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Evaluating the Effect of Uncertainty Visualisation in Open Learner Models on Students’ Metacognitive Skills

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Artificial Intelligence in Education (AIED 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10331))

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Abstract

Self-assessment is widely used in open learner models (OLMs) as a metacognitive process to enhance students’ self-regulated learning. Yet little research has investigated the impact of the visualisation when the OLM shows the conflict (i.e., uncertainty) between the system’s beliefs about student knowledge and students’ confidence in the correctness of their answers. We deployed such an OLM and studied its use. The impact of the uncertainty visualisation on student learning, confidence gains and actions was determined by comparing these measures across two treatment conditions and a control condition. Those who accessed the OLM performed significantly better on the post-test, and those in the treatment group who could see both sets of beliefs separately showed greater confidence gains and used the system more.

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References

  1. Boud, D.: The role of self-assessment in student grading. Assess. Eval. High. Educ. 14(1), 20–30 (1989)

    Article  Google Scholar 

  2. Jackson, S.A., Kleitman, S.: Metacognit. Learn. 9(1), 25–49 (2014). Springer

    Article  Google Scholar 

  3. Butler, D.L., Winne, P.H.: Feedback and self-regulated learning: a theoretical synthesis. Rev. Educ. Res. 65(3), 245–281 (1995)

    Article  Google Scholar 

  4. Kulhavy, R.W., Stock, W.A.: Feedback in written instruction: the place of response certitude. Educ. Psychol. Rev. 1(4), 279–308 (1989)

    Article  Google Scholar 

  5. Bull, S., Kay, J.: Open learner models as drivers for metacognitive processes. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies. SIHE, vol. 28, pp. 349–365. Springer, New York (2013). doi:10.1007/978-1-4419-5546-3_23

    Chapter  Google Scholar 

  6. Woolf, B.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-learning, pp. 49–94. Morgan Kaufmann, Burlington (2009)

    Google Scholar 

  7. Demmans Epp, C., Bull, S.: Uncertainty representation in visualizations of learning analytics for learners: current approaches and opportunities. IEEE Trans. Learn. Technol. 8(3), 242–260 (2015)

    Article  Google Scholar 

  8. Zwick, R., Zapata-Rivera, D., Hegarty, M.: Comparing graphical and verbal representations of measurement error in test score reports. Educ. Assess. 19(2), 116–138 (2014). Routledge

    Article  Google Scholar 

  9. Zapata-Rivera, J.D., Greer, J.E.: Interacting with inspectable bayesian models. Int. J. Artif. Intell. Educ. 14, 127–163 (2004)

    Google Scholar 

  10. Mohanarajah, S., Kemp, R.H., Kemp, E.: Opening a fuzzy learner model. In: Proceedings of Workshop on Learner Modelling for Reflection, International Conference on Artificial Intelligence in Education, pp. 62–71 (2005)

    Google Scholar 

  11. Aleven, V., Popescu, O., Ogan, A., Koedinger, K.R.: A formative classroom evaluation of a tutorial dialogue system that supports self-explanation. In: Aleven, V., Hoppe, U., Kay, J., Mizoguchi, R., Pain, H., Verdejo, F., Yacef, K. (eds.) Supplemental Proceedings of the 11th International Conference on Artificial Intelligence in Education, AIED 2003, vol. VI, pp. 345–355. School of Information Technologies, University of Sydney (2003)

    Google Scholar 

  12. Bull, S., Kay, J.: SMILI☺: a framework for interfaces to learning data in open learner models, learning analytics and related fields. Int. J. Artif. Intell. Educ. 26(1), 293–331 (2016)

    Article  Google Scholar 

  13. Mitrovic, A., Martin, B.: Evaluating the effect of open student models on self-assessment. Int. J. Artif. Intell. Educ. 17(2), 121–144 (2007)

    Google Scholar 

  14. Kerly, A., Bull, S.: Children’s interactions with inspectable and negotiated learner models. In: Woolf, Beverley P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 132–141. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69132-7_18

    Chapter  Google Scholar 

  15. Brusilovsky, P., Somyürek, S., Guerra, J., Hosseini, R., Zadorozhny, V.: The value of social: comparing open student modeling and open social student modeling. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 44–55. Springer, Cham (2015). doi:10.1007/978-3-319-20267-9_4

    Chapter  Google Scholar 

  16. Long, Y., Aleven, V.: Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 219–228. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39112-5_23

    Chapter  Google Scholar 

  17. Bull, S., Pain, H.: “Did i say what i think i said, and do you agree with me?” Inspecting and questioning the student model. In: Greer, J. (ed.) Proceedings of World Conference on Artificial Intelligence and Education. Association for the Advancement of Computing in Education, VA, USA, pp. 501–508 (1995)

    Google Scholar 

  18. Kinkeldey, C., MacEachren, A.M., Schiewe, J.: How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. Cartographic J. 51(4), 372–386 (2014). Taylor & Francis

    Article  Google Scholar 

  19. Al-Shanfari, L., Demmans Epp, C., Bull, S.: Uncertainty in open learner models: visualising inconsistencies in the underlying data. In: Bull, S., Ginon, B., Kickmeier-Rust, M., Kay, J., Johnson, M.D. (eds.) Workshop on Learning Analytics for Learners (LAK 2016), CEUR, pp. 23–30 (2016)

    Google Scholar 

  20. Bull, S., Jackson, T., Lancaster, M.: Students’ interest in their misconceptions in first year electrical circuits and mathematics courses. Int. J. Electr. Eng. Educ. 47(3), 307–318 (2010)

    Article  Google Scholar 

  21. Schön, D.A.: Educating the Reflective Practitioner. Jossey-Bass Publishers, San Francisco (1987)

    Google Scholar 

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Correspondence to Lamiya Al-Shanfari , Carrie Demmans Epp or Chris Baber .

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Al-Shanfari, L., Demmans Epp, C., Baber, C. (2017). Evaluating the Effect of Uncertainty Visualisation in Open Learner Models on Students’ Metacognitive Skills. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-61425-0_2

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