Abstract
In order to provide students with personalized practice recommendation in computer-aided intelligent education, a basic task is to predict students’ performance in future exercises, in which it is essential to track the changes of knowledge acquired by each student in practice activities. But the existing methods can only use students’ exercise records. Therefore, the rich information in the exercise is not fully utilized, so that it is impossible to achieve a more accurate prediction of student performance and a more explanatory analysis of knowledge acquisition. In this paper, we propose an overall study of student achievement prediction. In order to achieve the primary target of performance prediction, we use a variety of methods to fuse the exercise semantic information, and propose a deep knowledge tracking model that takes into account both knowledge concepts and exercise semantic information. In addition, in order to verify the portability of semantic extraction methods, we integrate semantic information into different deep knowledge tracing model. Through the experiment on the real online education data set, compared with the existing knowledge tracking model, the model proposed in this paper has better prediction performance.
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References
Pardos, Z.A., et al.: Ensembling predictions of student post-test scores for an intelligent tutoring system. In: EDM, pp. 189–198 (2011)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253–278 (1994)
Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 3–53. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_1
Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) Artificial Intelligence in Education. AIED 2013. LNCS, vol. 7926, pp. 171–180. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_18
Piech, C., et al.: Deep knowledge tracing. In: Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 1, pp. 505–513 (2015)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks, pp. 1310–1318. In: International Conference on Machine Learning. PMLR (2013)
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp. 6645–6649. IEEE (2013)
Hannun, A., et al.: Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Liu, Q., Huang, Z., Yin, Y., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 33(1), 100–115 (2019)
Mikolov, T., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265–283 (2016)
Wu, R., Xu, G., Chen, E., Liu, Q., Ng, W.: Knowledge or gaming? Cognitive modelling based on multiple-attempt response. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 321–329. International World Wide Web Conferences Steering Committee (2017)
Zhang, J., et al.: Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web 2017, pp. 765–774 (2017)
Acknowledgements
This work was supported by the National Nature Science Foundation of China (Grant No. 62077009 and 62177006) and the State Key Laboratory of Cognitive Intelligence under Grant iED2019-Z04, Deep knowledge tracking model with multiple features.
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Xiao, R., Zheng, R., Xiao, Y., Zhang, Y., Sun, B., He, J. (2021). Deep Knowledge Tracking Based on Exercise Semantic Information. In: Jia, W., et al. Emerging Technologies for Education. SETE 2021. Lecture Notes in Computer Science(), vol 13089. Springer, Cham. https://doi.org/10.1007/978-3-030-92836-0_24
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DOI: https://doi.org/10.1007/978-3-030-92836-0_24
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