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Xin Qiao; Akihito Kamata; Yusuf Kara; Cornelis Potgieter; Joseph Nese – Grantee Submission, 2023
In this article, the beta-binomial model for count data is proposed and demonstrated in terms of its application in the context of oral reading fluency (ORF) assessment, where the number of words read correctly (WRC) is of interest. Existing studies adopted the binomial model for count data in similar assessment scenarios. The beta-binomial model,…
Descriptors: Oral Reading, Reading Fluency, Bayesian Statistics, Markov Processes
A Sequential Bayesian Changepoint Detection Procedure for Aberrant Behaviors in Computerized Testing
Jing Lu; Chun Wang; Jiwei Zhang; Xue Wang – Grantee Submission, 2023
Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is pivotal to properly differentiate examinees' aberrant behaviors from solution behavior to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to…
Descriptors: Bayesian Statistics, Behavior Patterns, Computer Assisted Testing, Accuracy
Magliano, Joseph P.; Lampi, Jodi P.; Ray, Melissa; Chan, Greta – Grantee Submission, 2020
Coherent mental models for successful comprehension require inferences that establish semantic "bridges" between discourse constituents and "elaborations" that incorporate relevant background knowledge. While it is established that individual differences in the extent to which postsecondary students engage in these processes…
Descriptors: Reading Comprehension, Reading Strategies, Inferences, Reading Tests