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Using Adaptive Experiments to Rapidly Help Students

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

Abstract

Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probability of assigning students to conditions changes while more data is being collected, so students can be assigned to interventions that are likely to perform better. Digital educational environments lower the barrier to conducting such adaptive experiments, but they are rarely applied in education. One reason might be that researchers have access to few real-world case studies that illustrate the advantages and disadvantages of these experiments in a specific context. We evaluate the effect of homework email reminders in students by conducting an adaptive experiment using the Thompson Sampling algorithm and compare it to a traditional uniform random experiment. We present this as a case study on how to conduct such experiments, and we raise a range of open questions about the conditions under which adaptive randomized experiments may be more or less useful.

Keywords

  • Reinforcement learning
  • Randomized experiments
  • Multi-Armed bandits
  • A/B testing
  • Field deployment

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Correspondence to Angela Zavaleta-Bernuy .

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Zavaleta-Bernuy, A. et al. (2021). Using Adaptive Experiments to Rapidly Help Students. 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_75

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  • DOI: https://doi.org/10.1007/978-3-030-78270-2_75

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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)