Abstract
MOOCs are becoming more and more involved in the pedagogical experimentation of universities whose infrastructure does not respond to the growing mass of learners. These universities aim to complete their initial training with distance learning courses. Unfortunately, the efforts made to succeed in this pedagogical model are facing a dropout rate of enrolled learners reaching 90% in some cases. This makes the coaching, the group formation of learners, and the instructor/learner interaction challenging. It is within this context that this research aims to propose a predictive model allowing to classify the MOOCs learners into three classes: the learners at risk of dropping out, those who are likely to fail and those who are on the road to success. An automatic determination of relevant attributes for analysis, classification, interpretation and prediction from MOOC learners data, will allow instructors to streamline interventions for each class. To meet this purpose, we present an approach based on feature selection methods and ensemble machine learning algorithms. The proposed model was tested on a dataset of over 5,500 learners in two Stanford University MOOCs courses. In order to attest its performance (98.6%), a comparison was carried out based on several performance measures.
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Al-Shabandar, R., Hussain, A., Laws, A., Keight, R., Lunn, J., Radi, N. (2017). Machine learning approaches to predict learning outcomes in Massive open online courses. Int. Jt. Conf. Neural Networks (pp. 713—720).
Alonso-betanzos, A. (2007). Filter methods for feature selection. A comparative study. Proc. International Conference on Intelligent Data Engineering and Automated Learning (pp. 178—187). UK, Birmingham.
Alves, A. (2017). Stacking machine learning classifiers to identify Higgs bosons at the LHC. Journal of Instrumentation, 12, 1–19.
Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., et al. (2015). Spark SQL: Relational Data Processing in Spark. Proceedings of International Conference Management Data (pp. 1383—1394). Australia, Melbourne.
Burgos, C., Campanario, M.L., de la Pena, D., Lara, J.A., Lizcano, D., Martinez, M.A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computer Electrical Engineering, 66, 541–556.
Chaplot, D.S., Rhim, E., Kim, J. (2015). Predicting student attrition in MOOCs using sentiment analysis and neural networks. Proc. CEUR Workshop, 1432, 7–12.
Choudhury, S., & Bhowal, A. (2015). Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. Proceedings of International Conference in Smart Technology of Management Computer Communication Controlling Energy Material (pp. 89—95). India, Chennai.
Cross, S. (2013). Evaluation of the OLDS MOOC curriculum design course: participant perspectives expectations and experiences. OLDS MOOC Proj.
Crossley, S., Paquette, L., Dascalu, M., McNamara, D.S., Baker, R.S. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. Proc. Sixth Int. Conf. Learn. Anal. Knowl. (pp. 6—14). UK, Edinburgh.
Dinakar, K., Weinstein, E., Lieberman, H., Selman, R. (2014). Stacked Generalization Learning to Analyze Teenage Distress. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (pp. 81—90). USA, Michigan.
Fei, M., & Yeung, D.-Y. (2018). Temporal Models for Predicting Student Dropout in Massive Open Online Courses. IEEE International Conference on Data Mining Working (pp. 256—263). Singapore.
Gitinabard, N., Khoshnevisan, F., Lynch, C.F., Wang, E.Y. (2018). Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features. Proc. 11th International Conference on Educational Data Mining. Buffalo NY: In Press.
Healey, S.P., Cohen, W.B., Yang, Z., Brewer, C.K., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Hughes, M.J., Kennedy, R.E., et al. (2018). MApping forest change using stacked generalization: An ensemble approach. Remote Sensing Environment, 204, 717–728.
Jindal, P., & Kumar, D. (2019). A Review on Dimensionality Reduction Techniques, International Journal Pattern Recognition of Artificial Intelligence. In Press.
Jović, A., Brkić, K., Bogunović, N. (2015). A review of feature selection methods with applications Proceedings of 38th International Convenience of Information Communication Technology Electronic Microelectronics (pp. 1200—1205). Croatia, Opatija.
Kabir, A., Ruiz, C., Alvarez, S.A. (2014). Regression, Classification and Ensemble Machine Learning Approaches to Forecasting Clinical Outcomes in Ischemic Stroke. Biomedical Engineering Systems and Technologies, 452, 376–402.
Karegowda, A.G., Manjunath, A.S., Jayaram, M.A. (2010). Feature Subset Selection Problem using Wrapper Approach in Supervised Learning. International of Journal Computer Application, 1, 13–17.
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N. (2014). Predicting MOOC Dropout over Weeks Using Machine Learning Methods. Proc. Conf. Empir. Methods Nat. Lang. Process. (pp. 60—65).
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H. (2018). Feature selection: a data perspective, ACM Computer Survey, 50.
Liyanagunawardena, T.R., Parslow, P., Williams, S.A. (2014). Dropout: MOOC participants’ perspective. Proceedings of European MOOC Stakehold (pp. 95–100). Switzerland: Summit.
Martínez-España, R., Bueno-Crespo, A., Timón, I., Soto, J., Muñoz, A., Cecilia, J.M. (2018). Air-pollution prediction in smart cities through machine learning methods: A case of study in Murcia. Spain, Journal University of Computer Science, 24, 261–276.
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S. (2016). Others MLlib: Machine Learning in Apache Spark. Journal of Machine Learning Research, 17, 1235–1241.
