Disentangling Direct and Indirect Interactions in Polytomous Item Response Theory Models

Authors: Frank Nussbaum, Joachim Giesen

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We support our theoretical findings with experiments on synthetic and real-world data from polytomous item response theory studies.
Researcher Affiliation Academia 1 Friedrich-Schiller University, Jena, Germany 2 DLR Institute of Data Science, Jena, Germany {frank.nussbaum, joachim.giesen}@uni-jena.de
Pseudocode No The paper mentions using
Open Source Code No The paper does not provide any explicit statements or links indicating that its source code is publicly available.
Open Datasets Yes The first dataset is from a non-forced choice vocabulary IQ test (VIQT), where participants can indicate if they do not know an answer, otherwise answers are either correct or wrong. The dataset was obtained from the [Open-Source Psychometrics Project, 2019] and contains d = 45 variables and n = 12 173 samples. The second dataset contains the answers of n = 165 test takers to the d = 72 questions of the Cambridge face memory test (CFMT) [Duchaine and Nakayama, 2006,Itz et al., 2017].
Dataset Splits No The paper describes generating datasets with a certain number of samples to test asymptotic behavior but does not mention specific training, validation, or test splits or a cross-validation setup for its experiments.
Hardware Specification No The paper does not provide any specific details regarding the hardware used to run the experiments, such as CPU/GPU models, memory, or cloud computing resources.
Software Dependencies No The paper mentions the
Experiment Setup Yes Our choice of regularization parameters is guided by Corollary 1 and fixed for all models (λ = 1/50 p d log m/n, γ = 10). More specifically, we sample the latent-observed interaction parameters uniformly from [ 0.5, 0.2] [0.2, 0.5] and the parameters for the non-zero groups of S from [ 1.5, 0.5] [0.5, 1.5].