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Publications

Publications by CRACS

2020

Blockchain and Applications - 2nd International Congress, BLOCKCHAIN 2020, L'Aquila, Italy, 17-19 June, 2020

Authors
Prieto, J; Pinto, A; Das, AK; Ferretti, S;

Publication
BLOCKCHAIN

Abstract

2020

Blockchain and Applications - International Congress, BLOCKCHAIN 2019, Avila, Spain, 26-28 June, 2019

Authors
Prieto, J; Das, AK; Ferretti, S; Pinto, A; Corchado, JM;

Publication
BLOCKCHAIN

Abstract

2020

Revisiting Blockhain Use in Notary Services: An European Perspective

Authors
Pinto, A; Silva, J;

Publication
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

Preface

Authors
Prieto, J; Pinto, A; Das, AK; Ferretti, S;

Publication
Advances in Intelligent Systems and Computing

Abstract

2020

Preface

Authors
Prieto, J; Das, AK; Ferretti, S; Pinto, A; Corchado, JM;

Publication
Advances in Intelligent Systems and Computing

Abstract

2020

A Machine Learning Model to Early Detect Low Performing Students from LMS Logged Interactions

Authors
Cabral B.; Figueira Á.;

Publication
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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