## 1 Introduction  
The popularization of Internet access and the advances in the exploration of digital resources have led to a growing interest in distance education. This education has as main advantages the accessibility (students can follow a course from anywhere in the world) and the flexibility (students can fit their learning around their daily routine) [1]. The current distance studies could not be understood without a digital platform that provides fundamental features like the publication of the contents of the course, a channel to maintain professor-students communication or the tools to keep a control of the student evolution. These systems, called Virtual Learning Environments (VLEs), include course content delivery instruments, quiz modules and assignment submission components, among other functionalities [2]. In addition, VLEs are very useful to control the student involvement in the course, since all his/her activity is recorded in log files that can be analyzed [3]. Even though the history of distance courses is too recent, they have experimented a high expansion, with Massive Open Online Course (MOOCs) as the most popular example. This new format is opening a widespread investigation, due to its differences with respect to traditional face-to-face higher education. Among these features, an open environment regardless of the location of students stands out, as well as a higher number of enrollments [4]. This also implies an important growth in the number of dropouts and academic failures [5]. In this context, the prediction of student success according to their work collected by VLE's system has become an essential task to be able to discover the main features that describe to the students that pass satisfactorily a course. Thus, some works analyze the impact of the chosen learning platform [6; 7], others study the effectiveness of the student-instructor interaction in this engagement [8], while other works have focused on the dynamic adaptation of the e-learning system to the current level of knowledge of each student, based on the interaction with the exercises of the course [9] or prior knowledge and other social factors of students [10]. In this context, this work explores a little used factor to predict student success in distance learning analyzing how this information should be treated to extract all its potential. Specifically, the factor proposed is assignment information, understood as the tasks submitted by the students throughout the online course. The use of information about assignments is not extended as essential factor to determine the students' performance [1; 4; 11]. Preliminary, it could be estimated that delivered assignments may help predict the student performance more effectively than the number of accesses or clicks on the course resources, that it is the most used factor. Probably, the lower use of assignments information may be due to a combination of the little number of users who complete assignments, in relation to the total enrollments, and the substantial variation across courses in the assignments scheduled. In this context, our work proposes to use assignments information from a flexible data representation perspective based on Multiple Instance Learning (MIL). This learning framework introduced by [PERSON] et al. [12] is considered an extension of supervised traditional learning focused on weakly labeled data. MIL could make a better use of the information provided by submitted assignments to predict the impact of students' achievement. In the field of educational data mining there are not many public datasets due to the sensitivity of the data being worked with. This makes it difficult to compare different proposals since the data used by each study are usually so different to be compared. In this context, it is of great relevance the Open University Learning Analytics Dataset (OULAD) [13] availability, one of the few existing public datasets in the field.