Abstract
With the development of the Internet, e-learning has become a new trend for education. However, unlike traditional learning that is face-to-face, e-learning systems construct an environment where learners control their learning process. Many issues have occurred in online learning systems, such as low efficiency, high dropout rates, poor grades and so on. One of the leading causes is students’ low interest in e-learning content, and they cannot find attractive learning materials. Learning resource recommendations can solve this problem by recommending materials that learners may like. However, traditional recommendation methods omit that user’s identity as a student and face underperformance. In this paper, a new learning resource recommendation method based on Online Learning Style is proposed. By integrating learning style characteristics into collaborative filtering algorithm with association rules mining, experimental results showed that the proposed method achieved 25% improvement compared to the method without learners’ features.
Supported by National Natural Science Foundation of China (No. 61977003).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abrahamian, E., Weinberg, J.B., Grady, M., Stanton, C.M.: The effect of personality-aware computer-human interfaces on learning. J. Univers. Comput. Sci. 10(1), 17–27 (2004)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Al-Fraihat, D., Joy, M., Sinclair, J., et al.: Evaluating e-learning systems success: an empirical study. Comput. Hum. Behav. 102, 67–86 (2020)
Bourkoukou, O., El Bachari, E., El Adnani, M.: A recommender model in e-learning environment. Arab. J. Sci. Eng. 42(2), 607–617 (2017)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theor. Methods 3(1), 1–27 (1974)
Cao, W., Zhou, C., Wu, Y., Ming, Z., Xu, Z., Zhang, J.: Research progress of zero-shot learning beyond computer vision. In: Qiu, M. (ed.) ICA3PP 2020. LNCS, vol. 12453, pp. 538–551. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60239-0_36
Chen, H., Yin, C., Li, R., Rong, W., Xiong, Z., David, B.: Enhanced learning resource recommendation based on online learning style model. Tsinghua Sci. Technol. 25(3), 348–356 (2019)
Coffield, F., et al.: Learning styles and pedagogy in post-16 learning: a systematic and critical review (2004)
Dunn, R., Griggs, S.A., Olson, J., Beasley, M., Gorman, B.S.: A meta-analytic validation of the Dunn and Dunn model of learning-style preferences. J. Educ. Res. 88(6), 353–362 (1995)
Felder, R.M., Silverman, L.K., et al.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)
Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)
Giovannella, C.: What can we learn from long-time lasting measurements of felder-silverman’s learning styles? In: 2012 IEEE 12th International Conference on Advanced Learning Technologies, pp. 647–649 (2012)
Kolb, A.Y.: The kolb learning style inventory-version 3.1 2005 technical specifications. Boston, MA: Hay Resource Direct 200(72), 166–171 (2005)
Kuzilek, J., Hlosta, M., Zdrahal, Z.: Open university learning analytics dataset. Sci. Data 4(1), 1–8 (2017)
Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., Huang, Z.: From zero-shot learning to cold-start recommendation. Proc. AAAI Conf. Artif. Intell. 33, 4189–4196 (2019)
Li, R., Yin, C.: Analysis of online learning style model based on k-means algorithm. In: 3rd International Conference on Economics, Management, Law and Education (EMLE 2017), pp. 692–697 (2017)
Litzinger, T.A., Lee, S.H., Wise, J.C.: A study of the reliability and validity of the felder-soloman index of learning styles. In: Proceedings of the 2005 American Society for Education Annual Conference and Exposition, pp. 1–16 (2005)
Liu, Y.: Study on application of apriori algorithm in data mining. In: 2010 Second International Conference on Computer Modeling and Simulation, vol. 3, pp. 111–114 (2010)
Lourenco, J., Varde, A.S.: Item-based collaborative filtering and association rules for a baseline recommender in e-commerce. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 4636–4645 (2020)
Marton, F., Hounsell, D., Entwistle, N.J.: The experience of learning: implications for teaching and studying in higher education (1997)
Myers, I.B.: A Guide to the Development and Use of the Myers-Briggs Type Indicator: Manual (1985)
Nafea, S.M., Siewe, F., He, Y.: A novel algorithm for course learning object recommendation based on student learning styles. In: 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 192–201 (2019)
Nafea, S.M., Siewe, F., He, Y.: On recommendation of learning objects using felder-silverman learning style model. IEEE Access 7, 163034–163048 (2019)
Obidallah, W.J., Raahemi, B., Ruhi, U.: Clustering and association rules for web service discovery and recommendation: a systematic literature review. SN Comput. Sci. 1(1), 1–33 (2019). https://doi.org/10.1007/s42979-019-0026-8
Soonthornphisaj, N., Rojsattarat, E., Yim-ngam, S.: Smart e-learning using recommender system. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNAI), vol. 4114, pp. 518–523. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-37275-2_63
Xie, Z., Cao, W., Ming, Z.: A further study on biologically inspired feature enhancement in zero-shot learning. Int. J. Mach. Learn. Cybern. 12(1), 257–269 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yan, L., Yin, C., Chen, H., Rong, W., Xiong, Z., David, B. (2021). Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-82153-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82152-4
Online ISBN: 978-3-030-82153-1
eBook Packages: Computer ScienceComputer Science (R0)