Abstract
Teachers are never the only teacher in the class, especially in online e-learning environment. The key learner who is supposed to be more active and eager to spread knowledge and motivation to other classmates has a huge potentiality to improve the quality of teaching. However, the identification of such key learner is challenging which needs lots of human experience, especially when the contact channels between teachers and students are much more monotonous in online e-learning environment. Inspired by resistance distance theory, in this paper, we apply resistance distance and centrality into an interactive network of learners to identify key learner who can effectively motivate the whole class with discussion in e-learning platform. First, we define the terms of interactive network of learners with the node, edge, and graph. Then the distance between nodes is replaced with effective resistance distance to gain better understanding of propagation among the learners. Afterward, Closeness Centrality is utilized to measure the centrality of each learner in interactive network of learners. Experimental results show that the centrality we use can cover and depict the learners’ discussion activities well, and the key learner identified by our approach under apposite stimuli can effectively motivate the whole class’ learning performance.
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References
Rovai, A., Ponton, M., Wighting, M., Baker, J.: A comparative analysis of student motivation in traditional classroom and e-learning courses. Int. J. E-Learn. 6, 413–432 (2007)
Blagojević, M., Živadin, M.: A web-based intelligent report e-learning system using data mining techniques. Comput. Electr. Eng. 39(2), 465–474 (2013)
Chu, T.H., Chen, Y.Y.: With good we become good: understanding e-learning adoption by theory of planned behavior and group influences. Comput. Educ. s92–s93, 37–52 (2016)
Balaban, A.T., Klein, D.J.: Co-authorship, rational Erdős numbers, and resistance distances in graphs. Scientometrics 55(1), 59–70 (2002)
Wang, S., Hauskrecht, M.: Effective query expansion with the resistance distance based term similarity metric. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, pp. 715–716 (2010)
Schmidt, S.: Collaborative filtering using electrical resistance network models. In: The 7th Industrial Conference on Advances in Data Mining: Theoretical Aspects and Applications, Leipzig, Germany, pp. 269–282 (2007)
Fouss, F., Pirotte, A., Saerens, M.: The application of new concepts of dissimilarities between nodes of a graph to collaborative filtering. In: Workshop on Statistical Approaches for Web Mining (SAWM), Pisa, Italy (2004)
Kunegis, J., Schmidt, S., Albayrak, Ş., Bauckhage, C., Mehlitz, M.: Modeling collaborative similarity with the signed resistance distance kernel. In: Conference on ECAI 2008: European Conference on Artificial Intelligence, Patras, Greece, pp. 261–265 (2013)
Guo, G.Q., Xiao, W.J., Lu, B.: Similarity metric based on resistance distance and its applications to data clustering. Appl. Mech. Mater. 556–562, 3654–3657 (2014)
Aporntewan, C., Chongstitvatana, P., Chaiyaratana, N.: Indexing simple graphs by means of the resistance distance. IEEE Access 4(99), 5570–5578 (2017)
Ritzer, G.: The Blackwell encyclopedia of sociology. Math. Mon. 107(7), 615–630 (2007)
Badashian, A.S., Stroulia, E.: Measuring user influence in GitHub: the million follower fallacy. In: IEEE/ACM International Workshop on Crowdsourcing in Software Engineering, Austin, USA, pp. 15–21 (2016)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: ACM International Conference on Web Search and Data Mining, Hong Kong, China, pp. 45–54 (2011)
Boyd, D., Golder, S., Lotan, G.: Tweet, Tweet, Retweet: conversational aspects of retweeting on Twitter. In: Hawaii International Conference on System Sciences, Hawaii, USA, pp. 1–10 (2010)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: The Third ACM International Conference on Web Search and Data Mining, New York, USA, pp. 261–270 (2010)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: The 2008 International Conference on Web Search and Data Mining, Palo Alto, USA, pp. 231–240 (2008)
Kong, S., Feng, L., Sun, G., Luo, K.: Predicting lifespans of popular tweets in microblog. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, USA, pp. 1129–1130 (2012)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: International Conference on World Wide Web, Raleigh, USA, pp. 591–600 (2010)
Bozzo, E., Franceschet, M.: Resistance distance, closeness, and betweenness. Soc. Netw. 35(3), 460–469 (2013)
Acknowledgements
This research is supported by Major Project of the Tripartite Joint Fund of the Science and Technology Department of Guizhou Province under grant (LH[2015]7701).
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Lu, C., Zhang, F., Li, Y. (2020). Identifying Key Learner on Online E-Learning Platform: An Effective Resistance Distance Approach. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_49
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DOI: https://doi.org/10.1007/978-981-13-9710-3_49
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