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Evaluating the Impact of Research-Based Updates to an Adaptive Learning System

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Artificial Intelligence in Education (AIED 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12749))

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Abstract

ALEKS is an adaptive learning system covering subjects such as math, statistics, and chemistry. Several recent studies have looked in detail at various aspects of student knowledge retention and forgetting within the system. Based on these studies, various enhancements were recently made to the ALEKS system with the underlying goal of helping students learn more and advance further. In this work, we describe how the enhancements were informed by these previous research studies, as well as the process of turning the research findings into practical updates to the system. We conclude by analyzing the potential impact of these changes; in particular, after controlling for several variables, we estimate that students using the updated system learned 9% more on average.

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References

  1. Acharya, A., Blackwell, M., Sen, M.: Explaining causal findings without bias: detecting and assessing direct effects. Am. Polit. Sci. Rev. 110(3), 512 (2016)

    Article  Google Scholar 

  2. Averell, L., Heathcote, A.: The form of the forgetting curve and the fate of memories. J. Math. Psychol. 55, 25–35 (2011)

    Article  MathSciNet  Google Scholar 

  3. Bae, C.L., Therriault, D.J., Redifer, J.L.: Investigating the testing effect: retrieval as a characteristic of effective study strategies. Learn. Instr. 60, 206–214 (2019)

    Article  Google Scholar 

  4. Choffin, B., Popineau, F., Bourda, Y., Vie, J.J.: DAS3H: modeling student learning and forgetting for optimally scheduling distributed practice of skills. In: Proceedings of the 12th International Conference on Educational Data Mining, pp. 29–38 (2019)

    Google Scholar 

  5. Ebbinghaus, H.: Memory: A Contribution to Experimental Psychology. Originally published by Teachers College, Columbia University, New York (1885). Translated by Henry A Ruger and Clara E Bussenius (1913)

    Google Scholar 

  6. Goetgeluk, S., Vansteelandt, S., Goetghebeur, E.: Estimation of controlled direct effects. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 70(5), 1049–1066 (2008)

    Article  MathSciNet  Google Scholar 

  7. Joffe, M.M., Greene, T.: Related causal frameworks for surrogate outcomes. Biometrics 65(2), 530–538 (2009)

    Article  MathSciNet  Google Scholar 

  8. Kang, S.H.: Spaced repetition promotes efficient and effective learning: policy implications for instruction. Policy Insights Behav. Brain Sci. 3(1), 12–19 (2016)

    Article  Google Scholar 

  9. Karpicke, J.D., Roediger, H.L.: The critical importance of retrieval for learning. Science 319(5865), 966–968 (2008)

    Article  Google Scholar 

  10. 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 

  11. Matayoshi, J., Granziol, U., Doble, C., Uzun, H., Cosyn, E.: Forgetting curves and testing effect in an adaptive learning and assessment system. In: Proceedings of the 11th International Conference on Educational Data Mining, pp. 607–612 (2018)

    Google Scholar 

  12. Matayoshi, J., Uzun, H., Cosyn, E.: Deep (un)learning: using neural networks to model retention and forgetting in an adaptive learning system. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 258–269. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23204-7_22

    Chapter  Google Scholar 

  13. Matayoshi, J., Uzun, H., Cosyn, E.: Studying retrieval practice in an intelligent tutoring system. In: Proceedings of the Seventh ACM Conference on Learning @ Scale, pp. 51–62 (2020)

    Google Scholar 

  14. McGraw Hill ALEKS: What is ALEKS? (2021). https://www.aleks.com/about_aleks

  15. Pavlik, P.I., Anderson, J.R.: Using a model to compute the optimal schedule of practice. J. Exp. Psychol. Appl. 14(2), 101 (2008)

    Article  Google Scholar 

  16. Qiu, Y., Qi, Y., Lu, H., Pardos, Z.A., Heffernan, N.T.: Does time matter? Modeling the effect of time with Bayesian knowledge tracing. In: Proceedings of the 4th International Conference on Educational Data Mining, pp. 139–148 (2011)

    Google Scholar 

  17. Roediger III, H.L., Butler, A.C.: The critical role of retrieval practice in long-term retention. Trends Cogn. Sci. 15, 20–27 (2011)

    Article  Google Scholar 

  18. Roediger III, H.L., Karpicke, J.D.: The power of testing memory: basic research and implications for educational practice. Perspect. Psychol. Sci. 1(3), 181–210 (2006)

    Article  Google Scholar 

  19. Roediger III, H.L., Karpicke, J.D.: Test-enhanced learning: taking memory tests improves long-term retention. Psychol. Sci. 17(3), 249–255 (2006)

    Article  Google Scholar 

  20. Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with Python. In: 9th Python in Science Conference (2010)

    Google Scholar 

  21. Settles, B., Meeder, B.: A trainable spaced repetition model for language learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1848–1858 (2016)

    Google Scholar 

  22. Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez-Rodriguez, M.: Enhancing human learning via spaced repetition optimization. Proc. Natl. Acad. Sci. 116(10), 3988–3993 (2019)

    Article  MathSciNet  Google Scholar 

  23. Vansteelandt, S.: Estimating direct effects in cohort and case-control studies. Epidemiology 851–860 (2009)

    Google Scholar 

  24. Vansteelandt, S., et al.: On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects. Genet. Epidemiol.: Off. Publ. Int. Genet. Epidemiol. Soc. 33(5), 394–405 (2009)

    Article  Google Scholar 

  25. Wang, Y., Heffernan, N.T.: Towards modeling forgetting and relearning in ITS: preliminary analysis of ARRS data. In: Proceedings of the 4th International Conference on Educational Data Mining, pp. 351–352 (2011)

    Google Scholar 

  26. Weinstein, Y., Madan, C.R., Sumeracki, M.A.: Teaching the science of learning. Cogn. Res.: Principles Implicat. 3(1), 1–17 (2018). https://doi.org/10.1186/s41235-017-0087-y

    Article  Google Scholar 

  27. Xiong, X., Wang, Y., Beck, J.B.: Improving students’ long-term retention performance: a study on personalized retention schedules. In: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, pp. 325–329. ACM (2015)

    Google Scholar 

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Authors and Affiliations

  1. McGraw Hill ALEKS, Irvine, CA, USA

    Jeffrey Matayoshi, Eric Cosyn & Hasan Uzun

Authors
  1. Jeffrey Matayoshi
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  2. Eric Cosyn
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  3. Hasan Uzun
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Corresponding author

Correspondence to Jeffrey Matayoshi .

Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Haifa, Israel

    Ido Roll

  2. Arizona State University, Tempe, AZ, USA

    Danielle McNamara

  3. Utrecht University, Utrecht, The Netherlands

    Sergey Sosnovsky

  4. London Knowledge Lab, London, UK

    Rose Luckin

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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Matayoshi, J., Cosyn, E., Uzun, H. (2021). Evaluating the Impact of Research-Based Updates to an Adaptive Learning System. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_80

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  • DOI: https://doi.org/10.1007/978-3-030-78270-2_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78269-6

  • Online ISBN: 978-3-030-78270-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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