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How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts

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Book cover Artificial Intelligence in Education (AIED 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9112))

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Abstract

This research investigated key factors in learning conceptual material about statistics, and tested the effect of variability during retrieval practice. The goal was to build a model of learning for schedule-based interventions. Participants (n = 230) completed multiple reading and test trials with fill in the blank sentences about basic statistics concepts. The experiment was a 2 (trial type: read or drill) × 3 (learning trial spacing: wide medium, or narrow) × 2 (fill-in term during learning: variable or constant) × 2 (fill-in term during posttest: variable or constant) within-subjects design. The model of the results captures the data with recent and long-term components to explain posttest transfer and the testing and spacing effects. These results, and data on the conceptual confusions amongst statistical terms, are discussed with respect to implications for future intelligent learning systems.

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References

  1. Carpenter, S.K., Pashler, H., Wixted, J.T., Vul, E.: The Effects of Tests on Learning and Forgetting. Memory & Cognition 36, 438–448 (2008)

    Article  Google Scholar 

  2. Ebbinghaus, H.: Memory: A Contribution to Experimental Psychology. Teachers College, Columbia University, New York (1913/1885)

    Google Scholar 

  3. Smith, M.A., Karpicke, J.D.: Retrieval Practice with Short-Answer, Multiple-Choice, and Hybrid Tests. Memory 22, 784–802 (2014)

    Google Scholar 

  4. Karpicke, J.D., Roediger III, H.L.: The Critical Importance of Retrieval for Learning. Science 319, 966–968 (2008)

    Article  Google Scholar 

  5. Underwood, B.J., Ekstrand, B.R.: Effect of Distributed Practice on Paired-Associate Learning. Journal of Experimental Psychology 73, 1–21 (1967)

    Article  Google Scholar 

  6. Rohrer, D.: The Effects of Spacing and Mixing Practice Problems. Journal for Research in Mathematics Education 40, 4–17 (2009)

    Google Scholar 

  7. Roediger III, H.L., Karpicke, J.D.: Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention. Psychological Science 17, 249–255 (2006)

    Article  Google Scholar 

  8. Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. International Journal of Artificial Intelligence in Education 18, 181–208 (2008)

    Google Scholar 

  9. Baker, R.S.J.D., Yacef, K.: The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining 1, 3–17 (2009)

    Google Scholar 

  10. Pavlik Jr., P.I., Geno, A.: Deconstructing Cloze Practice. Manuscript submitted for publication (2015)

    Google Scholar 

  11. McDaniel, M.A., Anderson, J.L., Derbish, M.H., Morrisette, N.: Testing the Testing Effect in the Classroom. European Journal of Cognitive Psychology 19, 494–513 (2007)

    Article  Google Scholar 

  12. Paolacci, G., Chandler, J., Ipeirotis, P.G.: Running Experiments on Amazon Mechanical Turk. Judgment and Decision making 5, 411–419 (2010)

    Google Scholar 

  13. Pavlik Jr., P.I., Presson, N., Dozzi, G., Wu, S.-M., MacWhinney, B., Koedinger, K.R.: The FaCT (Fact and Concept Training) System: A New Tool Linking Cognitive Science with Educators. In: McNamara, D., Trafton, G. (eds.) Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society, pp. 1379–1384. Lawrence Erlbaum, Mahwah (2007)

    Google Scholar 

  14. Pavlik Jr., P.I.: The Microeconomics of Learning: Optimizing Paired-Associate Memory. Dissertation Abstracts International: Section B: The Sciences and Engineering 66, 5704 (2005)

    Google Scholar 

  15. Pavlik Jr., P.I., Cen, H., Koedinger, K.R.: Performance Factors Analysis – a New Alternative to Knowledge Tracing. In: Dimitrova, V., Mizoguchi, R., Boulay, B.D., Graesser, A. (eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education, Brighton, England, pp. 531-538 (2009)

    Google Scholar 

  16. Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)

    Article  Google Scholar 

  17. Pavlik Jr., P.I., Anderson, J.R.: Practice and Forgetting Effects on Vocabulary Memory: An Activation-Based Model of the Spacing Effect. Cognitive Science 29, 559–586 (2005)

    Article  Google Scholar 

  18. Appleton-Knapp, S.L., Bjork, R.A., Wickens, T.D.: Examining the Spacing Effect in Advertising: Encoding Variability, Retrieval Processes, and Their Interaction. Journal of Consumer Research 32, 266–276 (2005)

    Article  Google Scholar 

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Correspondence to Jaclyn K. Maass .

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© 2015 Springer International Publishing Switzerland

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Maass, J.K., Pavlik, P.I., Hua, H. (2015). How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_25

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19772-2

  • Online ISBN: 978-3-319-19773-9

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