Abstract
MOOC is an emerging online educational model in recent years. With the development of big data technology, a huge amount of learning behavior data can be mined by MOOC platforms. Mining learners’ past clickstream data to predict their future learning achievement by machine learning technology has become a hot research topic recently. Previous methods only consider the static counting-based features and ignore the correlative, temporal and fragmented nature of MOOC learning behavior, and thus have the limitation in interpretability and prediction accuracy. In this paper, we explore the effectiveness of N-gram features in clickstream data and model the MOOC learning achievement prediction problem as a multiclass classification task which classifies learners into four achievement levels. With extensive experiments on four real-world MOOC datasets, we empirically demonstrate that our methods outperform the state-of-the-art methods significantly.
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The minimal progress marks for attending final examinations are usually set in the range from 20% to 30% in Mengke. They can be changed by instructors before courses are opened.
References
Jiang, Z., Zhang, Y., Li, X.: Learning behavior analysis and prediction based on MOOC data. J. Comput. Res. Dev. 52(3), 614–628 (2015). (in Chinese)
Whitehill, J., Williams, J., Lopez, G., Coleman, C., Reich, J.: Beyond prediction: first steps toward automatic intervention in MOOC student stopout. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 222–230. EDM Press, Madrid (2015)
Xing, W., Chen, X., Stein, J., Marcinkowskid, M.: Temporal predication of dropouts in MOOCs: reaching the low hanging fruit through stacking generalization. Comput. Hum. Behav. 58, 119–129 (2016)
Qiu, J., Tang, J., Liu, T., Gong, J., Zhang, C., Zhang, Q., Xue, Y.: Modeling and predicting learning behavior in MOOCs. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining, pp. 93–102. ACM Press, San Francisco (2015)
Brooks, C., Thompson, C., Teasley, S.: A time series interaction analysis method for building predictive models of learners using log data. In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge, pp. 126–135. ACM Press, Poughkeepsie (2015)
Robinson, C., Yeomans, M., Reich, J., Hulleman, C., Gehlbach, H.: Forecasting student achievement in MOOCs with natural language processing. In: Proceedings of the 6th International Conference on Learning Analytics and Knowledge, pp. 383–387. ACM Press, Edinburgh (2016)
He, J., Bailey, J., Rubinstein, I., Zhang, R.: Identifying at-risk students in massive open online courses. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 1749–1755. AAAI Press, Austin (2015)
Chaplot, D., Rhim, E., Kim, J.: Predicting student attrition in moocs using sentiment analysis and neural networks. In: Proceedings of AIED 2015 Fourth Workshop on Intelligent Support for Learning in Groups, pp. 7–12. AIED Press, Madrid (2015)
Crossley, S., Paquette, L., Dascalu, M., McNamara, D., Baker, R.: Combining click-stream data with NLP tools to better understand MOOC completion. In: Proceedings of the 6th International Conference on Learning Analytics and Knowledge, pp. 6–14. ACM, Edinburgh (2016)
Zhou, Q., Mou, C., Yang, D.: Research Progress on Educational Data Mining A Survey. J. Softw. 26(11), 3026–3042 (2015). (in Chinese)
Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. SIGKDD Explor. 12, 40–48 (2010)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618 (2010)
Jiang, S., Williams, A., Schenke, K., Warschauer, M., Dowd, D.: Predicting MOOC performance with week 1 behavior. In: Proceedings of the 7th International Conference on Educational Data Mining, pp. 273–275. EDM Press, London (2014)
Kumar, M., Kan, M., Tan, B.: Learning instructor intervention from MOOC forums: early results and issues. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 218–225. EDM Press, Madrid (2015)
Yang, D., Piergallini, M., Howley, I., Rose, C.: Forum thread recommendation for massive open online courses. In: Proceedings of the 7th International Conference on Educational Data Mining, pp. 257–260. EDM Press, London (2014)
Davis, D., Chen, G., Hauff, C., Houben, G.: Gauging MOOC learners’ adherence to the designed learning path. In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 54–61. EDM Press, Raleigh (2016)
Ren, Z., Rangwala, H., Johri, A.: Predicting performance on MOOC assessments using multi-regression models. In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 484–489. EDM Press, Raleigh (2016)
Kennedy, G., Coffrin, C., Barba, P., Corrin, L.: Predicting success how learners’ prior knowledge, skills and activities predict MOOC performance. In: Proceedings of the 5th International Conference on Learning Analytics and Knowledge, pp. 136–140. ACM Press, Poughkeepsie (2015)
Sanchez-Santillan, M., Cerezo, R., Paule-Ruiz, M., Nuñez, J.: Predicting students’ performance: incremental interaction classifiers. In: Proceedings of the Third ACM Conference on Learning @ Scale, pp. 217–220. ACM Press, Edinburgh (2016)
Peter, J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput. Appl. Math. 20, 53–65 (1987)
Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web J. 19(6), 1151–1177 (2016)
Acknowledgments
We would like to thank the anonymous reviewers for their helpful comments to improve this paper. We also thank the support of Key Program of National Natural Science Foundation of China (61432020, 61532001) and Research Fund of Research Center for Online Education of Ministry of Education of China (2016YB150, 2016YB151).
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Li, X., Wang, T., Wang, H. (2017). Exploring N-gram Features in Clickstream Data for MOOC Learning Achievement Prediction. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_26
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