Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models
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USING RESPONSE TIMES AND RESPONSE ACCURACY TO MEASURE FLUENCY WITHIN COGNITIVE DIAGNOSIS MODELS
Shiyu Wang UNIVERSITY OF GEORGIA
Yinghan Chen UNIVERSITY OF NEVADA, RENO
The recent “Every Student Succeed Act" encourages schools to use an innovative assessment to provide feedback about students’ mastery level of grade-level content standards. Mastery of a skill requires the ability to complete the task with not only accuracy but also fluency. This paper offers a new sight on using both response times and response accuracy to measure fluency with cognitive diagnosis model framework. Defining fluency as the highest level of a categorical latent attribute, a polytomous response accuracy model and two forms of response time models are proposed to infer fluency jointly. A Bayesian estimation approach is developed to calibrate the newly proposed models. These models were applied to analyze data collected from a spatial rotation test. Results demonstrate that compared with the traditional CDM that using response accuracy only, the proposed joint models were able to reveal more information regarding test takers’ spatial skills. A set of simulation studies were conducted to evaluate the accuracy of model estimation algorithm and illustrate the various degrees of model complexities. Key words: response times, diagnostic classification model, fluency.
1. Introduction Diagnosing students’ learning outcomes, such as skills and abilities that students have at the completion of a course or a learning program, is an important task in Education. Cognitive diagnosis model (CDM) has emerged as an important statistical tool to help with this task, as it can use educational assessment results to identify a profile of strengths and weakness on a range of assessed skills. In this way, CDM can provide teachers with information about students’ strengths and identify their instructional needs (e.g., Ketterlin-Geller and Yovanoff 2009; Sia and Lim 2018). Research continues to document the benefits of CDM as a framework for classifying students into educationally relevant skill profiles and its wide applications in assessing students’ achievement across various instructional areas, such as spatial skills (Culpepper 2015), Englishlanguage proficiency (Chiu and Köhn 2015) and fraction subtraction (de la Torre and Douglas 2004). The recent research has also begun to consider the role of CDMs to analyze students’ growth across time in addition to diagnosing learning of skills at a given time point (e.g., Li et al. 2016; Wang et al. 2018a; Zhan et al. 2019). In spite of these benefits associated with CDM, one limitation to current research is that within CDM literature, skills are assessed strictly based upon the student’s ability to accurately complete a task. Skills that are completed with accuracy are referred to as mastered. Mastery of a skill, however, requires more than accuracy in a skill, it also requires the ability to complete the task with fluency. In the context of accuracy and speed of responding, this is a complex multidimensional
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