Edward Schultz, Ph.D. is an Associate Professor at the West College of Education at Midwestern State University in Witchita Falls, TX. In addition to teaching both undergraduate and graduate courses at MSU, he is also Program Coordinator for the Educational Diagnostician Program and co-author of the C-SEP model. His areas of expertise include SLD identification, multi-tiered systems of support, and emotional and behavioral disorders (EBD). Dr. Schultz provides an overview of the C-SEP model.
The Core-Selective Evaluation Process (C-SEP) model is defined as a third-method PSW approach to identify specific learning disabilities (SLD), and is an efficient and focused data-driven professional judgment process rooted in contemporary Cattell-Horn-Carroll (CHC) theory (Shrank, Stephens-Pisecco, & Schultz, 2017) and has been identified as an emerging practice in Texas. Specifically, using single-batteries of tests (e.g., Woodcock-Johnson IV [WJ IV], Wechsler Intelligence Scale for Children, Fifth Edition [WISC-V], and Wechsler Individual Achievement Test, Third Edition [WIAT-III], etc.) as the foundation of the evaluation, integrated with current policy and supporting data, the most salient features of SLD are assessed and an individual’s unique pattern of strengths and weaknesses are identified.
CSEP was conceptualized following the release of major revisions and improvements of individualized norm-referenced cognitive, language, and achievement tests. With the release of the new versions of test batteries, a decade of change of SLD identification, and feedback from the field, using CSEP to identify SLD represents an approach that is efficient, precise, and comprehensive. This process was developed after a critical analysis of all published “third method” PSW approaches and incorporated the strengths of all SLD identification models and addressed the limitations of current approaches.
All methods of SLD identification have similar features and processes. It is generally agreed that a student with SLD is: a) not responding to appropriate traditional and supplemental instruction, b) exhibiting a disorder of basic psychological processes is evident and directly impacts the identified academic area of concern. In addition, in the process of addressing the referral question: a) exclusionary factors need to be considered, b) an assessment of SLD should be linked to instructional recommendations, and c) the assessment should adhere to the Code of Federal Regulations (Flanagan & Alfonso, 2010). The following are important components and principles of the C-SEP model:
The C-SEP method is a PSW approach to SLD identification and assumes its application to reflect current Texas Policy regarding SLD identification using a PSW. C-SEP refers primarily to the ways in which norm-referenced tests are used in the context of integrated data analysis techniques, current policy, and current research regarding the construct of SLD.
In broad terms and directly related to the C-SEP, these four conditions must be satisfied in order to meet SLD eligibility requirements:
Data collected in order to show appropriate instruction prior to referral. This may be accomplished through response-to-intervention (RTI) systems or some other type of supplemental instruction.
The student does not achieve adequately for the child’s age or meet State-approved grade-level standards (IDEA, 2004). This requires the use of multiple measures in order to determine if the student is achieving adequately (e.g., Curriculum-Based Measurement (CBM), Curriculum-Based Assessment (CBA), state testing, grades, work samples, benchmarks etc.).
According to Texas regulations, the pattern is evident by significant variance: among specific areas of cognitive function such as working memory and verbal comprehension; or between specific areas of cognitive function and academic achievement). Significant variance is not defined in TX regulations, however, the variation must be important and meaningful (practical) when scores differ by ~1 SD when considering confidence intervals of norm-referenced tests. Such patterns are identified using norm-referenced tests of cognition, language, and achievement along with the integration of all other data sources.
The evaluation of specific learning disability (SLD) requires an assessment and consideration of exclusionary factors that may be the primary cause of a student’s academic skill weaknesses and learning difficulties. These factors include vision/ hearing, or motor disabilities, intellectual disability (ID), social/emotional or psychological disturbance, environmental or economic disadvantage, cultural and linguistic factors (e.g., limited English proficiency).The assessment team must rule out any of these factors as being the primary cause of a student’s academic and learning difficulties, however, the degree of influence or contributions to the learning problems must be also be addressed.
Distinguishing Features of the Core-Selective Evaluation Process
Expressive (Oral Expression) and Receptive Language (Listening Comprehension) are formally tested and considered with every evaluation. These results are compared with cognitive measures, academic measures, and classroom functioning. This not only provides diagnostic information but also provides insight into teaching and learning.
Statistical analysis is conducted using actual norms and software/tables from the publisher. Data can be viewed using multiple lenses. Data collected from other batteries are included in the assessment using integrated data analysis (Schultz, Simpson, & Lynch, 2012).
Statistical analysis informs decision-making and professional judgment instead of being the primary vehicle for the eligibility decision. Integrated data analysis, including pattern seeking techniques, are used to make eligibility decisions.
All tests administered including the core should be administered in a purposeful and deliberate manner based on referral concerns. Testing should only occur to provide new or previously unknown information. Examiner time is dedicated to the interpretation and integrating data instead of test administration.
Academic underachievement is determined using multiple sources. Standard scores obtained from norm-referenced testing are used to understand the relationship between cognitive and language constructs. Standard scores are never used as the sole determinate of a discrepancy or variance with a cognitive or language measure.
The C-SEP model requires professional judgment (Schultz & Stephens, 2009) be utilized when making eligibility decisions. Discrepancy analysis is used to show variance and to identify and support patterns that emerged from the data.
Special education policy and assurances are strictly adhered to in order to provide the most comprehensive and appropriate evaluation and outcome.
The imperfect ability to listen, think, speak are salient features of the SLD definition and are critical assessment areas when identifying a PSW and instructional implications.
CSEP represents an approach to norm-referenced testing within the context of the overall process of SLD identification. It is dependent, as all other SLD identification methods are, on the quality of standards-based teaching and supplemental instruction (e.g., RTI), and other data considered as part of the assessment. Ecological validity of norm-referenced tests can only occur when actual achievement data can be cross-validated (Kwaitak & Schultz, 2014). In addition, the exclusionary factors must be comprehensively examined. Assessment personnel involved in the identification of SLD should have a variety of strategic approaches to answer complex questions. CSEP represents a strategic approach that is effective for a variety of referral questions.
For more information about CSEP, contact: Edward.email@example.com
Kwiatek, R., & Schultz, E.K. (2014). Using Informal Assessment Data to Support the Diagnosis of Specific Learning Disability., The Dialog, 43,12-15.
Schrank, F.A, Stephens-Pisecco, T.L., & Schultz, E.K. (2017). The Core-Selective Evaluation Process Applied to Identification of Specific Learning Disability (Woodcock-Johnson IV Assessment Service Bulletin No. 8). Itasca, IL: Houghton Mifflin Hardcourt.
Schultz, E.K., Simpson, C., & Lynch, S. (2012). Specific learning disability identification: What constitutes a pattern of strengths and weaknesses? Learning Disabilities, 18, 87-97.
Schultz, E.K., & Stephens, T.L. (2009). Utilizing professional judgment within the SLD eligibility determination process: Guidelines for educational diagnosticians and ARD committee members. The Dialog, 38, 3-6.