Naghibi, S.A., Ahmadi, K., Daneshi, A. (2017). Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resources Management, 31, 2761–2775.
Nagi, S., & Bhattacharyya, D.K. (2013). Classification of microarray cancer data using ensemble approach. Network Modelling Analysis of Health Informatics Bioinforma, 2, 159–173.
Onah, D.F., & Sinclair, J. (2014). Boyatt Dropout Rates of Massive Open Online Courses: Behavioural Patterns MOOC Dropout and Completion: Existing Evaluations, Proceedings of 6th International Conference on Education (pp. 1–10). Spain: New Learn. Technol.
Panthong, R., & Srivihok, A. (2015). Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm. Procedia Computer Science, 72, 162–169.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Prieto, L.P., Rodríguez-Triana, M.J., Kusmin, M., Laanpere, M. (2017). Smart school multimodal dataset and challenges. Proceedings of CEUR Workshop, 1828, 53–59.
Qi, Q., Liu, Y., Wu, F., Yan Xi., Wu, N. (2018). Temporal Models for Personalized Grade Prediction in Massive Open Online Courses. Proceedings of ACM Turing Celebration Conference (pp. 67—72).
Qiu, L., Liu, Y., Hu, Q., Liu, Y. (2018a). Student dropout prediction in massive open online courses by convolutional neural networks. bSoft Computer, 22, 1–15.
Qiu, L., Liu, Y., Liu, Y. (2018b). An integrated framework with feature selection for dropout prediction in massive open online courses. IEEE Access, 6, 71474–71484.
Ren, Y., Zhang, L., Suganthan, P.N. (2016). Ensemble Classification and Regression-Recent Developments, Applications and Future Directions. IEEE Computer of Intelligence Magazine, 11, 41–53.
Salcedo-Sanz, S., Cornejo-Bueno, L., Prieto, L., Paredes, D., García-Herrera, R. (2018). Feature selection in machine learning prediction systems for renewable energy applications. Renewable and Sustainable Energy Reviews, 90, 728–741.
Sanchez-Gordon, S., & Luján-Mora, S. (2016). How could MOOCs become accessible? The case of edX and the future of inclusive online learning. Journal University of Computer Science, 22, 55–81.
Sikora, R., & Al-Laymoun, O. (2014). A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms. Handbook of Research on Organizational Transformations through Big Data Analytics, 23, 43–53.
Sinha, T., Jermann, P., Li, N., Dillenbourg, P. (2014). Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions. Proceedings of Conference Empirial Methods Nat. Lang. Process. (pp. 6—14).
Talavera, L. (2005). An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering. Proceedings of International Symposium on Intelligent Data Analysis (pp. 440—451). Spain, Madrid.
Tang, C., Ouyang, Y., Rong, W., Zhang, J., Xiong, Z. (2018). Time series model for predicting dropout in massive open online courses, Proc. International conference on artificial intelligence in education (pp. 353–357). UK.
Vitiello, M., Walk, S., Helic, D., Chang, V., Gütl, C. (2018). User behavioral patterns and early dropouts detection: Improved users profiling through analysis of successive offering of MOOC. Journal University of Computer Science, 24, 1131–1150.
White, T. (2012). Hadoop: The definitive guide. USA: O’Reilly Media, Inc.
Witten, I. (2016). Data mining: Practical machine learning tools and techniques. Burlington: MorganKaufmann.
Xing, W., Chen, X., Stein, J., Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Comput. Human Behav., 58, 119–129.
Xu, S., Lu, B., Baldea, M., Edgar, T.F., Nixon, M. (2018). An improved variable selection method for support vector regression in NIR spectral modeling. Journal Process Control, 67, 83–93.
Yang, D., Sinha, T., Adamson, D. (2016). ’Turn on, Tune in, Drop out’: Anticipating student dropouts in Massive Open Online Courses. Proc. NIPS Work. Data Driven Educ. (pp. 1—8).
Yuan, L., & Powell, S. (2013). MOOCS and disruptive innovation: Implications for higher education. In-depth eLearning Papers, 33, 1–7.
Zhu, Y., Xie, C., Wang, G.J., Yan, X.G. (2017). Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Computer Applications, 28, 41–50.
Zitlau, R., Hoyle, B., Paech, K., Weller, J., Rau, M.M., Seitz, S. (2016). Stacking for machine learning redshifts applied to SDSS galaxies. Monthly Not. R. Astron. Soc., 460, 3152–3162.
Acknowledgements
This research was done through Stanford University’s Advanced Research Center on Online Learning (CAROL), which we thank immensely for all the facilities they provided for us. We also wish to express our full gratitude to Ms. Kathy Mirzaei for her responsiveness as well as her collaboration. We wish to warmly thank Mr. Mitchell Stevens, Director of Digital Research and Planning, as well as all the CAROL commission for the trust they have given us.
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Youssef, M., Mohammed, S., Hamada, E.K. et al. A predictive approach based on efficient feature selection and learning algorithms’ competition: Case of learners’ dropout in MOOCs. Educ Inf Technol 24, 3591–3618 (2019). https://doi.org/10.1007/s10639-019-09934-y
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DOI: https://doi.org/10.1007/s10639-019-09934-y