Dein Suchergebnis zum Thema: Model

Randomized variants of tasks in mathematics computer-aided assessment—a theoretical model

https://www.leibniz-ipn.de/de/forschen/publikationen/randomized-variants-of-tasks-in-mathematics-computer-aided-assessment-a-theoretical-model-and-empirical-validation

The present paper addresses this gap by proposing a cross-content model for the difficulty – The model includes two key elements of mathematical tasks: the solution path and – This paper provides details on the proposed model, along with empirical evidence – from a study involving 105 mathematics freshmen, to support the model and its validity – implications for future research as well as practical implications of how the proposed model
Randomized variants of tasks in mathematics computer-aided assessment—a theoretical model

Randomized variants of tasks in mathematics computer-aided assessment—a theoretical model

https://www.leibniz-ipn.de/de/forschen/publikationen/randomized-variants-of-tasks-in-mathematics-computer-aided-assessment-a-theoretical-model-and-empirical-validation?show_navhelper=1

The present paper addresses this gap by proposing a cross-content model for the difficulty – The model includes two key elements of mathematical tasks: the solution path and – This paper provides details on the proposed model, along with empirical evidence – from a study involving 105 mathematics freshmen, to support the model and its validity – implications for future research as well as practical implications of how the proposed model
Randomized variants of tasks in mathematics computer-aided assessment—a theoretical model

Using a large language model to provide individualized feedback for pre-service physics

https://www.leibniz-ipn.de/de/forschen/publikationen/using-a-pretrained-language-model-to-provide-individualized-feedback-for-pre-service-physics-teachers-written-reflections

Consequently, we investigate ways in which AI in the form of a large language model – To address this issue, we used a pre-trained large language model and fine-tuned – Our results show that the pre-trained language model allowed a sufficient classification
EnglishDeutsch Startseite Forschen Alle Publikationen des IPN Using a large language model

Using a large language model to provide individualized feedback for pre-service physics

https://www.leibniz-ipn.de/de/forschen/publikationen/using-a-pretrained-language-model-to-provide-individualized-feedback-for-pre-service-physics-teachers-written-reflections?show_navhelper=1

Consequently, we investigate ways in which AI in the form of a large language model – To address this issue, we used a pre-trained large language model and fine-tuned – Our results show that the pre-trained language model allowed a sufficient classification
EnglishDeutsch Startseite Forschen Alle Publikationen des IPN Using a large language model

A note on using scale sum scores in path analysis – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/a-note-on-using-scale-sum-scores-in-path-analysis

Model.) has cautioned against employing sum scores as predictor variables in subsequent – alternative, structural equation modeling (SEM) based on a unidimensional factor model—where – measurement error correction methods when the assumption of a unidimensional measurement model – design-based reliability indices, such as Cronbach’s alpha, are preferred over model-based
Model.) has cautioned against employing sum scores as predictor variables in subsequent

A note on using scale sum scores in path analysis – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/a-note-on-using-scale-sum-scores-in-path-analysis?show_navhelper=1

Model.) has cautioned against employing sum scores as predictor variables in subsequent – alternative, structural equation modeling (SEM) based on a unidimensional factor model—where – measurement error correction methods when the assumption of a unidimensional measurement model – design-based reliability indices, such as Cronbach’s alpha, are preferred over model-based
Model.) has cautioned against employing sum scores as predictor variables in subsequent

A note on Tutz’s pairwise separation estimator – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/a-note-on-tutzs-pairwise-separation-estimator?show_navhelper=1

The Rasch model has the desirable property that item parameter estimation can be – Pairwise estimation approaches in the Rasch model exploit this principle by estimating – The present article examines the asymptotic behavior of the TPSE within the Rasch model
10.3390/appliedmath6010013 Publikationsstatus Veröffentlicht – 01.2026 The Rasch model

A note on Tutz’s pairwise separation estimator – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/a-note-on-tutzs-pairwise-separation-estimator

The Rasch model has the desirable property that item parameter estimation can be – Pairwise estimation approaches in the Rasch model exploit this principle by estimating – The present article examines the asymptotic behavior of the TPSE within the Rasch model
10.3390/appliedmath6010013 Publikationsstatus Veröffentlicht – 01.2026 The Rasch model

A supervised learning approach to estimating IRT models in small samples – Leibniz-Institut

https://www.leibniz-ipn.de/de/forschen/publikationen/a-supervised-learning-approach-to-estimating-irt-models-in-small-samples?show_navhelper=1

We describe and evaluate our approach for the three-parameter logistic model; however – , it is applicable to any model with an item characteristic curve. – supporting the obtainment of both point estimates and confidence intervals for IRT model
We describe and evaluate our approach for the three-parameter logistic model; however

A supervised learning approach to estimating IRT models in small samples – Leibniz-Institut

https://www.leibniz-ipn.de/de/forschen/publikationen/a-supervised-learning-approach-to-estimating-irt-models-in-small-samples

We describe and evaluate our approach for the three-parameter logistic model; however – , it is applicable to any model with an item characteristic curve. – supporting the obtainment of both point estimates and confidence intervals for IRT model
We describe and evaluate our approach for the three-parameter logistic model; however