Abstract
Learning performance analysis is such a research field that draws much attention from researchers though it has just been emerged in recent years. On the one hand, analyzing learning behaviors can help learners to choose their learning methods and allocate their study time in a more appropriate way. On the other hand, learning analysis can provide valuable feedbacks for teachers and administrators to improve teaching efficiency and quality. This paper studies and analyzes more than 640,000 learning data from the MOOC platform edX. A tree-based model along with an information gain measure is applied to identify the usefulness of data features. A back-propagation neural network model is further adopted to train data and achieve a prediction model of learning performance. In addition, a genetic algorithm calculates learning score conditions and return feedbacks as suggestions to learners. Experiment results demonstrate the effectiveness of the utilization of the methods in the predication of online learning performance.
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Song, Y., Chen, X.L., Hao, T.Y., Liu, Z.N., Lan, Z.X.: Exploring two decades of research on classroom dialogue using bibliometric analysis. Comput. Educ. 137(2019), 12–31 (2019)
Qu, Y., Yu, Z., Cong, H., Hao, T.: Pedagogical principle based e-learning exploration: a case of construction mediation training. In: Huang, T.-C., Lau, R., Huang, Y.-M., Spaniol, M., Yuen, C.-H. (eds.) SETE 2017. LNCS, vol. 10676, pp. 539–547. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71084-6_63
Kashiwao, T., Nakayama, K., Ando, S., et al.: A neural network-based local rainfall prediction system using meteorological data on the Internet: a case study using data from the Japan Meteorological Agency. Appl. Soft Comput. 56, 317–330 (2017)
Lou, W.: The research of high-dimensional data mining technology for big data. Shanghai University (2013)
Chen, E., Heritage, M., Lee, J.: Identifying and monitoring students’ learning needs with technology. J. Educ. Students Placed Risk 2010(3), 309–332 (2010)
Wang, X., Hao, T.: Designing interactive exercises for corpus-based English Learning with Hot Potatoes software. In: Huang, T.-C., Lau, R., Huang, Y.-M., Spaniol, M., Yuen, C.-H. (eds.) SETE 2017. LNCS, vol. 10676, pp. 485–494. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71084-6_57
Gu, X.Q., Zhang, J.L., Cai, H.Y.: Learning analysis: emerging data technology. J. Distance Educ. 30(01), 18–25 (2012)
Wei, S.P.: Learning analysis technology: mining the value of educational data in the age of big data. Mod. Educ. Technol. 02, 5–11 (2013)
Hao, T., Chen, W., Xie, H., Nadee, W., Lau, R. (eds.): SETE 2018. LNCS, vol. 11284. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03580-8. ISSN 0302-9743, ISBN 978-3-030-03579-2
Tu, Y.: Application of BP neural network in welfare lottery prediction. Intelligent Computing Branch of China Operational Research Society. Third China Intelligent Computing Congress. Intelligent Computing Branch of China Operational Research Society, vol. 2009, no. 4, pp. 16–19 (2009)
Xie, H.: Prediction of driving condition for plug-in hybrid electric vehicles. Chongqing University, Chongqing, China (2014)
Shu, Y., Gu, B.W., Zhang, Y.Q.: Performance prediction of building thermal insulation materials based on bp neural network algorithm. Build. Energy Conserv. 04, 52–55 (2017)
Lun, Z.M.: Short-term traffic flow prediction based on BP neural network optimized by modified chaotic genetic algorithms. Comput. Program. Skills Maintenance 05, 18–20 (2017)
He, C.K., Wu, M.: Analysis and prediction of learning behavior of educational big data based on edX Platform. Distance Educ. China 06, 54–59 (2016)
Li, D.Z., Du, L.Y.: Application of data mining-based student performance prediction. Heilongjiang Sci. Technol. Inf. 7, 156–157 (2017)
Sun, L., Cheng, Y.X.: Research and realization of learning achievement prediction of network education in the big data era - taking the english examination for undergraduate public courses as an example. Open Educ. Res. 21(03), 74–80 (2015)
He, W.: Examining students’ online interaction in a live video streaming environment using data mining and text mining. Comput. Hum. Behav. 29(1), 90–102 (2013)
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Shen, Y., Liu, W., Wu, Q., Chen, R., Liu, K. (2020). Leveraging Neural Network for Online Learning Performance Prediction and Learning Suggestion. In: Popescu, E., Hao, T., Hsu, TC., Xie, H., Temperini, M., Chen, W. (eds) Emerging Technologies for Education. SETE 2019. Lecture Notes in Computer Science(), vol 11984. Springer, Cham. https://doi.org/10.1007/978-3-030-38778-5_29
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