Educational Data Mining to Support the Teacher's Decision-Making Process
Abstract
This paper presents an approach to identify which factors are most relevant in e-learning student’s performance. So, we use classification algorithms on study of relevant factors in the sample. The results showed that the score of student’s participation and good performance in your tasks influence the positive student’s results in the course.
References
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Baker, R.S.J. (2011) Data Mining for Education. International Encyclopedia of Education, 3rd ed., edited by B. McGaw, P. Peterson, and E. Baker. Oxford, UK: Elsevier.
Chikalov, I. (2011). Average Time Complexity of Decision Trees. Springer. Disponivel em: [link]. ISBN 978-3-642-22660-1.
Corrigan, J. A. (2012). The implementation of e-tutoring in secondary schools: A diffusion study. Computers & Education. 59 (3) pages 925–936. Elsevier.
Russell, S., Norvig, P. (2004) Artificial Intelligence – A Modern Approach, Prentice-Hall, 2a Edição.
Baker, R.S.J. (2011) Data Mining for Education. International Encyclopedia of Education, 3rd ed., edited by B. McGaw, P. Peterson, and E. Baker. Oxford, UK: Elsevier.
Chikalov, I. (2011). Average Time Complexity of Decision Trees. Springer. Disponivel em: [link]. ISBN 978-3-642-22660-1.
Corrigan, J. A. (2012). The implementation of e-tutoring in secondary schools: A diffusion study. Computers & Education. 59 (3) pages 925–936. Elsevier.
Russell, S., Norvig, P. (2004) Artificial Intelligence – A Modern Approach, Prentice-Hall, 2a Edição.
Published
2013-07-23
How to Cite
MORAIS, Alana M.; FECHINE, Joseana.
Educational Data Mining to Support the Teacher's Decision-Making Process. In: WORKSHOP ON COMPUTING EDUCATION (WEI), 21. , 2013, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 478-483.
ISSN 2595-6175.
