Abstract: The proposed research aims to develop a hybrid learning analytics model that incorporates clickstream and sentiment mining techniques to accurately predict student retention in Learning Management Systems (LMS) in Manila-based HEIs. This proposed research aims to improve the limitations of single prediction modeling. The proposed research utilized a quantitative explanatory research design and was applied to undergraduate students taking general education courses. The proposed research utilized stratified sampling to select a total of 345 students. Clickstream and text-based interaction data were analyzed to obtain sequence and sentiment-based metrics to train the hybrid prediction model using Logistic Regression and XGBoost. The engagement tool was also able to reach Cronbach's alpha = 0.91, which is a good measure of internal consistency. This shows that the click-only model had an AUC of 0.78, but the hybrid model was enhanced to 0.84, with the top predictors being attempts at quizzes per week and negative sentiment ratio. This shows the importance of multimodal analytics in the improvement of early warning systems and proactive interventions. The implication of this can be seen in the administration of the institution and the academic support, which is a basis for evidence-based retention and fairness-based predictive models in Philippine Higher Education.