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Automatic Interpretable Personalized Learning

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Title: Automatic Interpretable Personalized Learning
Language: English
Authors: Prihar, Ethan, Haim, Aaron, Sales, Adam, Heffernan, Neil
Source: Grantee Submission. 2022.
Peer Reviewed: Y
Page Count: 11
Publication Date: 2022
Sponsoring Agency: National Science Foundation (NSF)
Institute of Education Sciences (ED)
Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR)
Office of Naval Research (ONR) (DOD)
Contract Number: 2118725
2118904
1950683
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R305D210031
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R305A170243
R305A180401
R305A120125
U411B190024
N000141812768
R305A170641
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Individualized Instruction, Educational Technology, Technology Uses in Education, Electronic Learning, Instructional Effectiveness, Simulation, Educational Environment, Teaching Methods
DOI: 10.1145/3491140.3528267
Abstract: Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students' education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students' learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms. [This paper was published in: "Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S '22), June 1-3, 2022, New York City, NY, USA," ACM, 2022.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2022
Accession Number: ED619825
Database: ERIC
FullText Text:
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  Data: National Science Foundation (NSF)<br />Institute of Education Sciences (ED)<br />Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR)<br />Office of Naval Research (ONR) (DOD)
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  Data: Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students' education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students' learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms. [This paper was published in: "Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S '22), June 1-3, 2022, New York City, NY, USA," ACM, 2022.]
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        Value: 10.1145/3491140.3528267
    Languages:
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