# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 2 Related Work  
Numerous studies have explored the correlation between student adaptability and academic performance, retention rates, and overall learning outcomes. The link between learning flexibility and self-regulated learning (SRL) in junior high school students is examined in the study by [PERSON] et al. [12], with an emphasis on the mediating roles of academic motivation and self-management. Under China's \"double reduction\" educational program, the research looks at how these elements work together to support SRL, which is an important ability for students to succeed. The study's conclusions show the independent and cumulative mediation functions of academic motivation and self-management, as well as the noteworthy benefits of learning adaptability on SRL. This offers information to help students improve their learning practices and adjust to changes in schooling. [PERSON] et al. 's systematic review [13] summarizes research on adaptive learning from 2009 to 2018, with an emphasis on publishing patterns, instructional settings, techniques, and technology. After analyzing 61 articles, the study found that the number of publications peaked in 2015 and that higher education particularly in Taiwan and the US was the main topic of interest. The study focuses on several stages of instructional design in the field of adaptive learning research, such as learner characteristics, needs assessment, and system design and implementation. Furthermore, it classifies adaptive technologies into systems, applications, teaching techniques, and design solutions, and adaptive strategies into categories based on learner models, content, and instructional models. In the review, it is emphasized how important it is to have more diverse research and rigorous methods in adaptive learning. In their investigation of student adaptation following COVID-19-induced online learning, [PERSON] et al. [14] explore Education 4.0. By using Explainable Machine Learning (XML), they investigate students' acceptance of Industry 4.0 features. Using a range of machine learning techniques, the study finds that Neural Networks are the most accurate (93%) at classifying adaptation. The study emphasizes how crucial explainability is for machine learning models. It uses dataset analysis, model performance research, and SHAP techniques to clarify feature importance and model predictions. In the end, it emphasizes how important elements affecting students' capacity to adjust include age, socioeconomic situation, and type of internet access. This thorough study helps teachers comprehend how pupils adjust to online learning and what effects it has. [PERSON] et al. [15] present an XAI model that predicts student performance in virtual learning environments at various course stages with detailed interpretability, utilizing Random Forest among other algorithms and incorporating SHAP values for insight into decision-making processes. This enhances educational data mining by enabling early performance prediction. In a related context, [PERSON] et al. [16] develop an interpretable machine learning framework to analyze and predict student performance, focusing on commitment and grades, and improving framework efficiency through algorithmic tuning and GPU-based infrastructure. Another study [5] explores early prediction of student performance using machine learning and XAI. Logistic regression yielded the best results, and the integration of XAI helped educators understand the predictions, aiming to support at-risk students promptly. The adaptability of Bangladesh pupils to online learning amid the COVID-19 pandemic is investigated by [PERSON] et al. [1]. The study uses machine learning models including Decision Tree, Random Forest, Naive Bayes, SVM, KNN, and ANN and conducts surveys across different educational levels. The Random Forest Classifier, which focuses on sociodemographic characteristics, has the best accuracy (89.63%).