Sequence based course recommender for personalized curriculum planning

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10948 LNAI pp. 531 - 534
Issue Date:
2018-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
PROOF_469815_1_En_100_Chapter_Author.pdfAccepted Manuscript version278.15 kB
Adobe PDF
© Springer International Publishing AG, part of Springer Nature 2018. Students in higher education need to select appropriate courses to meet graduation requirements for their degree. Selection approaches range from manual guides, on-line systems to personalized assistance from academic advisers. An automated course recommender is one approach to scale advice for large cohorts. However, existing recommenders need to be adapted to include sequence, concurrency, constraints and concept drift. In this paper, we propose the use of recent deep learning techniques such as Long Short-Term Memory (LSTM) Recurrent Neural Networks to resolve these issues in this domain.
Please use this identifier to cite or link to this item: