The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Recommender system for predicting student performance
Open accessAbstract
Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results.
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Cites
Matrix Factorization Techniques for Recommender Systems
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Evaluating collaborative filtering recommender systems
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Factorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772773 · Cited by 2,692
Self-citationPairwise interaction tensor factorization for personalized tag recommendation
Rendle, S., & Schmidt-Thieme, L. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718498 · Cited by 675
Self-citation“Learning factors analysis-a general method for cognitive model evaluation and improvement,”
Cen, H., Koedinger, K., & Junker, B. (2006) · Lecture notes in computer science · doi:10.1007/11774303_17 · Cited by 572
Addressing the assessment challenge with an online system that tutors as it assesses
Feng, M., Heffernan, N., & Koedinger, K. (2009) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-009-9063-7 · Cited by 502
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
Bell, R. M., & Koren, Y. (2007) · Seventh IEEE International Conference on Data Mining (ICDM 2007) · doi:10.1109/icdm.2007.90 · Cited by 499
Building a recommender agent for e-learning systems
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Data Mining Algorithms to Classify Students
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Recommender Systems in Technology Enhanced Learning
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_12 · Cited by 367
Cited by
Beyond Parity: Fairness Objectives for Collaborative Filtering
Yao, S., & Huang, B. (2017) · arXiv · doi:10.48550/arxiv.1705.08804 · Cited by 169
Saint+: Integrating temporal features for ednet correctness prediction
Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448188 · Cited by 141
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
Shen, J. T., Yamashita, M., Prihar, E., Heffernan, N., Wu, X., Graff, B., & Lee, D. (2021) · arXiv · doi:10.48550/arxiv.2106.07340 · Cited by 47
Metadata
- Authors
- Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2010
- Volume
- 1
- Issue
- 2
- Pages
- 2811-2819
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2045871438
- Cited by
- 306
- References
- 23
- FWCI
- 7.56
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science. https://doi.org/10.1016/j.procs.2010.08.006
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
