Ngai Cheong
ABSTRACT
With the advancement of technology and the development of IT, such as big data, artificial intelligence (AI), and optimization theory have triggered revolutions in many fields. Along with the massive digital dataset, it also gives the reform motivation to promote traditional teaching and learning. How to find the appropriate information that students are interested, valuable, and easy to be understood in the vast amount of information, and bring their creative ability, is still a difficult thing. A Recommender System (RS) for e-learning based on learners’ Science Knowledge (SK) is a powerful tool to solve such problems, which can guarantee the quality and efficiency of the science teaching and learning in the international context. It is one of science research directions with great research value. This paper discusses the overall framework design of a wisdom RS for conceptual learning (CL) in science, which analysis models are based on SK of the students, using a big data software platform. The research sample is made of totally 621 junior students who take the subject of an introductory in science concepts (SC) in Computer Studies of Program, Macao Polytechnic Institution, from 2006 to 2021 academic year. According to the students’ SK, their CL methods have been classified into six types of thinking modes, which has three different corresponding understanding levels for each mode. The appropriate recommended materials to the students who are interested in the information provided, such as, text-based teaching materials, computer assisted materials, simulation tools and game activities, can increases the motivations of the students to learn, considered their different characteristics and understanding. The performance of this RS has been tracked over a period of 16 years, which can effectively improve the personalized teaching quality in the area of computer science, since it may be useful in heightening students’ motivation and interest in CL in science. The recommendation algorithm presented in this paper may be applied for a similar fashion across different domains, topics and contexts.
- Ingrid Nunes, Dietmar Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction, vol. 27, pp. 393.Google Scholar
Digital Library
- Ngai Cheong. 2019. Application and Research of Modelling Intelligent Learning Systems based on Recommendation System, Proceedings of The 11th Higher Education International Conference on Innovation and Developments in TL Quality Assurance, MacauGoogle Scholar
- Ngai Cheong. 2018. Teaching in engineering and science areas, From “Learning and Practicing, to “Practicing and Learning”, Proceedings of The 10th Higher Education International Conference on Innovation and Developments in TL Quality Assurance, MacauGoogle Scholar
- George A. Sielis, Christos Mettouris, Aimilia Tzanavari, George A. 2012. Papadopoulos, Educational Recommender Systems and Technologies.Google Scholar
- Pearl Pu, Li Chen & Rong Hu. 2012. Evaluating recommender systems from the user's perspective: survey of the state of the art, User Modeling and User-Adapted Interaction, vol. 22, pp. 317.Google ScholarDigital Library
- Michael Jugovac and Dietmar Jannach. 2017. Interacting with Recommenders-Overview and Research Directions, ACM Transactions on Interactive Intelligent Systems, vol. 7, pp. 1.Google ScholarDigital Library
- Ninos P. Maleka, Joshua C. Hallb, Collin Hodgesc. 2014. A review and analysis of the effectiveness of alternative teaching methods on student learning in economics. Perspectives on Economic Education Research, 9(1), 75-85.Google Scholar
- Allison J. Jaeger, Jennifer Wiley & Thomas Moher. 2016. Leveling the playing field: Grounding learning with embedded simulations in geoscience. Cognitive Research: Principles and Implications1(1), 23.Google Scholar
- Aher, S.B., Lobo, L. 2013. Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowledge-Based Systems, 51:1-14.Google ScholarDigital Library
- Ghauth, K.I., Abdullah, N.A. 2010. Learning materials recommendation using good learners’ ratings and content-based filtering”, Educational Technology Research and Development, 58(6):711-727.Google ScholarCross Ref
- Liang G., Kong, W.N., Luo, J.Z. 2006. Courseware recommendation in e-learning system, Berlin: Springer,.10-24.Google Scholar
- Milićević, A.K., Vesin, B., Ivanović, M., Budimac, Z.E. (2011). Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3):885-899.Google ScholarDigital Library
- Kurilovas, E., Zilinskiene, I., Dagiene, V. (2015). Recommending suitable learning paths according to learners’ preferences: Experimental research results. Computers in Human Behavior, 51:945-951.Google ScholarDigital Library
- Intayoad W., Becker, T., Temdee, P. (2017). Social Context-Aware Recommendation for Personalized Online Learning”, Wireless Personal Communications, 97(1):163-179.Google ScholarDigital Library
- Zhou, Y. W., Huang, C. Q. (2018). Personalized learning full-path recommendation model based on LSTM neural networks. Information Sciences, 444:135-152.Google ScholarCross Ref
- Liao, Y. C., Liao, C. W., Chen, C. H., & Liao, Y. H. (2016). A study on learning effects of integrating information technology into electronics curriculum teaching. International Journal of Information and Education Technology,6(11), 831.Google ScholarCross Ref
- Neo, M., & Neo, T. K. 2009. Engaging students in multimedia-mediated Constructivist learning-Students’ perceptions. Journal of Educational Technology & Society, 12(2), 254.Google Scholar
- Amirault J. Ray. 2015. Technology transience and the challenges it poses to higher education. Quarterly Review of Distance Education, 16(2), 1.Google Scholar
Index Terms
- Knowledge-based Recommender System of Conceptual Learning in Science
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