Knowledge Discovery Through Time Series Applied to Students' Grades
Resumo
Assessment is a constant activity in education, in the school system, and the teaching-learning process. The traditional approach classifies the students learning level through grades. This paper shows an application of knowledge discovery and data mining through classification and clustering via time series modeling on students' grades from high school. We collected historical data from an institute of technology, from this, we created models that can be used to extract patterns to help teachers to understand the profile of the students and provide early warns about possible poor results.
Palavras-chave:
Technical Courses, Data mining, Feature Extractions
Referências
Abu Tair, M. M. and El-Halees, A. M. (2012). Mining educational data to improve students’ performance: a case study. Mining educational data to improve students’ performance: a case study, 2(2).
Assunção, J., Fernandes, P., Lopes, L., and Normey, S. (2014). A dimensionality reduction process to forecast events through stochastic models. In The 26th International Conference on Software Engineering & Knowledge Engineering, SEKE 2014, pages 534–539. ISBN 13: 978-1-891706-35-7.
de Paula Santos, F., Lechugo, C. P., and Silveira-Mackenzie, I. F. (2016). “speak well” or “complain” about your teacher: A contribution of education data mining in the evaluation of teaching practices. In 2016 International Symposium on Computers in Education (SIIE), pages 1–4.
Keogh, E., Wei, L., Xi, X., Lee, S.-H., and Vlachos, M. (2006). Lb keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In Proceedings of the 32nd international conference on Very large data bases, VLDB ’06, pages 882–893. VLDB Endowment.
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, DMKD ’03, pages 2–11, New York, NY, USA. ACM.
Luckesi, C. C. (2014). Avaliação da aprendizagem escolar: estudos e proposições. Cortez editora
Osmanbegovic, E. and Suljié, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1):3–12.
Othman, E. H., Abdelali, S., and Jaber, E. B. (2016). Education data mining: Mining moocs videos using metadata based approach. In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pages 531–534.
Richmond, G., Salazar, M. d. C., and Jones, N. (2019). Assessment and the future of teacher education.
Assunção, J., Fernandes, P., Lopes, L., and Normey, S. (2014). A dimensionality reduction process to forecast events through stochastic models. In The 26th International Conference on Software Engineering & Knowledge Engineering, SEKE 2014, pages 534–539. ISBN 13: 978-1-891706-35-7.
de Paula Santos, F., Lechugo, C. P., and Silveira-Mackenzie, I. F. (2016). “speak well” or “complain” about your teacher: A contribution of education data mining in the evaluation of teaching practices. In 2016 International Symposium on Computers in Education (SIIE), pages 1–4.
Keogh, E., Wei, L., Xi, X., Lee, S.-H., and Vlachos, M. (2006). Lb keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In Proceedings of the 32nd international conference on Very large data bases, VLDB ’06, pages 882–893. VLDB Endowment.
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, DMKD ’03, pages 2–11, New York, NY, USA. ACM.
Luckesi, C. C. (2014). Avaliação da aprendizagem escolar: estudos e proposições. Cortez editora
Osmanbegovic, E. and Suljié, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1):3–12.
Othman, E. H., Abdelali, S., and Jaber, E. B. (2016). Education data mining: Mining moocs videos using metadata based approach. In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pages 531–534.
Richmond, G., Salazar, M. d. C., and Jones, N. (2019). Assessment and the future of teacher education.
Publicado
30/06/2020
Como Citar
ASSUNÇÃO, Joaquim; OLIVEIRA, Fernando; CORREA, Claiton.
Knowledge Discovery Through Time Series Applied to Students' Grades. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 7. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 93-100.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2020.11073.
