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.

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.

This integrative review examines the motivational benefits of computerized adaptive tests (CATs), and demonstrates that they can have important advantages over conventional tests in both identifying instances when examinees are exhibiting low effort, and effectively addressing the validity threat posed by unmotivated examinees.

This article describes the decisions made in the development of CATs that influence and might threaten content alignment. It outlines a process for evaluating alignment that is sensitive to these threats and gives an empirical example of the process.

Predictive analytics in education can offer a benefit as long as educators heed the differences between how the tools are used in industry and how they should be used differently in schooling. Perhaps most important, teachers already know a great deal about their students — far more than an investor knows about a stock or a baseball scout about an up-and-coming pitcher.

The nearly 10 million English Language Learners (ELLs) represent the fastest-growing segment of the US’s public school student population. While research continually finds that ELL parents, generally speaking, place a high value on their children’s education, many immigrant, refugee, and ELL parents experience their relationships with their children’s schools very differently from mainstream English-speaking families.

Modeling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modeling and teacher value-added modeling are complex.