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 1917808 1931523 1940236 1917713 1903304 1822830 1759229 1724889 1636782 1535428 R305N210049 R305D210031 R305A170137 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 |
| 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.] |
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| DOI: | 10.1145/3491140.3528267 |
