Accuracy of ML classifiers.

This study significantly contributes to the sphere of educational technology by deploying state-of-the-art machine learning and deep learning strategies for meaningful changes in education. The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. That indeed puts into great perspective the huge potential it possesses for accuracy measures while predicting in educational setups. The CNN model, which predicted with an accuracy of 89%, showed quite impressive capability in sentiment analysis to acquire further insight into the emotional status of the students. RCNN, Random Forests, and Decision Trees contribute to the possibility of educational data complexity with valuable insight into the complex interrelationships within ML models and educational contexts. The application of the bagging XGBoost algorithm, which attained a high accuracy of 88%, further stamps its utility toward enhancement of academic performance through strong robust techniques of model aggregation. The dataset that was used in this study was sourced from Kaggle, with 1205 entries of 14 attributes concerning adaptability, sentiment, and academic performance; the reliability and richness of the analytical basis are high. The dataset allows rigorous modeling and validation to be done to ensure the findings are considered robust. This study has several implications for education and develops on the key dimensions: teacher effectiveness, educational leadership, and well-being of the students. From the obtained information about student adaptability and sentiment, the developed system helps educators to make modifications in instructional strategy more efficiently for a particular student to enhance effectiveness in teaching. All these aspects could provide critical insights for the educational leadership to devise data-driven strategies that would enhance the overall school-wide academic performance, as well as create a caring learning atmosphere. The integration of sentiment analysis within the structure of education brings an inclusive, responsive attitude toward ensuring students’ well-being and, thus, a caring educational environment. The study is closely aligned with sustainable ICT in education objectives and offers a transformative approach to integrating AI-driven insights with practice in this field. By integrating notorious ML and DL methodologies with educational challenges, the research puts the basis for future innovations and technology in this area. Ultimately, it contributes to sustainable improvement in the educational system.

本研究通过部署前沿机器学习（machine learning）与深度学习（deep learning）策略以推动教育领域的实质性变革，为教育技术领域作出了重要贡献。本研究采用以决策树（Decision Trees）、随机森林（Random Forest）与XGBoost为基学习器、梯度提升（Gradient Boosting）为元学习器的混合堆叠框架，实现了出色的模型部署，其分类准确率可达90%。这充分展现了该方法在教育场景下的预测任务中所具备的高精度应用潜力。
卷积神经网络（Convolutional Neural Network, CNN）模型的预测准确率达89%，其在情感分析任务中表现出优异的性能，可进一步挖掘学生的情绪状态。循环卷积神经网络（Recurrent Convolutional Neural Network, RCNN）、随机森林与决策树则针对教育数据的复杂性展开研究，为理解机器学习模型与教育场景间的复杂交互关系提供了宝贵视角。
装袋XGBoost算法的应用准确率达88%，该方法凭借稳健的模型集成技术，进一步验证了其在提升学业表现方面的实用价值。本研究使用的数据集源自Kaggle平台，共包含1205条样本，涵盖适应性、情感与学业表现共14项属性，分析基础具备较高的可靠性与丰富度。该数据集支持开展严谨的建模与验证工作，可确保研究结论具备稳健性。
本研究对教育领域具有多维度启示，主要围绕教师效能、教育管理与学生福祉三大核心方向展开。基于学生适应性与情感状态的相关分析结果，本研究开发的系统可帮助教育者更高效地针对个体学生调整教学策略，从而提升教学效能。上述研究成果可为教育管理者提供关键参考，助力其制定数据驱动的策略，以提升全校整体学业表现，并营造关怀型的学习氛围。
将情感分析融入教育体系，可推动形成包容且灵活的应对机制，以保障学生福祉，进而构建关怀型教育环境。本研究与教育领域的可持续信息与通信技术（Information and Communication Technology, ICT）目标高度契合，提供了一种将人工智能（Artificial Intelligence）驱动的分析洞察与教育实践相融合的变革性路径。通过将先进的机器学习与深度学习方法与教育挑战相结合，本研究为该领域未来的技术创新奠定了基础。最终，本研究助力教育系统实现可持续性改进。