Abstract
With the development of technology, the teaching environment has changed greatly. As an educational resource, exercise plays an important role in students’ personalized learning service. Therefore, how to recommend appropriate exercises to students is a key problem to be solved urgently. The exercise recommendation method analyses students’ history answer sequences and provides personalized exercise recommendation service for students. Previous exercise recommendation methods assume that students’ knowledge states are fixed, so these methods cannot recommend exercise according to the changes of student ability. In addition, the existing methods do not take concept prerequisite relations into consideration. In this paper, we propose Exercise Recommendation method based on Knowledge Tracing and Concept Prerequisite relations (ER-KTCP). Firstly, ER-KTCP can capture the changes of students’ knowledge states. Secondly, ER-KTCP can adjust the details of recommendation strategy according to the changes of students’ knowledge states. Thirdly, ER-KTCP recommends exercises according to the relations between concepts and the difficulty of exercises, which makes selected exercises more reasonable. Besides, we propose a new metric to evaluate the improvement of student’s score after he has done the recommended exercises. Experiments on multiple data sets show that ER-KTCP performs better in exercise recommendation than state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization[J]. Stat 1050, 21 (2016)
Badache, I., Fournier, S., Chifu, A.-G.: Harnessing ratings and aspect-sentiment to estimate contradiction intensity in temporal-related reviews[J]. Procedia Comput. Sci. 112, 1711–1720 (2017). https://doi.org/10.1016/j.procs.2017.08.197
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering[C]. Morgan Kaufmann, pp. 43–52 (1998)
Bridge, R.G.: All our children learning: Benjamin S. Bloom. New York: McGraw-Hill, 1981. Pp. 275. $14.95 (cloth).[J]. Econ. Educ. Rev. 2(2): 197–200 (1982). https://doi.org/10.1016/0272-7757(82)90042-5
Chuan, Y., Jieping, X., Xiaoyong, D.U.: Recommendation algorithm combining the user-based classified regression and the item-based filtering[C]. In: Proceedings of the 8th International Conference on Electronic Commerce: The New e-Commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, pp. 574–578 (2006)
Dai, Z., Yang, Z., Yang, Y., et al.: Transformer-XL: attentive language models beyond a fixed-length context[C]. ACL (1) (2019)
Dascalu, M.I., Bodea, C.N., Moldoveanu, A., et al.: A recommender agent based on learning styles for better virtual collaborative learning experiences[J]. Comput. Hum. Behav. 45(1), 243–253 (2015). https://doi.org/10.1016/j.chb.2014.12.027
De La Torre, J.: The generalized DINA model framework[J]. Psychometrika 76(2), 179–199 (2011)
Dibello, L.V., Roussos, L.A., Stout, W.: 31A review of cognitively diagnostic assessment and a summary of psychometric models[J]. Handbook Statist. 26(06), 979–1030 (2006). https://doi.org/10.1016/S0169-7161(06)26031-0
Embretson, S.E., Reise, S.P.: Item response theory[M]. Psychology Press (2013)
Ericsson, A., Pool, R.: Peak: Secrets from the new science of expertise[M]. Houghton Mifflin Harcourt (2016)
Haertel, E.: An application of latent class models to assessment data[J]. Appl. Psychol. Meas. 8(3), 333–346 (1984). https://doi.org/10.1177/014662168400800311
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition[C]. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90
Junker, B.W., Sijtsma, K.: Cognitive assessment models with few assumptions, and connections with nonparametric item response theory[J]. Appl. Psychol. Meas. 25(3), 258–272 (2001). https://doi.org/10.1177/01466210122032064
Kim, B.M., Li, Q., Park, C.S., et al.: A new approach for combining content-based and collaborative filters[J]. J. Intell. Inf. Syst. 27(1), 79–91 (2006)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems[J]. Computer 42(8), 30–37 (2009)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization[J]. Nature 401(6755), 788–791 (1999)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering[J]. IEEE Internet Comput. 7(1), 76–80 (2003)
Maimaimaia12318. Education data sets: doudouyun[OL]. https://blog.csdn.net/maimaimaia12138/article/details/115521865, 2021–04–08.
