Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns
Metadata
Show full item recordEditorial
Springer
Materia
Course recommendation Sequential pattern mining Emerging patterns Supervised descriptive pattern mining
Date
2022-04-19Referencia bibliográfica
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]
Sponsorship
CRUE-CSIC; Springer Nature; Spanish Government PID2020-115832GBI00; University of Cordoba UCO-FEDER 18 REF.1263116 MOD.AAbstract
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.