IJBTT

Predictive Models for Dropout Rates Affected by COVID-19 Using Classification and Feature Selection Techniques

Predictive Models for Dropout Rates Affected by COVID-19 Using Classification and Feature Selection Techniques
	© 2023 by IJETT Journal
	Volume-71 Issue-7
	Year of Publication : 2023
	Author : Patchara Nasa-Ngium, Wongpanya S. Nuankaew, Kanakarn Phanniphong, Phaisarn Jeefoo, Pratya Nuankaew
	DOI : 10.14445/22315381/IJETT-V71I7P233

How to Cite?

Patchara Nasa-Ngium, Wongpanya S. Nuankaew, Kanakarn Phanniphong, Phaisarn Jeefoo, Pratya Nuankaew, "Predictive Models for Dropout Rates Affected by COVID-19 Using Classification and Feature Selection Techniques," International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 349-356, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I7P233

Abstract
The COVID-19 outbreak in Thailand has severely affected students in higher education. This research, therefore, had three significant objectives 1) to develop a model to predict the dropout rate and graduation rate of students in tertiary education, 2) to assess the effectiveness of the prediction model, and 3) to determine the contributing factors that affect the dropout rate and the graduation rate of students in higher education. This research highlighted the students’ academic achievement in the Bachelor of Business Administration Program in Management program at the Faculty of Business Administration and Information Technology at the Rajamangala University of Technology Tawan-ok: Chakrabongse Bhuvanarth Campus, Bangkok, Thailand. The data collected was 547 students in this educational program during the academic year 2015 – 2022. It contains 14,402 transactions. The research tool used educational data mining development and applied machine learning techniques for factor analysis. The research results found that the COVID-19 pandemic had significant implications for the reduced graduation of students in the Business Administration Program in Management programs. It was also found that factors related to 21st-century skills influenced the student’s termination.

Keywords
Academic achievement model, COVID-19, Dropout analysis, Educational data mining, Feature selection.

