Abstract
The aim of this study is to explore navigational patterns of university students in a learning management system (LMS). After a close review of the literature, a scarcity of research on the relation between online learners’ navigational patterns and their learning performance was found. To contribute to this research area, the study aims to examine whether there is a potential difference in navigational patterns of the learners in terms of their academic achievement (pass, fail). The data for the study comes from 65 university students enrolled in online Computer Network and Communication. Navigational log records derived from the database were converted into sequential database format. According to students’ achievement (pass, failure) at the end of the academic term, these data were divided into two tables. Page connections of the users were transformed into interaction themes, namely homepage, content, discussion, messaging, profile, assessment, feedback, and ask the instructor. Data transformed into sequential patterns by the researchers were organized in navigational pattern graphics by taking frequency and ratio into consideration. The z test was used to test the significance of the difference between the ratios calculated by the researchers. The findings of the research revealed that although learners differ in terms of their achievement, they draw upon similar processes in the online learning environments. Nevertheless, it was observed that students differ from each other when considering their system interaction durations. According to this, learning agents, interventional feedbacks, and leaderboards can be used to keep failed students in the online learning environment. Studies were also proposed on the ordering of these LMS navigational themes, which are important in the e-learning process. Findings from these studies can guide designers and researchers in the design of adaptive e-learning environments, which are also called next-generation digital learning environments.
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References
Akçapınar, G., Altun, A., & Aşkar, P. (2015). Modeling students’ academic performance based on their interactions in an online learning environment. Elementary Education Online, 14(3), 815–824.
Ally, M. (2008). Foundations of educational theory for online learning. Theory and Practice of Online Learning, 2, 15–44.
Altun, A. (2016). Understanding cognitive profiles in designing personalized learning environments. In B. Gros, Kinshuk, & M. Maina (Eds.), The future of ubiquitous learning (pp. 259–271). Berlin, Germany: Springer.
Bagarinao, R. T. (2015). Students’ navigational pattern and performance in an E-learning environment: A case from UP Open University, Philippines. Turkish Online Journal of Distance Education, 16(1), 101–111.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. Cambridge, UK: Cambridge University Press.
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (Vol. 13, pp. 61–75). New York, NY: Springer.
Chen, L. H. (2010). Web-based learning programs: Use by learners with various cognitive styles. Computers & Education, 54, 1028–1035.
Chen, S. Y., & Ford, N. J. (1998). Modelling user navigation behaviours in a hyper-media-based learning system: An individual differences approach. Knowledge Organization, 25(3), 67–78.
Dagger, D., O’Connor, A., Lawless, S., Walsh, E., & Wade, V. P. (2007). Service-oriented e-learning platforms: From monolithic systems to flexible services. IEEE Internet Computing, 11(3), 28–35.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.
Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation, and learning: An empirical study. Journal of Educational Multimedia and Hypermedia, 9(4), 281–312.
Galusha, J. M. (1998). Barriers to learning in distance education. Hattiesburg, MS: University of Southern Mississippi.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. London, UK: Taylor & Francis.
Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments-A context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.
Kim, M., Han, S., Cui, Y., & Lee, H. (2013). Design and implementation of mongoDB-based unstructured log processing system over cloud computing environment. Journal of Internet Computing and Services, 14(6), 71–84.
Lu, J., Yu, C. S., & Liu, C. (2003). Learning style, learning patterns, and learning performance in a WebCT-based MIS course. Information & Management, 40(6), 497–507.
Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. Internet and Higher Education, 24, 26–34.
Malikowski, S. R., Thompson, M. E., & Theis, J. G. (2007). A model for research in to course management systems: Bridge technology & learning theory. Journal of Educational Computing Research., 36(2), 149–173.
Martin, T., & Sherin, B. (2013). Learning analytics and computational techniques for detecting and evaluating patterns in learning: An Introduction to the Special Issue. Journal of the Learning Sciences, 22(4), 511–520. https://doi.org/10.1080/10508406.2013.840466
Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7.
O’Connor, C., Sceiford, E., Wang, G., Foucar-Szocki, D., & Griffin, O. (2003). Departure, abandonment, and dropout of e-learning: Dilemma and solutions. In TechLearn 2003 Conference.
Osmanbegović, E., Suljić, M., & Agić, H. (2015). Determining dominant factor for students performance prediction by using data mining classification algorithms. Tranzicija, 16(34), 147–158.
Premlatha, K. R., Dharani, B., & Geetha, T. V. (2016). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments, 24(6), 1054–1075.
Puntambekar, S., & Stylianou, A. (2005). Designing navigation support in hypertext systems based on navigation patterns. Instructional Science, 33(5–6), 451–481.
Puntambekar, S., Sullivan, S. A., & Hübscher, R. (2013). Analyzing navigation patterns to scaffold metacognition in hypertext systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (Vol. 26, pp. 261–275). New York, NY: Springer.
Rezaei, A. R., & Katz, L. (2004). Evaluation of the reliability and validity of the cognitive styles analysis. Personality and Individual Differences, 36, 1317–1327.
Rezende, F., & de Souza Barros, S. (2008). Students’ navigation patterns in the interaction with a mechanics hypermedia program. Computers & Education, 50(4), 1370–1382.
Ribbe, E., & Bezanilla, M. J. (2013). Scaffolding learner autonomy in online university courses. Digital Education Review, 24, 98–112.
Richey, R. C., Silber, K. H., & Ely, D. P. (2008). Reflections on the 2008 AECT definitions of the field. TechTrends, 52(1), 24–25.
Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Eds.). (2010). Handbook of educational data mining. Boca Raton, FL: CRC Press.
Roy, M., & Chi, M. T. (2003). Gender differences in patterns of searching the web. Journal of Educational Computing Research, 29(3), 335–348.
Şahin, M., Keskin, S., Özgür, A., & Yurdugül, H. (2017). Determination of interaction profiles based on learner characteristics in e-learning environments. Educational Technology Theory and Practice, 7(2), 172–192.
Şahin, M., Keskin, S. & Yurdugül, H. (2017, May). Determination of learner characteristics and interaction variables for e-learning design by feature selection algorithms 11. International Computer and Instructional Technology Symposium (ICITS 2017), Malatya, Turkey. http://icits2017.inonu.edu.tr/dosya/1493369308092697100.pdf
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30.
Wagner, E. D. (1994). In support of a functional definition of interaction. American Journal of Distance Education, 8(2), 6–26.
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Keskin, S., Şahin, M., Yurdugül, H. (2019). Online Learners’ Navigational Patterns Based on Data Mining in Terms of Learning Achievement. In: Sampson, D., Spector, J.M., Ifenthaler, D., Isaías, P., Sergis, S. (eds) Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-15130-0_7
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