scholarly journals Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

2017 ◽  
Vol 25 (02) ◽  
pp. 36
Author(s):  
Marco Aurélio Silva Cruz ◽  
Julio Cesar Duarte ◽  
Ronaldo Ribeiro Goldschmidt
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Learning Environment ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Machine Learning Techniques ◽  
Keystroke Dynamics ◽  
Learning Techniques

The authentication of users on a Virtual Learning Environment (VLE) is, in general, punctual and intrusive, occurring when the user connects to the environment, by typing his password. Such approach allows, after the initial login, that unauthenticated users take the role of authenticated users and perform tasks in the environment, causing, among other things, distortions in the perception about the academic performance of students. The objective of this work is, thus, to propose a mechanism to execute periodic and non-intrusive authentications of users in VLEs. The proposed mechanism uses machine learning techniques to build recognition models based on the keystroke dynamics of users and it is also independent of the used VLE. A prototype of the proposed mechanism, integrated with Moodle, was implemented and applied to a postgraduate course with seventeen users. The recognition models generated by the prototype in the case study showed a performance above 92% of accuracy, which is a positive indication about the viability of the utilization of the proposed mechanism.

scholarly journals Estudo de Caso sobre o Uso das Ferramentas Notas e Relatórios do Moodle na Prática Pedagógica do Tutor a Distância em Cursos Brasileiros de Formação Continuada de Professores

EAD em FOCO ◽  
2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Thaís Tenório ◽  
Fabiana Aparecida Rodrigues ◽  
André Tenório
Keyword(s):  
Academic Performance ◽  
Learning Environment ◽  
Management System ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Pedagogical Practice ◽  
Main Difficulty ◽  
E Learning ◽  
On Line

O sistema Moodle de gerenciamento de ambientes virtuais de aprendizagem disponibiliza ferramentas, como relatórios e quadro de notas, destinadas ao acompanhamento da assiduidade e do progresso de alunos de cursos a distância. Entretanto, em geral, o emprego e as possibilidades dessas ferramentas são pouco discutidos. Foram investigadas, por meio de questionário on-line, as percepções de 88 tutores a distância atuantes em cursos de formação continuada de professores de dois estados brasileiros. Todos tinham ciência das ferramentas de relatórios e notas, mas alguns declararam não usá-las frequentemente. Os relatórios eram empregados, segundo os pesquisados, principalmente, para verificar a frequência de acesso dos cursistas ao ambiente virtual de aprendizagem e a realização das atividades propostas. A grande maioria indicou atribuir os graus de desempenho dos alunos com a ferramenta de notas e informou utilizá-la para comentar (feedback) as tarefas submetidas para avaliação e monitorar os desempenhos acadêmicos. Para alguns, a principal dificuldade no manuseio das ferramentas envolvia desconhecimento dos recursos. A atuação tutorial é indispensável para envolver os alunos nas atividades dos cursos. Paralelamente, para bem desempenhar suas funções, esses profissionais carecem de capacitação quanto às ferramentas disponíveis.Palavras-chave: Moodle, Relatórios, Notas, Tutor.  Case Study about the Use of Grade and Report Moodle Tools on the Pedagogical Practice of E learning Tutors of Brazilian Teacher-upgrading CoursesAbstractMoodle management system of virtual learning environment offers tools such as reports and grades, both used to verify attendance and progress of students in e learning courses. However, in general, employment and possibilities of these tools are little discussed. We investigated the perceptions of eighty eight tutors of teacher-upgrading e learning courses of two Brazilian states by on-line questionnaire. Tutors were aware of reports and grades tools, but some declared not using it. According to the subjects, reports were utilized to check the frequency of access of students to virtual learning environment and the realization of the proposed activities. Most of subjects affirmed use grades tool for assign scores achieved by students, give feedback of activities and check academic performance. Some respondents pointed out the little knowledge of tools as the main difficulty in handle it. The actions of tutors are essential to involve students in the activities of the courses. At the same time, to well perform their responsibilities, these professionals need of training about the available tools of virtual learning environment.Keywords: Moodle, Reports, Grades, Tutor.

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prone Area ◽  
Flood Prone Area

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

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