Abstract
As a user modeling method, Bayesian Knowledge Tracing (BKT) has been extensively used in the area of Intelligent Tutoring Systems (ITS). Thereafter the various schemes based on BKT are proposed to model student knowledge state and learning process. However, these schemes seldom consider the situation when a student first encounters a knowledge component (KC). That is, the existing models cannot be applied directly to predict student performance on a first-encounter KC. To solve this issue, combined user-based collaborative filtering and BKT model, a novel student performance prediction scheme PSFK is proposed in this paper. The PSFK scheme contains three major steps: first, building BKT models for each student and each KC he or she has encountered; then, finding the top-k similar students for a specified student S; finally, predicting S’s response on first-encounter KC. We evaluate our scheme on a real-world data set (which contains 4883 students and 177 KCs). The experiments show that the student performance prediction results of the proposed PSFK are acceptable (the RMSE can be decreased to 0.403).
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Proce-dural Knowledge. UMUAI. 4, 253–278 (1995)
Ma, Y., Liu, B., Wong, C.K., Yu, P.S., Lee, S.M.: Targeting the right students using data mining. In: SIGKDD, pp. 457–464 (2000)
Baker, R.S.J.d.: Modeling and understanding students’ off-task behavior in intelligent tutoring system. In: CHI, pp. 1059–1068 (2007)
Sahebi, S., Huang, Y., Brusilovsky, P.: Predicting student performance in solving parameterized exercises. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 496–503. Springer, Heidelberg (2014)
Piech, C., Huang, J., Nguyen, A., Phulsuksombati, M., Sahami, M., Guibas, L.: Learning program embeddings to propagate feedback on student code. In: ICML, vol. 37 (2015)
Romero, C., Ventura, S.: Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics 40(6) (2010)
Thai-Nghe, N.: Predicting Student Performance in an Intelligent Tutoring System. PhD thesis, University of Hildesheim (2011)
Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a bayesian networks implementation of knowledge tracing. In: Chin, D., Kobsa, A., De Bra, P. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255–266. Springer, Heidelberg (2010)
Pardos, Z.A., Heffernan, N.T.: Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. In: KDD cup 2010: Improving Cognitive Models with Educational Data Mining (2010)
Cetintas, S., Si, L., Xin, Y.P., Hord, C.: Predicting correctness of problem solving in its with a temporal collaborative filtering approach. In: Mostow, J., Kay, J., Aleven, V. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 15–24. Springer, Heidelberg (2010)
http://teacherwiki.assistments.org/index.php/Assistments_2009-2010_Full_Dataset
Adhatrao, K., GayKar, A., Dhawan, A., Jha, R., Honrao, V.: Predicting Students’ Perfor-mance Using ID3 and C4.5 Classification Algorithm. IJDKP 3(5) (2013)
Elbadrawy, A., Studham, R.S., Karypis, G.: Collaborative multi-regression models for predicting students’ performance in course activities. In: LAK, pp. 16–20 (2015)
Naser, S.A., Zaqout, I., Ghosh, M.A., Atallah, R., Alajrami, E.: Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology. IJHIT 8(2), 221–228 (2015)
Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L.: Recommender system for predicting student performance. In: RecSysTEL, vol. 01, pp. 1–9 (2010)
Thai-Nghe, N., Drumond, L., Horváth, T., Nanopoulos, A., Schmidt-Thieme, L.: Matrix and tensor factorization for predicting student performance. In: CSEDU, pp. 69–78 (2011)
Feng, M., Heffernan, N.T., Koedinger, K.R.: Addressing the assessment challenge in an Intelligent Tutoring System that tutors as it assesses. The Journal of User Modeling and User-Adapted Interaction. 19, 243–266 (2009)
Vázquez, M.R., Romero, F.P., Vanoye, J.R., Olivas, J.A., Guerrero, J.S.: An extension of fuzzy deformable prototypes for predicting student performance on web-based tutoring systems. In: IFSA, pp. 556–563 (2015)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Song, Y., Jin, Y., Zheng, X., Han, H., Zhong, Y., Zhao, X. (2015). PSFK: A Student Performance Prediction Scheme for First-Encounter Knowledge in ITS. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-25159-2_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25158-5
Online ISBN: 978-3-319-25159-2
eBook Packages: Computer ScienceComputer Science (R0)