Curriculum-Based Measurement progress data: Effects of graph pattern on ease of interpretation
Open accessAbstract
Curriculum-Based Measurement (CBM) is a system for monitoring the progress of and evaluating instructional program effectiveness for students with learning difficulties. Although a large amount of research has been conducted on CBM, little has focused on the interpretation and use of the data for instructional decision-making, despite the fact that it is data use that leads to performance gains. In this study, we examine factors affecting the interpretation of CBM data. Specifically, we examine the effect of CBM graph patterns on ease of graph interpretation. Thirty college/university students completed a two-part study in which they viewed various slope-to-goal and slope-to-slope patterns of CBM-graphed data and answered decision-making questions. Response times and accuracy were measured. Results revealed that graph patterns differed in terms of ease of interpretation. Differences depended on the type of question. Implications for interpretation and use of CBM data for decision-making are discussed.
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Specific training to link data to instruction is necessary for teachers to do so effectively.
“Thus, specific training to link data to instruction is necessary for teachers to do so effectively [46].”
From Using Evidence-Based Practice and Data-Based Decision Making in Inclusive Education (2021) · cites this paper as [46]
The Discussion (Slope-to-goal comparisons; page unavailable) recommends focused CBM training that explicitly connects graph interpretation to decisions about changing instruction. But it says the effects of that training require future study; it does not establish that such training is necessary for teachers to use data effectively. The Discussion (Limitations and future research; page unavailable) also notes that the study needs replication with teachers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEven when teachers can read and interpret data and progress monitoring graphs, it is still difficult for them to link the data to instruction.
“However, even when teachers can read and interpret data and progress monitoring graphs, it is still difficult for them to link the data to instruction [46].”
From Using Evidence-Based Practice and Data-Based Decision Making in Inclusive Education (2021) · cites this paper as [46]
The Introduction’s CBM graph interpretation/comprehension section says teachers have difficulty linking graphed information to instruction. The Discussion’s “The need to read beyond the data” section explains that instructional decisions require more than reading the data. Neither passage establishes the claim’s stronger condition that teachers who can already interpret the graphs still have difficulty making that link. (LLM, conf=0.78)
Experimental — accuracy not yet measuredTeachers need training in DBDM, including collecting, interpreting, and using CBM and other progress monitoring tools to provide instruction.
“Additionally, teachers need training in DBDM [10], including in collecting, interpreting, and using CBM and other progress monitoring tools to provide instruction [46].”
From Using Evidence-Based Practice and Data-Based Decision Making in Inclusive Education (2021) · cites this paper as [46]
The Introduction’s CBM graph interpretation/comprehension section says teachers likely need specific training in interpreting CBM graphs and links interpretation to instructional decisions. The cited paper describes collecting CBM data, but does not say teachers need training in collecting it or in using other progress-monitoring tools. The separate claim about DBDM training is attached to citation [10], not this source. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Cites
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Flagged first, then by citations
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Cited by
Sorted by citations
van den Bosch, R. M., Espin, C. A., Chung, S., & Saab, N. (2017) · Learning Disabilities Research & Practice · doi:10.1111/ldrp.12122 · Cited by 62 · see citing works
Self-citation
- Authors
- Espin, C. A., Saab, N., Pat-El, R., Boender, P. D. M., & van der Veen, J.
- Affiliations
- Leiden University (NL)
- Corresponding author
- Espin, Christine A.
- Venue
- Zeitschrift für Erziehungswissenschaft
- Published
- 2018-07-02
- Volume
- 21
- Issue
- 4
- Pages
- 767-792
- ISSN
- 1434-663X (ISSN-L)
- 1862-5215
- PMC
- PMC6428331
- OpenAlex
- W2809991245
- Cited by
- 16
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 1 · 2017: 1
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Zeitschrift für Erziehungswissenschaft (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion)
- DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Leiden University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- curriculum-based measurement, graph interpretation, graph patterns, instructional decision-making, learning difficulties, progress monitoring, university students, data utilization
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