A review of knowledge tracking

doi:10.3969/j.issn.1000-5641.2019.05.001

Journal of East China Normal University(Natural Sc ›› 2019, Vol. 2019 ›› Issue (5): 1-15.doi: 10.3969/j.issn.1000-5641.2019.05.001

• Data-driven Computational Education • Previous Articles Next Articles

A review of knowledge tracking

LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge

Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Received:2019-07-29 Online:2019-09-25 Published:2019-10-11

Abstract

Abstract: In the field of education, scientifically and purposefully tracking the progression of student knowledge is a topic of great significance. With a student's historical learning trajectory and a model for the interaction process between students and exercises, knowledge tracking can automatically track the progression of a student's learning at each stage. This provides a technical basis for predicting student performance and achieving personalized guidance and adaptive learning. This paper first introduces the background of knowledge tracking and summarizes the pedagogy and data mining theory involved in knowledge tracking. Then, the paper summarizes the research status of knowledge tracking based on probability graphs, matrix factorization, and deep learning; we use these tools to classify the tracking methods according to different characteristics. Finally, the paper analyzes and compares the latest knowledge tracking technologies, and looks ahead to the future direction of ongoing research.

Key words: knowledge tracking, cognitive diagnosis, deep learning, probability map model

CLC Number:

TP301.6

LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge. A review of knowledge tracking[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(5): 1-15.

References

[1] YUDELSON M V, KOEDINGER K R, GORDON G J. Individualized Bayesian knowledge tracing models[C]//Artificial Intelligence in Education. Springer Berlin Heidelberg, 2013.
[2] SCHUSTER M, PALIWAL K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45(11):2673-2681.
[3] PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing[C]//NIPS, 2015:505-513.
[4] DIBELLO L, ROUSSOS L, STOUT W. 31a review of cognitively diagnostic assessment and a summary of psychometric models[J]. Handbook of Statistics, 2006, 26(12):979-1030.
[5] EMBRETSON S E, REISE S P. Item response theory[M].[S.l.]:Psychology Press, 2013.
[6] TORRE D L. DINA model and parameter estimation:A didactic[J]. Journal of Educational and Behavioral Statistics, 2008, 34(1):115-130.
[7] WU R, LIU Q, LIU Y, et al. Cognitive modelling for predicting examinee performance[C]//International Conference on Artificial Intelligence. AAAI Press, 2015.
[8] THAINGHE N, HORVATH T, SCHMIDTTHIEME L, et al. Factorization models for forecasting student performance[C]//Educational Data Mining, 2011:11-20.
[9] XIONG L, CHEN X, HUANG T K, et al. Temporal collaborative filtering with Bayesian probabilistic tensor factorization[C]//Proceedings of the 2010 SIAM International Conference on Data Mining, 2010:211-222
[10] SU Y, LIU Q, LIU Q, et al. Exercise-enhanced sequential modeling for student performance prediction[C]//AAAI, 2018:2435-2443.
[11] CHEN Y, LIU Q, HUANG Z, et al. Tracking knowledge proficiency of students with educational priors[C]//CIKM. ACM, 2017:989-998.
[12] KINGMA D, BA J. Adam:A method for stochastic optimization[C]//International Conference on Learning Representations, 2015.
[13] SHI Y, PENG Z, WANG H. Modeling student learning styles in MOOCs[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017:979-988.
[14] GRAVES A, MOHAMED A, HINTON G E. Speech recognition with deep recurrent neural networks[C]//ICASSP. IEEE, 2013:6645-6649.
[15] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet Classification with Deep Convolutional Neural Networks[C]//NIPS. Curran Associates Inc, 2012.
[16] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems, 2013.
[17] HUANG Z, LIU Q, CHEN E, et al. Question difficulty prediction for reading problems in standard tests[C]//National Conference on Artificial Intelligence, 2017:1352-1359.
[18] ZHANG J, SHI X, KING I, et al. Dynamic key-value mem

A review of knowledge tracking

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References