## 6 Conclusions and Future Work  
The results of the use of four supervised Machine Learning models (RF, MLR, SVM, and XGB) to predict whether students will pass or fail an introductory programming course are presented in this study. The contributions of this can be summarised as follows:  
1. By removing correlated variables, it was possible to obtain results that were closely comparable to those achieved in prior work [27] with simpler and more explainable models. 2. Models were built using all the students in the sample, as well as subsets based on programme duration and the department responsible for the programme. Except for MLR, all of the methods outperform the others in certain scenarios, with SVMs surpassing the others in the majority of cases. 3. With only two features--the students' GPA and the result on the entrance language test--a model was created that can accurately predict whether students pass or fail the theory part of FCYP with an accuracy of 67.71%. 4. Specific models were built for each subset of students. With the exception of Geography and Metallurgy specialisations, all departmental models improved the accuracy of the base model, with Informatics, Industry, Civil Engineering, and Electricity showing the best results. Although it may appear paradoxical, the optimal scenario would be for the model, assuming it is correctly trained, to yield results that are virtually random. This would imply that students have the capability to overcome the factors that predestine their outcomes. A model that can reasonably predict a student's course performance a semester in advance suggests the presence of environmental barriers that a student may not surmount within a single semester. The results open interesting possibilities for future work. Selected predictors vary greatly between specialisations, implying the existence of diverse student profiles, which should be further investigated. One approach to doing so would be the use of unsupervised Machine Learning techniques, such as clustering or association rules, to see if there are distinguishable student profiles and to determine if (and how) these overlap with programme and academic department choices. Applying the same process to a more recent data set is relatively straightforward. However, as these models were calibrated using pre-COVID-19 data, the prospect of evaluating their performance with students currently enrolled in FCYP presents a more compelling inquiry. This would facilitate an assessment of the models' relevance over time and in varying settings, just as it would allow us to determine how various student profiles match a given model. It would also be interesting to apply the same process to courses that are taught simultaneously in order to rule out variables that are general predictors of academic achievement and differentiate domain-specific variables for common STEM courses such as Physics, Calculus, Algebra, Statistics, and Programming. **Author Contributions:** Conceptualisation, J.L.J., J.K. and L.H. ; data curation, J.K. and L.H. ; formal analysis, J.L.J. and J.K.; funding acquisition, J.L.J. ; methodology, J.L.J. ; project administration, J.L.J. ; software, J.K.; supervision, J.L.J. ; validation, J.K and L.H. ; visualisation, J.K. and L.H. ; writing--original draft, L.H. and J.K; writing--review and editing, J.K., L.H. and J.L.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is partially supported by the Facultad de Ingenieria of Universidad de Santiago de Chile (FING-USACH), the Direccion de Pregrado of Universidad de Santiago de Chile PID 032-2019, the National Agency for Research and Development (ANID), and Scholarship programme, DOC-TORADO BECAS CHILE, 7608/2020, and the ANID-Subdireccion de Capital Humano/Doctorado Nacional/2022-21220979. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used in this work are available from the corresponding author upon reasonable request.