## 3 Results
### Learning Analytics with Traditional Models
#### 3.2.3 Academic Performance Prediction in Face-to-Face Classrooms  
Predicting academic performance in face-to-face higher education settings remains a core objective within learning analytics (LA), primarily aiming at the early identification of students at risk. This chapter synthesizes findings from 26 studies utilizing traditional machine learning (ML) models in classroom contexts. The most frequently employed ML algorithms include random forest (RF), support vector machines (SVM), decision trees (DT), and logistic regression, each favored due to their effectiveness with structured academic data. Specifically, RF has reliably detected underperformance through historical grades [56]. However, its predictive accuracy is notably reduced in small or homogenous student groups [57]. Conversely, SVM demonstrated strong performance with smaller datasets [58; 59], though its applicability to larger classrooms remains inadequately explored. Decision trees are valued for their interpretability in guiding interventions [60] but often face challenges with imbalanced datasets [61]. Meanwhile, artificial neural networks (ANNs) effectively model complex multimodal datasets [62; 63], though their inherent opacity complicates practical deployment. Supervised learning methodologies--primarily classification and regression--dominated the reviewed studies, largely based on academic records and limited learning management system (LMS) logs. Despite the inclusion of innovative approaches such as natural language processing (NLP) for feedback analysis [62; 64] and sensor-based physical interaction data [65], face-to-face contexts continue to present significant barriers due to insufficient digital interaction data for adaptive and longitudinal modeling. Advanced analytical techniques, including concept inventories [66], gesture analytics [67], and LMS-integrated predictive models [68], contributed positively to predictive accuracy but were often limited by small sample sizes, affecting generalizability. Additionally, early assessments [69; 70], demographic indicators (e.g., socioeconomic status, language proficiency, background) [71; 72], and institutional data emerged as reliable predictors. Curriculum personalization emerged as a notable application. Recommendation systems were proposed based on academic pathways [73; 74], aiding program design. Key applications include:  
* Performance forecasting enabling timely academic interventions [57; 62; 63; 65; 75];
* Dropout prediction identifying risk patterns for preventive actions [56; 60; 61; 76; 77; 78; 79];
* Personalized feedback via NLP [64];
* Curriculum personalization through predictive analytics informing tailored educational pathways [73; 74; 80; 81]. Challenges and opportunities: Despite significant progress, key limitations persist. Generalizability remains constrained by small or imbalanced datasets and institutional variability [57; 58; 67]. Face-to-face contexts lack continuous digital traces, limiting adaptive and multimodal analytics. Moreover, critical variables such as motivation, emotion, and socio-economic status are often excluded despite their predictive value [60; 66; 71; 75; 76]. Addressing these issues demands not only methodological refinement but also ethical vigilance. Incorporating sensitive data raises concerns about privacy, consent, and bias. Future research must adopt privacy-preserving practices, transparent data governance, and inclusive frameworks to ensure that predictive systems are equitable, respectful of student rights, and contextually appropriate across diverse educational environments.