2020
Autores
Prieto, J; Pinto, A; Das, AK; Ferretti, S;
Publicação
BLOCKCHAIN
Abstract
2020
Autores
Prieto, J; Das, AK; Ferretti, S; Pinto, A; Corchado, JM;
Publicação
BLOCKCHAIN
Abstract
2020
Autores
Pinto, A; Silva, J;
Publicação
BLOCKCHAIN
Abstract
Notary services have long been identified as a recurrent example for dematerialisation through blockchain adoption, but has failed to become a world wide reality. The key issue being the distinct legal frameworks throughout the world. Europe in this context has a more restrictive legal context with regard to blockchain use. In this work, we briefly discuss the European role of the Notary, review the existing European solutions and identify related open issues that are not resolved in the existing solutions.
2020
Autores
Prieto, J; Pinto, A; Das, AK; Ferretti, S;
Publicação
Advances in Intelligent Systems and Computing
Abstract
2020
Autores
Prieto, J; Das, AK; Ferretti, S; Pinto, A; Corchado, JM;
Publicação
Advances in Intelligent Systems and Computing
Abstract
2020
Autores
Cabral B.; Figueira Á.;
Publicação
Learning and Analytics in Intelligent Systems
Abstract
Grade prediction has been for a long time a subject that interests both teachers and researchers. Before the digital age this type of predictions was something nearly impossible to achieve. With the increasing integration of Learning Management Systems in education, grade prediction seems to have become a viable option. The general adoption of this type of systems brings to the research area a database known as “registry”, or more simply known as logged data. Using this new source of information several attempts regarding the prediction of student grades have been proposed. The methodology proposed in this study is capable of, analyzing student online behavior, using the information collected by the Moodle system and making a prediction on what the final grade of the student will be, at any point in the semester. Our novel approach uses the gathered information to examine the academic path of the student in order to determine an interaction pattern, then it tries to establish a link with other, present or past, known successful paths. Making this comparison, the model can automatically determine if a student is going to fail or pass the course, which then would leave a space for the teacher or the student to circumvent the situation. Our results show that the system is not only viable, as it is also robust to make prediction at an early stage in the course.
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