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LOOL: Towards Personalization with Flexible & Robust Estimation of Heterogeneous Treatment Effects

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Title: LOOL: Towards Personalization with Flexible & Robust Estimation of Heterogeneous Treatment Effects
Language: English
Authors: Duy M. Pham, Kirk P. Vanacore, Adam C. Sales, Johann A. Gagnon-Bartsch
Source: Grantee Submission. 2024Paper presented at the International Conference on Educational Data Mining (17th, Atlanta, GA, Jul 2024).
Peer Reviewed: Y
Page Count: 9
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D210031
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Middle School Students, Middle School Teachers, Middle School Mathematics, Algebra, Mathematics Instruction, Individualized Instruction, Educational Technology, Computer Assisted Testing, Student Characteristics, Predictive Measurement, Prior Learning, English (Second Language), Computer Managed Instruction
DOI: 10.5281/zenodo.12729840
Abstract: Effective personalization of education requires knowing how each student will perform under certain conditions, given their specific characteristics. Thus, the demand for interpretable and precise estimation of heterogeneous treatment effects is ever-present. This paper outlines a new approach to this problem based on the Leave-One-Out Potential Outcomes (LOOP) Estimator, which unbiasedly estimates individual treatment effects (ITE) from experiments. By regressing these estimates on a set of moderators, we obtain parameterized and easily interpretable estimates of conditional average treatment effects (CATE) that allow us to understand which individuals will likely benefit from each condition. We implement this approach with real-world data from an efficacy study that included four experimental conditions for instructing middle-school algebra. Our models indicate that treatment effect heterogeneity is significantly associated with students' prior subject knowledge and whether English is their native language. We then discuss possibilities for applications to enhance personalized assignments. [This paper was published in: "Proceedings of the 17th International Conference on Educational Data Mining," edited by B. PaaBen and C. D. Epp, International Educational Data Mining Society, 2024, pp. 376-84.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED670843
Database: ERIC
FullText Text:
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  Data: LOOL: Towards Personalization with Flexible & Robust Estimation of Heterogeneous Treatment Effects
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  Data: <searchLink fieldCode="AR" term="%22Duy+M%2E+Pham%22">Duy M. Pham</searchLink><br /><searchLink fieldCode="AR" term="%22Kirk+P%2E+Vanacore%22">Kirk P. Vanacore</searchLink><br /><searchLink fieldCode="AR" term="%22Adam+C%2E+Sales%22">Adam C. Sales</searchLink><br /><searchLink fieldCode="AR" term="%22Johann+A%2E+Gagnon-Bartsch%22">Johann A. Gagnon-Bartsch</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2024Paper presented at the International Conference on Educational Data Mining (17th, Atlanta, GA, Jul 2024).
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  Data: 10.5281/zenodo.12729840
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  Data: Effective personalization of education requires knowing how each student will perform under certain conditions, given their specific characteristics. Thus, the demand for interpretable and precise estimation of heterogeneous treatment effects is ever-present. This paper outlines a new approach to this problem based on the Leave-One-Out Potential Outcomes (LOOP) Estimator, which unbiasedly estimates individual treatment effects (ITE) from experiments. By regressing these estimates on a set of moderators, we obtain parameterized and easily interpretable estimates of conditional average treatment effects (CATE) that allow us to understand which individuals will likely benefit from each condition. We implement this approach with real-world data from an efficacy study that included four experimental conditions for instructing middle-school algebra. Our models indicate that treatment effect heterogeneity is significantly associated with students' prior subject knowledge and whether English is their native language. We then discuss possibilities for applications to enhance personalized assignments. [This paper was published in: "Proceedings of the 17th International Conference on Educational Data Mining," edited by B. PaaBen and C. D. Epp, International Educational Data Mining Society, 2024, pp. 376-84.]
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        Value: 10.5281/zenodo.12729840
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      – Text: English
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      – SubjectFull: Middle School Students
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      – SubjectFull: Middle School Teachers
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      – SubjectFull: Predictive Measurement
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      – TitleFull: LOOL: Towards Personalization with Flexible & Robust Estimation of Heterogeneous Treatment Effects
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              Y: 2024
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