Curriculum-Based Measurement in Science Learning

Abstract

The technical adequacy of curriculum-based measures in the form of short and simple vocabulary-matching probes to predict students’ performance and progress in science at the secondary level was investigated. Participants were 198 seventh-grade students from 10 science classrooms. Curriculum-based measurements (CBM) were 5-min vocabulary-matching probes administered once weekly over a period of 14 weeks. Criterion measures were knowledge pre- and posttests, the science subtest of the Iowa Test of Basic Skills (ITBS), and course grades. Alternate-form reliability coefficients ranged from r = .64 to .84. Coefficients increased over time and by combining scores across probes. Correlations between scores on the vocabulary-matching and criterion measures ranged from r = .55 to .76. The estimated mean group growth rate on the vocabulary-matching measure was .63 correct matches per week, which was significantly different from zero. The measures produced significant interindividual differences in growth rates, and growth on the measures was related to performance on the ITBS, course grades, and pre–post gains on the knowledge test. Results provide initial support for the technical adequacy of vocabulary-matching as an indicator of performance and progress in science.

CBM reading, mathematics, and written expression at the secondary level: Examining latent composite relations among indices and unique predictions with a state achievement test.

“…Future research could include additional measures of CBM-R to assess the extent to which this phenomenon may be replicated. Fifth, content measures such as vocabulary matching for science (Espin et al, 2013) and social studies (Espin, Busch, Shin, & Kruschwitz, 2001) display adequate technical evidence and might be considered in future research. Finally, this study did not examine diagnostic accuracy, screening efficiency, or individual student differences such as special education designation or gender, which have been found to differ depending on grade level in previous research (see Yeo, Fearrington, & Christ, 2012).…”

Conceptual Replications of the Critical Content Monitoring General Outcome Measure in Science Content

“…The present study incorporated conceptual replications of CCM validity research (Mooney & Lastrapes, 2016) with larger and more representative samples than had been included previously. Participant pools in individual content general outcome measurement studies have generally been small in number, with less than 200 students in a given study and singular in grade level, locale, and content area (e.g., Espin et al, 2013). For CCM, previous research studies have included 50 to 100 participants in single grades and school settings targeting both science and social studies content (Mooney & Lastrapes, 2016; Mooney, McCarter, Russo et al, 2013, 2014).…”

“…Second, as noted previously, criterion validity studies in the published literature have targeted single grades to date, focusing primarily on middle/junior high school students. Moderate concurrent correlations have been reported for individual samples in Grades 5 through 7 and 10 (e.g., Espin et al, 2013). Mooney and Lastrapes (2016) reported moderate predictive correlations for CCM fall ( r = .56) and winter (.51) benchmark probe scores with those of spring statewide accountability test scores.…”

“…The present study provided the first set of data for the curriculum-sampled measures of academic vocabulary. Fourth, growth data have only been reported for vocabulary matching studies (e.g., Espin et al, 2013; Espin, Shin, & Busch, 2005) and focused on weekly administrations over 11- to 25-week time frames. What has yet to be reported, both in the general outcome measure literature for science and social studies content and for CCM specifically, are benchmark growth scores.…”

A Comparison of Two Content Area Curriculum-Based Measurement Tools

“…Curriculum-based measurement (CBM; Deno, 1985) has been widely accepted as a valid and reliable technology for assisting educators with making data-based screening (i.e., identifying students at risk of academic difficulties) and progress decisions (i.e., measuring growth over time) in reading, mathematics, and writing (M. K. Hosp, Hosp, & Howell, 2016). CBM tools in content areas such as social studies (e.g., Beyers, Lembke, & Curs, 2013;Espin, Busch, Shin, & Kruschwitz, 2001;Mooney, McCarter, Russo, & Blackwood, 2013), and more recently science (e.g., Borsuk, 2010;Espin et al, 2013;Ford & Hosp, in press; J. L. Hosp & Ford, 2014;Mooney, Lastrapes, Marcotte, & Matthews, 2016) have also increasingly been examined.…”