In a joint effort with NOVA SBE, NOVA IMS will actively collaborate on various project activities, particularly in the characterization of dropout and academic success phenomena at UNL. Together, we will implement intervention strategies aimed at reducing dropout rates and fostering academic success.
Within the research element of this project, NOVA IMS will take the lead in conducting comprehensive data analysis and predictive modelling. Our team of researchers will leverage diverse data analysis and predictive modelling techniques to explore factors associated with dropout and academic success. This includes a comprehensive exploratory data analysis (EDA) encompassing descriptive analyses, correlation studies, cluster analyses, performance analysis, and frequency analysis. These insights will be crucial for identifying patterns and understanding student behaviour.
To advance our understanding, we will utilize a range of sophisticated predictive modeling techniques, including Decision Trees, k-Nearest Neighbors, Support Vector Machines, Random Forests, Gradient Boosting, Neural Networks, Deep Learning Neural Networks, and Ensemble Learning algorithms. These advanced approaches will enhance our ability to predict dropout probabilities and academic success based on student characteristics, based on various student characteristics. This, in turn, will pave the way for more effective intervention strategies and support mechanisms tailored to individual needs.
