Abstract
The relationship between one online formative assessment program in reading and state test scores in reading was examined using existing data (N=208) in four cohorts across elementary, middle, and high school from 2004/2005 to 2009/2010. The following research question was addressed: (1) What is the relationship between online formative assessment score growth and state test score growth? Two-Level Time-Varying Covariate Growth Models were used. The results indicated that gains in online formative assessment scores over time covaried significantly and positively with state test score gains. Although causal inference is limited, the demonstrated relationship can provide teachers/administrators with evidence of the benefits of technology-based formative assessment practices. This relationship is reassuring given the number of educators who are using technology-based and/or online teaching tools in the classroom, and the number of administrators who are seeking to increase the use of technology as a learning tool in their schools.
TopBackground
E-learning (i.e., learning that is facilitated by electronic technologies) is referred to as part of the equipment of 21st Century scholarship (Buzzetto-More & Guy, 2006). However, e-learning is only half of the equation as government mandates have required schools to use data to inform decision making. The use of data has necessitated the development of improved information technology and access to computers and high-speed Internet in schools (Petrides, 2006). Thus, the other half of the equation is the use of data rendered from e-learning, or e-assessment, which entails using electronic technologies to drive student learning and assessment as with FA (Ridgway, McCusker, & Pead, 2004).
FA can be briefly defined as the use of diagnostic formal and informal assessments to provide feedback to teachers and students over the course of instruction for the purpose of improving performance and achievement (e.g., Black, 2015; Boston, 2002). Previous research in this area has primarily focused on traditional FA practices (e.g., paper-and-pencil quizzes), with the current literature beginning to examine the effectiveness of Internet-based, automated FA programs (e.g., Chua & Don, 2013; Kingston & Nash, 2011). The overall consensus from the traditional body of literature is that FA is an essential component of classroom procedure, and that its proper use can raise standards and achievement (e.g., Black & Wiliam, 1998a; Carlson, Borman, & Robinson, 2011; Gulikers, Biemans, Wesselink, & van der Wel, 2013; Merino & Beckman, 2010), with the latest studies of technology-based FA beginning to echo these findings. Many theories have attempted to describe FA in terms of multilevel relationships (i.e., students, teachers, schools, school districts, etc.), with few studies focusing on statistically accounting for these nested associations, and hardly any examining technology-based FA practices (Black & Wiliam, 2009).
Key Terms in this Chapter
Proficiency Test: An exam that evidences how competent or skilled a student or learner is in a particular activity or field of study.
E-Learning: Learning that uses electronic technology or media (e.g., the Internet) to access education outside of the traditional brick and mortar classroom.
Covariation: Variation or variance that is correlated between two or more variables.
Formative Assessment: Formal and informal assessment methods conducted by educators concurrent with student learning used to adapt teaching and learning activities to improve student achievement.
Time-Varying Covariate: This statistical term (also called a time-dependent covariate) is used in multilevel growth modeling or survival analysis, and indicates that a covariate in the growth model is not constant throughout.
Summative Assessment: Assessment methods that are used to evaluate student learning and/or achievement at the end of an instructional cycle.
Multilevel Modeling: Statistical models (e.g., generalizations of linear models such as linear regression) of parameters that vary at more than one level (e.g., nested data).