Multi-instance genetic programming for predicting student performance in web based educational environments

A considerable amount of e-learning content is available via virtual learning environments. These platforms keep track of learners’ activities including the content viewed, assignments submission, time spent and quiz results, which all provide us with a unique opportunity to apply data mining methods. This paper presents an approach based on grammar guided genetic programming, G3P-MI, which classifies students in order to predict their final grade based on features extracted from logged data in a web based education system. Our proposal works with multiple instance learning, a relatively new learning framework that can eliminate the great number of missing values that appear when the problem is represented by traditional supervised learning. Experimental results are carried out on data sets with information about several courses and demonstrate that G3P-MI successfully achieves better accuracy and yields trade-off between such contradictory metrics as sensitivity and specificity compared to the most popular techniques of multiple instance learning. This method could be quite useful for early identification of students at risk, especially in very large classes, and allows the instructor to provide information about the most relevant activities to help students have a better chance to pass a course.

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► We present an approach based on grammar guided genetic programming, G3PMI, for solving the problem of predicting a student's performance.
► The proposal works with a representation based on multiple instance learning.
► It is compared with the most popular algorithms used in multiple instance learning.
► The comparison shows the effectiveness of the proposal to achieve the best accuracy values and the best balance between sensitivity and specificity.
► Also, it is shown that the proposal provides information about the most relevant activities to help students have a better chance to pass a course.