Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique

References

• Adomavicius, G., and A. Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (6):734–491. doi:10.1109/TKDE.2005.99.
   Web of Science ®Google Scholar
• Aher, S. B., and L. M. R. J. Lobo. 2012. Course recommender system in E-learning. International Journal of Computer Science and Communication 3 (1):159–64.
   Google Scholar
• Al Mamunur, R., G. Karypis, and J. Riedl. 2008. Learning preferences of new users in recommender systems: An information theoretic approach. ACM SIGKDD Explorations Newsletter Journal 10 (2):90–100. doi:10.1145/1540276.1540302.
   Google Scholar
• Alashkar, T., S. Jiang, S. Wang, and Y. Fu (2017, February). Examples-rules guided deep neural network for makeup recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence ( Vol. 31, No. 1).
   Google Scholar
• Bernhard, T. J. (1997). Challenges and strategies for electrical engineering education. In 27th Annual Conference Frontiers in Education: Teaching and Learning in an Era of Change (pp. 1459–1462). Pittsburg, Pennsylvania, USA. doi:10.1109/FIE.1997.632708.
   Google Scholar
• Bhojak, H., S. Jain, and V. Muralidharan (2012). Instructor guided personalization of learningpath-adopting SCORM. In 4th International Congress on Engineering Education – ImprovingEngineering Education: Towards Sustainable Development. New York, USA. doi:10.1109/ICEED.2012.6779280.
   Google Scholar
• Bourkoukou, O., and E. El Bachari. 2018. Toward a hybrid recommender system for e-learning personnalization based on data mining techniques. JOIV: International Journal on Informatics Visualization 2 (4):271–78. doi:10.30630/joiv.2.4.158.
   Google Scholar
• Bourkoukou, O., and O. Achbarou. 2018. Weighting based approach for learning resources recommendations. JOIV: International Journal on Informatics Visualization 2 (3):104–09. doi:10.30630/joiv.2.3.124.
   Google Scholar
• Cayzer, S., and U. Aickelin (2002). A Recommender System based on the Immune Network.In Congress on Evolutionary Computation CEC2002, (pp. 807–813), Honolulu, USA. doi:10.1109/CEC.2002.1007029.
   Google Scholar
• Chen, C., P. Zhao, L. Li, J. Zhou, X. Li, and M. Qiu (2017a, April). Locally connected deep learning framework for industrial-scale recommender systems. In Proceedings of the 26th international conference on World Wide Web companion (pp. 769–770).
   Google Scholar
• Chen, J., H. Zhang, X. He, L. Nie, W. Liu, and T. S. Chua (2017b, August). Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 335–344).
   Google Scholar
• Chin Ming, H., C. Chih Ming, and C. Mei Hui (2005). Personalized Learning Path Generation Approach for Web-based Learning. In 4th WSEAS International Conference on E-activities (pp. 62–68). Miami, Florida, USA.
   Google Scholar
• Chin Ming, H., C. Chih Ming, C. Mei Hui, and C. Shin Chia (2007). Intelligent web-basedtutoring system with personalized learning path guidance. In 7th IEEE InternationalConference on Advanced Learning Technologies, ICALT 2007 (pp. 512–516). Nigata, Japan.
   Google Scholar
• Covington, P., J. Adams, and E. Sargin. (2016). Deep neural networks for youtube recommendations. In Recsys. 191–98.
   Google Scholar
• Cremonesi, P., F. Garzotto, S. Negro, A. V. Papadopoulos, and R. Turrin (2011). Looking for good Recommendations: A comparative Evaluation of Recommender Systems. In P. Campos, N. Graham, J. Jorge, N. Nunes, P. Palanque, and M. Winckler (Eds.), 13th IFIP TC 13 International Conference INTERACT,2011,(pp. 152–168).Lisbon, Portugal. doi:10.1007/978-3-642-23765-2_11.
   Google Scholar
• De Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci, F., and Semeraro, G. (2009). Preference learning in recommender systems. Preference Learning, 41, 41-55.
