Active Learning for Improving Machine Learning of Student Explanatory Essays

  • Peter HastingsEmail author
  • Simon Hughes
  • M. Anne Britt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10947)


There is an increasing emphasis, especially in STEM areas, on students’ abilities to create explanatory descriptions. Holistic, overall evaluations of explanations can be performed relatively easily with shallow language processing by humans or computers. However, this provides little information about an essential element of explanation quality: the structure of the explanation, i.e., how it connects causes to effects. The difficulty of providing feedback on explanation structure can lead teachers to either avoid giving this type of assignment or to provide only shallow feedback on them. Using machine learning techniques, we have developed successful computational models for analyzing explanatory essays. A major cost of developing such models is the time and effort required for human annotation of the essays. As part of a large project studying students’ reading processes, we have collected a large number of explanatory essays and thoroughly annotated them. Then we used the annotated essays to train our machine learning models. In this paper, we focus on how to get the best payoff from the expensive annotation process within such an educational context and we evaluate a method called Active Learning.


  1. 1.
    Osborne, J., Erduran, S., Simon, S.: Enhancing the quality of argumentation in science classrooms. J. Res. Sci. Teach. 41(10), 994–1020 (2004)CrossRefGoogle Scholar
  2. 2.
    Achieve Inc.: Next generation science standards (2013)Google Scholar
  3. 3.
    Hastings, P., Britt, M.A., Rupp, K., Kopp, K., Hughes, S.: Computational analysis of explanatory essay structure. In: Millis, K., Long, D., Magliano, J.P., Wiemer, K. (eds.) Multi-Disciplinary Approaches to Deep Learning. Routledge, New York (2018). Accepted for publicationGoogle Scholar
  4. 4.
    Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., Tsujii, J.: brat: a web-based tool for NLP-assisted text annotation. In: Proceedings of the Demonstrations Session at EACL 2012, Avignon, France, Association for Computational Linguistics, April 2012Google Scholar
  5. 5.
    Stenetorp, P., Topić, G., Pyysalo, S., Ohta, T., Kim, J.D., Tsujii, J.: BioNLP shared task 2011: Supporting resources. In: Proceedings of BioNLP Shared Task 2011 Workshop, Portland, Oregon, USA, Association for Computational Linguistics, pp. 112–120, June 2011Google Scholar
  6. 6.
    Goldman, S.R., Greenleaf, C., Yukhymenko-Lescroart, M., Brown, W., Ko, M., Emig, J., George, M., Wallace, P., Blaum, D., Britt, M.: Project READI: Explanatory modeling in science through text-based investigation: Testing the efficacy of the READI intervention approach. Technical Report 27, Project READI (2016)Google Scholar
  7. 7.
    Shermis, M.D., Hamner, B.: Contrasting state-of-the-art automated scoring of essays: analysis. In: Annual National Council on Measurement in Education Meeting, pp. 14–16 (2012)Google Scholar
  8. 8.
    Deane, P.: On the relation between automated essay scoring and modern views of the writing construct. Assessing Writ. 18(1), 7–24 (2013)CrossRefGoogle Scholar
  9. 9.
    Roscoe, R.D., Crossley, S.A., Snow, E.L., Varner, L.K., McNamara, D.S.: Writing quality, knowledge, and comprehension correlates of human and automated essay scoring. In: The Twenty-Seventh International Flairs Conference (2014)Google Scholar
  10. 10.
    Shermis, M.D., Burstein, J.: Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge (2013)Google Scholar
  11. 11.
    Dikli, S.: Automated essay scoring. Turk. Online J. Distance Educ. 7(1), 49–62 (2015)Google Scholar
  12. 12.
    Condon, W.: Large-scale assessment, locally-developed measures, and automated scoring of essays: Fishing for red herrings? Assessing Writ. 18(1), 100–108 (2013)CrossRefGoogle Scholar
  13. 13.
    Riaz, M., Girju, R.: Recognizing causality in verb-noun pairs via noun and verb semantics. EACL 2014, 48 (2014)Google Scholar
  14. 14.
    Rink, B., Bejan, C.A., Harabagiu, S.M.: Learning textual graph patterns to detect causal event relations. In: Guesgen, H.W., Murray, R.C. (eds.) FLAIRS Conference. AAAI Press (2010)Google Scholar
  15. 15.
    Hughes, S., Hastings, P., Britt, M.A., Wallace, P., Blaum, D.: Machine learning for holistic evaluation of scientific essays. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 165–175. Springer, Cham (2015). Scholar
  16. 16.
    Hughes, S.: Automatic inference of causal reasoning chains from student essays. Ph.D. thesis, DePaul University, Chicago, IL (2018)Google Scholar
  17. 17.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)CrossRefGoogle Scholar
  18. 18.
    Hastings, P., Hughes, S., Blaum, D., Wallace, P., Britt, M.A.: Stratified learning for reducing training set size. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 341–346. Springer, Cham (2016). Scholar
  19. 19.
    Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009)Google Scholar
  20. 20.
    Sharma, M., Bilgic, M.: Most-surely vs. least-surely uncertain. In: 13th International Conference on Data Mining (ICDM), pp. 667–676. IEEE (2013)Google Scholar
  21. 21.
    Ferdowsi, Z.: Active learning for high precision classification with imbalanced data. Ph.D. thesis, DePaul University, Chicago, IL, USA, May 2015Google Scholar
  22. 22.
    Cawley, G.C.: Baseline methods for active learning. In: Active Learning and Experimental Design Workshop in Conjunction with AISTATS 2010, pp. 47–57 (2011)Google Scholar
  23. 23.
    Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001)zbMATHGoogle Scholar
  24. 24.
    Mirroshandel, S.A., Ghassem-Sani, G., Nasr, A.: Active learning strategies for support vector machines, application to temporal relation classification. In: Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 56–64 (2011)Google Scholar
  25. 25.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM (1998)Google Scholar
  26. 26.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefGoogle Scholar
  27. 27.
    Joachims, T.: Learning to Classify Text Using Support Vector Machines - Methods, Theory, and Algorithms. Kluwer/Springer, New York (2002)CrossRefGoogle Scholar
  28. 28.
    Olsson, F.: A literature survey of active machine learning in the context of natural language processing. Technical Report T2009:06, Swedish Institute of Computer Science (2009). Accessed 8 Feb 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputingDePaul UniversityChicagoUSA
  2. 2.Psychology DepartmentNorthern Illinois UniversityDeKalbUSA

Personalised recommendations