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization[J]. Adv. Neural Inf. Process. Syst. 20 (2007)
Niu, W., Niu, Z., Tang, S., et al.: A learning resource recommendation method combining user sequential interaction with collaborative filtering[C]. Modell. Identific. Control. 2015.
Pandey, P., Karypis, G.: A self attentive model for knowledge tracin[C]. In: Proceedings of the 12th International Conference on Educational Data Mining (2019)
Piech, C., Spencer, J., Huang, J. et al.: Deep knowledge tracing[J/OL]. arXiv:1506.05908[cs], 2015[2021–10–07]
Qi, B., Zou, H., Wang, Y., et al.: Question recommendation based on collaborative filtering and cognitive diagnosis[J]. Comput. Sci. 46(11), 235–240 (2019)
Rasch, G.: On general laws and the meaning of measurement[C]. In: Psychology. Proceedings 4tb Berkeley Symposium Mathematics Statistics and Probability. 5: 321–333 (1961)
Resnick, P., Iacovou, N., Suchak, M., et al.: Grouplens: an open architecture for collaborative filtering of netnews[C]. Proc. ACM Conf. Comput. Support. Cooperat. Work 1994, 175–186 (1994)
Rollinson, J.E.: From predictive models to instructional policies [J/OL]. Int. Educ. Data Min. Soc. (2015) [2021–10–07].
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms[C]. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Shan, R., Luo, Y., Sun, Y.: Collaborative filtering algorithm based on cognitive diagnosis[J]. Comput. Syst. Appl. 27(03), 136–142 (2018)
Sun, Z., Anbarasan, M., Praveen, K.D.: Design of online intelligent English teaching platform based on artificial intelligence techniques[J]. Comput. Intell. 37(3), 1166–1180 (2021)
Torre, D.L.J.: DINA model and parameter estimation: a didactic[J]. J. Educ. Behav. Stat. 34(1), 115–130 (2009). https://doi.org/10.3102/1076998607309474
Van Loan, C.F.: Generalizing the singular value decomposition[J]. SIAM J. Numer. Anal. 13(1), 76–83 (1976)
Vaswani, A., Shazeer, N., Parmar, N., et al. Attention is all you need[C]. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Vukicevic, M., Jovanovic, M.Z., Delibasic, B., et al.: Recommender system for selection of the right study program for Higher Education students[J]. In: Hofmann, M., Klinkenberg, R., (eds) RapidMiner: Data Mining Use Cases and Business Analytics Applications (2013)
Walker, A.E., Recker, M.M., Lawless, K., et al.: Collaborative information filtering: a review and an educational application[J]. Int. J. Artif. Intell. Educ. 14(1), 1–26 (2004)
Yang, C.: Learning resource recommendation method based on particle swarm optimization algorithm [J]. J. Comput. Appl. 34(05), 1350–1353 (2014)
Yeung, C. K., Yeung, D.Y.: Addressing two problems in deep knowledge tracing via prediction-consistent regularization[C/OL]. In: the Fifth Annual ACM Conference.[2021–10–07]
Yu, J., Luo, G., Xiao, T., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs[C]. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3135–3142 (2020)
Zhang, J., Shi, X., King, I., et al.: Dynamic key-value memory networks for knowledge tracing[C]. In: International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 765–774 (2017)
Zhu, T., Huang, Z., Chen, E., et al.: Cognitive diagnosis based personalized question recommendation method[J]. Chin. J. Comput. 40(01), 176–191 (2017)
Acknowledgements
The authors would like to express gratitude to all those who helped us during the writing of this paper, including but not limited to Zhijing Sun of Shanghai Jiao Tong University, Youming Zhang of Newyork University, Haochen Zhang of University of Birmingham, Yida Zhang of Purdue University.
Funding
This research was supported by Youth Innovation Promotion Association of the Chinese Academy of Sciences.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
He, Y., Wang, H., Pan, Y. et al. Exercise recommendation method based on knowledge tracing and concept prerequisite relations. CCF Trans. Pervasive Comp. Interact. 4, 452–464 (2022). https://doi.org/10.1007/s42486-022-00109-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42486-022-00109-2