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Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

Knowledge decays across breaks in instruction. Learners lack the metacognition to self-assess their knowledge decay and effectively self-direct review, as well as lacking interactive exercises appropriate to their individual knowledge level. Adaptive learning systems offer the potential to mitigate these issues, by providing open learner models to facilitate learner’s understanding of their knowledge levels and by presenting personalized practice exercises. The current study analyzes differences in knowledge decay between learners randomly assigned to an intervention where they could use an adaptive system during a long gap between courses, compared with a control condition. The experimental condition used the Personal Assistant for Life-Long Learning (PAL3), a tablet-based adaptive learning system integrating multiple intelligent tutoring systems and conventional learning resources. It contained electronics content relevant to the experiment participants, Navy sailors who graduated from apprentice electronics courses (A-School) awaiting assignment to their next training (C-School). The study was conducted over one month, collecting performance data with a counterbalanced pre-, mid-, and post-test. The control condition exhibited the expected decay. The PAL3 condition showed a significant difference from the control, with no significant knowledge decay in their overall knowledge, despite substantial variance in usage for PAL3 (e.g., most of overall use in the first week, with fewer participants engaging as time went on). Interestingly, while overall decay was mitigated in PAL3, this result was primarily through gains in some knowledge offsetting losses in other knowledge. Overall, these results indicate that adaptive study tools can help prevent knowledge decay, even with voluntary usage.

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Acknowledgements

PAL3 was supported by the Office of Naval Research (ONR) through Army Research Lab W911NF-04-D-0005 and ONR N00014-12-C-0643. However, the contents of this paper are the responsibility of the authors alone.

Author information

Authors and Affiliations

  1. Institute for Intelligent Systems, University of Memphis, Memphis, TN, 38152, USA

    Andrew J. Hampton, Philip I. Pavlik & Arthur C. Graesser

  2. Institute for Creative Technologies, University of Southern California, Los Angeles, CA, 90007, USA

    Benjamin D. Nye & William R. Swartout

  3. California State Polytechnic University, Pomona, CA, 91768, USA

    Joseph Gunderson

Authors
  1. Andrew J. Hampton
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  2. Benjamin D. Nye
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  3. Philip I. Pavlik
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  4. William R. Swartout
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  5. Arthur C. Graesser
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  6. Joseph Gunderson
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Corresponding author

Correspondence to Andrew J. Hampton .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen , Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Hampton, A.J., Nye, B.D., Pavlik, P.I., Swartout, W.R., Graesser, A.C., Gunderson, J. (2018). Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_23

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