Curriculum-Based Measurement of Reading Growth: Weekly Versus Intermittent Progress Monitoring

Jenkins, James J.; Schulze, Margaret A.; Marti, Allison; Harbaugh, Allen G.

doi:10.1177/0014402917708216

Cited by 14 publications

(98 citation statements)

References 26 publications

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“…Whereas less frequent progress monitoring might run the risk of detecting a need for instructional change too late, Jenkins and Terjeson () found that reduced frequency, along with ambitious long‐term goals and use of trend‐based decision rules, actually led to more prompts to modify instruction in reading. Further, Jenkins, Schulze, Marti, and Harbaugh () found no evidence that intermittent progress monitoring schedules (e.g., two or three CBM forms administered on one occasion every two to six weeks) would change decision‐making accuracy compared to weekly CBM administration. However, they cautioned that replication is needed.…”

Section: Discussionmentioning

confidence: 99%

Effects of Data‐Based Individualization for Students with Intensive Learning Needs: A Meta‐Analysis

Jung

McMaster

Kunkel

et al. 2018

Learn Disabil Res Pract

104

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We examined the mean effect of teachers’ use of data‐based individualization (DBI) on the performance of students with intensive learning needs across academic areas and factors influencing the effects of DBI on student achievement. A total of 57 effect sizes from 14 studies were categorized into two comparisons: DBI Only (comparisons between DBI and a business‐as‐usual control) and DBI Plus (comparisons in which DBI implementers had access to additional information on student performance while they implemented DBI, compared to a control). The mean effect of DBI Only on student performance was g = 0.37; the mean effect of DBI Plus was g = 0.38. Differential effects of DBI were found depending on the nature of CBM tasks, frequency of CBM administration, and type and frequency of supports provided to teachers. Findings support the use of DBI to enhance student outcomes across academic areas.

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Section: Discussionmentioning

confidence: 99%

Effects of Data‐Based Individualization for Students with Intensive Learning Needs: A Meta‐Analysis

Jung

McMaster

Kunkel

et al. 2018

Learn Disabil Res Pract

104

View full text Add to dashboard Cite

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“…Jenkins and colleagues (2017) hypothesized that intermittent PM schedules could decrease time commitments related to assessment administration and, therefore, enhance the feasibility of using CBM and the DBI process for teachers. Jenkins et al argued that decreasing the time demands related to CBM administration would provide more time for teachers to use the data to inform instruction, particularly for students with inadequate response to intervention.…”

Section: Considering Alternative Pm Schedules: Jenkins Et Al (2017)mentioning

confidence: 99%

“…Jenkins et al (2017) administered three ORF probes each week to 56 students (demographics in Table 1). From the total set of 42 passages administered to each student, Jenkins et al simulated six PM schedules (i.e., one a week, two every 2 weeks, and three every 3, 4, 5, and 6 weeks).…”

Section: Considering Alternative Pm Schedules: Jenkins Et Al (2017)mentioning

confidence: 99%

“…Jenkins et al (2017) established a goal growth rate of 1.0-WRC increase each week, citing this goal as a reasonable rate of growth for second to sixth graders (Deno et al, 2001). For each week, Jenkins et al assessed whether TG and weekly slopes indicated adequate or inadequate progress compared to the goal of 1.0-WRC increase per week.…”

Section: Considering Alternative Pm Schedules: Jenkins Et Al (2017)mentioning

confidence: 99%

“…Teachers must select PM schedules that are sufficiently accurate while considering the timeliness of instructional adaptations for students who are not responding adequately (Jenkins et al, 2017). Teachers may be able to make instructional changes for students more rapidly if they use fewer than 8 to 12 weeks of CBM data; however, they will be doing so with less reliable information, thus increasing the probability of inaccurate decisions.…”

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confidence: 99%

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Comparing Schedules of Progress Monitoring Using Curriculum-Based Measurement in Reading: A Replication Study

Gesel

Lemons

2020

Exceptional Children

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Curriculum-based measurement (CBM) is a systematic, ongoing assessment framework that allows special educators to monitor students’ progress and determine the need for instructional adaptations. Jenkins and colleagues examined the accuracy and timeliness of six different schedules of CBM progress monitoring (PM). The authors found that weekly and intermittent PM schedules were similarly accurate and timely. This study replicated and extended the work of Jenkins and colleagues by examining the accuracy and timeliness of different PM schedules for 51 students with disabilities. Results indicated that the accuracy and timeliness of the PM schedules for the current sample was poorer than the accuracy and timeliness reported by Jenkins and colleagues. In line with the results of the original study, however, these results indicated that intermittent PM schedules sufficiently predicted student true growth compared to weekly PM schedule. Implications for research and practice are discussed.

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Assessing the consequential validity of early literacy progress monitoring data: An investigation of the accuracy of decision rules to evaluate response to instruction.

Norman¹,

Nelson²

2019

School Psychology

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A growing body of research suggests that growth in early literacy skills, including letter sound fluency, is predictive of later reading outcomes. In turn, educators and school psychologists use measures of letter sound fluency to monitor student response to early reading instruction. Limited research has evaluated whether decision-making frameworks that educators apply to early reading time series data to make instructional decisions (e.g., to continue or change the intervention) yield accurate recommendations. Further, it is unclear how long data need to be collected and which type of decision rule (e.g., data point, trend line) will produce the most accurate recommendations. We conducted a series of simulations to investigate the impact of data collection duration (4 to 16 weeks) and decision-rule type (data point, trend line, and median) on the accuracy of data-based decisions using early literacy data. Results suggest that the median and trend-line rules produced recommendations that were sufficiently accurate in identifying students that were not making adequate progress (sensitivity) after about 12 weeks when data were collected once a week. However, the median and trend-line rules did not produce recommendations that were sufficiently accurate to continue an intervention (specificity) across all progress monitoring durations. The opposite pattern was observed for the data-point rule. Outcomes suggest that recommendations developed from other progress monitoring measures (e.g., oral reading fluency) should not be extrapolated to other measures without empirical investigation. More research is needed to identify appropriate decision rules to evaluate early literacy progress monitoring data.

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Curriculum-Based Measurement of Reading Growth: Weekly Versus Intermittent Progress Monitoring

Cited by 14 publications

References 26 publications

Effects of Data‐Based Individualization for Students with Intensive Learning Needs: A Meta‐Analysis

Effects of Data‐Based Individualization for Students with Intensive Learning Needs: A Meta‐Analysis

Comparing Schedules of Progress Monitoring Using Curriculum-Based Measurement in Reading: A Replication Study

Assessing the consequential validity of early literacy progress monitoring data: An investigation of the accuracy of decision rules to evaluate response to instruction.

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