Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns Al-Twijri, Mohammed Ibrahim Herrera Triguero, Francisco Course recommendation Sequential pattern mining Emerging patterns Supervised descriptive pattern mining Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Spanish Ministry of Science and Innovation, project PID2020-115832GBI00, and the University of Cordoba, project UCO-FEDER 18 REF.1263116 MOD.A. Both projects were also supported by the European Fund of Regional Development. 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)2 P (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)2 P 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. 2022-04-29T09:54:47Z 2022-04-29T09:54:47Z 2022-04-19 info:eu-repo/semantics/article Al-Twijri... [et al.]. Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns. Cogn Comput (2022). [https://doi.org/10.1007/s12559-022-10015-5] http://hdl.handle.net/10481/74648 10.1007/s12559-022-10015-5 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Springer