Abstract
Online learning has developed rapidly, but the participation of learners is very low. So it is of great significance to construct a prediction model of learning results, to identify students at risk in time and accurately. We select nine online learning behaviors from one course in Moodle, take one week as the basic unit and 5 weeks as the time node of learning behavior, and the aggregate data and sequence data of the first 5 weeks, the first 10 weeks, the first 15 weeks, the first 20 weeks, the first 25 weeks, the first 30 weeks, the first 35 weeks and the first 39 weeks are formed. Eight classic machine learning methods, i.e. Logistic Regression (LR), Naive Bayes (NB), Radom Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Iterative Dichotomiser3 (ID3), Classification and Regression Trees (CART), and Neural Network (NN), are used to predict the learning results in different time nodes based on aggregate data and sequence data. The experimental results show that sequence data is more effective than aggregate data to predict learning results. The prediction AUC of RF model on sequence data is 0.77 at the lowest and 0.83 at the highest, the prediction AUC of CART model on sequence data is 0.70 at the lowest and 0.83 at the highest, which are the best models of the eight classic prediction models. Then Radom Forest (RF) model, Classification and Regression Trees (CART) model, recurrent neural network (RNN) model and long short term memory (LSTM) model are used to predict learning results on sequence data; the experimental results show that long short term memory (LSTM) is a model with the highest value of AUC and stable growth based on sequence data, and it is the best model of all models for predicting learning results.
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References
Deng, R.Q., Benckendorff, P., Gannaway, D.: Progress and new directions for teaching and learning in MOOCs. Comput. Educ. 129, 48–60 (2019)
Jayaprakash, S.M., Moody, E.W., Lauría, J.M., Regan, R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1(1), 6–47 (2014)
Hu, Y.H., Lo, C.L., Shih, S.P.: Developing early warning systems to predict students’ online learning performance. Comput. Hum. Behav. 36, 469–478 (2014)
Chen, W.Y., Brinton, C.G., Cao, D., Mason-Singh, A., Lu, C., Chiang, M.: Early detection prediction of learning outcomes in online short-courses via learning behaviors. IEEE Trans. Learn. Technol. 12(1), 44–58 (2018)
Tempelaara, D.T., Rienties, B., Giesbers, B.: In search for the most informative data for feedback generation: learning analytics in a data-rich context. Comput. Hum. Behav. 47, 157–167 (2015)
Zacharis, N.Z.: A multivariate approach to predicting student outcomes in web-enabled blended learning courses. Internet High. Educ. 27, 44–53 (2015)
You, J.W.: Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High. Educ. 29, 23–30 (2016)
Marbouti, F., Diefes-Dux, H.A., Madhavan, K.: Models for early prediction of at-risk students in a course using standards-based grading. Comput. Educ. 103, 1–15 (2016)
Howarda, E., Meehana, M., Parnell, A.: Contrasting prediction methods for early warning systems at undergraduate level. Internet High. Educ. 37, 66–75 (2018)
Pardo, A., Han, F., Ellis, R.A.: Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Trans. Learn. Technol. 10(1), 82–92 (2017)
Gašević, D., Dawson, S., Rogersb, T., Gasevic, D.: Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High. Educ. 28, 68–84 (2016)
Fan, Y.Z., Wang, Q.: Prediction of academic performance and risk: a review of literature on predicative indicators in learning analytics. Distance Educ. China 1, 05–15+44+79 (2018). (in Chinese)
Conijn, R., Snijders, C., Kleingeld, A., Matzat, U.: Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Trans. Learn. Technol. 10(1), 17–29 (2017)
Moreno-Marcos, P.M., Alario-Hoyos, C., Muñoz-Merino, P.J., Kloos, C.D.: Prediction in MOOCs: a review and future research directions. IEEE Trans. Learn. Technol. 12(3), 384–401 (2019)
Arnold, K.E., Hall, Y., Street, S.G., Lafayette, W., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270 (2012)
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Wolf, A.: OU analyse: analysing at-risk students at The Open University. Learning Analytics Review, LAK15–1, 1–16 (2015)
Acknowledgements
This work is supported by the Fundamental Research Funds for Central Universi-ties (CCNU18JCK05), the National Science Foundation of China (No. 61532008; No. 61572223), the National Key Research and Development Program of China (No. 2017YFC0909502), and the Ministry of Education of Humanities and Social Science project (No. 20YJCZH046).
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Yuan, S., Huang, H., He, T., Hou, R. (2021). Early Detecting the At-risk Students in Online Courses Based on Their Behavior Sequences. In: Deze, Z., Huang, H., Hou, R., Rho, S., Chilamkurti, N. (eds) Big Data Technologies and Applications. BDTA WiCON 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-72802-1_2
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