Comparison of Two Item Preknowledge Detection Approaches Using Response Time

Author(s):  
Chunyan Liu
2020 ◽  
Author(s):  
Murat Kasli ◽  
Cengiz Zopluoglu ◽  
Sarah Linnea Toton

Response time (RT) information has recently attracted a significant amount of attention in the literature as it may provide meaningful information about item preknowledge. In this study, a Deterministic Gated Lognormal Response Time (DG-LNRT) model is proposed to identify examinees with potential item preknowledge using RT information. The proposed model is applied to a real experimental dataset provided by Toton and Maynes (2019) in which item preknowledge was manipulated, and its performance is demonstrated. Then, the performance of the DG-LNRT model is investigated through a simulation study. The model is estimated using the Bayesian framework via Stan. The results indicate that the proposed model is viable and has the potential to be useful in detecting cheating by using response time differences between compromised and uncompromised items.


2020 ◽  
Author(s):  
Cengiz Zopluoglu ◽  
Murat Kasli ◽  
Sarah Linnea Toton

Response time information has recently attracted a significant amount of attention in the literature as it may provide meaningful information about item preknowledge. The methods that propose the use of response time information in identifying examinees with potential item preknowledge make an implicit assumption that the examinees with item preknowledge differ in their response time patterns compared to other examinees without item preknowledge. In this study, we analyzed the differences in response time of examinees with potential item preknowledge and examinees without item preknowledge based on a real experimental dataset provided by Toton and Maynes (2019). A multiple-group extension of van der Linden’s Lognormal Response Model with a gating mechanism was used to capture the differences in latent speed for control and experimental groups on disclosed and undisclosed items. The model used in the study and estimated parameters from this experimental dataset may inform future simulation studies in this area of research to simulate realistic datasets with item preknowledge behavior.


2016 ◽  
Vol 35 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Hong Qian ◽  
Dorota Staniewska ◽  
Mark Reckase ◽  
Ada Woo

Author(s):  
Roberto Limongi ◽  
Angélica M. Silva

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.


2000 ◽  
Author(s):  
Michael Anthony ◽  
Robert W. Fuhrman
Keyword(s):  

2014 ◽  
Author(s):  
Gabriel Tillman ◽  
Don van Ravenzwaaij ◽  
Scott Brown ◽  
Titia Benders

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