A Survey of Learner and Researcher Related Challenges in E-learning Recommender Systems

In recent years, recommender systems have been widely used to support online learning in educational institutions. However, there are still some challenges experienced by learners and researchers hindering the full implementation and utilization of recommender systems in e-learning environments. In this paper, we review the main learner and researcher related challenges of e-learning recommender systems. This was achieved by carrying out a systematic literature review of relevant journal papers on e-learning recommender systems with a view to identifying and classifying the challenges as either learner or researcher challenges. The results of the survey reveal that successful implementation and utilization of e-learning recommender systems is hindered by some challenges categorized in this review as learner and researcher challenges. The paper also identifies some possible solutions from different studies for alleviating the challenges as well as the limitations. The implications of this study will be vital in assisting learners and educational institutions utilize recommender systems to support online teaching and learning.

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

[2]  Gordon I. McCalla,et al.  A Multidimensional Paper Recommender: Experiments and Evaluations , 2009, IEEE Internet Computing.

[3]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[4]  Zoran Budimac,et al.  E-Learning personalization based on hybrid recommendation strategy and learning style identification , 2011, Comput. Educ..

[5]  Hendrik Drachsler,et al.  Recommender Systems in Technology Enhanced Learning , 2011, Recommender Systems Handbook.

[6]  Hung Nguyen,et al.  A Context-Aware Recommendation Framework in E-Learning Environment , 2015, FDSE.

[7]  Tiffany Ya Tang,et al.  Further Thoughts on Context-Aware Paper Recommendations for Education , 2014, Recommender Systems for Technology Enhanced Learning.

[8]  Abdelwahab Hamou-Lhadj,et al.  Educational Recommender Systems: A Pedagogical-Focused Perspective , 2013 .

[9]  Iraklis Varlamis,et al.  A Trust-Aware System for Personalized User Recommendations in Social Networks , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Miguel Torres Ruiz,et al.  An ontology-based approach for representing the interaction process between user profile and its context for collaborative learning environments , 2015, Comput. Hum. Behav..

[11]  Enrique Herrera-Viedma,et al.  A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling , 2015, Inf. Sci..

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

[13]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[14]  Yi Li,et al.  A hybrid recommendation algorithm adapted in e-learning environments , 2012, World Wide Web.

[15]  John K. Tarus,et al.  Challenges of implementing e-learning in Kenya: A case of Kenyan public universities , 2015 .

[16]  Zafar Ali,et al.  Recommender Systems: Issues, Challenges, and Research Opportunities , 2016 .

[17]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[18]  Katrien Verbert,et al.  Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities , 2016, Expert Syst. Appl..

[19]  Katrien Verbert,et al.  Panorama of Recommender Systems to Support Learning , 2015, Recommender Systems Handbook.

[20]  Friedrich-Alexander-University Erlangen-Nuremberg,et al.  Challenges for Nutrition Recommender Systems , 2011 .

[21]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[22]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[23]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[24]  Zhendong Niu,et al.  A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining , 2017, Future Gener. Comput. Syst..

[25]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[26]  John K. Tarus,et al.  E‐Learning in Kenyan Universities: Preconditions for Successful Implementation , 2015 .

[27]  Hangjung Zo,et al.  Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty , 2012, Comput. Educ..

[28]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[29]  Christoph Rensing,et al.  Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey , 2015, IEEE Transactions on Learning Technologies.

[30]  Erik Duval,et al.  Dataset-Driven Research to Support Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[31]  Hendrik Drachsler,et al.  Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model , 2008, Int. J. Learn. Technol..

[32]  Caroline Herssens,et al.  Knowledge-Based Recommendation Systems: A Survey , 2014, Int. J. Intell. Inf. Technol..

[33]  Dragan Gasevic,et al.  Ontologies for Effective Use of Context in e-Learning Settings , 2007, J. Educ. Technol. Soc..

[34]  Jano Moreira de Souza,et al.  Bringing knowledge into recommender systems , 2013, J. Syst. Softw..

[35]  Hendrik Drachsler,et al.  Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning , 2009, J. Digit. Inf..

[36]  Mojtaba Salehi,et al.  Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree , 2013, Knowl. Based Syst..