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Reseach Article

Enhanced Retention Performance Modeling for Intelligent Tutoring System

by N. Sharada, M. Shashi, Xiaolu Xiong
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 10
Year of Publication: 2016
Authors: N. Sharada, M. Shashi, Xiaolu Xiong
10.5120/ijca2016911914

N. Sharada, M. Shashi, Xiaolu Xiong . Enhanced Retention Performance Modeling for Intelligent Tutoring System. International Journal of Computer Applications. 151, 10 ( Oct 2016), 1-4. DOI=10.5120/ijca2016911914

@article{ 10.5120/ijca2016911914,
author = { N. Sharada, M. Shashi, Xiaolu Xiong },
title = { Enhanced Retention Performance Modeling for Intelligent Tutoring System },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 10 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number10/26266-2016911914/ },
doi = { 10.5120/ijca2016911914 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:42.721736+05:30
%A N. Sharada
%A M. Shashi
%A Xiaolu Xiong
%T Enhanced Retention Performance Modeling for Intelligent Tutoring System
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 10
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of Personalized Adaptive Scheduling System is not only to maximize learning, but also to minimize the tendency to forget. Empirical studies from psychology and cognitive theory of memory indicate that repetition with gradually increasing spacing interval is necessary to promote optimal long-term retention. While all individuals differ in their capacity to learn and retain information, the existing personalized systems attempt to model student retention performance based on learning capabilities only. The present study aims to investigate the extent to which individual differences in learning and forgetfulness help in predicting retention performance, which is essential for designing personalized retrieval practice schedules for a student. This study was conducted using data from Personalized Adaptive Scheduling System in ASSISTments, an adaptive mathematical tutor. The results illustrate the importance of student learning and forgetfulness features in predicting retention performance of a new skill, the proposed prediction model showed a significant improvement from an R2 of 0.2975 with an existing baseline model to an R2 value of 0.3989. In addition the newly identified features are used to predict the retention interval of a student, since ideal personalized retention schedules should be roughly equal to the retention interval of the student. The predictive accuracy of the linear regression model thus obtained was found to be statistically significant with an R2 value of 0.413.

References
  1. Corbett, A.T. and Anderson, J.R., 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), pp.253-278.
  2. Pardos, Z. and Heffernan, N., 2010, June. Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm. In Educational Data Mining 2010.
  3. Lee, J.I. and Brunskill, E., 2012. The Impact on Individualizing Student Models on Necessary Practice Opportunities. International Educational Data Mining Society.
  4. d Baker, R.S., Corbett, A.T. and Aleven, V., 2008, June. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In International Conference on Intelligent Tutoring Systems (pp. 406-415). Springer Berlin Heidelberg.
  5. Yudelson, M.V., Koedinger, K.R. and Gordon, G.J., 2013, July. Individualized bayesian knowledge tracing models. In International Conference on Artificial Intelligence in Education (pp. 171-180). Springer Berlin Heidelberg.
  6. Wang, Y. and Heffernan, N., 2010, June. Towards modeling forgetting and relearning in ITS: preliminary analysis of ARRS data. In Educational Data Mining 2011.
  7. Pashler, H., Rohrer, D., Cepeda, N.J. and Carpenter, S.K., 2007. Enhancing learning and retarding forgetting: Choices and consequences. Psychonomic bulletin & review, 14(2), pp.187-193.
  8. Wang, Y. and Beck, J., 2012, June. Incorporating Factors Influencing Knowledge Retention into a Student Model. In Educational Data Mining 2012.
  9. Xiong, X., Li, S. and Beck, J.E., 2013, May. Will You Get It Right Next Week: Predict Delayed Performance in Enhanced ITS Mastery Cycle. In FLAIRS Conference.
  10. Landauer, T.K. and Bjork, R.A., 1978. Optimum rehearsal patterns and name learning. Practical aspects of memory, 1, pp.625-632.
  11. Kang, S.H., Lindsey, R.V., Mozer, M.C. and Pashler, H., 2014. Retrieval practice over the long term: Should spacing be expanding or equal-interval? Psychonomic bulletin & review, 21(6), pp.1544-1550.
  12. Xiong, X. and Beck, J.E., 2014, June. A study of exploring different schedules of spacing and retrieval interval on mathematics skills in ITS environment. In International Conference on Intelligent Tutoring Systems (pp. 504-509). Springer International Publishing.
  13. Xiong, X., Wang, Y. and Beck, J.B., 2015, March. Improving students' long-term retention performance: a study on personalized retention schedules. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 325-329). ACM.
  14. Wang, Yutao, and Joseph E. Beck., 2012 Using Student Modeling to Estimate Student Knowledge Retention. International Educational Data Mining Society.
Index Terms

Computer Science
Information Sciences

Keywords

Retention Performance Personalized Adaptive Scheduling System Automatic Reassessment and Relearning System.