ARCHIVES

1. Abu Tair, M.M. and El-Halees, A.M. (2012). Mining educational data to improve students' performance: a case study. International
Journal of Information and Communication Technology Research., 2(2):140-146.
2. Adekitan, A.I. and Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result
using educational data mining. Heliyon., 5(2): e01250.
3. Bravo, J. and Ortigosa, A. (2009). Detecting Symptoms of Low Performance Using Production Rules. International working group on
educational data mining.,4(2):31-40.
4. Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers operations research.,
13(5):533-549.
5. Ramos, J.L.C., e Silva, R.E.D., Silva, J.C.S., Rodrigues, R.L. and Gomes, A.S. (2016). A comparative study between clustering methods
in educational data mining. IEEE Latin America Transactions., 14(8):3755-3761.
6. Urbina Nájera, A.B., De La Calleja, J. and Medina, M.A. (2017). Associating students and teachers for tutoring in higher education
using clustering and data mining. Computer Applications in Engineering Education., 25(5):823-832.