Forecasting Graduation By Data Mining Techniques
Article Sidebar
Main Article Content
Abstract
This study intends to: 1) investigate the selection of significant features for data analysis; 2) create a predictive model for students' academic success in Pibulsongkram Rajabhat University's Faculty of Science and Technology; and 3) use data mining techniques to evaluate the model's efficacy. The 1, 082 records with 30 features that made up the data evaluated in this study were obtained from Rajabhat Pibulsongkram University's educational services department during the academic years 2560-2562. The study used information gain, and Chi Squared as feature selection methods in the analysis to determine the elements influencing academic achievement. According to certain parameters, each strategy entailed lowering variables with low weights. There were fifteen sets of data in the dataset. The data were split into two categories for model creation: training data (80%) and test data (20%). The model's performance was assessed by 10-Fold Cross Validation using data mining techniques, specifically Decision Tree, Random Forest, and NaÏve Bayes. Accuracy and F1-Score were the evaluation criteria. According to experimental results, the Random Forest model performed with the best overall accuracy when Information Gain values from dataset IG5 were matched with it.
The accuracy was 96.03%, and the average F1-Score was 88.65%.
Article Details
References
SC., Vision Mission Philosophy. Available Online at http://202.29.80.54/vision/, accessed on 05 April 2023.
Office of Educational Quality Assurance, Educational quality assurance system. Available Online at https://qa.chandra.ac.th/index.php, accessed on 15 June 2023.
T. Thongthammachart. "The Feature Selection to Creating Models for Predicting Learning Achievement using Data Mining Techniques." Report following the 4th national academic conference Kamphaeng Phet Rajabhat University, pp. 338-347, 2017.
J. Jareanying. The Prediction of Student Performance Using Data Mining Techniques with RapidMiner. Master's Thesis, Information Technology Program Faculty of Science Srinakharinwirot University, 2020.
S. Sinsomboon. Data Mining. 1st ed., Bangkok: Jamjuree Product, 2015.
P. N. wichian, P. Manair, Y. Chuchuen, and S. Mak-on. "Optimization Feature Selection for Classification of Manuscript Grouping." Journal of Science and Technology Songkla University, Vol. 1, No. 1, January-June, 2020.
S. Euawatthanamongkol. Data Mining. 2nd ed., Bangkok: National Institute of Development Administration, (n.d.), 2019.
N. Hongboonmee and P. Trepanichkul. "Comparison of Data Classification Efficiency to Analyze Risk Factors that Affect the Occurrence of Hyperthyroidusing Data Mining Techniques." Journal of Information Science and Technology, Vol. 9, No. 1, pp. 41-51, January-June, 2019.
K. Satangmongkol, K-Fold Cross Validation. Available Online at https://datarockie.com/blog/k-fold-cross-validation/, accessed on 10 June 2023.
P. Rawengwan and P. Seresangtakul. "A model for forecasting educational status of students." Proceedings of the Graduate Research Presentation Conference National and International Levels Khon Kaen University, Vol. 10, pp. 273-283, March, 2017.
T. Klaythong and C. Srisawat. "Forecasting Dropout of Undergraduates Pibulsongkram Rajabhat University with Data Mining Technique." Journal of Applied Informatics and Technology, Vol. 5, No. 1, pp. 1-17, January-June, 2023.
S. Sittichat. "Study of Educational Attributes Using Data Mining Technique." Information Technology Journal, Vol. 13, No. 2, pp. 20-28, July-December, 2017.
N. Janchum and C. Cheewaviriyanon. "Using Data Mining Techniques to Develop a Model for Scratch Programming Assessment." Information Technology Journal, Vol. 18, No. 1, pp. 96-105, January-June, 2022.
S. Vanont, T. Areerat, and C. Saenrat. "A Study of Techniques in Predicting Career Counseling for Undergraduate Students of the Computer Program by Using Data Mining Technique." Journal of Technology Management Rajabhat Maha Sarakham University, Vol. 5, No. 1, January-June, 2018.
REG PSRU, Measurement of educational evaluation. Available Online at https://reg.psru.ac.th/reg2018/student.php, accessed on 20 June 2023.
W. Jaidee and N. Wannapee. "The Study of Factors Affecting for On-time Graduation of Ungraduated Student Using Feature Selection Technique on Imbalanced Datasets." Journal of Information Science and Technology, Vol. 10, No. 1, pp. 75-84, January-June, 2020.
A. Montaphan. "Comparison of Feature Selection Methods to Improve Breast Cancer Prediction." Royal Thai Air Force Medical Gazette, Vol. 65, No. 2, pp. 49-56, May-August, 2019.
A. Phutthala and S. Saensri. "The Searching Relationship of Results High School and Bachelor in Case Study: The Graduate Student in Year 2017 at KU.CSC." The 8th Asia Undergraduate Conference on Computing (AUCC 2020), pp. 277-285, 2023.
D. Hunthong, T. Ngernwilai, and S. Sinsomboonthong. "Efficiency Comparison inReplace Missing Value Using Regression Imputation, Multiple Imputation and Expectation Maximizationfor Classification in Data Mining." Thai Journal of Science and Technology, Vol. 9, No. 5, pp. 575-588, September-October, 2020.