Comparative Study of Techniques Used in Prediction of Student Performance
Languages of publication
Providing high quality education is a major concern for higher educational institutions. The quality of education in higher institutions can be assessed by the teaching and learning process. The quality of the teaching learning process depends on the performance of instructor as well as performance of students involved. Analysis and prediction of student performance is key step to identify the poor academic performance. On the basis of prediction, the corrective actions must be taken to improve performance of students and enhance the quality of education system. In this study we surveyed the techniques commonly used to predict the performance of students and also analysed the factors affecting the student academic performance.
-  Goga, M., Kuyoro, S. and Goga, N., A recommender for improving the student academic performance. Procedia - Social and Behavioral Sciences, 180, pp. 1481-1488, (2015).
-  Altujjar, Y., Altamimi, W., Al-Turaiki, I. and Al-Razgan, M., Predicting Critical Courses Affecting Students Performance: A Case Study. Procedia Computer Science, 82, pp. 65-71, (2016).
-  Gokmen, G., Akinci, T.Ç., Tektaş, M., Onat, N., Kocyigit, G. and Tektaş, N., Evaluation of student performance in laboratory applications using fuzzy logic. Procedia - Social and Behavioral Sciences, 2(2), pp. 902-909, (2010).
-  Badr, G., Algobail, A., Almutairi, H. and Almutery, M., Predicting students’ performance in university courses: a case study and tool in KSU mathematics department. Procedia Computer Science, 82, pp. 80-89, (2016).
-  Patel, S., Sajja, P. and Patel, A., Fuzzy logic based expert system for students performance evaluation in data grid environment. International Journal of Scientific & Engineering Research 5(1), (2014).
-  Shahiri, A.M. and Husain, W., A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, pp. 414-422, (2015).
-  Yadav, R.S. and Singh, V.P., Modeling academic performance evaluation using soft computing techniques: A fuzzy logic approach. International Journal on Computer Science and Engineering, 3(2), pp. 676-686, (2011).
-  Chauhan, M., Rastogi, A. and Kapoor, U., Fuzzy Based Prediction of Student’s Performance in University Exam: A Case Study, International Journal of Research in Engineering, IT and Social Sciences, Volume 08, Special Issue, Page 138-145, (2018).
-  Sakthivel, E., Kannan, K.S. and Arumugam, S., Optimized evaluation of students performances using fuzzy logic. International Journal of Scientific & Engineering Research, 4(9), pp. 1128-1133, (2013).
-  Hamsa, H., Indiradevi, S. and Kizhakketthottam, J. J., Student Academic Performance prediction model using decision tree and fuzzy genetic algorithm, Procedia Technology, Vol. 25, 326-332, (2016).
-  Alfiani, A.P. and Wulandari, F.A., Mapping student's performance based on data mining approach (a case study). Agriculture and Agricultural Science Procedia, 3, pp. 173-177, (2015).
-  Kabra, R.R. and Bichkar, R.S., Performance prediction of engineering students using decision trees. International Journal of Computer Applications, 36(11), pp. 8-12, (2011).
-  Oyelade, O.J., Oladipupo, O.O. and Obagbuwa, I.C., Application of k Means Clustering algorithm for prediction of Students Academic Performance. International Journal of Computer Science and Information Security, Vol. 7, No. 1, 292-295, 2010 arXiv preprint arXiv:1002.2425 (2010).
-  Baradwaj, B.K. and Pal, S., Mining educational data to analyze students' performance. International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 63-69, 2011 arXiv preprint arXiv:1201.3417
-  Altaher, A. and BaRukab, O., Prediction of Student's Academic Performance Based on Adaptive Neuro-Fuzzy Inference. International Journal of Computer Science and Network Security 17(1), p. 165, (2017).
-  Ibrahim, Z. and Rusli, D., Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In 21st Annual SAS Malaysia Forum, 5th September, (2007).
-  Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S. and Wani, E., Prediction of student academic performance by an application of data mining techniques. In International Conference on Management and Artificial Intelligence IPEDR Vol. 6, No. 1, Pp. 110-114, (2011).
-  Borkar, S. and Rajeswari, K., Predicting students academic performance using education data mining. International Journal of Computer Science and Mobile Computing, 2(7), pp. 273-279, (2013).
Publication order reference