   Google Scholar
• El-Bishouty, M. M., T. W. Chang, S. Graf, and N. S. Chen. 2014. Smart e-course recommender based on learning styles. Journal of Computers in Education 1 (1):99–111. doi:10.1007/s40692-014-0003-0.
   Google Scholar
• Fleming, N. D. (1995, July). I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. In Research and development in higher education, Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA), HERDSA (Vol. 18, pp. 308–313).
   Google Scholar
• Gallego, D. L., E. Barra, A. Gordillo, and G. Huecas (2013).Enhanced recommendationsfor e-learning authoring tools based on a proactive context-aware recommender. In IEEE Frontiers in Education Conference (pp. 1393–1395). Oklahoma City, USA.doi:10.1109/FIE.2013.6685060.
   Google Scholar
• Gong, Y., and Q. Zhang (2016). Hashtag recommendation using attention-based convolutional neural network. In IJCAI, 2782–88.
   Google Scholar
• Gulzar, Z., A. A. Leema, and G. Deepak. 2018. Pcrs: Personalized course recommender system based on hybrid approach. Procedia Computer Science 125:518–24. doi:10.1016/j.procs.2017.12.067 .
   Google Scholar
• Hidasi, B., A. Karatzoglou, L. Baltrunas, and D. Tikk (2016). Session-based recommendations with recurrent neural networks. In Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico, 2–4 May 2016, 1–10.
   Google Scholar
• Hoic-Bozic, N., M. HolenkoDlab, and V. Mornar. 2016. Recommender system and web 2.0 Tools to enhance a blended learning model. IEEE Transactions on Education 59 (1):39–44. doi:10.1109/TE.2015.2427116.
   Web of Science ®Google Scholar
• Hu, B., C. Shi, W. X. Zhao, and P. S. Yu (2018, July). Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1531–40).
   Google Scholar
• Huang, Z., D. Zeng, and H. Chen. 2007. A comparison of collaborative-filtering recommendation algorithms for ecommerce. IEEE Intelligent Systems 22 (5):5. doi:10.1109/MIS.2007.4338497.
   Web of Science ®Google Scholar
• Huimin, Q., C. Ming, and X. Mingming (2010, May). A personalized resource recommendation system using data mining. In 2010 International Conference on E-Business and E-Government (pp. 5365–5368). IEEE.
   Google Scholar
• Imran, H., M. Belghis-Zadeh, T. W. Chang, and S. Graf. 2016. PLORS: A personalized learning object recommender system. Vietnam Journal of Computer Science 3 (1):3–13. doi:10.1007/s40595-015-0049-6.
   Google Scholar
• Kuzilek, J., M. Hlosta, and Z. Zdrahal, Open university learning analytics dataset, https://www.kaggle.com/vjcalling/oulad-open-university-learning-analytics-dataset
   Google Scholar
• Latha, C. B. C., and E. Kirubakaran. 2013. Personalized learning path delivery in webbased educational systems using a graph theory based approach. Journal of American Science 9 (12):981–92.
   Google Scholar
• Lu, J., A personalized e-learning material recommender system, Macquarie Scientific Publishing, pp. 374–379, 2004.
   Google Scholar
• Manouselis, N., H. Drachsler, K. Verbert, and E. Duval. 2012. Recommender Systems for Learning. Springer Science & Business Media.
   Google Scholar
• Marian, C. M., P. S. Popescu, and I. Costel (2015). Intelligent tutor recommender system for on-line educational environments. In 8th International Conference on Educational Data Mining (pp. 516–519). Madrid, Spain.
   Google Scholar
• Mubarak, A. A., H. Cao, and S. A. Ahmed. 2021. Predictive learning analytics using deep learning model in MOOCs’ courses videos. Education and Information Technologies 26 (1):371–92. doi:10.1007/s10639-020-10273-6.
