Academic journal article Exceptional Children

Curriculum-Based Measurement of Oral Reading: Quality of Progress Monitoring Outcomes

Academic journal article Exceptional Children

Curriculum-Based Measurement of Oral Reading: Quality of Progress Monitoring Outcomes

Article excerpt

Curriculum-based measurement (CBM) was developed to monitor progress in the basic skill areas of reading, mathematics, written expression, and spelling (Deno, 1985, 1986, 2003; Deno & Mirkin, 1977). In the initial stages of research and development, the priorities were to develop progress monitoring measures that were (a) reliable and valid, (b) simple and efficient for use to frequently assess and evaluate instructional effects, (c) easily understood by educators to guide instructional decisions, and (d) inexpensive to use (Deno, 1985). Notwithstanding limitations, some of which are addressed here, substantial research over the past 30 years contributed to establish CBM as a set of procedures that are uniquely suited to improve student achievement (Stecker, Fuchs, & Fuchs, 2005) within a problem-solving (Shinn, 2008) or response-to-intervention framework of service delivery (D. Fuchs, Fuchs, McMaster, & Al Otaiba, 2003). Among the most distinct and highly touted benefits conferred by CBM is program evaluation through progress monitoring for the individual child. Indeed, systematic manipulation, instructional hypothesis testing, and progress monitoring are central features of school-based problem solving (Christ, 2008; Deno, 1995, 2002; Deno & Mirkin, 1977); although there are relatively few progress monitoring options, CBM of oral reading (CBM-R) is one of the most well established.

Research provides modest support for progress monitoring practice in general, with robust theoretical (L. S. Fuchs, Fuchs, Hosp, & Jenkins, 2001; Shinn, Good, Knutson, Tilly, & Collins, 1992), empirical (L. S. Fuchs et al., 2001; Stecker et al., 2005; Wayman, Wallace, Wiley, Ticha, & Espin, 2007), and psychometric evidence (Wayman et al., 2007) for CBM-R. Multiple studies have provided evidence that student achievement improves when data are collected and used along with decision rules to guide instruction (L. S. Fuchs, Deno, & Mirkin, 1984; L. S. Fuchs & Fuchs, 1986; L. S. Fuchs, Fuchs, & Hamlett, 1989a, 1989b; L. S. Fuchs, Fuchs, Hamlett, & Ferguson, 1992; L. S. Fuchs, Fuchs, Hamlett, & Phillips, 1994; L. S. Fuchs, Fuchs, Hamlett, & Stecker, 1991). In general, the research literature indicates that progress monitoring improves student outcomes if time series data are collected, graphically depicted, and evaluated with predefined rules for decision making (L. S. Fuchs & Fuchs, 1986). However, a recent review of the literature by Ardoin, Christ, Morena, Cormier, and Klingbeil (2010) identified only sparse evidence for the technical adequacy of prescribed decision rules associated with CBM.

Ardoin and colleagues (2010) reviewed 50 years of literature relevant to progress monitoring and decision rules. They classified decision rules as either (a) a data point rule or (b) a trend line rule. The data point decision rules depend on the evaluation of data points against an aim line, which defines the expected trajectory of growth (White & Haring, 1980). Each data point is plotted on a time-series graph and evaluated to be above or below the aim line. When some number of consecutive data points--usually three, four, or five--fall below the aim line, growth is deemed insufficient and a change in intervention is expected. If that number of consecutive data points fall above the aim line, then growth is in excess and the goal might be set more ambitiously.

In contrast to the data point rule, the trend line rule depends on estimates of the observed growth rate, or slope (L. S. Fuchs & Shinn, 1989; Good & Shinn, 1990; Shinn, Good, & Stein, 1989). Instead of analyzing individual data points, the trend line decision rule relies on the composite estimate of growth derived from the data set, which is typically summarized as CBM units gained per week. In the case of CBM-R, the slope is expected to approximate or exceed 1.5 words read correct per minute (WRCM) per week (Deno, Fuchs, Marston, & Shin, 2001; L. …

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