Smart Learning Management Systems for Inclusive Education Using Cloud Computing Technologies

Abstract

The rapid advancement of smart educational technologies has significantly transformed digital learning environments through the integration of cloud computing, intelligent learning management systems (LMS), and educational analytics. The present study investigated students’ adaptability toward smart learning management systems within cloud-enabled inclusive educational environments using machine learning-based predictive analysis. A publicly available educational dataset containing 1,205 student records and 14 categorical variables related to demographic characteristics, educational background, technological accessibility, and online learning behavior was utilized for analysis. Data preprocessing and label encoding were performed before statistical and machine learning analysis. Exploratory data analysis, correlation analysis, and Random Forest classification modeling were conducted to evaluate factors influencing adaptability toward smart LMS systems. The findings demonstrated that educational accessibility, technological infrastructure, and socioeconomic conditions significantly influence adaptability levels in online learning environments. Correlation analysis revealed that class duration, institution type, and location positively influenced adaptability, whereas financial condition demonstrated a substantial relationship with adaptability disparities. The Random Forest classifier achieved an overall prediction accuracy of 90.87%, indicating strong predictive capability for adaptability classification. Feature importance analysis identified class duration, age, financial condition, network quality, and device accessibility as major predictors affecting adaptability toward smart educational systems. The results further highlighted the importance of cloud-enabled educational accessibility, LMS participation, and digital infrastructure in supporting inclusive and adaptive learning environments. The study demonstrates that machine learning and educational analytics can effectively support adaptive digital learning research and inclusive educational management. Overall, the integration of smart learning management systems and cloud computing technologies provides substantial opportunities for improving accessibility, learner engagement, and adaptive educational delivery within future intelligent learning ecosystems.