Abstract
At present, online teaching has become more and more popular especially in the context of the current epidemic, and quality governance has become the internal needs of modern education development, while there is no simple and easy to use learning situation risk cognition method for specific online teaching class. In order to deal with this problem, in this study a data-driven method of learning situation risk cognition and measurement for online teaching is provided, which uses student initiative degree, concentration degree, duration degree and interaction degree to measure comprehensive learning effective degree and reflect student learning situation risk in an online class. Besides, normalized score earned by student in knowledge point test after online class is used to validate the calculation method designed, and the obtained results show that it is promising and easy to calculation. It provides a basis for decision making of students’ learning situation risk early warning and also provides a data-driven management method for the guarantee of online teaching quality, which has both academic and practical significance.
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References
Jishan, S.T., Rashu, R,I., Haque, N., et al. Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decis. Anal. 2015(1), 1–25 (2015)
Mat, U.B., Buniyamin, N., Arsad, P.M., et al.: An overview of using academic analytics to predict and improve students’ achievement: a proposed proactive intelligent intervention. In: ICEE. Proceedings of the IEEE 5th International Conference on Engineering Education, pp. 126–130. IEEE, Selangor (2013)
Christian, T.M., Ayub, M.: Exploration of classification using NB tree for predicting students’ performance. In: ICODSE. Proceedings of the International Conference on Data and Software Engineering, pp. 1–6. IEEE, Bandung (2014)
Romero, C., López, M.I., Luna, J.M., et al.: Predicting students’ final performance from participation in on-line discussion forums. Comput. Educ. 68, 458–472 (2013)
Zhang, W.Y.: Design and development of online learning environment evaluation model, index system and evaluation scale. China Educ. Technol. 7, 29–33 (2004)
Minaei-Bidgoli, B., Punch, W.F.: Using genetic algorithms for data mining optimization in an educational web-based system. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 2252–2263. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45110-2_119
Campbell, J.P.: Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. Indiana: Purdue University (2007)
Baker, R.S., Lindrum, D., Lindrum, M.J., et al.: Analyzing early at-risk factors in higher education e-learning courses. In: International Educational Data Mining Society. Proceedings of the 8th International Conference on Educational Data Mining, pp. 150–155. National University for Distance Education, Madrid (2015)
Yang, S.J.H., Lu, O.H.T., Huang, A.Y.Q., et al.: Predicting students’ academic performance using multiple linear regression and principal component analysis. J. Inform. Process. 26, 170–176 (2018)
Bravo, J., Sosnovsky, S., Ortigosa, A.: Detecting symptoms of low performance using prediction rules. International Working Group on Educational Data Mining. In: Barnes, T., Desmarais, M., Romero, C., et al. Proceedings of the 2nd Educational Data Mining Conference, pp. 31–40. Universidad de Cordoba, Cordoba (2009)
Sandeep, M.J., Erik, W.M., Eitel, J.M.L., et al.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1, 6–47 (2014)
Hamoud, A.K., Humadi, A.M., Awadh, W.A., et al.: Students’ success prediction based on bayes algorithms. Int. J. Comput. Appl. 7, 6–12 (2017)
Zhong, X.: Learning situation warning based on naive bayesian classifier based on feature weighting. J. Shanxi Datong Univ. (Nat. Sci.), 2019(2), 46–49 (2019)
Aybek, H.S.Y., Okur, M.R.: Predicting achievement with artificial neural networks: the case of anadolu university open education system. Int. J. Assess. Tools Educ. 3, 474–490 (2018)
Xing, W., Guo, R., Petakovic, E., et al.: Participation-based student final performance prediction model through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)
Mi, C., Deng, Q., Lin, J., et al.: A dynamic early warning method of student study failure risk based on fuzzy synthetic evaluation. Int. J. Perform. Eng. 4, 639–646 (2018)
Pistilli, M.D., Arnold, K.E.: Purdue signals: Mining real-time academic data to enhance student success. About Campus 3, 22–24 (2010)
Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: Using learning analytics to increase student success. LAK12. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM, Vancouver (2012)
Chen, X., Li, Y.: Interpretation of 2020 EDUCAUSE Horizon ReportTM (Teaching and Learning Edition) and its enlightenments: challenges and transformation of higher education under the epidemic situation. J. Distan. Educ. 2020(2), 3–16 (2020)
Wang, L., Ye, Y., Yang, X.: Design of online learning early-warning model based on big data. Mod. Educ. Technol. 7, 5–11 (2016)
Hu, Z., Zhu, L., Wu, G.: Construction of postgraduate education management information platform for quality monitoring and early warning. Mod. Educ. Technol. 10, 54–59 (2019)
Huang, T., Zhao, Y., Geng, J., Wang, H., Zhang, H., Yang, H.: Evaluation mechanism and method for data-driven precision learning. Mod. Distance Educ. Res. 1, 3–12 (2021)
Wang, Y., Zheng, Y.: Multimodal data fusion: the core driving force to solve the key problems of intelligent education. Mod. Distance Educ. Res. 2, 93–102 (2022)
Acknowledgment
We are very thankful that this study is supported by the General Program of Humanities and Social Sciences of the Ministry of Education of China (Grant No.19YJC880064), the Scientific Research Project of Hunan Provincial Department of Education (Grant No.19B447) and the Scientific Research Project of Huaihua University(Grant No.HHUY2018-40).
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Mi, C., Deng, Q., Zhao, C., Yin, D., Liu, Y. (2023). Learning Situation Risk Cognition and Measurement Based on Data-Driven. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1812. Springer, Singapore. https://doi.org/10.1007/978-981-99-2446-2_48
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DOI: https://doi.org/10.1007/978-981-99-2446-2_48
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