Rafael Dias Araujo

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

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.

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

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.

Interactive web interfaces modeling, simulation and analysis using Colored Petri Nets

Interaction modeling is a relevant activity during software development processes. Created relying on Petri Nets theory and aiming to represent discrete time events, Colored Petri Nets (CPNs) are a graphical formal language developed and widely employed for system modeling. While traditional CPNs only have elements with ordinary stylization and behaviors, in this article we explore the key ideas behind Web Interaction Modeling Using Colored Petri Nets (wiCPN), a modeling style developed with focus on representing Web interactions as an incremental improvement of CPNs. We review wiCPN’s refinements over CPNs and the modeling of the Web interface of Classroom eXperience (CX), a ubiquitous educational platform, thus verifying the model’s properties to ensure it was able to represent the different access levels among its users and how wiCPN displayed suitability to comprehend this requirement on the generated model. We have also improved the originally developed model with the modification of elements to make it finite and fully analyzable. Also, we added temporization capabilities to the model and ran corresponding user simulation to observe the average time that users with different roles tend to spend during interactions. We compared wiCPN results with Unified Modeling Language (UML) Activity and Use Case diagrams, observing, as outcomes, that the generated model represented CX’s interactive flow correctly and maintained a concise notation—a single wiCPN diagram was sufficient to depict the same interactive flow that, in UML, would require several diagrams, something that could overload the design team in actual software development scenarios. We also included new user experiments comprising qualitative results from experts. Finally, we created a reachability graph for the new model and generated a full state space report, analyzing Petri Nets properties such as boundedness, liveness and home marking.

Learning Objects Authoring Supported by Ubiquitous Learning Environments

Learning objects authoring is still a complex and time-consuming task for instructors, which requires attention to technical and pedagogical aspects. However, one can take advantage of the Ubiquitous Learning Environments characteristics to make it a mild process by means of automatic or semi-automatic processes. In this way, this paper presents an approach for creating learning objects and their metadata in such environments considering collaborative interactions among users. The proposed approach is being integrated to a real multimedia capture system used as a complementary tool in a university.

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

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 Automatic and Dynamic Knowledge Assessment Module for Adaptive Educational Systems

Adaptive Educational Systems (AES) make use of Artificial Intelligence techniques aiming at adapting themselves to the real needs of the student, and through such provide a personalized and individualized teaching. In order for this adaptation to be successful, it is important that the system knows the level of knowledge concerning the real cognitive state of the students. In this manner, this article presents an approach for predicting academic performance based on ontologies and Bayesian networks. A knowledge assessment module was developed and integrated to a Ubiquitous Learning Environment (ULE) in real and actual use. During three semesters, five graduate classes were analyzed with the intent of verifying the correlation between the grades generated by the module and the actual grades obtained by the students. It was noted that 80% of the analyzed samples obtained very high or substantial correlations. This indicates that the proposed module is adequate for identifying and predicting academic performance.