## 3 Results
### Learning Analytics with Traditional Models
#### 3.2.4 Feedback and Performance Modeling in Hybrid Learning with ML  
Machine learning is essential in hybrid learning contexts, especially for modeling academic performance and automating feedback. Artificial neural networks (ANN), random forest (RF), and support vector machines (SVM) dominate due to their effectiveness with varied educational data. ANN frequently models complex behaviors like cognitive engagement, though it faces interpretability challenges [82, 83, 84]. SVM excels at classifying high-dimensional textual data, identifying cognitively relevant content [84, 85]. RF balances predictive accuracy and interpretability, analyzing variables such as peer interactions and digital distractions [83, 86].  
Recent use of transformer-based models like BERT has advanced analysis of emotional and behavioral signals in collaborative settings, enabling real-time personalized feedback and co-regulated learning [87].  
Multimodal datasets typically combine structured data (grades, surveys) with unstructured inputs (forum posts, interaction logs, keystroke dynamics). Natural language processing (NLP) is crucial for preprocessing text, classifying engagement, and assessing discourse relevance [83, 84]. Keystroke analytics reveal writing fluency and cognitive processes, while LMS logs highlight self-regulated learning patterns [88, 89]. Hierarchical regression isolates cognitive, affective, and behavioral influences on student performance [90].  
Explainable AI (XAI) techniques like LIME and SHAP have begun enhancing transparency and informing pedagogical decisions, yet broader integration is underdeveloped [91, 92].  
Key applications include:  
* Cognitive engagement via discourse analysis [84];
* Analyzing distraction and peer interactions using NLP-classified discussions [83, 89];
* Real-time, emotion-sensitive feedback leveraging transformer-based models [87];
* Early identification of academic risks using RF with academic and behavioral data [93, 94, 95];
* Instructional design optimization through behavioral predictors of satisfaction and performance [96].  
Challenges and opportunities: Despite progress, interpretability and adaptive responsiveness remain challenges. Consistent use and pedagogical value of XAI techniques require further integration and validation [85, 89, 90]. Current multimodal integration mostly focuses on textual or structured inputs, limiting personalization and scalability. Incorporating audio, video, and physiological signals within diverse, multi-institutional datasets could significantly enhance analytical robustness [81, 92, 93]. Moreover, personalized interventions based on predictive analytics need rigorous longitudinal evaluation to confirm their educational impact [94].  
Recommendations for future research:* Integrate richer multimodal data (audio, video, physiological signals) to enhance analytical capabilities;
* Systematically apply advanced XAI techniques for increased model interpretability;
* Develop and validate adaptive, real-time feedback systems through robust longitudinal and experimental research.  
Emerging technologies such as generative AI, multimodal analytics, and advanced XAI offer new opportunities to overcome current limitations in feedback and performance modeling. Their potential lies in enhancing accuracy, personalization, and pedagogical value, but this requires rigorous validation, integration into real hybrid learning contexts, and alignment with instructional goals. Future systems must evolve toward real-time, interpretable, and adaptive support that scales to diverse learner needs.