## 3 Related Work
### MIL in Educational Data Mining  
MIL has been used in a wide range of application domains, including classification, regression, ranking and clustering tasks [14]. This framework has experienced a growing \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Work** & **Algorithm** & **Criteria** & **Prediction** \\ \hline
[28] & Decision Tree & Demographic data. Number of clicks per day. Assignments data. & Final outcome. All courses together. \\ \hline
[40] & Decision Tree & Assignments data. & Final outcome: Fail vs. all. In courses CCC and FFF. \\ \hline
[44] & Decision Tree & Demographic data. & Final outcome, excluding Withdraw. In course AAA, per presentation. \\ \hline
[30,31] & J48 & Number of clicks per resource. & Final outcome binarized in Pass+Distinction/Fail+Withdraw. All courses separately. \\ \hline
[33] & J48 & Number of clicks per resource. & Engagement to the course: a combination of the first assignment score, course final result and total number of clicks. \\ \hline
[47] & Random Forest & Demographic data. Number of clicks per day. Assignments score. & Final outcome binarized in Pass+Distinction/Fail+Withdraw. All courses together. At different percentages of course length \\ \hline
[36,46] & XGBoost & Demographic data. Statistics over of clicks until the first assignment of the course. & Deadline compliance. In courses BBB, DDD, EE, FFF, only last presentation \\ \hline
[38] & Naive Bayes & Demographic data. Total number of clicks, only in web page resource. & Final outcome, only Pass or Fail. All courses together. \\ \hline
[27] & Support Vector Machine & Demographic data. Number of clicks per day. & Final outcome binarized in Pass+Distinction/Fail+Withdraw. All courses together. \\ \hline
[29] & Gaussian Mixture Model & Number of clicks and number of sessions per resource and time-interval. & Final outcome: Withdraw vs. all Course BBB. At different intervals of the course. \\ \hline
[37] & Dynamic Incremental Semi-Supervised Fuzzy C-Means & Demographic data. Number of clicks per resource. Assignments average score and number of submissions. & Final outcome binarized in Pass+Distinction/Fail+Withdraw. Course DDD. \\ \hline
[26] & Time Series Forest & Number of clicks per resource and day, only in 3 resources. & Final outcome: Withdraw vs. all. All courses and presentations separately. \\ \hline
[32] & Markov Chains & Number of clicks per week in planned and non-planned activities. & Final outcome: Withdraw vs. all. Course FFF, one presentation. \\ \hline
[3] & Artificial Neural Network (ANN) & Demographic data. Number of clicks per assignment. Assignment score. & Regression of final score. Course DDD, by presentations. \\ \hline
[41] & Deep Artificial Neural Network & Demographic data. Number of clicks. Assignments data. & Final outcome: Fail vs. all. In all courses. At different quarties of course. \\ \hline
[42] & Joint Neural Network Model & Demographic data. Number of clicks per resource and day. & Final outcome, only Pass or Fail. Courses BBB, CCC, FFF, one presentation. \\ \hline
[39] & Recurrent Neural Network & Demographic data. Number of clicks per week and resource. Assignment data. & Final outcome binarized in Pass+Distinction/Fail+Withdraw. All courses together. At different weeks. \\ \hline
[43] & Convolutional and recurrent deep model & Demographic data. Number of clicks. Assignment score. & Final score discretized in six ranges. Course AAA. \\ \hline
[45] & Up-sampling based on Adversarial Network + ANN & Number of clicks per resource and course quartiles. & Final outcome, only Pass or Fail. All courses together. \\ \hline
[34] & LSTM & Number of clicks per week of 25 first weeks. & Final outcome: Withdraw vs. all. \\ \hline
[35] & LSTM & Number of clicks per week. & Final outcome, only Pass or Fail. All courses together. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between proposals that use OULAD. interest to represent problems because of its characteristics in data representation.