## 3 Results
### Learning Analytics with Traditional Models
#### 3.2.1 Engagement Prediction in Online Learning with Traditional ML Models  
The analysis of student engagement in online and hybrid environments has progressed through the integration of ML within LA. This cluster, comprising 11 studies, focuses on predicting engagement, academic performance, and dropout risk using structured and unstructured data sources.  
Most studies rely on classical ML algorithms, such as random forest, decision tree, SVM, and logistic regression, often enhanced with ensemble techniques like AdaBoost and Bagging. These models are widely used in MOOCs and online courses to support an early detection of at-risk behavior. However, few studies, including those addressing engagement and behavioral prediction [22; 23; 24; 25; 26], assess whether such predictions lead to real-time, actionable interventions.  
Figure 3: Geographical distribution of learning analytics studies in higher education (2018–2025) by AI model type.  
The choice of algorithm often reflects the nature of the data: SVM and artificial neural networks (ANN) are used for fine-grained engagement prediction [24], while decision tree and logistic regression are suited to interpret behavioral traces such as video interaction logs [25]. Feature engineering, clustering, and regression remain central analytical techniques [27].  
A notable methodological trend is the use of natural language processing (NLP) to analyze forum content and course reviews. Word embeddings--Doc2 Vec, Word2 Vec, FastText, and GloVe--support sentiment classification and behavioral inference [28; 29]. Deep learning models, such as LSTM and GRU, enhance performance in text-based engagement tasks, with GloVe consistently yielding superior accuracy [28].  
Among the most comprehensive approaches is the work of [PERSON] [28], which combines traditional classifiers, ensemble techniques, and deep architectures like CNN, GRU, LSTM, and attention-based RNN. The model achieves 95.80% accuracy using LSTM with GloVe embeddings, demonstrating the synergy between affective analytics and attention mechanisms--an intersection between traditional ML and GenAI.  
Emerging studies also explore spatiotemporal learning behaviors. [PERSON] et al. [30] show that consistent study patterns correlate with academic success. [PERSON] et al. [31] apply clustering to blended learning profiles, while [PERSON] et al. [32] investigate strategy use in learner sourcing platforms, linking it to instructional design quality.  
Key applications include:  
* Dropout detection and risk assessment [22; 25];
* Engagement forecasting and behavioral profiling [24; 26; 27];
* Sentiment analysis of learner feedback [28];
* Modeling learning routines and platform interactions [30; 31; 32].  
Figure 4: Top 10 AI models applied in learning analytics using traditional machine learning techniques, categorized by educational context.  
Challenges and opportunities: Multimodal integration remains limited, with most studies focusing on a single data type. Longitudinal analyses are rare, limiting insights into lasting engagement patterns. Although XAI is occasionally applied [23; 26], most models lack transparency, reducing their practical utility in educational settings. Recommendations for future research:  
* Combine behavioral, textual, and emotional data sources;
* Conduct longitudinal studies on sustained engagement;
* Enhance model explainability through tailored XAI tools;
* Link predictions to adaptive instructional strategies.  
In summary, progressing toward multimodal and learner-centric LA systems is essential for fully leveraging ML in higher education contexts.