## 3 Results
### Learning Analytics with Traditional Models
#### 3.2.2 Dropout Prediction in Digital Education with Traditional ML Models  
Student attrition remains a persistent concern in digital higher education, often preceded by disengagement--behavioral, cognitive, or emotional. Predicting these early indicators is crucial for intervention. Predictive learning analytics (PLA) and machine learning (ML) are widely used to identify at-risk students before dropout. This section synthesizes findings from 23 peer-reviewed studies applying AI models across online, blended, and MOOC environments.  
Random forest and SVM are the most used models, with each in nine online learning studies. Decision tree, naive Bayes, and logistic regression remain favored for their simplicity and interpretability. Ensemble methods such as random forest and boosting reduce overfitting and handle feature interactions effectively [33; 34; 35]. In MOOCs, random forest reached F1-scores above 0.88 based on daily progress [34]. Hybrid ensembles combining classification and regression enabled early risk detection before midterms [36]. During the pandemic, extra trees and logistic regression surpassed 90% specificity in asynchronous learning [37].  
Neural models like LSTM and GRU outperform traditional classifiers. LSTM exceeded CNNs and MLPs from week six in STEM courses [38]; GRU achieved 98% accuracy in remedial English [39]. IOHMM and sequential logistic regression predicted dropout by week eight, outperforming SVM and logistic models [40].  
Interpretable models like decision trees and logistic regression remain valuable for deployment [41; 42], often as base learners in ensembles [43]. Semi-supervised models with SHAP explanations combine diverse data types [44]. Dashboards like OU analyse supported instructor monitoring and improved student outcomes [45]. Behavioral profiling through LMS logs and motivational indicators guided personalization [46; 47]. Ensemble models also supported post-pandemic readiness [48], and SHAP facilitated fairness-aware predictions [49]. Self-reports on motivation were included in some models [50]. Sentiment analysis with SVM and decision trees reached over 90% accuracy [51]. Random matrix theory and community detection uncovered engagement-success links in programming education [52]. SMOTE addressed class imbalance [39]. Hybrid ensembles also explored fuzzy logic [53]. Generative models remain rare [54], and model generalizability is still limited [55].  
Key applications include:  
* Early warning systems in initial weeks [35; 38];
* Instructor dashboards for real-time monitoring [45];
* Adaptive feedback based on predictions [35];
* Behavioral profiling using LMS and motivation data [46; 47];
* Post-pandemic readiness via ensembles [48];
* Fairness-aware interventions using SHAP [49].  
Challenges and opportunities: Generative AI is underused [38; 54]. LMS logs dominate, while multimodal inputs are scarce [47]. Few systems operate in real time or are deployed in institutions [35; 45]. Explainability tools lack integration into actionable dashboards [49]. Generalizability is limited due to narrow datasets [55]. Recommendations for future research:  
* Apply generative models for feedback and simulation;
* Incorporate multimodal sources (e.g., forums, self-reports);
* Deploy real-time tools in institutional settings;
* Embed explainability in instructor-facing systems;
* Expand validation across contexts and institutions.