03826nas a2200157 4500008004100000245008500041210006900126260005500195520325800250653000803508653002203516653002303538100001603561700002003577856007103597 2017 eng d00aA Large-Scale Progress Monitoring Application with Computerized Adaptive Testing0 aLargeScale Progress Monitoring Application with Computerized Ada aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Many conventional assessment tools are available to teachers in schools for monitoring student progress in a formative manner. The outcomes of these assessment tools are essential to teachers’ instructional modifications and schools’ data-driven educational strategies, such as using remedial activities and planning instructional interventions for students with learning difficulties. When measuring student progress toward instructional goals or outcomes, assessments should be not only considerably precise but also sensitive to individual change in learning. Unlike conventional paper-pencil assessments that are usually not appropriate for every student, computerized adaptive tests (CATs) are highly capable of estimating growth consistently with minimum and consistent error. Therefore, CATs can be used as a progress monitoring tool in measuring student growth.

This study focuses on an operational CAT assessment that has been used for measuring student growth in reading during the academic school year. The sample of this study consists of nearly 7 million students from the 1st grade to the 12th grade in the US. The students received a CAT-based reading assessment periodically during the school year. The purpose of these periodical assessments is to measure the growth in students’ reading achievement and identify the students who may need additional instructional support (e.g., academic interventions). Using real data, this study aims to address the following research questions: (1) How many CAT administrations are necessary to make psychometrically sound decisions about the need for instructional changes in the classroom or when to provide academic interventions?; (2) What is the ideal amount of time between CAT administrations to capture student growth for the purpose of producing meaningful decisions from assessment results?

To address these research questions, we first used the Theil-Sen estimator for robustly fitting a regression line to each student’s test scores obtained from a series of CAT administrations. Next, we used the conditional standard error of measurement (cSEM) from the CAT administrations to create an error band around the Theil-Sen slope (i.e., student growth rate). This process resulted in the normative slope values across all the grade levels. The optimal number of CAT administrations was established from grade-level regression results. The amount of time needed for progress monitoring was determined by calculating the amount of time required for a student to show growth beyond the median cSEM value for each grade level. The results showed that the normative slope values were the highest for lower grades and declined steadily as grade level increased. The results also suggested that the CAT-based reading assessment is most useful for grades 1 through 4, since most struggling readers requiring an intervention appear to be within this grade range. Because CAT yielded very similar cSEM values across administrations, the amount of error in the progress monitoring decisions did not seem to depend on the number of CAT administrations.

Session Video

10aCAT10aLarge-Scale tests10aProcess monitoring1 aBulut, Okan1 aCormier, Damien uhttps://drive.google.com/open?id=1uGbCKenRLnqTxImX1fZicR2c7GRV6Udc