Predicting Student Study Failure Risk Using Artificial Neural Network Method

Author(s): Mi Chunqiao*, Deng Qingyou, Peng Xiaoning, Lin Jing.

Journal Name: Recent Patents on Engineering

Volume 13 , Issue 4 , 2019

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Graphical Abstract:


Background: Study failure in a course is very complicated which is always affected by many different factors and is characterized by uncertainties. We reviewed previous literatures and patents relating to student failure prediction, while there seems to be no ideal method or tool which can easily learn the relationship between failure results and reasons.

Methods: In this study a method of artificial neural network was provided to predict student failure risk in course study. A three-layered network topology was used including input layer with nine neural nodes, hidden layer with ten optimized neural nodes, and output layer with one neural node, which is advantageous for dealing with this complicated and uncertain issue. The whole modeling process includes four stages: output inference, loss evaluation, weights and biases training, and model testing.

Results: In our sample data there are 577 students in total, including 433 train cases and 144 test cases, and for each sample, there are nine input dependent variables and one output target variable. All calculation and optimization results were implemented based on the TensorFlow and Python. The model accuracy measurements of relative root mean square error on the total, test and train data sets were 0.1637, 0.1596, and 0.1607 respectively, and consistency was shown by testing the predicted results with our observed data, which indicated that the method was promising for predicting student failure risk in course study.

Conclusion: It can be used to identify at-risk students with study difficulty and is of practical significance for educators to provide pedagogical supports and interventions in early time to the at-risk students to help them avoid academic failure, and it is also of theoretic significance to improve the whole efficiency of early warning education management.

Keywords: Study failure risk prediction, at-risk student, artificial neural network, uncertainties, failure prediction, network topology.

M.J. Sandeep, W.M. Erik, J.M.L. Eitel, J.R. Regan, and J.D. Baron, "Early alert of academically at-risk students: an open source analytics initiative", J. Learn. Anal., vol. 1, pp. 6-47, 2014.
B.B. Minaei, and W. Punch, "Using genetic algorithms for data mining optimization in an educational web-based system In ", Proceedings of Genetic and Evolutionary Computational Conference Chicago, Illinois, USA, 2003, pp. 2252-2263.
L.V. Morris, S. Wu, and C. Finnegan, "Predicting retention in online general education courses", Am. J. Dist. Edu., vol. 19, pp. 23-36, 2005.
J.P. Campbell, Utilizing student data within the course management system to determine undergraduate student academic success: an exploratory study. Doctoral dissertation, Purdue University, pp. 31-61, 2007.
L.P. Macfadyen, and S. Dawson, "Mining LMS data to develop an early warning system for educators: a proof of concept", Compu. Edu., vol. 54, pp. 588-599, 2010.
R.S. Baker, D. Lindrum, M.J. Lindrum, and D. Perkowski, "Analyzing early at-risk factors in higher education e-learning courses In ", Proceedings of the 8th International Conference on Educational Data Mining, 2015pp. 150-155
B. Jay, M. James, Z. Anne, L. Eitel, M.J. Sandeep, and B. Josh, "Using learning analytics to predict at-risk students in online graduate public affairs and administration education", J. Public Aff. Educ., vol. 21, pp. 247-262, 2015.
S.J.H. Yang, O.H.T. Lu, A.Y.Q. Huang, J.C.H. Huang, H. Ogata, and A.J.Q. Lin, "Predicting students’ academic performance using multiple linear regression and principal component analysis", J. Inform. Process., vol. 26, pp. 170-176, 2018.
G. Chen, C. Liu, K. Ou, and B.J. Liu, "Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology", J. Educ. Comput. Res., vol. 23, pp. 305-332, 2000.
Y. Ma, B. Liu, C.K. Wong, P.S. Yu, and S.M. Lee, "Targeting the right students using data mining In ", Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining Boston, Massachusetts, USA pp. 457-464, 2000.
J. Bravo, S. Sosnovsky, and A. Ortigosa, "Detecting symptoms of low performance using prediction rules In ", Proceedings of the 2nd Educational Data Mining Conference Universidad de Cordoba, Cordoba, Spain,, 2009, pp. 31-40.
S. Mack, L. Jaime, R. Huzefa, and J. Aditya, "Next-term student performance prediction: a recommender systems approach", Edu. Data Mining, vol. 8, pp. 22-51, 2016.
C. Mollica, and L. Petrella, "Bayesian binary quantile regression for the analysis of bachelor-to-master transition", J. Appl. Stat., vol. 44, pp. 2791-2812, 2017.
A.K. Hamoud, A.M. Humadi, W.A. Awadh, and A.S. Hashim, "Students’ success prediction based on bayes algorithms", Int. J. Comput. Appl., vol. 178, pp. 6-12, 2017.
C. Geng, L. Dandan, W. Haowen, and W. Guochang, "Analysis and prediction model of financial income in guangzhou", Stat. Appl., vol. 4, pp. 187-195, 2015.

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Article Details

Year: 2019
Page: [382 - 386]
Pages: 5
DOI: 10.2174/1872212112666180917115140
Price: $25

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