Abstract
The absence of learning emotional support has great impact on online learning. This paper presents an adaptive students-mood prediction framework to improve learning emotion in Mobile Learning (M-Learning), in which a mechanism is adopted to detect a kind of depressed students and provide guidance for tutors to help them with intervention. To effectively predict students’ mood level, we propose a novel fully-unsupervised multi-feature fusion joint sentiment-topic model and detail its inference. The proposed model adds an additional pedagogical features layer into the existing layers based on two classical models, and applies IG (Information Gain) feature selection to optimize performance. Experiments have been conducted on various students’ interactive emotion texts. The results show that our approach is a robust and reliable solution for automatic students’ mood prediction in M-Learning.
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References
Crompton, H., Burke, D.: The use of mobile learning in higher education: a systematic review. Comput. Educ. 123, 53–64 (2018)
Sharples, M., Spikol, D.: Mobile learning. In: Duval, E., Sharples, M., Sutherland, R. (eds.) Technology Enhanced Learning, pp. 89–96. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-02600-8_8
Jing, X.: Application of mobile learning system in phonetics teaching. In: International Conference on E-Education, E-Business, E-Management and E-Learning, pp. 19–23 (2017)
Chee, K.N., Yahaya, N., Hasan Ibrahim, N.H., Noor, M.: Review of mobile learning trends 2010–2015. J. Educ. Technol. Soc. 20(2), 113–126 (2017)
Veenhoven, R.: Healthy happiness: effects of happiness on physical health and the consequences for preventive health care. J. Happiness Stud. 9(3), 449–469 (2008)
Lopezmartinez, D., Rudovic, O., Picard, R.: Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning 56(2), 126–130 (2017)
Hwang, G.J., Wu, P.H.: Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008–2012 publications in selected SSCI journals. Inderscience Publishers (2017)
Sun, C.Y., Chang, K.Y.: Design and development of a location-based mobile learning system to facilitate english learning. Univ. Access Inf. Soc. 15(3), 1–13 (2016)
Sidan, L.I., Chen, Y., Qin, M., Wang, B., Zhou, X., Zhu, G., et al.: Mobile learning in medical education. China Medical Herald, China (2017)
Alrasheedi, M., Capretz, L.F.: Determination of critical success factors affecting mobile learning: a meta-analysis approach. Turk. Online J. Educ. Technol. 14(2), 41 (2018)
Kechaou, Z., Ammar, M.B., Alimi, A.M.: Improving e-learning with sentiment analysis of users’ opinions. In: IEEE Global Engineering Education Conference, vol. 72, pp. 1032–1038. IEEE, Shanghai (2011)
Al-Hmouz, A., Shen, J., Yan, J.: A machine learning based framework for adaptive mobile learning. In: Spaniol, M., Li, Q., Klamma, R., Lau, R.W.H. (eds.) ICWL 2009. LNCS, vol. 5686, pp. 34–43. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03426-8_4
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)
Bo, P., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales, pp. 115–124 (2005)
Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: Proceedings of CIKM, p. 6, Springer, Berlin (2005)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. Arch. 3(1), 993–1022 (2003)
Yut, L., Zhang, C., Shao, Y., Cui, B., Yut, L., Zhang, C., et al.: LDA*: a robust and large-scale topic modeling system. Proc. VLDB Endow. 10(11), 1406–1417 (2017)
Lu, B., Ott, M., Cardie, C., Tsou, B.K.: Multi-aspect sentiment analysis with topic models. In: IEEE International Conference on Data Mining Workshops, pp. 81–88. IEEE (2012)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: ACM Conference on Information and Knowledge Management, vol. 217, pp. 375–384. ACM (2009)
The Liru Online Courses Homepage. http://www.moodle.scnu.edu.cn. Accessed 7 Aug 2018
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61370229 and 61750110516), the S&T Projects of Guangdong Province (No.2016B010109008 and 2015A030401087), the GDUPS (2015), and the Innovation Project of Graduate School of South China Normal University (No.2017LKXM051).
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Wang, X., Zhao, M., Huang, C., Zhu, J., Tang, Y. (2019). Predicting Students’ Mood Level Using Multi-feature Fusion Joint Sentiment-Topic Model in Mobile Learning. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_23
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