USING CURRICULUM-BASED MEASURES FOR ASSESSING ACHIEVEMENT IN CHILDREN WHO MAY HAVE POTENTIAL RISK -FACTORS FOR READrNG DIFFICULTY

Millions of school-aged children experience reading difficulty every year. Research indicates it is essential to identify reading problems early so that intervention may be appropriately implemented. There is debate regarding what risk-factors are related to reading difficulty, what reading instruction is best, and what methods are best to screen students for potential reading problems. This research reviews the existing literature on poverty, family size, and birth order as potential risk-fhctors for reading difficulty. Curriculum-based measures, specifically DIBELS, were examined in regards to their utility in identifjing and monitoring students at-risk of reading failure. Reading instruction was also addressed. A critical analysis of the relevant literature includes implications for current practice as well as future research. TABLE OF CONTENTS . Page . ABSTRACT 11 . Chapter I: Introduction '. 1 . Statement of the Problem $ 4 . Purpose of the Stu& 5 . Research Questions 5 . Definition of Terms 5 . Assumptions and Limitations of the Stu& 7 . Chapter 11: Literature Review 8 . Potential Risk-Factors 8 Poverty . 8 Birth Order and Family Size . 13 Curriculum-Based Measurement . 18 Utility of Curriculum-Based Measures . 18 Psychometric Properties . 19 E#ect on Teacher Instruction . 20 Reading Instruction . -22 Phonemic Awareness Instruction . 22 . Phonics Instruction 23 . DIBELS . : -25 . Chapter III: Summary and Implications 28 . Summary of Mazn Findings -28 . Implicatiom for Practice 3 0 . Implications for Future Research 32 . References 34 Chapter I: Introduction According to the National Reading Panel (2004), national longitudinal studies indicated that more that 17.5% of children, roughly 10 million, will experience reading difficulty in their first three years of schooling. Evidence suggests that successfL1 reading begins early and once established, trajectories are difficult to change (National Reading Panel, 2004). VanDerHeyden, Witt, Naquin, and Noel1 (2001) proposed that early academic performance is likely to influence later academic performance; therefore, it is essential to identify reading problems and determine interventions early. There is a potential link between certain family factors such as poverty, family size, and birth order to reading difficulty. Being knowledgeable about what students' risk-factors are for reading failure can aid early identification so intervention strategies can be employed. Adams, Foorman, Lundberg, and Beeler (1998) suggested that proficient phonological awareness distinguished economically disadvantaged preschoolers f? om more advantaged children. Furthermore, poverty can impact parenting practices and the overall home environment, which can influence academic functioning. Papalia, Olds, and Feldman (2002) indicated that the effects of poverty lead to behavioral, emotional, and academic difficulties in children. Family size and poverty are often related. More recently, families have decreased in size; however many large families still exist and many face financial difficulty. Past research has implied that the larger the family, the lower a child's IQ tends to be (Rogers, Cleveland, van den Oord, & Rowe, 2000). Factors associated with large families such as overcrowding within the home, limited family resources, and a less sophisticated linguistic environment may all contribute to the trend of large families and lower IQ. Others argue this is indeed not the case (Rogers et al., 2000; Esping, 2000). According to Rodgers et al. (2000), parental IQ has a strong link to family IQ, and often low-IQ parents have many children, perhaps leading to the conclusion that large families are linked to children who struggle academically. Along with family size, the position of a child within the family may also have an impact on academic success. Theorist Alfred Adler claimed that there are birth order differences in achievement potential (cited in Prochaska & Norcross, 2003). Francis Galton also conducted research to determine a link between IQ and birth order. His work proposed that firstborn children are more intelligent than laterborn children (cited in Esping, 2003). However, Rodgers et al. (2000) concluded that birth order does not directly decrease the intelligence of laterborn children. Poverty, family size, and birth order may be potential predictors for reading difficulty. It is important for educators to know the risk-factors for underachievement so they can screen at-risk children and provide them with the appropriate interventions. One way to screen underachievement is through curriculum-based measures. Curriculum-based measures have been researched thoroughly and have been found to hold high reliability and validity (Deno, 2003). These psychometric properties have been achieved through standardized observational procedures in the repeated study of student performance in reading, writing, and mathematical skills @eno, 2003). DIBELS, a form of curriculum-based measurement (CBM), is a set of individually administered standardized measures that relate to early literacy development. According to Mr. Don Sibley (WSPA conference, October 28,2005), an expert in the field, DIBELS is designed to measure the development of pre-reading and early reading skills of elementary school children. According to Manzo (2005), DIBELS has become the national assessment tool for Reading First, a federal program adopted under the No Child Left Behind Act of 2001. More than 40 states use DIBELS to screen students grades K-3 for potential reading problems. The tests are replicable, take little time, and are simple to administer, which appeals to many school districts. OfEcials use DIBELS scores to identify children at-risk for reading failure, hold schools accountable for student achievement, and aid educators in informing their teaching instruction (Manzo, 2005). Curriculum-based measures, such as the DIBELS, can also assist with the referral process for special education programs. Teachers are typically the referral source for students potentially in need of special education based on classroom performance. There is human error involved in teachers refemng based on their perceptions of classroom success (Deno, 2003). This suggests that teacher perception might not be the best "test" of student success at school. CBM has been used to make the practice of referring for special education services more objective and measurable (Deno, 2003). Perhaps one of the greatest benefits of curriculum-based measures is that they allow the freedom of administration in the natural classroom setting. This greatly contributes to the utility of CBM for instructors and allows for a more comprehensive assessment of academic skills (VanDerHeyden et al., 2001). Furthermore, curriculum-based measures, such as DIBELS, allow for the assessment of retained curriculum and generalization of learning. Scores can be compared across students or can be used to measure the learning of an individual student over time (Hintze & Silberglitt, 2005). Providing instructors with sensitive indicators of curriculum retention informs practice and reveals which teaching methods best meet students' learning needs. When teachers adapt their instruction to aid in successfbl learning, it has been shown that students demonstrate higher rates of achievement (Fuchs & Fuchs, 1986). The scores derived fiom CBM can also be used to accurately predict which students will struggle with curriculum (Hintze & Silberglitt, 2005). Many practitioners believe early intervention is promising because it can prevent students fiom falling behind in their class, which often makes it harder for them to catch up. The possibility of reducing deficits when students are young is appealing and gives instructors a real sense of hope in the remediation of reading problems (VanDerHeyden et al., 2001). Statement of t