The academic performance of university students provides key indicators to evaluate the quality of their learning. In the digital era, digital skills are important in favoring such performance since they allow students to use technology as an adjuvant in their formative process. The research objective was to identify patterns in achievement, dropout, and failure of university students in Tabasco. The research approach was quantitative since classification techniques were applied using a decision tree algorithm to a Dataset collected through student surveys. For the development of this research, the KDD (Knowledge Discovery in Databases) process and the Weka data mining tool were used as a methodology. According to the interpretation of the results, key patterns were identified that show that even in adverse situations, such as students who had scholarships, were working, and lacked Internet connection, they expressed total disagreement in withdrawing from the school cycle. The above was because the teachers offered them study alternatives if they did not have the necessary resources to carry out their academic activities; these factors influenced the students to avoid dropping out of the school year they were attending so that their use of the program was perceived as balanced.