Deep Knowledge Tracing for Learning Analytics

DEEP KNOWLEDGE TRACING FOR LEARNING ANALYTICS
Gabriella Casalino
Online International Conference on Recent Advances in Deep Learning (ICRADL-2021)
gabriella.casalino@uniba.it

http://www.di.uniba.it/~cilab
Deep Knowledge Tracing for Learning Analytics gabriella.casalino@uniba.it

http://www.di.uniba.it/~cilab
AI 2.0-Enabled Next Generation Intelligence of Things for Smart Enterprise Systems", Computers, Materials & Continua, IF. 4.89

EDUCATIONAL DATA

LEARNING ANALYTICS AND EDUCATIONAL DATA MINING
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

LEARNING ANALYTICS
"The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of
understanding and optimizing learning and the environments in which it occurs".
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
STUDENTS’ DATA
DEEP LEARNING
MONITORING/
PREDICTION
STUDENTS/TEACHERS/
TUTORS/INSTITUTIONS

THE IMPORTANCE OF TIME
Does the time matter when analyzing educational data?

KNOWLEDGE TRACING
➤ Proposed by Corbett and Anderson (1994)
➤ Basic Idea:
student is able to master a subject if
➤ the domain knowledge is organized in a hierarchical
structure of skills
➤ this hierarchy is proposed during the learning experience
S8
S3 S4 S7
S1 S2 S5 S6
E1 E2 E3 E4 E5 En
S1 S2 S3
S4
S1
S5
S3
S8
S4
S7
Time

KNOWLEDGE TRACING
➤ Ideal student model:
➤ set of the rules (skills and sub-skills) that the student should have known to
acquire the domain knowledge
➤ The probability that the student has learned each rule is evaluated and a guidance
is eventually provided
➤ Bayesian Classifiers, Factor Analysis, Deep Learning
E1 E2 E3 E4 E5 En
S1 S2 S3
S4
S1
S5
S3
S8
S4
S7
Time

ARTIFICIAL NEURAL NETWORKS

DEEP KNOWLEDGE TRACING
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in neural information processing systems, 28, 505-513.

CLASSICAL RNN APPROACHES FOR KT
➤ Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing.
arXiv preprint arXiv:1506.05908.
➤ introduced formally the concept of Deep Knowledge Tracing;
➤ RNN for tackling the temporal task of modelling the student’s knowledge;
➤ Analyzing data representing the student’s history to trace the acquired knowledge and to predict his/her
future performances

CLASSICAL RNN APPROACHES FOR KT
➤ Xiong, X., Zhao, S., Van Inwegen, E. G., & Beck, J. E. (2016). Going deeper with deep knowledge
tracing. International Educational Data Mining Society.
➤ revised the results the preliminary score presented by Piech et al. uncovering issues
that were not considered;
➤ RNN was re-tested showing overall better behavior but the performance gap was
reduced (up to 20%);

ENHANCED DKT
➤ Zhang, J., Shi, X., King, I., & Yeung, D. Y. (2017, April). Dynamic key-value memory networks
for knowledge tracing. In Proceedings of the 26th international conference on World Wide Web
(pp. 765-774).
➤ Dynamic Key-Value Memory Networks is capable of learning a schema concerned
the concepts mastered for each student
➤ Zhang, L., Xiong, X., Zhao, S., Botelho, A., & Heﬀernan, N. T. (2017, April). Incorporating rich
features into deep knowledge tracing. In Proceedings of the fourth (2017) ACM conference on
learning@ scale (pp. 169-172).
➤ proposed to enrich the features exploited by the classical DKT (e.g students
response time, attempt number)

ENHANCED DKT
➤ Transformers models:
➤ Pandey, S., & Karypis, G. (2019). A self-attentive model for knowledge tracing.
arXiv preprint arXiv:1907.06837.
➤ Self-attentive model for knowledge tracing (SAKT)
➤ He, Y., Hu, X., Xu, Z., & Sun, A. G. (2020, May). KT-XL: A Knowledge Tracing
Model for Predicting Learning Performance Based on Transformer-XL. In
Proceedings of the ACM Turing Celebration Conference-China (pp. 175-179).
➤ Transformer-XL

CONCLUSIONS
➤ Deep learning for Learning Analytics has been presented
➤ Deep Knowledge Tracing (DKT)
➤ it is able to model the student’s learning process in term of hidden skills
➤ it is able to predict the student’s outcome
➤ it overcomes other machine learning approaches for KT
➤ It is eﬀective for both in presence and online learning
➤ Future directions:
➤ Systematic review
➤ Explainable artificial intelligence

REFERENCES
➤ Casalino G., Grilli L., Limone P
., Santoro D., Schicchi D. (in
press) Deep learning for knowledge tracing in learning
analytics: an overview
➤ Casalino G., Castellano G., Vessio G. (2021) Exploiting
Time in Adaptive Learning from Educational Data
➤ Casalino G., Castellano G., Mannavola A., Vessio G.
(2020) Educational Stream Data Analysis: A Case Study
➤ Alonso J.M., Casalino G. (2019) Explainable Artificial
Intelligence for Human-Centric Data Analysis in Virtual
Learning Environments
➤ Casalino G., Castellano G., Mencar C. (2019) Incremental
and adaptive fuzzy clustering for Virtual Learning
Environments data analysis

THANKS!
https://sites.google.com/site/cilabuniba/people/gabriella-casalino

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