Abstract
More and more higher education institutions are adopting computer based learning management system to boost learning of the students. Networking and collaboration through social media platforms are vital realities. Learning is not merely limited to class rooms, which is now independent of location and time. Understanding how students learn in this realm is a mighty challenge for teaching professionals. Fortunately, data is abundantly available through learning management systems and social media platforms. Analyzing this vast data could give an insight into how learning is happening in these days. Data mining techniques are vastly being used for this purpose. In this paper, we present a statistical analysis of e-leaning data obtained from SCHOLAT, a scholar oriented social networking system. The analysis aims at getting data oriented perspectives of learning, e.g., which factor to what extent impacts learning. The analysis revealed factors which positively or negatively affect learning achievement of the students, i.e., course final scores.
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References
Chen, B., Breyer, T.: Investigating instructional strategies for using social media in formal and informal learning. Int. Rev. Res. Open Distance Learn. 13(1), 87–104 (2012)
Hrastinski, S.: A theory of on-line learning as on-line participation. Comput. Educ. 52, 78–82 (2009)
Junco, R., Helbergert, G., Loken, E.: The effect of Twitter on college student engagement and grades. J. Comput. Assist. Learn. 27, 119–132 (2011)
Hurt, N.E., Moss, G.S., Bradley, C.L., Larson, L.R., Lovelace, M.D., Prevost, L.B., et al.: The ‘Facebook’ effect: college students’ perceptions of online discussions in the age of social networking. Int. J. Sch. Teach. Learn. 6(2), 2–14 (2012)
Fewkes, M., McCabe, M.: Facebook: learning tool or distraction? J. Digital Learn. Teacher Educ. 28(3), 92–98 (2012)
Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., Heiner, C.: An educational data mining tool to browse tutor-student interactions: Time will tell!. In: Proceedings of the Workshop on Educational Data Mining, pp. 15–22, Pittsburgh, USA (2005)
Dewey, J.: Experience and education, New York: Collier, 1938/1963
Vygotsky, L.S.: Mind in society: the development of higher psychological processes. Harvard University Press, Cambridge Mass (1978)
Cole, J.: Using Moodle. O’Reilly, Sebastopol (2005)
Dawson, S.: A study of the relationship between student social networks and sense of community. Educ. Technol. Soc. 11(3), 224–238 (2008)
Yu, A.Y., Tian, S.W., Vogel, D., Kwok, R.C.: Can learning be virtually boosted? an investigation of on-line social networking impacts. Comput. Educ. 55, 1494–1503 (2010)
Junco, R.: Too much face and not enough book: the relationship between multiple indices of Facebook use and academic performance. Comput. Hum. Behav. 28, 187–198 (2012)
Lamb, L., Johnson, L.: Bring back the joy: creative teaching, learning, and librarian-ship. Teach. Librarian 38(2), 61–66 (2010)
Klosgen, W., Zyttow, J.: Handbook of Data Mining And Knowledge Discovery. Oxford University Press, New York (2002)
Romero, C., Ventura, S., Garcia, E.: Data mining in course management systems: moodle case study and tutorial. Comput. Educ. 51(1), 368–384 (2008)
Romero, C., Ventura, S.: Data Mining in E-Learning, Southampton. Wit Press, UK (2006)
Mazza, R., Milani, C.: Exploring usage analysis in learning systems: gaining insights from visualization. In: Workshop on Usage Analysis in Learning Systems at 12th International Conference on Artificial Intelligence in Education, pp. 1–6, New York, USA (2005)
Mor, E., Minguillon, J.: E-learning personalization based on itineraries and long term navigational behavior. In: Proceedings of the 13th International World Wide Web Conference, pp. 264–265 (2004)
Talavera, L., Gaudioso, E.: Mining students data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL, pp. 17–23, Valencia, Spain (2004)
Akram, A., Chengzhou, F., Yong, T., Yuncheng, J., Xueqin, L.: Exposing the hidden to the eyes: analysis of SCHOLAT E-learning data. In: Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design, pp. 693–698 (2016)
Romero, C., Ventura, S.: Data mining in education. WIREs Data Min. Knowl. Disc. 3, 12–27 (2013). https://doi.org/10.1002/widm.1075
Huck, S.W.: Reading Statistics and Research, vol. 2(28). Pearson Education, Boston (2012)
Huck, S.W.: Reading Statistics and Research, vol. 2(24). Pearson Education, Boston (2012)
Huck, S.W.: Reading Statistics and Research, vol. 3(49). Pearson Education, Boston (2012)
Acknowledgment
This work is supported by the National Nature Science Foundation of China (Grant Nos. 61272066, 61272067, 61300104), the Applied Technology Research and Development Foundation of Guangdong Province (2016B010124008), the Technology Innovation Platform Project of Fujian Province (Grant Nos. 2009J1007, 2014H2005), the Fujian Collaborative Innovation Center for Big Data Applications in Governments.
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Akram, A., Fu, C., Tang, Y., Jiang, Y., Guo, K. (2018). Digging Deep Inside: An Extended Analysis of SCHOLAT E-Learning Data. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_44
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DOI: https://doi.org/10.1007/978-3-319-74521-3_44
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