Overview of 4-Day Series
This four-day training series provides a contemporary framework for conducting comprehensive evaluations that are educationally meaningful, instructionally relevant, and aligned with current research and policy. Participants will examine updated perspectives on the role of norm-referenced testing, with an increased emphasis on purposeful test selection, using learning data to identify the presence and manifestations of a learning disability and test data to explain how a student learns. The series emphasizes deconstructing academic achievement areas to gain deeper insight into the skills and processes underlying student performance and integrating multiple sources of evidence into meaningful, theme-based explanations of learning rather than relying primarily on standard scores. Participants will also learn to use artificial intelligence as a practical tool to enhance work-sample analysis, task-demand analysis, data synthesis, impact statement development, and report writing, while maintaining professional judgment and strengthening the ecological validity of comprehensive evaluations.
Day 1, September 14, The Role of Norm-Referenced Testing and Purposeful Test Selection
Day 1 establishes the foundation for contemporary comprehensive evaluations by examining the role of norm-referenced testing within current policy, research, and a preponderance-of-data framework. Participants will explore how intellectual development is conceptualized and assessed, review updates to the Cattell-Horn-Carroll (CHC) Theory of Intelligence and the interpretation of global scores, and learn to deconstruct academic achievement constructs to guide purposeful test selection. The session concludes with a practical framework for selecting assessments that answer meaningful questions and directly inform intervention and educational decision-making.
Learning Outcomes:
• Describe the evolving role of norm-referenced testing within comprehensive evaluations in the context of Texas policy, guidance, and current research.
• Explain how intellectual development is conceptualized and assessed within contemporary evaluation practices.
• Explain current updates to the Cattell-Horn-Carroll (CHC) Theory of Intelligence and the appropriate interpretation of global cognitive scores.
• Deconstruct academic achievement constructs to better understand the skills and processes measured by standardized assessments.
• Apply a question-driven framework for purposeful test selection by determining whether an assessment provides new information or meaningful insight into student learning.
Day 2 , October 5th, Applying Purposeful Test Selection Through Case-Based Analysis
Day 2 focuses on applying the concepts from Day 1 through authentic case studies. Participants will analyze referral information, educational history, intervention data, classroom performance, and assessment results to evaluate a preponderance of data and make defensible educational decisions. The session introduces three advanced analytical frameworks—task demand analysis, conditional analysis, and integrated data analysis—and provides guided practice using these frameworks to synthesize evidence into meaningful conclusions that support eligibility and intervention planning.
Learning Outcomes:
• Apply the principles of purposeful test selection to authentic case studies.
• Conduct a task demand analysis to identify the knowledge and skills required for academic success.
• Use conditional analysis to evaluate factors that may influence student performance and interpretation of assessment results.
• Integrate multiple sources of assessment, educational, and observational data into cohesive, evidence-based conclusions that support intervention planning and eligibility decision-making.
• Evaluate a preponderance of educational, intervention, observational, and assessment data when making evaluation decisions.
Day 3, October 19th, Gathering Instructional Response Data to Strengthen Comprehensive Evaluations
Day 3 focuses on using instructional response data to strengthen comprehensive evaluations. Participants will examine the developmental foundations of reading, writing, mathematics, oral language, attention, and cognitive processing, including the PASS Theory, and learn how these models inform evaluation practices. The session also introduces AI-assisted work sample analysis as a practical method for analyzing authentic student work, strengthening ecological validity, and connecting classroom performance to evaluation findings.
Learning Outcomes:
• Describe the developmental foundations of reading, writing, mathematics, oral language, attention, and cognitive processing.
• Explain the role of PASS Theory in comprehensive evaluations.
• Apply AI-assisted work sample analysis to authentic student work.
• Integrate work sample analysis with standardized assessment findings.
• Strengthen the ecological validity of comprehensive evaluations.
Day 4, November 9th, From Sorting Data to Synthesizing Data: Explaining Learning Through Integrated Evaluation Practices
The final day focuses on integrating the concepts from the previous three days through authentic case studies. Participants will move from a test-by-test reporting approach to an integrated, theme-based approach that synthesizes multiple sources of data to explain student learning. Throughout the session, participants will apply AI to support task demand analyses, data synthesis, impact statement development, and report writing while maintaining professional judgment and producing educationally meaningful evaluation reports.
Learning Outcomes
• Synthesize multiple sources of data into integrated, theme-based explanations of student learning.
• Move beyond test-by-test reporting and strengths-and-weaknesses models to develop explanatory evaluation narratives.
• Organize evaluation findings around educationally meaningful themes.
• Use AI to support task demand analyses, data synthesis, and impact statement development.
• Apply the principles from the previous three days to develop comprehensive, educationally meaningful evaluation reports.