References
[1] Laura Calvet Liñán, and Ángel Alejandro Juan Pérez, “Educational Data Mining and Learning Analytics: Differences, Similarities, and Time Evolution,” RUSC, Universities and Knowledge Society Journal, vol. 12, no. 3, pp. 98-112, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Surjeet Kumar Yadav, and Saurabh Pal, “Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification,” Machine Learning, arXiv, vol. 2, no. 2, pp. 51-56, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Pavlo D. Antonenko, Serkan Toy, and Dale S. Niederhauser, “Using Cluster Analysis for Data Mining in Educational Technology Research,” Educational Technology Research and Development, vol. 60, no. 3, pp. 383–398, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[4] John S Kinnebrew, and Biswas Gautam, “Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution,” International Educational Data Mining Society, 2012.
[Google Scholar] [Publisher Link]
[5] Hogyeong Jeong, and Gautam Biswas, “Mining Student Behavior Models in Learning-by Teaching Environments,” Educational Data Mining, pp. 127–136, 2008.
[Google Scholar] [Publisher Link]
[6] Camilo Palazuelos, Diego García-Saiz, and Marta Zorrilla, “Social Network Analysis and Data Mining: An Application to the E-Learning Context,” Computational Collective Intelligence, Technologies and Applications, pp. 651–660, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Putu Eka Juliana Jaya, “Increasing Learning Achievement and Student Participation through Independent Learning Design,” SSRG International Journal of Economics and Management Studies, vol. 8, no. 10, pp. 50-56, 2021.
[CrossRef] [Publisher Link]
[8] Pratya Nuankaew, “Dropout Situation of Business Computer Students, University of Phayao,” International Journal of Emerging Technologies in Learning, vol. 14, no. 19, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Concepción Burgos, “Data Mining for Modeling Students’ Performance: A Tutoring Action Plan to Prevent Academic Dropout,” Computers & Electrical Engineering, vol. 66, pp. 541–556, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Marzieh Karimi-Haghighi, Carlos Castillo, and Davinia Hernández-Leo, “A Causal Inference Study on the Effects of First Year Workload on the Dropout Rate of Undergraduates,” Artificial Intelligence in Education, pp. 15–27, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Pratya Nuankaew et al., “Analysis Dropout Situation of Business Computer Students at University of Phayao,” The Impact of the 4th Industrial Revolution on Engineering Education, pp. 419–432, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Sittichai Bussaman et al., “Prediction Models of Learning Strategies and Learning Achievement for Lifelong Learning,” IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering, pp. 192–197, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wongpanya Nuankaew, and Pratya Nuankaew, “Educational Engineering for Models of Academic Success in Thai Universities during the COVID-19 Pandemic: Learning Strategies for Lifelong Learning,” International Journal of Engineering Pedagogy, vol. 11, no. 4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Pranav Dabhade et al., “Educational Data Mining for Predicting Students’ Academic Performance using Machine Learning Algorithms,” Materials Today: Proceedings, vol. 47, pp. 5260–5267, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Paniah Emeko Kodzo, “The Determinants of Educational Performance in Togo,” SSRG International Journal of Economics and Management Studies, vol. 9, no. 11, pp. 6-16, 2022.
[CrossRef] [Publisher Link]
[16] Diego A. Forero et al., “Global Perspectives and Recommendations for Curriculum Design in Academic Programs in the Health Sciences,” Educación Médica, vol. 23, no. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Wongpanya Nuankaew, and Pratya Nuankaew, “The Study of the Factors and Development of Educational Model: The Relationship between the Learner Context and the Curriculum Context in Higher Education,” International Journal of Emerging Technologies in Learning, vol. 14, no. 21, pp. 205–226, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fuseini Inusah et al., “Data Mining and Visualisation of Basic Educational Resources for Quality Education,” International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 296-307, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Rasha M. Abdelrahman, “Metacognitive Awareness and Academic Motivation and their Impact on Academic Achievement of Ajman University Students,” Heliyon, vol. 6, no. 9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Pratya Nuankaew et al., “Perception and Attitude toward Self-Regulated Learning in Educational Data Mining,” 6th International Conference on Technical Education, pp. 1–5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Elham Hussein et al., “Exploring Undergraduate Students’ Attitudes towards Emergency Online Learning during COVID-19: A Case from the UAE,” Children and Youth Services Review, vol. 119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Myunghee Kang et al., “Developing an Educational Performance Indicator for New Millennium Learners,” Journal of Research on Technology in Education, vol. 43, no. 2, pp. 157–170, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Kanakarn Phanniphong et al., “Clustering of Learners Performance based on Learning Outcomes for Finding Significant Courses,” Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, pp. 192–196, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Élise Lavoué et al., “Analyzing the Relationships between Learners’ Motivation and Observable Engaged Behaviors in a Gamified Learning Environment,” International Journal of Human-Computer Studies, vol. 154, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Lilah F. Morris-Wiseman, and Valentine N. Nfonsam, “Early Detection and Remediation of Problem Learners,” Surgical Clinics of North America, vol. 101, no. 4, pp. 611–624, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Suman, “Analysis on the Mis in Primary Schools,” International Journal of Computer and Organization Trends, vol. 11, no. 1, pp. 10-14, 2021.
[Publisher Link]
[27] Gang Chen, and Jin Chen, “A Novel Wrapper Method for Feature Selection and its Applications,” Neurocomputing, vol. 159, pp. 219–226, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Gazi Mahabubul Alam, “Does Online Technology Provide Sustainable HE or Aggravate Diploma Disease? Evidence from Bangladesh—a Comparison of Conditions before and during COVID-19,” Technology in Society, vol. 66, pp. 1–10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] B.Ajay Babu, and K. Nimala, “A Study on Techniques for Online Feature Choice Method,” SSRG International Journal of Computer Science and Engineering, vol. 2, no. 3, pp. 1-5, 2015.
[CrossRef] [Publisher Link]
[30] Cristina Hava Muntean, “Improving Learner Quality of Experience by Content Adaptation Based on Network Conditions,” Computers in Human Behavior, vol. 24, no. 4, pp. 1452–1472, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Mitali Sonowal, and Mun Kalita, “Metacognitive Awareness and Academic Achievement of Higher Secondary Level Arts Stream Students of Dibrugarh District, Assam,” SSRG International Journal of Humanities and Social Science, vol. 6, no. 4, pp. 17-23, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Marina Segura, Jorge Mello, and Adolfo Hernández, “Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?,” Mathematics, vol. 10, no. 18, p. 3359, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Joana R. Casanova et al., “Dimensionality and Reliability of a Screening Instrument for Students At-Risk of Dropping out From Higher Education,” Studies in Educational Evaluation, vol. 68, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Sinsuda Dechsupa, and Suvichada Assawakosri, “Positive Impact of Lockdown on COVID-19 Outbreak in Thailand,” Travel Medicine and Infectious Disease, vol. 36, 2020.
[CrossRef] [Google Scholar] [Publisher Link]