means to integrate and access such content. By merging the two concepts, users may benefit from tools that allow them to retrieve, view and interact with the captured media artifacts. In this paper, we present a contextual access architecture for personalization and recommendation of interactive multimedia content captured in an instrumented ed- ucational environment. Our approach takes into account context information, user preferences and presentation constraints in order to personalize the access experience, tailoring it to users' needs.

Personalization of Interactive Digital Media in Ubiquitous Educational Environments

Dynamic adaptation of educational content has been an important research topic. Therefore, in order for it to run effectively, student models that properly describe and monitor the cognitive state of students are needed. In this sense, this paper presents a hybrid student model approach that combines ontologies and Bayesian Networks to identify the knowledge of each student based on their characteristics and behavior while using an Adaptive Educational System. Experiments were performed with real student participants in a higher education course using an experimental prototype developed to verify the viability of the approach, which showed satisfactory results.

An Automatic and Dynamic Student Modeling Approach for Adaptive and Intelligent Educational Systems Using Ontologies and Bayesian Networks

Adaptive and Intelligent Educational Systems are interesting resources for supporting teaching-learning activities. Such environments make use of intelligent techniques to adapt educational content to the real needs of students. With the increasing amount of educational content becoming available, there is a good reason to believe that smart data analysis and machine learning techniques will become indispensable ingredients for educational progress. Therefore, this work proposes an approach for automatic and dynamic analysis of learning objects repositories in which an ontology models the relations between learning objects attributes and learning styles. Promising results have been obtained, and they are presented in this work.

An Approach for Automatic and Dynamic Analysis of Learning Objects Repositories through Ontologies and Data Mining Techniques for Supporting Personalized Recommendation of Content in Adaptive and Intelligent Educational Systems

Capture and access applications are developed to support the recording and later recall of multimedia information. The capture phase demands complex mechanisms for the acquisition and synchronization of user-generated data, while the access phase requires infrastructures that allow the personalization of the captured content. In this paper, we present a ubiquitous computing platform for the capture and access of educational activities in an instrumented classroom. Our approach uses context information in order to personalize the access experience according to users preferences. Transparent communication mechanisms complement our proposal by supporting data transfer and synchronization among associated storage services.

Classroom Experience: A Platform for Multimedia Capture and Access in Instrumented Educational Environments

In Smart Learning Environments, students need to be aware of their academic performance so they can self-regulate their learning process. Likewise, the teaching process can also be improved if instructors are able to supervise the progress of students, both individually and globally, and anticipate proper pedagogical strategies. Thus, effective Student Models, capable of identifying and predicting the level of knowledge of students, are a key requirement in modern educational systems. In this article, we revisit OSM-V, an Open Student Model with Information Visualization capabilities that allow students and instructors to assess performance-related information in educational systems. We detail its architecture and how it was integrated into Classroom eXperience, a Smart Learning Environment with multimedia capture capabilities. We also present extended results from experiments that evaluate both the perception of utility and behavioral changes in students who used OSM-V, showing that it can positively impact students’ learning and positively influence their study habits.

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Hiran Ferreira

Papers

Personalization of Interactive Digital Media in Ubiquitous Educational Environments

An Automatic and Dynamic Student Modeling Approach for Adaptive and Intelligent Educational Systems Using Ontologies and Bayesian Networks

An Approach for Automatic and Dynamic Analysis of Learning Objects Repositories through Ontologies and Data Mining Techniques for Supporting Personalized Recommendation of Content in Adaptive and Intelligent Educational Systems

Classroom Experience: A Platform for Multimedia Capture and Access in Instrumented Educational Environments

Technology-enhanced assessment visualization for smart learning environments