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In the domain of adaptable educational environments, our study is dedicated to achieving three key objectives: forecasting the adaptability of student learning, predicting and evaluating student performance, and employing aspect-based sentiment analysis for nuanced insights into student feedback. Using a systematic approach, we commence with an extensive data preparation phase to ensure data quality, followed by applying efficient data balancing techniques to mitigate biases. By emphasizing higher education or educational data mining, feature extraction methods are used to uncover significant patterns in the data. The basis of our classification method is the robust WideResNeXT architecture, which has been further improved for maximum efficiency by hyperparameter tweaking using the simple Modified Jaya Optimization Method. The recommended WResNeXt-MJ model has emerged as a formidable contender, demonstrating exceptional performance measurements. The model has an average accuracy of 98%, a low log loss of 0.05%, and an extraordinary precision score of 98.4% across all datasets, demonstrating its efficacy in enhancing predictive capacity and accuracy in flexible learning environments. This work presents a comprehensive helpful approach and a contemporary model suitable for flexible learning environments. WResNeXt-MJ’s exceptional performance values underscore its capacity to enhance pupil achievement in global higher education significantly.在自适应教育环境领域，本研究致力于达成三项核心目标：预测学生学习适配性、预估与评估学生学习表现，以及运用基于方面的情感分析（aspect-based sentiment analysis）以获取学生反馈的精细化洞察。本研究采用系统化研究路径，首先开展大规模数据预处理工作以保障数据质量，随后应用高效的数据均衡技术以缓解数据偏差。本研究聚焦高等教育或教育数据挖掘领域，通过特征提取方法挖掘数据中的关键潜在模式。本研究的分类方法以鲁棒的WideResNeXT架构为基础，通过采用简易的改进型Jaya优化算法（Modified Jaya Optimization Method）进行超参数调优，进一步优化模型以实现极致运行效率。所提出的WResNeXt-MJ模型已成为极具竞争力的方案，展现出优异的性能指标。该模型在所有数据集上均实现了98%的平均准确率、0.05%的极低对数损失（log loss），以及98.4%的出色精确率，证实其可有效提升灵活学习环境中的预测能力与预测精度。本研究提出了一套完整实用的研究方案，以及适配灵活学习环境的新型模型。WResNeXt-MJ优异的性能指标进一步证明，其可显著提升全球高等教育场景中学生的学业成效。