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Behavioural Cloning of Teachers for Automatic Homework Selection

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Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

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Abstract

We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.

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Notes

  1. 1.

    http://www.engineeringchallenges.org/challenges/learning.aspx.

  2. 2.

    https://isaacphysics.org.

  3. 3.

    https://www.isaacbooks.org.

References

  1. Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). www.tensorflow.org

  2. Aboalela, R., Khan, J.: Model of learning assessment to measure student learning: inferring of concept state of cognitive skill level in concept space. In: 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 189–195. IEEE (2016)

    Google Scholar 

  3. Anderson, M., et al.: RACOFI: a rule-applying collaborative filtering system. In Proceedings of IEEE/WIC COLA 2003 (2003)

    Google Scholar 

  4. Ba-Omar, H., Petrounias, I., Anwar, F.: A framework for using web usage mining to personalise e-learning. In: Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (2007)

    Google Scholar 

  5. Bloom, B.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Res. 13, 4–16 (1984)

    Article  Google Scholar 

  6. Bratko, I., Urbančič, T., Sammut, C.: Behavioural cloning: phenomena, results and problems. IFAC Proc. Volumes 28(21), 143–149 (1995)

    Article  Google Scholar 

  7. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (1998)

    Google Scholar 

  8. Brinton, C., Rill, R., Ha, S., Chiang, M., Smith, R., Ju, W.: Individualization for education at scale: MIIC design and preliminary evaluation. IEEE Trans. Learn. Technol. 8, 136–148 (2015)

    Article  Google Scholar 

  9. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12, 331–370 (2002)

    Article  Google Scholar 

  10. Chi, M., VanLehn, K.: Porting an intelligent tutoring system across domains. Front. Artif. Intell. Appl. 158, 551 (2007)

    Google Scholar 

  11. Chollet, F., et al.: Keras (2015). https://keras.io

  12. Craig, S.D., et al.: Learning with ALEKS: the impact of students’ attendance in a mathematics after-school program. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS (LNAI), vol. 6738, pp. 435–437. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21869-9_61

    Chapter  Google Scholar 

  13. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22, 143–177 (2004)

    Article  Google Scholar 

  14. Dettmers, S., Trautwein, U., Lüdtke, O., Kunter, M., Baumert, J.: Homework works if homework quality is high: Using multilevel modeling to predict the development of achievement in mathematics. J. Educ. Psychol. 102(2), 467 (2010)

    Article  Google Scholar 

  15. Dron, J., Mitchell, R., Siviter, P., Boyne, C.: CoFIND – an experiment in n-dimensional collaborative filtering. J. Netw. Comput. Appl. 23, 131–142 (2000)

    Article  Google Scholar 

  16. Khan, F., Mutlu, B., Zhu, X.: How do humans teach: on curriculum learning and teaching dimension. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1449–1457 (2011)

    Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  18. Lindsey, R.V., Shroyer, J.D., Pashler, H., Mozer, M.C.: Improving students’ long-term knowledge retention through personalized review. Psychol. Sci. 25(3), 639–647 (2014)

    Article  Google Scholar 

  19. Munro, A., Höök, K., Benyon, D.: Personal and Social Navigation of Information Space. Springer, London (1999). https://doi.org/10.1007/978-1-4471-0837-5

    Book  MATH  Google Scholar 

  20. Murray, T.: Authoring intelligent tutoring systems: an analysis of the state of the art. Int. J. Artif. Intell. Educ. (IJAIED) 10, 98–129 (1999)

    Google Scholar 

  21. Nadolski, R., et al.: Simulating lightweight personalised recommender systems in learning networks: a case for pedagogy-oriented and rating based hybrid recommendation strategies. J. Artif. Soc. Soc. Simul. 12 (2009)

    Google Scholar 

  22. Novak, J.D., Cañas, A.J.: The theory underlying concept maps and how to construct and use them. Technical Report, Institute for Human and Machine Cognition (2008)

    Google Scholar 

  23. Pardos, Z.A., Tang, S., Davis, D., Le, C.V.: Enabling real-time adaptivity in MOOCs with a personalized next-step recommendation framework. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, pp. 23–32. ACM (2017)

    Google Scholar 

  24. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  25. Pomerleau, D.A.: Alvinn: an autonomous land vehicle in a neural network. In: Advances in Neural Information Processing Systems, pp. 305–313 (1989)

    Google Scholar 

  26. Recker, M., Walker, A., Lawless, K.: What do you recommend? implementation and analyses of collaborative filtering of web resources for education. Instr. Sci. 31, 229–316 (2003)

    Article  Google Scholar 

  27. Rosen, Y., et al.: The effects of adaptive learning in a massive open online course on learners’ skill development. In: Proceedings of Learning @ Scale (2018)

    Google Scholar 

  28. Sampson, D., Karagiannidis, C.: Personalised learning: educational, technological and standardisation perspective. Interact. Educ. Multimedia 4, 24–39 (2002)

    Google Scholar 

  29. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW 2001 (2001)

    Google Scholar 

  30. Sharp, C.: Should schools set homework. Nat. Found. Educ. Res. 27, 1–4 (2002)

    Google Scholar 

  31. Tang, T.Y., McCalla, G.: Smart recommendation for an evolving e-learning system. Int. J. E-learn. 4, 105–129 (2005)

    Google Scholar 

  32. Tarus, J., Niu, Z., Mustafa, G.: Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif. Intell. Rev. 50, 21–48 (2018)

    Article  Google Scholar 

  33. VanLehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Educ. (IJAIED) 16, 227–265 (2006)

    Google Scholar 

  34. VanLehn, K., et al.: The Andes physics tutoring system: five years of evaluations. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education, pp. 678–685 (2005)

    Google Scholar 

  35. Wang, P.-Y., Yang, H.-C.: Using collaborative filtering to support college students’ use of online forum for english learning. Comput. Educ. 59, 628–637 (2012)

    Article  Google Scholar 

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Acknowledgements

This paper reports on research supported by Cambridge Assessment, University of Cambridge. We thank members of the Isaac Physics team, our colleagues in the ALTA Institute, and the three anonymous reviewers for their valuable feedback.

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Authors and Affiliations

  1. ALTA Institute, Department of Computer Science and Technology, University of Cambridge, 15 J. J. Thomson Avenue, Cambridge, UK

    Russell Moore, Andrew Caines, Andrew Rice & Paula Buttery

Authors
  1. Russell Moore
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  2. Andrew Caines
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  3. Andrew Rice
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  4. Paula Buttery
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Corresponding author

Correspondence to Russell Moore .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Moore, R., Caines, A., Rice, A., Buttery, P. (2019). Behavioural Cloning of Teachers for Automatic Homework Selection. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23203-0

  • Online ISBN: 978-3-030-23204-7

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