LSBCTR: A Learning Style-Based Recommendation Algorithm

This Research Full Paper presents a hybrid algorithm for the recommendation of Learning Objects (LO) aimed at students’ learning profiles. In this sense, the Learning Styles-based Collaborative Topic Recommender (LSBCTR) algorithm was developed based on the Collaborative Topic Regression (CTR) model, a hybrid recommendation algorithm that combines a method of Collaborative Filtering (CF) and probabilistic topic modeling. The Learning Style is incorporated into the CTR to predict LO classification. The proposed model controls which classifications are more effective in the students’ learning process and which LO recommendations fit better to the student’s learning profile. Experiments were carried out in a real-world dataset collected from a Virtual Learning Environment (VLE) that was based on the inventory proposed by Felder and Soloman.

[1]  André Prisco Vargas,et al.  Um Sistema de Recomendação Baseado em um Modelo Cognitivo de Aprendizagem , 2017 .

[2]  Nima Jafari Navimipour,et al.  Recommender systems: A systematic review of the state of the art literature and suggestions for future research , 2018, Kybernetes.

[3]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[4]  Anas Alzogbi.  Time-aware Collaborative Topic Regression: Towards Higher Relevance in Textual Item Recommendation , 2018, BIRNDL@SIGIR.

[5]  Erik Duval,et al.  Context-Aware Recommender Systems for Learning: A Survey and Future Challenges , 2012, IEEE Transactions on Learning Technologies.

[6]  Thiago de Paulo Faleiros,et al.  Modelos probabilísticos de tópicos: desvendando o latent Dirichlet allocation. , 2016 .

[7]  Hai Liu,et al.  A content-based recommendation algorithm for learning resources , 2017, Multimedia Systems.

[8]  Hugo Minni,et al.  Identifier management and resolution: conforming the IEEE standard for learning object metadata , 2008 .

[9]  Tao Dai,et al.  Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network , 2017, Journal of Ambient Intelligence and Humanized Computing.

[10]  Zhendong Niu,et al.  Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning , 2017, Artificial Intelligence Review.

[11]  Yan Wang,et al.  EXPLORE: EXPLainable item-tag CO-REcommendation , 2019, Inf. Sci..

[12]  Jianjun Cao,et al.  Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization , 2018, IEEE Access.

[13]  Joseph A. Konstan,et al.  Introduction to recommender systems , 2008, SIGMOD Conference.

[14]  Xiaoyi Deng,et al.  A Trust-aware Neural Collaborative Filtering for E-learning Recommendation , 2018 .

[15]  Melvin A. Ballera,et al.  Classification of Learning Styles in Virtual Learning Environment Using J48 Decision Tree. , 2017 .

[16]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[17]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[18]  Richard M. Felder,et al.  Index of Learning Styles , 2019 .

[19]  Itana Stiubiener,et al.  Sistemas Híbridos para Recomendações Educacionais: Uma Revisão Sistemática da Literatura , 2019, Anais do XXX Simpósio Brasileiro de Informática na Educação (SBIE 2019).

[20]  Miller M. Mendes,et al.  Uma Abordagem Para a Organização Automática de Objetos de Aprendizagem em Ambientes Educacionais Ubíquos com Base em Estilos de Aprendizagem , 2020 .

[21]  Lior Rokach,et al.  Recommender Systems: Introduction and Challenges , 2015, Recommender Systems Handbook.

[22]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[23]  Alexandros Nanopoulos,et al.  Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions , 2015, Artificial Intelligence Review.

[24]  K. Pichara,et al.  Understanding Learning Resources Metadata for Primary and Secondary Education , 2018, IEEE Transactions on Learning Technologies.

[25]  Ying He,et al.  A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles , 2019, 2019 International Conference on Innovative Trends in Computer Engineering (ITCE).

[26]  Graça Bressan,et al.  e-LORS: uma abordagem para recomendação de objetos de aprendizagem , 2012 .

[27]  R. Felder,et al.  Learning and Teaching Styles In Foreign and Second Language Education , 1995 .

[28]  Dit-Yan Yeung,et al.  Relational Stacked Denoising Autoencoder for Tag Recommendation , 2015, AAAI.

[29]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[30]  Ke Wang,et al.  Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks , 2014, ECIR.

[31]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[32]  Hongrong Cheng,et al.  An Efficient and Simple Graph Model for Scientific Article Cold Start Recommendation , 2016, ER.