Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan

Rimsha Asad, Saud Altaf, Shafiq Ahmad, Adamali Shah Noor Mohamed, Shamsul Huda and Sofia Iqbal
Additional contact information
Rimsha Asad: University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, Pakistan
Shafiq Ahmad: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Adamali Shah Noor Mohamed: Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Shamsul Huda: School of Information Technology, Deakin University, Burwood, VIC 3128, Australia
Sofia Iqbal: Space and Upper Atmosphere Research Commission, Islamabad 44000, Pakistan

Sustainability, 2023, vol. 15, issue 3, 1-22

Abstract: With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student’s participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students’ performance.

Keywords: precision education; personalized learning; machine learning; early prediction; educational data mining; learning analytics; digital learning platforms; COVID-19 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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