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Analysis and Prediction of the Factors Influencing Students’ Grades Based on Their Learning Behaviours in MOOCs

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Computer Science and Education (ICCSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1812))

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Abstract

The outbreak of COVID-19 brought new challenges to learning and teaching, and MOOCs (massive open online courses), as online distance learning platforms, provide new opportunities for teaching and learning activities. However, student learning efficiency is difficult to ensure in distance learning. Researchers have studied the relationship between students’ grades and behaviours such as forum participation and video viewing; however, less research has been performed on students’ submission behaviours. In this paper, we investigate the influence of learning attitudes reflected by students’ submission behaviour and the trend in attitude change on grades. First, by studying students’ submission behaviours, we identify new features that affect students’ grades, such as students’ resubmission behaviours. Second, we define positive attitudinal trends that students possess through student behaviour studies: more adequate code, more page viewing actions, and more aggressive submission details performance. Finally, we use the selected features to predict the students’ performance. In the experiment, we predict student performance with an accuracy of 86.48%. This study will help teachers understand students’ attitudes based on student behaviours and identify students who are struggling academically.

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References

  1. Coman, C.,  Țîru, L.G., Meseșan-Schmitz, L., Stanci, C., Bularca, M.C.: Online teaching and learning in higher education during the coronavirus pandemic: students’ perspective. Sustainability 12(24), 10367 (2020)

    Google Scholar 

  2. Ayanbode, O.F., Fagbe, A., Owolabi, R., Oladipo, S., Ewulo, O.R., Islam, A.: Students’ interactions, satisfaction and perceived progress in an online class: empirical evidence from Babcock university Nigeria. Cogent Educ. 9, 1 (2022)

    Google Scholar 

  3. Meier, Y., Xu, J., Atan, O., van der Schaar, M.: Predicting grades. In: IEEE Transactions on Signal Processing, vol. 64, no. 4, pp. 959–972 (2016)

    Google Scholar 

  4. Hooshyar, D., Yang, Y.: Predicting course grade through comprehensive modelling of students’ learning behavioral pattern. Complexity 2021, 7463631, 12 (2021)

    Google Scholar 

  5. Wu, M., Zhao, H., Yan, X., Guo, Y., Wang, K.: Student achievement analysis and prediction based on the whole learning process. In: 2020 15th International Conference on Computer Science & Education (ICCSE), pp.123–128 (2020)

    Google Scholar 

  6. Liao, P., Xu, J., Gong, S., Liu, W., Yi, Y.: Clustering analysis of learners’ watching sequences on MOOC videos. In: 2021 16th International Conference on Computer Science & Education (ICCSE), pp. 111–116 (2021)

    Google Scholar 

  7. Wang, H., Hao, X., Jiao, W., Jia, X.: Causal association analysis algorithm for mooc learning behavior and learning effect. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 202–206 (2016)

    Google Scholar 

  8. EI, S., Ghada, R.: Understanding how learners use massive open online courses and why they drop out: thematic analysis of an interview study in a developing country. J. Educ. Comput. Res. 55, 0735633116681302 (2016)

    Google Scholar 

  9. Zhao, C., Mi, C.: A Study on the differences of attitude learning and learning behavior sequences for university computer MOOC+ SPOC course. In: 2020 15th International Conference on Computer Science & Education (ICCSE), pp. 365–369 (2020)

    Google Scholar 

  10. Chen, P., Chen, Y.: Massive open online course study group: interaction patterns in face-to-face and online (facebook) discussions. Front. Psychol. 12, 1664–1078 (2022)

    Article  Google Scholar 

  11. Onah, D.F.O., Pang, E.L.L., Sinclair, J.E.: Cognitive optimism of distinctive initiatives to foster self-directed and self-regulated learning skills: a comparative analysis of conventional and blended-learning in undergraduate studies. Educ. Inf. Technol. 25(5), 4365–4380 (2020). https://doi.org/10.1007/s10639-020-10172-w

    Article  Google Scholar 

  12. Rienties, B., Toetenel, L.: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules. Comput. Human Behav. 60, 333–341, 0747–5632 (2016)

    Google Scholar 

  13. Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., Loumos, V.: Early and dynamic student achievement prediction in e-learning courses using neural networks. J. Am. Soc. Inform. Sci. Technol. 60(2), 372–380 (2014)

    Article  Google Scholar 

  14. Fang, N., Lu, J.G.: A Decision tree approach to predictive modeling of student performance in engineering dynamics. Int. J. Eng. Educ. 26, 87–95 (2010)

    Google Scholar 

  15. Er, E., Gómez-Sánchez, E., Bote-Lorenzo, M.L., Dimitriadis, Y., Asensio-Pérez, J.I.: Generating actionable predictions regarding MOOC learners’ engagement in peer reviews. Behav. Inf. Technol. 39, pp. 1356–1373 (2020)

    Google Scholar 

  16. Huang, S., Fang, N.: Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput. Educ. 61, 133–145 (2013)

    Google Scholar 

  17. Injadat, M., Moubayed, A., Nassif, A.B., Shami, A.: Multi-split optimized bagging ensemble model selection for multi-class educational data mining. Appl. Intell. 50(12), 4506–4528 (2020). https://doi.org/10.1007/s10489-020-01776-3

    Article  Google Scholar 

  18. Migueis, V.L., Freitas, A., Garcia, P.J.V., Silva, A.: Early segmentation of students according to their academic performance: a predictive modelling approach. Decision Supp. Syst. 115, 36–51 (2018)

    Google Scholar 

  19. Chen, H.M., Nguyen, B.A., Yan, Y.X., Dow, C.R.: Analysis of learning behavior in an automated programming assessment environment: a code quality perspective. In: IEEE Access, vol. 8, pp. 167341–167354, 2169–3536 (2020)

    Google Scholar 

  20. Nigam, K., Mccallum, A.K., Sebastian, T., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learn. 39, 103–134 (2000)

    Google Scholar 

  21. Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627–2636 (1998)

    Google Scholar 

  22. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)

    Article  MATH  Google Scholar 

  23. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Information Center, Beijing Institute of Technology, Beijing, China

    Ziyi Zhao & Shaojie Qu

  2. School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China

    Fengxi Kang & Jing Wang

  3. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Binhui Chen

  4. School of Xuteli, Beijing Institute of Technology, Beijing, China

    Mingxuan Yang

Authors
  1. Ziyi Zhao
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  2. Fengxi Kang
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  3. Jing Wang
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  4. Binhui Chen
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  5. Mingxuan Yang
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  6. Shaojie Qu
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Corresponding author

Correspondence to Shaojie Qu .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Wenxing Hong

  2. Sichuan University, Chengdu, China

    Yang Weng

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhao, Z., Kang, F., Wang, J., Chen, B., Yang, M., Qu, S. (2023). Analysis and Prediction of the Factors Influencing Students’ Grades Based on Their Learning Behaviours in MOOCs. 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_33

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  • DOI: https://doi.org/10.1007/978-981-99-2446-2_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2445-5

  • Online ISBN: 978-981-99-2446-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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