Al‑Twijri, Mohammed Ibrahim Luna, J.M. Herrera, Francisco Ventura Soto, S. 2022-05-16T07:37:04Z 2022-05-16T07:37:04Z 2022 http://hdl.handle.net/10396/22935 To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES)2P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES)2P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process. application/pdf eng Springer https://creativecommons.org/licenses/by/4.0/ Al-Twijri, M. I., Luna, J. M., Herrera, F., & Ventura Soto, S. (2022). Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns. Cognitive Computation, 14(4), 1474-1495. https://doi.org/10.1007/s12559-022-10015-5 Course recommendation Sequential pattern mining Emerging patterns Supervised descriptive pattern mining Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns info:eu-repo/semantics/article https://doi.org/10.1007/s12559-022-10015-5 Gobierno de España. PID2020-115832GBI00 Junta de Andalucía. 1263116 (UCO-FEDER) info:eu-repo/semantics/openAccess