An intelligent CAT that can deal with disengaged test taking
Wise, S. (2020). An intelligent CAT that can deal with disengaged test taking. In H. Jiao & R. W. Lissitz (Eds), Application of artificial intelligence to assessment (pp. 161-174). Information Age Publishing.
By: Steven Wise
This book presents varied applications of artificial intelligence (AI) in test development, including research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing.
This book was published outside of NWEA. The full text can be found at the link above.
By: Cindy Jiban
Most previous research involving the study of response times has been conducted using locally developed instruments. The purpose of the current study was to examine the amount of rapid-guessing behavior within a commercially available, low-stakes instrument.
By: Steven Wise, J. Carl Setzer, Jill R. van den Heuvel, Guangming Ling
The effect of nonignorable missing data in computerized adaptive test on item fit statistics for polytomous item response models
These studies are conducted based on assumptions under regular conditions for fixed test forms, such as no missing responses and normal distribution of unidimensional ability for a population.
By: Shudong Wang, Hong Jiao
Construct validity and measurement invariance of computerized adaptive testing: Application to Measures of Academic Progress (MAP) using confirmatory factor analysis
This study, using real data, provides empirical evidence of construct and invariance construct of MAP scales across grades at different academic calendars for 10 different states.
By: Shudong Wang, Marth S. McCall, Hong Jiao, Gregg Harris
Validation of longitudinal achievement constructs of vertically scaled computerised adaptive tests: a multiple-indicator, latent-growth modelling approach
The current investigative study uses a multiple-indicator, latent-growth modelling (MLGM) approach to examine the longitudinal achievement construct and its invariance for MAP Growth.
By: Shudong Wang, Hong Jiao, Liru Zhang
In this article, the authors explain how CAT provides a more precise, accurate picture of the achievement levels of both low-achieving and high-achieving students by adjusting questions as the testing goes along. The immediate, informative test results enable teachers to differentiate instruction to meet individual students’ current academic needs.
By: Edward Freeman
When New York state released the first results of the exams under the Common Core State Standards, many wrongly believed that the results showed dramatic declines in student achievement. A closer look at the results showed that student achievement may have increased.
By: John Cronin, Nate Jensen
Topics: Measurement & scaling