Such changes could include additional lesson time or a different type of lesson. Students identified as being at some risk are likely not to meet a later learning goal unless instruction is changed in some way. Such scores are predictive of some current or later learning difficulty. This descriptor applies to students with scores between the 15th and 40th percentiles. In this regard, such students are described as being at low risk of learning problems. Keep in mind that there is no such thing as no risk because many factors influence a student’s school performance and no one assessment can predict with 100% certainty if a student will succeed in the future. Students whose screening score is at or above the 40th percentile on FastBridge assessments are very likely to meet later learning goals. The resulting score ranges are described in relation to different levels of risk. For example, researchers will identify what score a student needs to earn in the fall in order to have a strong likelihood of meeting the grade level goal (e.g., proficiency) in the spring of the same school year. In the FastBridge system, scores at the 40th and 15th percentile ranks have been identified as the default benchmark levels. The percentiles selected for specific score ranges are often based on the data about the measure’s predictive validity. Finally, the scores corresponding to specific percentile ranks are identified. First, the scores in the norms are rank ordered for each grade level of students. The norms can be organized into score ranges and these ranges are used to develop benchmarks. Typically, norms are drawn from students from diverse backgrounds that represent the total population of students at each grade level that the assessment can be used. Another way to examine the accuracy of a new assessment is to see how well it predicts performance on a later assessment. For example, students could complete the new assessment in the fall and then the established assessment in the spring. If the new, fall, assessment provides data that predicts how students perform on the later spring assessment, the measure can be understood to have predictive validity.īoth concurrent and predictive validity are used as indicators of what an assessment measures. With validity evidence in hand, a test publisher can then establish norms. Norms are created from the scores of large numbers of students who have completed the assessment. Such correlations are indicators of concurrent validity. When students’ scores on both assessments are very similar they are highly correlated. In order to determine whether an assessment measures students’ skills as well as another assessment given at the same time, both assessments are given and the scores compared. Predictive validity refers to how well a certain assessment predicts a student’s future performance on a different assessment of the same skill.Ĭoncurrent Validity. Concurrent validity refers to how well a certain assessment provides information that is similar to other assessments of the same skill. This blog will explain how benchmark scores are developed, what they predict, and how to use them to provide additional instruction for students at risk of not meeting benchmark goals.īenchmark goals are developed from data sets containing information about student learning at different grades and over time. Benchmarks are typically based on two types of data: concurrent and predictive validity. These benchmarks are scores on certain assessments that have been validated through research to predict that a student will meet later learning goals. The term benchmark is widely used in education to indicate grade-level learning goals for all students. In order to know if a student is making progress toward specific learning goals, comparison with some type of standard or benchmark of success is needed.
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