Abstract
With the increasing use of blended learning approaches in classroom, various kinds of technologies are incorporated to provide digital teaching and learning resources to support students. These resources are often centralized in learning management systems (LMSs), which also store valuable learning data of students. The data could assist teachers in their pedagogical decision making but they are often not well utilized. This paper proposes the use of data mining and visualization techniques as learning analytics to provide a more comprehensive overview of students’ learning online based on log data from LMSs . The focus of this study is the discovery of frequent navigational patterns by sequential pattern mining techniques and the demonstration of how presentation of patterns through hierarchical clustering and sunburst visualization could facilitate the interpretation of patterns. The data in this paper were collected from a blended statistics course for undergraduate students.
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References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE (1995)
Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanced Learn. 4(5–6), 304–317 (2012)
Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In: MICAI 2008, pp. 765–778 (2008)
GĂłmez-Aguilar, D.A., Hernández-GarcĂa, A., GarcĂa-Peñalvo, F.J., TherĂłn, R.: Tap into visual analysis of customization of grouping of activities in elearning. Comput. Hum. Behav. 47, 60–67 (2015)
Graf, S., Liu, T.C.: Analysis of learners’ navigational behaviour and their learning styles in an online course. J. Comput. Assist. Learn. 26(2), 116–131 (2010)
Hirate, Y., Yamana, H.: Generalized sequential pattern mining with item intervals. J. Comput. 1(3), 51–60 (2006)
Kang, J., Liu, M., Qu, W.: Using gameplay data to examine learning behavior patterns in a serious game. Comput. Hum. Behav. 72, 757–770 (2016)
Kerr, D.: Visualizing changes in strategy use across attempts via state diagrams: a case study. Int. J. Comput. Games Technol. 2016, 4 (2016)
Kong, S.C., Song, Y.: An experience of personalized learning hub initiative embedding BYOD for reflective engagement in higher education. Comput. Educ. 88, 227–240 (2015)
Mahajan, R., Sodhi, J., Mahajan, V.: Usage patterns discovery from a web log in an Indian e-learning site: a case study. Educ. Inf. Technol. 21(1), 123–148 (2016)
Mazza, R.: Visualization in educational environments. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.D. (eds.) Handbook of Educational Data Mining, pp. 9–26 (2010)
Mooney, C.H., Roddick, J.F.: Sequential pattern mining — approaches and algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013)
Poon, L.K.M., Kong, S.C., Wong, M., Yau, T.: Mining sequential patterns of students’ access on learning management system. In: The Second International Conference on Data Mining and Big Data (DMBD 2017) (2017)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE (2001)
Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern-growth methods. J. Intell. Inf. Syst. 28, 133–160 (2007)
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2009)
Psaromiligkos, Y., Orfanidou, M., Kytagias, C., Zafiri, E.: Mining log data for the analysis of learners behaviour in web-based learning management systems. Oper. Res. 11(2), 187–200 (2011)
Reimann, P., Hesse, F., Freebody, P., Cierniak, G., Imhof, B., Wasson, B., Hansen, C., Utz, W., Vatrapu, R., Bull, S., et al.: Supporting teachers in capturing and analyzing data in the technology-rich classroom. In: Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012), pp. 33–40. International Society of the Learning Sciences (2012)
Rodden, K.: Applying a sunburst visualization to summarize user navigation sequences. IEEE Comput. Graph. Appl. 34(5), 36–40 (2014)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor. Newsl. 1(2), 12–23 (2000)
Van Barneveld, A., Arnold, K.E., Campbell, J.P.: Analytics in higher education: establishing a common language. EDUCAUSE Learn. Initiative 1(1), 1–11 (2012)
Zhou, M.: Using traces to investigate self-regulatory activities: a study of self-regulation and achievement goal profiles in the context of web search for academic tasks. J. Cogn. Educ. Psychol. 12(3), 287–305 (2013)
Zhou, M., Xu, Y., Nesbit, J.C., Winne, P.H.: Sequential pattern analysis of learning logs: methodology and applications. In: Handbook of Educational Data Mining, pp. 107–121 (2010)
Ziebarth, S., Chounta, I.A., Hoppe, H.U.: Resource access patterns in exam preparation activities. In: Design for Teaching and Learning in a Networked World, pp. 497–502. Springer (2015)
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The study was funded by Teaching Development Grant (Ref: HKIED7/T&L/12-15) under the Hong Kong University Grants Committee.
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Poon, L.K.M., Kong, SC., Yau, T.S.H., Wong, M., Ling, M.H. (2017). Learning Analytics for Monitoring Students Participation Online: Visualizing Navigational Patterns on Learning Management System. In: Cheung, S., Kwok, Lf., Ma, W., Lee, LK., Yang, H. (eds) Blended Learning. New Challenges and Innovative Practices. ICBL 2017. Lecture Notes in Computer Science(), vol 10309. Springer, Cham. https://doi.org/10.1007/978-3-319-59360-9_15
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