# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 7 Conclusions, Limitations, and Future Research  
The present paper analyzed different aspects of data involving students enrolled in courses at the Universidad de la Republica in Uruguay. Precisely, we collected data from 4529 students of three programs and through three different sources: the academic system, an academic survey, and a VLE. We applied data science techniques (visualizations, statistics, and data mining) to understand how different combinations of the datasets could help predict students' final status in the subjects and the role that different attributes played in this task. The results presented an overview of the institutional patterns regarding the use of the VLE, and this will help pave the way for the implementation of future policies in institutions to diminish student failures and increase persistence. Among the findings was an association between the use of the VLE and the final status of the students (success and fail) and also the different types of activities inside the VLE presenting different levels of importance in this association. Examples of institutional policies that could emerge from these findings are as follows: the allocation of extra computational resources for improving VLE infrastructure and its widespread use in the university, the development of new tools for following students' trajectory and detecting at-risk students at early stages of their courses, and the construction of more institutional policies to mitigate students' failure based on other relevant attributes (e.g., the number of subjects the student is enrolled in, the student's mother's education, and the student's neighborhood). The proposed methodology for combining different data sources, as well as their pre-processing and feature engineering, demonstrated that the combination of data had a high predictive power. In this regard, the combination of the survey variables, academic system, and virtual environment showed a high capacity for early prediction. Thus, it was possible to achieve prediction rates with outstanding discrimination as soon as in the fourth week of the course. This characteristic satisfies the temporal factor of precocity, which is considered to be a determining factor in identifying and attempting to reverse the problem [31; 50]. This proposed approach model, although initially restricted to only three university programs, can serve as a basis for future work that seeks to implement methods of online information and prediction on student behavior, such as academic dashboards. However, for these steps, it is still necessary to clarify two key points: how this approach would behave with more data and the analysis of its acceptance regarding the technology and the reliability of the methods by the stakeholders, teachers, and students. The present work can help the university to develop user profiles based on the students practices inside the VLE, thus, allowing the future development of systems able to continuously deliver indicators related to the learning processes. In this way, it contributes to the production of primary information that can potentially help to the evaluation of quality and the definition of strategies that guide the university teaching and learning processes. One limitation of the present work is the lack of a qualitative analysis of the scenarios. Future work could explore the opinions of students and professors regarding the usage and importance of the VLE in their teaching and learning processes. Another limitation is the restricted number of courses used in this study. As mentioned before, Udelar has 100 undergraduate courses and the number of courses studied here (only three) can not be considered representative of the whole university, even though it serves for the purpose of an initial assessment. Future work could expand the data analyzed by increasing the number of courses. Future work could also include new data covering the period of the COVID-19 pandemic and evaluate how this period influenced the behavior of the students inside the VLE.