   Web of Science ®Google Scholar
• Muthukumar, V., and N. Bhalaji. 2020. MOOCVERSITY-deep learning based dropout prediction in MOOCs over weeks. Journal of Soft Computing Paradigm (JSCP) 2 (3):140–52. doi:10.36548/jscp.2020.3.001.
   Google Scholar
• Negnevitsky, M. 2005. Artificial intelligence – A Guide to Intelligent Systems. London: Addison-Wesley, Pearson Education Ltd.
   Google Scholar
• Onah, D. F., and J. E. Sinclair (2015, March). Massive open online courses: An adaptive learning framework. In Proceedings of the 9th International Conference on Technology, Education and Development (pp. 1258–1266).
   Google Scholar
• Qiao, C., and X. Hu (2018, July). Discovering student behavior patterns from event logs: Preliminary results on a novel probabilistic latent variable model. In 2018 ieee 18th international conference on advanced learning technologies (ICALT) (pp. 207–211). IEEE.
   Google Scholar
• Romero, C., S. Ventura, and E. García. 2008. Data mining in course management systems: Moodle case study and tutorial. Computers & Education 51 (1):368–84. doi:10.1016/j.compedu.2007.05.016.
   Web of Science ®Google Scholar
• Santos, C. O., and G. J. Boticario. 2011. Requirements for semantic educational recommender systems in formal e-learning scenarios. Open Access Journal: Algorithms 4 (1):131–54. doi:10.3390/a4030131.
   Google Scholar
• Sengupta, S., S. Sahu, and R. Dasgupta. 2011. Construction of learning path using ant colony optimization from a frequent pattern graph. International Journal of ComputerScience 8 (6):314–21.
   Google Scholar
• Shishehchi, S., S. Y. Banihashem, N. A. M. Zin, and S. A. M. Noah (2011, June). Review of personalized recommendation techniques for learners in e-learning systems. In 2011 international conference on semantic technology and information retrieval (pp. 277–281). IEEE.
   Google Scholar
• Sunil, L., and D. Saini (2013). Design of a Recommender System for Web Based Learning. In World Congress on Engineering WCE2013 (pp. 3–8). London, England.
   Google Scholar
• Tejeda-Lorente, A., J. Bernabe-Moreno, C. Porcel, P. Galindo-Moreno, and E. Herrera-Viedma. 2015. A dynamic recommender system as reinforcement for personalized education by a fuzzy linguistic web system. Elsevier - Procedia ComputerScience 55 (1):1143–50. doi:10.1016/j.procs.2015.07.084.
   Google Scholar
• Wang, J., L. Yu, W. Zhang, Y. Gong, Y. Xu, B. Wang, … D. Zhang (2017, August). Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 515–24).
   Google Scholar
• Wang, Q., H. Yin, Z. Hu, D. Lian, H. Wang, and Z. Huang (2018, July). Neural memory streaming recommender networks with adversarial training. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2467–75).
   Google Scholar
• Xu, Z., T. Lukasiewicz, C. Chen, Y. Miao, and X. Meng (2017, August). Tag-aware personalized recommendation using a hybrid deep model. AAAI Press/International Joint Conferences on Artificial Intelligence.
   Google Scholar
• Yago, H., J. Clemente, D. Rodriguez, and P. Fernandez-de-cordoba. 2018. On-smmile: Ontology network-based student model for multiple learning environments. Data & Knowledge Engineering 115:48–67. doi:10.1016/j.datak.2018.02.002.
   Web of Science ®Google Scholar
• Zhang, H., T. Huang, L. V. Zhihan, S. Liu, and H. Yang. (2018). MOOCRC: A highly accurate resource Recommendation model for use in MOOC environments. Springer, Mobile Networks and Applications.
   Google Scholar
• Zhang, S., L. Yao, and A. Sun (2017). Deep learning based recommender system: A survey and new perspectives. arXiv, arXiv:1707.07435.
   Google Scholar
• Zheng, L., V. Noroozi, and Y. Ph. (2017). Joint deep modeling of users and items using reviews for recommendation. In WSDM.
   Google Scholar