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Fully Latent Principal Stratification with Misspecified Measurement Models in Intelligent Tutoring Systems

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Title: Fully Latent Principal Stratification with Misspecified Measurement Models in Intelligent Tutoring Systems
Language: English
Authors: Yanping Pei, Adam C. Sales, Hyeon-Ah Kang, Tiffany A. Whittaker
Source: International Educational Data Mining Society. 2025.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 10
Publication Date: 2025
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D210036
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Secondary Education
Descriptors: Intelligent Tutoring Systems, Measurement, Computation, Simulation, Randomized Controlled Trials, Outcomes of Treatment, Models, Secondary School Students, Algebra
Abstract: Fully-Latent Principal Stratification (FLPS) offers a promising approach for estimating treatment effect heterogeneity based on patterns of students' interactions with Intelligent Tutoring Systems (ITSs). However, FLPS relies on correctly specified models. In addition, multiple latent variables, such as ability, participation, and epistemic beliefs, can influence the effect of an ITS. Consequently, any attempt to model the latent space will inevitably involve some misspecification. In this paper, we extend prior work by investigating a more realistic scenario: assessing the impact of model misspecification on the estimation of the Local Average Treatment Effect (LATE) using simulated data. Our simulation setup is grounded in a real Randomized Controlled Trial (RCT) of Cognitive Tutor Algebra 1, an intelligent tutoring platform. This approach minimizes subjective parameter specification by relying on data-driven methods, effectively mimicking real RCT data. Our analysis reveals that FLPS remains robust in estimating LATE even under latent variable misspecification--specifically when two latent variables are used in data simulation while only a single latent variable is used in FLPS estimation. This holds regardless of whether the true LATE is zero or nonzero. These findings highlight FLPS's resilience to certain model misspecifications, reinforcing its applicability in real-world educational research. [For the complete proceedings, see ED675583.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED675630
Database: ERIC
FullText Text:
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  Data: Fully-Latent Principal Stratification (FLPS) offers a promising approach for estimating treatment effect heterogeneity based on patterns of students' interactions with Intelligent Tutoring Systems (ITSs). However, FLPS relies on correctly specified models. In addition, multiple latent variables, such as ability, participation, and epistemic beliefs, can influence the effect of an ITS. Consequently, any attempt to model the latent space will inevitably involve some misspecification. In this paper, we extend prior work by investigating a more realistic scenario: assessing the impact of model misspecification on the estimation of the Local Average Treatment Effect (LATE) using simulated data. Our simulation setup is grounded in a real Randomized Controlled Trial (RCT) of Cognitive Tutor Algebra 1, an intelligent tutoring platform. This approach minimizes subjective parameter specification by relying on data-driven methods, effectively mimicking real RCT data. Our analysis reveals that FLPS remains robust in estimating LATE even under latent variable misspecification--specifically when two latent variables are used in data simulation while only a single latent variable is used in FLPS estimation. This holds regardless of whether the true LATE is zero or nonzero. These findings highlight FLPS's resilience to certain model misspecifications, reinforcing its applicability in real-world educational research. [For the complete proceedings, see ED675583.]
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
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      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Measurement
        Type: general
      – SubjectFull: Computation
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      – SubjectFull: Randomized Controlled Trials
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      – SubjectFull: Outcomes of Treatment
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      – SubjectFull: Models
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      – SubjectFull: Secondary School Students
        Type: general
      – SubjectFull: Algebra
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    Titles:
      – TitleFull: Fully Latent Principal Stratification with Misspecified Measurement Models in Intelligent Tutoring Systems
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            NameFull: Yanping Pei
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            NameFull: Adam C. Sales
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            NameFull: Hyeon-Ah Kang
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              Y: 2025
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