A System-Oriented Analytical Framework for Assessing Digital Learning Platforms and Student Psychological Adaptation Using Data-Driven Methods

Digital learning has enabled more flexible, accessible, and intelligent learning experiences. The present evaluation
methods for digital learning platforms focus mainly on educational results and system usability, neglecting the mental adaptability of learners in dynamic online learning contexts. Based on the mentioned gap, this study proposes a new cognitive resilience assessment model in digital learning environments based on the system-oriented adaptive
cognitive resilience framework (SACRF), which evaluates digital learning platforms and analyzes students' mental
adaptability by making data-driven intelligent assessments on the basis of behavioral learning analytics, engagement
features, psychological resilience variables, emotion adaptation, and system performance characteristics integrated into a single system. Furthermore, a novel dynamic psychological adaptation index (DPAI) is proposed to measure students' coping ability under the impact of academic stress, changing environments, and the transition of technology adaptation. A machine learning model and explainable artificial intelligence (XAI) approach are integrated to make the influential features and intelligent prediction on adaptation and learning results as explainable as possible, as well as an adaptive intervention method, which is implemented to deliver immediate, individualized recommendations to improve learning engagement, mitigate psychological stress, and maintain the learning process. The proposed SACRF can attain an adaptation prediction accuracy up to 95.4% with the average 30-35% improvement on cognitive
resilience and about 25% improvement on early risk detection compared to benchmark approaches on different
evaluation indices. The conclusion suggests that psychological adaptation analytics are necessary in the digital learning platform evaluation to support an adaptive, intelligent, and learner-centric learning environment, and the framework presents a stable and well-scaled method for boosting sustainable digital education.