# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 2 Related Work
In the end, the authors demonstrated that students who had performance problems or dropped out at the end of the first year received an average of 43% more alerts on the tool. The problem of college-going students taking longer to graduate than their parental generations was tackled by [33]. The authors presented a prediction model to identify students at-risk of failing courses that they plan to take in the next term (or future). Differentmodels are learned from different courses. To predict a student's grades in the next courses, his grades from prior courses are fed into corresponding models. To capture the sequential characteristics of students' grades in prior courses, they modeled the learning behavior and performance using recurrent neural networks with long short term memory (LSTM). In Latin America, a number of initiatives proposed approaches to the use of Educational Data Mining and Learning Analytics at the higher educational level [23]. In this context, [4] presented a proposal that aimed at early prediction of university student retention at Chile. For that, the authors applied a number of different data mining algorithms (decision trees, k-nearest neighbors, logistic regression, naive Bayes, random forest, and support vector machines) over students socioeconomic information and previous achievements in their courses. The results demonstrated an accuracy on the classification higher than 80% in all tested scenarios. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **Goal** & **Technique** & **Algorithm** & **Edu. Level** & **Features Used** \\ \hline
[29] & To predict students at-risk of fail & Classification & Logistic regression & Higher & Student factors (IMD area, price area, and disability), Previous studies (highest qualification on entry), Student course (total credits studying in a year and late registration), Previous progress at the university (best previous score and number of fails) \\ \hline
[27] & To support academic advising scenarios & Multilevel clustering & Fuzzy C-means & Higher & Grades and the number of courses students took during the semester \\
[26] & To predict students at-risk of fail & Not-mentioned & Not-mentioned & Higher & Demographic data versus only VLE participation data \\
[4] & To predict student retention & Classification & Decision trees, k-nearest neighbors, logistic regression, naive Bayes, random forest, and SVM & Higher & Educational score and the community poverty index and university grades. \\
[30] & To predict students at-risk of fail & Statistical Analysis at-risk of fail & Correlation and regression analysis & Higher & Click stream data, self-reported measures, and course performance. \\
[24] & To predict both marginal and at-risk students of fail & Classification & Training vector-based SVM & Higher & Demographic data and interaction with a virtual learning environment. \\
[25] & To select best features to improve predicting students performance & Feature selection to improve supervised learning classifiers & Deep learning with LSTM & Higher & Metrics from navigation events that are combined in the LSTM network. \\
[28] & To predict students at-risk of dropout & Classification & Random forest and boosted decision & School & Attendance and course performance. \\
[32] & To identify learners personality & Classification & Naive bayes & Higher & Participation in forums and chats, access to supplementary course materials, delay in assignment delivering, score, accomplishment of assignments, time solving of quizzes, and number of entrances in the system. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. The learning process in which students are responsible for defining their goals and constantly auto-regulating their objectives towards some content or course is named Self-Regulated Learning (SLR) [34]. [PERSON] et al.