Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning

Authors

  • Pratya Nuankaew School of Information and Communication Technology, University of Phayao, Phayao, Thailand. https://orcid.org/0000-0002-3297-4198
  • Patchara Nasa-ngium Faculty of Science and Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand
  • Wongpanya Sararat Nuankaew Faculty of Information Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand

DOI:

https://doi.org/10.3991/ijim.v15i22.24069

Keywords:

Learning Analytics, Dropping Out, Educational Data mining, Eruptive Technology, Disruptive Technology

Abstract


The purpose of the research is to identify the risk of dropping out in tertiary students with an application. The components of the research goal aim (1) to develop the students’ achievement prediction model and (2) to construct a prototype application for the predictions of the tertiary students dropping out. The research tools consisted of three parts, (1) tool for developing predictive prototypes uses a tool called the CRISP-DM process with Decision Tree Classification, Feature Selection methods, Confusion Matrix performance, Cross-Validation methods, Accuracy, Precision and Recall measurements, (2) tool for application development used the SDLC with V-method, and (3) tool to assess application satisfaction used questionnaires and statistical analysis. Data sample were collected from 401 students enrolled in the Business Computer Program at the School of Information and Communication Technology, University of Phayao during the academic year 2012-2016. The results showed that the prediction model had a very high percentage of accuracy (82.29%). The prototype test results with the data gathered had a very high score level (84.04%; correct 337 out of 401 training examples). An overview of the underlying application with the utmost integrity by the researchers planned to put the application to the test in the first semester of the academic year 2021 at the School of Information Technology and Communication, University of Phayao. For future research, the researchers plan to create a mobile application for mentors in the University of Phayao to monitor learner on both Android and iOS systems.

Author Biographies

Pratya Nuankaew, School of Information and Communication Technology, University of Phayao, Phayao, Thailand.

Pratya Nuankaew received a B.Ed. Degree in Educational Technology in 2001, M.Sc. degree in Information Technology in 2008 from Naresuan University, and a Ph.D. degree in Computer Engineering in 2018 from Mae Fah Luang University. He is currently a lecturer at the School of Information and Communication Technology, University of Phayao, Phayao, 56000 Thailand. His research interests are in Digital Technologies, Educational Data Mining, Educational Engineering, Educational Technology, Informatics and Applications, Learning Analytics Modeling, Learning Strategies for Lifelong Learning, Learning Styles, Mentoring Relationships, Online Mentoring Model, Social Network Analysis, Ubiquitous Computing, and Ubiquitous Learning.

Patchara Nasa-ngium, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand

Patchara Nasa-Ngium received the B.S. degree in computer science from Rajabhat Maha Sarakham University, the M.S. degree and Ph.D. degree in computer science from Khon Kaen University, Thailand. He is also a Lecturer with the Department of Computer Science, Rajabhat Maha Sarakham University. His research interests include artificial intelligence, machine learning, evolutionary computing, and data mining.

Wongpanya Sararat Nuankaew, Faculty of Information Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand

Wongpanya Nuankaew received a B.Sc. degree in Computer Science in 2004, and M.Sc. degree in Information Technology in 2007 from Naresuan University. She is currently a lecturer at the Faculty of Information Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand. 

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Published

2021-11-19

How to Cite

Nuankaew, P., Nasa-ngium, P., & Nuankaew, W. S. (2021). Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning. International Journal of Interactive Mobile Technologies (iJIM), 15(22), pp. 22–43. https://doi.org/10.3991/ijim.v15i22.24069

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Section

Papers