Multiple instance learning for classifying students in learning management systems

In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.

► We describe a new representation for solving the problem of predicting a student’s performance.
► The new representation is based on multiple instance representation.
► It is compared with a traditional representation based on single instances.
► The comparison shows the effectiveness of this representation to eliminate scattering of data.
► Also, it is shown that algorithms using the new representation achieve significantly better solutions.