Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.

Cho, Sun‐Joo; Brown‐Schmidt, Sarah; Boeck, Paul De; Naveiras, Matthew

doi:10.1037/met0000444

Cited by 6 publications

(2 citation statements)

References 82 publications

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“…While older simulation studies (Binder & Tutz, 2008) and current complex applications (Cho, Brown-Schmidt et al, 2022;Cho, Preacher et al, 2022) of GAMMs show that current estimation algorithms perform well when ample binary data is available, we have not investigated what the lowest feasible sample size for the SGLDRM is. We expect that it is hard to give general recommendations, as performance will depend on factors like number of items, number of measurement occasions, as well as distributions of latent variables and item difficulties.…”

Section: Appendix 1 Estimation Proceduresmentioning

confidence: 99%

Modeling a smooth course of learning and testing individual deviations from a global course

Brunn¹,

Freise²,

Doebler³

2022

JERO

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Formative assessment supplies valuable feedback for teachers and learners, and has been facilitated by computerized implementations. While longitudinal within-student assessment or within-class comparisons are useful, a normative interpretation of an individual’s course of learning can only be given relative to a reference population. As current computerized assessment systems sample items from pools or adapt tests, monitored students might work on non-overlapping item sets, so that classic sum scores cannot be compared directly. To meet this challenge, the Smooth Growth and Linear Deviations Rasch Model (SGLDRM) is introduced, an extension of Rasch’s item response theory model for binary test data. With the help of spline functions a smooth global course of learning is included. The model is flexible enough to accommodate increases and/or decreases of the mean ability level, which might be more or less pronounced at each measurement occasion. On the individual level, a random slope and a random intercept with amenable interpretations modify the global course of learning. Two measurement occasions suffice to estimate person-specific courses. A likelihood ratio test allows identifying students whose performance differs from the mean course. The methodology is illustrated with data from an online dyscalculia assessment and training.

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Section: Appendix 1 Estimation Proceduresmentioning

confidence: 99%

Modeling a smooth course of learning and testing individual deviations from a global course

Brunn¹,

Freise²,

Doebler³

2022

JERO

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Section: Interpretation In Applicationmentioning

confidence: 99%

Untitled

2022

JERO

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wenden Sie sich ebenfalls an diese Adresse, sollte Ihnen eine fristgerechte Einreichung des Abstracts nicht möglich sein. Alle eingereichten Abstracts werden von den Herausgeber:innen des Schwerpunktheftes sowie der Schriftleitung des JERO begutachtet. Auf Basis dieser Begutachtung wird die Einreichung von Manuskripten eingeladen. Die Manuskripte sollen einen Umfang von 50 000 Zeichen (einschließlich Leerzeichen) nicht übersteigen. Formal sind die Richtlinien für Autor:innen zur Vorbereitung von Manuskripten für das Journal for Educational Research Online zu beachten. Die Begutachtung durch unabhängige Expert:innen erfolgt dem regulären Verfahren entsprechend im Doppelblindverfahren. Die Entscheidung über die Annahme der Manuskripte zur Veröffentlichung im JERO trifft die Schriftleitung. Die Frist zur Einreichung der vollständigen Manuskripte ist der 1. Dezember 2022.

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Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

Cho,

Brown-Schmidt,

Clough

et al. 2024

Psychometrika

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This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available package (Wood in Package ‘mgcv.’ https://cran.r-project.org/web/packages/mgcv/mgcv.pdf, 2023) in . The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

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Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.

Cited by 6 publications

References 82 publications

Modeling a smooth course of learning and testing individual deviations from a global course

Modeling a smooth course of learning and testing individual deviations from a global course

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Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

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