Abstract
Massive Open Online Courses (MOOCs) allow accessing qualitative online educational resources for huge amounts of online students. In this context, the dropout phenomenon is known as a nasty problem faced by several existing studies proposing methods and techniques to make predictions on students who are at risk of dropping out. Although the majority of such studies adopt traditional classification algorithms based on supervised methods, the present work proposes a sequential approach based on Three-Way Decisions and Neighborhood Rough Sets. The underlying idea is to exploit weekly data in order to classify, with high levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach has the advantage to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The sequential application of the approach makes the recall increase as new data is gathered.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adams, A., Liyanagunawardena, T., Williams, S.: MOOCs: a systematic study of the published literature 2008–2012. Int. Rev. Res. Open Dist. Learn. 14, 202–227 (2013)
Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week’s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) ITS 2019. LNCS, vol. 11528, pp. 163–173. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22244-4_20
Blundo, C., Fenza, G., Fuccio, G., Loia, V., Orciuoli, F.: A time-driven FCA-based approach for identifying students’ dropout in MOOCs. Int. J. Intell. Syst. 37(4), 2683–2705 (2021)
Deng, R., Benckendorff, P., Gannaway, D.: Progress and new directions for teaching and learning in MOOCs. Comput. Educ. 129, 48–60 (2019)
Impey, C., Formanek, M.: MOOCs and 100 days of COVID: enrollment surges in massive open online astronomy classes during the coronavirus pandemic. Soc. Sci. Humanit. Open 4(1), 100177. ISSN 2590-2911 (2021)
Jupyter. https://jupyter.org/
Knowledge Discovery and Data Mining. MOOC dataset from KDD cup 2015 (2015). http://data-mining.philippe-fournier-viger.com/the-kddcup-2015-dataset-download-link/
Kumar, S.U., Inbarani, H.H.: A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Comput. Sci. 47, 351–359. ISSN 1877-0509 (2015)
Liu, T.-Y., Li, X.: Finding out reasons for low completion in MOOC environment: an explicable approach using hybrid data mining methods. DEStech Trans. Soc. Sci. Educ. Hum. Sci. 376–384 (2017)
Menardi, G., Torelli, N.: Training and assessing classification rules with imbalanced data. Data Min. Knowl. Discov. 28, 92–122 (2014)
Numpy. https://numpy.org/
Pandas. https://pandas.pydata.org/
Pawlak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99(1), 48–57 (1997)
Python 3. https://www.python.org/
Qian, Y., et al.: Local rough set: a solution to rough data analysis in big data. Int. J. Approximate Reasoning 97, 38–63. ISSN 0888-613X (2018)
Shao, M.-W., Leung, Y., Wu, W.-Z.: Rule acquisition and complexity reduction in formal decision contexts. Int. J. Approximate Reasoning 55(1, Part 2), 259–274. ISSN 0888-613X (2014). Special issue on Decision-Theoretic Rough Sets
Sun, B., Chen, X., Zhang, L., Ma, W.: Three-way decision making approach to conflict analysis and resolution using probabilistic rough set over two universes. Inf. Sci. 507, 809–822 (2019)
Wang, Q., Qian, Y., Liang, X., Guo, Q., Liang, J.: Local neighborhood rough set. Knowl. Based Syst. 153, 53–64 (2018)
Wang, W., Yu, H., Miao, C.: Deep model for dropout prediction in MOOCs. In: ICCSE 2017: Proceedings of the 2nd International Conference on Crowd Science and Engineering, pp. 26–32 (2017)
Xing, W., Du, D.: Dropout prediction in MOOCs: using deep learning for personalized intervention. J. Educ. Comput. Res. 57, 073563311875701 (2018)
XuetanX. https://www.xuetangx.com/
Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353. ISSN 0020-0255 (2010)
Yao, Y.: Granular computing and sequential three-way decisions. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 16–27. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41299-8_3
Yao, Y., Deng, X.: Sequential three-way decisions with probabilistic rough sets. In: Proceedings of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011, pp. 120–125 (2011)
Zhang, J., Li, T., Ruan, D., Liu, D.: Neighborhood rough sets for dynamic data mining. Int. J. Intell. Syst. 27, 317–342 (2012)
Zhang, T., Yuan, B.: Visualizing MOOC user behaviors: a case study on XuetangX. In: Yin, H., et al. (eds.) IDEAL 2016. LNCS, vol. 9937, pp. 89–98. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46257-8_10
Acknowledgement
Thanks to Tiziana Coppola for the initial discussion on the used dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Blundo, C., Fenza, G., Fuccio, G., Loia, V., Orciuoli, F. (2022). Sequential Three-Way Decisions for Reducing Uncertainty in Dropout Prediction for Online Courses. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-99584-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99583-6
Online ISBN: 978-3-030-99584-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)