University dropout : Prevention patterns through the application of educational data mining

2020

Recently, the use of educational data mining techniques has gained great relevance when applied to performance prediction, creation of predictive retention models, behaviour profiles and school failure, amongst others. For the present paper we applied an attribute selection algorithm to identify the most important factors influencing drop out decision. Decision trees were used to define patterns that can alert an imminent dropout. A tool was adapted and administered online to 300 students from public HEIs, and 200 students from private HEIs currently enrolled on a higher education program. By means of the attribute selection algorithm, 27 relevant factors were found. Within the three main f…