Cover page and Table of Contents: PDF (size: 919KB)
Full Text (PDF, 919KB), PP.1-9
Views: 0 Downloads: 0
Machine Learning, Prediction, Clustering, Grade Data Patterns, Study Plan
This research aims to enhance the achievement of the students on their study plan. The problem of the students in the university is that some students cannot design the efficient study plan, and this can cause the failure of studying. Machine Learning techniques are very powerful technique, and they can be adopted to solve this problem. Therefore, we developed our techniques and analyzed data from 300 samples by obtaining their grades of students from subjects in the curriculum of Computer Science, Faculty of Science and Technology, Sakon Nakhon Rajabhat University. In this research, we deployed CGPA prediction models and K-means models on 3rd-year and 4th-year students. The results of the experiment show high performance of these models. 37 students as representative samples were classified for their clusters and were predicted for CGPA. After sample classification, samples can inspect all vectors in their clusters as feasible study plans for next semesters. Samples can select a study plan and predict to achieve their desired CGPA. The result shows that the samples have significant improvement in CGPA by applying self-adaptive learning according to selected study plan.
Nipaporn Chanamarn, Kreangsak Tamee, "Enhancing Efficient Study Plan for Student with Machine Learning Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.3, pp.1-9, 2017. DOI:10.5815/ijmecs.2017.03.01
M. S. Amirah., H.Wahidah, and A.R. Nuraini, “A Review on Predicting Student’s Performance using Data Mining Techniques”, Procedia Computer Science, vol. 72, 2015, pp. 414 – 422.
A. Shaterloo and G. Mohammadyari,"Students counselling and academic achievement", Procedia - Social and Behavioral Sciences, vol.30, 2011, pp.625-628.
H. Choi and Y.Kang, "Statistical Data Analysis and Prediction Model for Learning Assessment in Korean High Schools Based on EduData", International Journal of u- and e- Service, Science and Technology, vol.9, No. 1, 2016, pp.117-122.
A. K. Pal and S. Pal, " Classification Model of Prediction for Placement of Students", International Journal of Modern Education and Computer Science, vol. 5, No.11, 2013, pp. 49-56
A. Mueen, B. Zafar, and U.Manzoor, “Modeling and Predicting Sudents’ Academic Performance Using Data Mining Techniques”, International Journal of Modern Education and Computer Science, vol.8, no.11, 2016, pp. 36-42.
P. Flach, “Machine Learning: The Art and Science of Algorithms That Make Sense of Data”. Cambridge: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511973000, 2012.
G.Jianghua, et al., "Prediction of Power Generation in China Using Process Neural Network", International Journal of u- and e- Service, Science and Technology”, vol.8, no.5, 2015, pp.141-146.
P. Rahul, C. Pavan, and M. Abhishek, “Disease Prediction System using Data Mining Hybrid Approach Communications on Applied Electronics (CAE)”, Foundation of Computer Science (FCS), New York, USA, vol. 4, no.9, 2016, pp. 48-51.
T. T. El-Midany, M. A. El-Baz, and M. S. Abdelwahed, “Improve Characteristics of Manufactured Products Using Artificial Neural Network Performance Prediction Model”, International Journal of Recent advances in Mechanical Engineering (IJMECH), vol. 2, no. 4, 2013, pp. 23-34.
S. D. Snehal and V. R., “Agricultural Crop Yield Prediction Using Artificial Neural Network Approach”, International Journal of Innovative Research in Electrical, Electronic, Instrumentation and Control Engineering, vol. 2, Issue 1, 2014, pp. 683-686.
P. M. Arsad, N. Buniyamin, and J. A. Manan, "Neural Network Model to Predict Electrical Students’ Academic Performance", International Congress on Engineering Education (ICEED), Park Royal Penang Malaysia, 2012, Dec 5-7.
C. E. Moucary, M. Khair, and W. Zakhem, "Improving Student’s Performance Using Data Clustering and Neural Networks in Foreign-Language Based Higher Education", IJJ: The Research Bulletin of Jordan ACM, V o l . 2, No. 3, 2011, pp.27-34.
S. Huang, “Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course”, All Graduate Thesis and Dissertations, 2011.
S. Huang and N. Fang, “Predicting Student Academic performance in an Engineering dynamics course: A Comparison of Four types of Predictive Mathematical Models”, Computer & Education, vol. 61, 2013, pp.133-145.
B. Rahmani and H. Aprilianto, “Early Model of Student's Graduation Prediction Based on Neural Network”, TELKOMNIKA, vol.12, no.2, 2014, pp. 465-474.
U. B. Mat, N. Buniyamin and P. A. Arshad, “Educational Data Mining Classifier for Semester One Performance to Improve Engineering Students Achievement”, Middle-East Journal of Scientific Research, vol. 24, no. 2, 2016, pp. 338-346.
A. A. Mashael and A. Muna, “Predicting Students Final GPA Using Decision Trees: A Case Study”, International Journal of Information and Education Technology, vol. 6, no. 7, 2016, pp. 528-533.
A. Padmapriya, “Prediction of Higher Education Admissibility using Classification Algorithms”, International Journal of Advanced Research in Computer Science and Software Engineering, vol.2, no. 11, 2012, pp. 330-336.
O. C. Asogwa and A. V. Oladugba, “Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs)”, American Journal of Applied Mathematics and Statistics, vol. 3, no. 4, 2015, pp.151-155.
K. Adhatrao, et al., “Predicting Students’ Performance using ID3 and C4.5 Classification Algorithm”, International Journal of Data Mining & Knowledge Management Process., vol. 3, no. 5, 2013, pp. 39-51.
P. A. Kumar and S. Pal, “Analysis and Mining of Educational Data for Predicting the Performance of Students”, International Journal of Electronics Communication and Computer Engineering, vol. 4, Issue 5, 2013, pp. 1560-1565.
T. Mishra, D. Kumar, and S. Gupta, “Mining Students’ Data for Performance Prediction”, Fourth International Conference on Advanced Computing & Communication Technologies, 2014, pp. 255-262.
M. Pandey and S. Taruna, “A Multi-level Classification Model Pertaining to The Student's Academic Performance Prediction”, International Journal of Advances in Engineering & Technology, vol. 7, Issue 4, 2014, pp. 1329-1341.
S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Predicting Students’ Performance in Distance learning using Machince Learning Techniques”, Applied Artificial Intelligence, vol. 5, 2010, pp. 411-426.
P.M. Arsad, N. Buniyamin and J.-L.A. Manan, "Neural Network Model to Predict Electrical Students’ Academic Performance", IEEE International Conference, 2012, pp. 1-5.
P.M. Arsad, N. Buniyamin and J.-L.A. Manan, "Prediction of Engineering Students’ Academic Performance Using Artificial Neural Network and Linear Regression: A Comparison", IEEE 5th Conference on Engineering Education (ICEED), 2013, pp. 43-48.
A.Tekin, "Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach", Eurasian Journal of Educational Research. Issue 54, 2014, pp. 207-226.
O. J. Oyelade , O. O. Oladipupo, and I. C. Obagbuwa, "Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance", International Journal of Computer Science and Information Security, Vol. 7, No. 1, 2010, pp. 292-295.
N. A. M. Nasir, N. S. Rasid, and N. Ahmad, "Grouping Students Academic Performance Using One-Way Clustering", International journal of Science Commerce and Humanities, Vol.2, No. 3, 2014, pp. 131-138.
MATLAB, “MATLAB Environment”, from http://www.mathworks.com/products/matlab/, 2016.