An Intelligent DGGO-Based Machine Learning Framework for

Predicting Student Adaptability in Online Education

Osama Alabedallat1,*

1 School of Information Communication and Technology, Bahrain Polytechnic, Isa Town, Bahrain

Received: August 04, 2025 Revised: October 09, 2025 Accepted: December 10, 2025 ⋆ Corresponding author

ABSTRACT

The recent shift to online education worldwide has highlighted the urgent need to evaluate and improve students’

flexibility in the online learning environment. The paper solves that problem by creating a predictive framework

that enables measuring students’ adaptability using modern techniques for advanced perception of adaptability and

advanced machine learning (ML) and metaheuristic optimization methods. The research proposes an improved

variant of geese’s cooperative flight behavior, called the Dynamic Greylag Goose Optimization (DGGO) algorithm,

to optimize the hyperparameters of a base model, a Decision Tree (DT). A comparison of the baseline DT showed

an accuracy of 93.81% and a sensitivity (True Positive Rate, TPR) of 93.14%. The DGGO optimization further

improved the DT model’s performance to 97.15% accuracy and 96.86% sensitivity, with improved convergence

and tuning. To be flexible to changes, the optimized model reduces classification errors and improves predictive

consistency. The findings demonstrate the effectiveness of metaheuristic-based optimization for educational data

mining and provide preliminary evidence that DGGO can serve as a robust, scalable, and intelligent framework to

enhance predictive analytics and decision-support capabilities in the modern e-learning environment.

Keywords: Dynamic Greylag Goose Optimization (DGGO) Machine Learning in Education Decision Tree Classification

Student Adaptability Prediction Metaheuristic Optimization

1. INTRODUCTION

Due to the COVID-19 pandemic and other global upheavals,

there has been a significant shift in the educational setting,

accelerating the shift from face-to-face to online and blended

learning. This dramatic change has presented significant problems

to students, educators and educational institutions globally

[1]. Alterations that would take years to plan, introduce,

and test in a traditional educational system were forced to be

implemented almost immediately. This consequently forced

teachers and learners to quickly adjust to digital platforms,

redesigned pedagogies and online forms of communication.

Students especially had a lot of challenges as they had to

move out of on campus systems, which were marked by physical

presence, face-to-face interaction and rigid schedules to

learning systems over the internet where they needed to have

self-discipline, study and learn on their own and use the new

digital tools that were not familiar to them in their systems

of learning [2]. This sudden change made adaptability a significant

issue, as students had to change their approaches to

academic success almost overnight. Additionally, psychological

and behavioral responses to the new conditions of

online learning were defined by shifts in motivation, stress

coping, and patterns of new interaction, and these are not

the only ones in the new online learning environment. [3].

The above experiences have shown that adaptability is not a

technical skill or knowledge in itself, but a multidimensional

factor that involves emotional, cognitive and behavioral flexi-