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

Abstract

<span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">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. <a name="_Hlk71439748"></a>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.</span>

Bibliometric insights into data mining in education research: A decade in review

“…Not only that, the author Nuankaew, P. also has two recent publications, Nuankaew and Nuankaew (2022) and Nuankaew et al (2021), which also focused on predicting student performance and achievements using different models. Nuankaew and Nuankaew (2022) highlighted that future research should focus on utilizing different machine learning models to improve student performance prediction and provide better learning experiences for each learner.…”

“…With the many interactions of learners through LMS, researchers have been able to use these large amounts of data to understand and evaluate educational processes in conjunction with making decisions to improve the effectiveness of the current education system (Fischer et al, 2020). A study conducted by Nuankaew and Nuankaew (2021) suggests that e-learning and the traditional settings of the educational context contribute equally to students' academic achievements based on the data collected before and during the COVID-19 pandemic. Hence, this analysis concludes that the prevalent research areas that researchers can focus on for subsequent research may revolve around predicting student performances using machine learning algorithms and data mining in e-learning.…”