Adaptive Content Sequencing without Domain Information

Carlotta Schatten, Lars Schmidt-Thieme

2014

Abstract

In Intelligent Tutoring Systems, adaptive sequencers can take past student performances into account to select the next task which best fits the student’s learning needs. In order to do so, the system has to assess student skills and match them to the required skills and difficulties of available tasks. In this scenario two problems arise: (i) Tagging tasks with required skills and difficulties necessitate experts and thus is time-consuming, costly, and, especially for fine-grained skill levels, also potentially subjective. (ii) Learning adaptive sequenc- ing models requires online experiments with real students, that have to be diligently ethically monitored. In this paper we address these two problems. First, we show that Matrix Factorization, as performance predic- tion model, can be employed to uncover unknown skill requirements and difficulties of tasks. It thus enables sequencing without explicit domain knowledge, exploiting the Vygotski concept of Zone of Proximal Devel- opment. In simulation experiments, this approach compares favorably to common domain informed sequenc- ing strategies, making tagging tasks obsolete. Second, we propose a simulation model for synthetic learning processes, discuss its plausibility and show how it can be used to facilitate preliminary testing of sequencers before real students are involved.

References

  1. Beck, J., Woolf, B. P., and Beal, C. R. (2000). Advisor: A machine learning architecture for intelligent tutor construction. AAAI/IAAI, 2000:552-557.
  2. Chi, M., VanLehn, K., Litman, D., and Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and UserAdapted Interaction, 21(1-2):137-180.
  3. Cichocki, A., Zdunek, R., Phan, A. H., and Amari, S.-i. (2009). Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley. com.
  4. Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4):253-278.
  5. D Baker, R. S., Corbett, A. T., and Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In Intelligent Tutoring Systems, pages 406-415. Springer.
  6. Koedinger, K. R., Pavlik Jr, P. I., Stamper, J. C., Nixon, T., and Ritter, S. (2011). Avoiding problem selection thrashing with conjunctive knowledge tracing. In EDM, pages 91-100.
  7. Konda, V. R. and Tsitsiklis, J. N. (2000). Actor-critic algorithms. Advances in neural information processing systems, 12:1008-1014.
  8. Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30-37.
  9. Krohn-Grimberghe, A., Busche, A., Nanopoulos, A., and Schmidt-Thieme, L. (2011). Active learning for technology enhanced learning. In Towards Ubiquitous Learning, pages 512-518. Springer.
  10. Malpani, A., Ravindran, B., and Murthy, H. (2011). Personalized intelligent tutoring system using reinforcement learning. In Twenty-Fourth International FLAIRS Conference.
  11. Pardos, Z. A. and Heffernan, N. T. (2010). Modeling individualization in a bayesian networks implementation of knowledge tracing. In User Modeling, Adaptation, and Personalization, pages 255-266. Springer.
  12. Pardos, Z. A. and Heffernan, N. T. (2011). Kt-idem: introducing item difficulty to the knowledge tracing model. In User Modeling, Adaption and Personalization, pages 243-254. Springer.
  13. Pavlik, P. I., Cen, H., and Koedinger, K. R. (2009). Performance factors analysis-a new alternative to knowledge tracing. In AIEd, pages 531-538.
  14. Sarma, B. S. and Ravindran, B. (2007). Intelligent tutoring systems using reinforcement learning to teach autistic students. In Home Informatics and Telematics: ICT for The Next Billion, pages 65-78. Springer.
  15. Sutton, R. S. and Barto, A. G. (1998). Reinforcement learning: An introduction, volume 1. Cambridge Univ Press.
  16. Thai-Nghe, N., Drumond, L., Horvath, T., KrohnGrimberghe, A., Nanopoulos, A., and SchmidtThieme, L. (2011). Factorization techniques for predicting student performance. Educational Recommender Systems and Technologies: Practices and Challenges (In press). IGI Global.
  17. Thai-Nghe, N., Drumond, L., Horvath, T., and SchmidtThieme, L. (2012). Using factorization machines for student modeling. In UMAP Workshops.
  18. Vygotski, L. L. S. (1978). Mind in society: The development of higher psychological processes. Harvard university press.
  19. Wang, Y. and Heffernan, N. T. (2012). The student skill model. In Intelligent Tutoring Systems, pages 399- 404. Springer.
Download


Paper Citation


in Harvard Style

Schatten C. and Schmidt-Thieme L. (2014). Adaptive Content Sequencing without Domain Information . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 25-33. DOI: 10.5220/0004753000250033


in Bibtex Style

@conference{csedu14,
author={Carlotta Schatten and Lars Schmidt-Thieme},
title={Adaptive Content Sequencing without Domain Information},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2014},
pages={25-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004753000250033},
isbn={978-989-758-020-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Adaptive Content Sequencing without Domain Information
SN - 978-989-758-020-8
AU - Schatten C.
AU - Schmidt-Thieme L.
PY - 2014
SP - 25
EP - 33
DO - 10.5220/0004753000250033