## 1 Introduction
OULAD collects a large number of student data from an important distance university during two academic years, including demographic data, student interactions with the VLE and assignments submitted. Many works have used it to predict students' performance but mainly centered in clicks activity, as it is discussed in related work section. This work uses OULAD to obtain assignments information, adapt the representation to the MIL paradigm and store it in ARFF format to work with a popular framework for data mining called Weka. Thus, following the open access philosophy, these files have been made public online for the scientific community that wants to continue the line of this study. Summarizing, this work carries out an exhaustive study to determine the relevance that information about assignments has to predict the student's performance. Specifically, this work addresses the following research questions:  
1. How should the information about assignments be represented? Previous works in distance learning use a classical representation based on single instances. However, each course has different type and number of assignments, and these are submitted by few students, which suggests a high sparsity in the data. Representation should be adapted to this environment so that machine learning algorithms can perform well. We propose to use an optimized representation based on MIL able to adapt to the specific information available for each student. 2. Are machine learning algorithms affected by the way that assignments are represented? It is analyzed a wide set of machine learning algorithms using two different representations of assignments information: representation based on single instances(it is used in previous studies) and based on MIL (the representation that it has been proposed in the previous step). A significant performance difference between the same algorithms using both representations shows the relevance of an appropriate representation so that assignments can be considered a very influential factor for predicting students' performance. 3. Is information about assignments a relevant feature to predict the student performance? The accuracy in predicting student performance using MIL is compared with previous studies that use different factors such as demographic features and interactions on VLEs to address the same problem. Algorithms using only information about submitted assignments reach competitive results achieving better accuracy in relation to the previous works that predict academic performance using other factors provided in the same dataset. This justifies the relevance of assignments to predict students' performance, if it is represented appropriately. This paper is organized in following sections. Section 2 presents a briefly introduction to MIL and other concepts of background for this study. In Section 3, a review of related work for solving students' success prediction tasks in distance education is presented. Moreover, this section briefly introduces MIL and its application to the educational environment. Section 4 addresses an in-depth analysis of problem representation and the available information. In Section 5, it is presented the experimentation carried out and the obtained results. Finally, Section 6 draws some conclusions and proposes some ideas for future work.