Decision-Making Validity in Response to Intervention

Author(s):  
David W. Barnett ◽  
Renee Hawkins ◽  
David Prasse ◽  
Janet Graden ◽  
Melissa Nantais ◽  
...  
Education ◽  
2013 ◽  
Author(s):  
Amy Eppolito ◽  
Kathryn White ◽  
Janette Klingner

Response to intervention (RTI) is a comprehensive, systematic approach to teaching and learning designed to monitor academic and behavioral progress for all students, provide early interventions of increasing intensity to struggling learners, and potentially identify learners with more significant learning disabilities. The model is implemented with multitiered instruction, intervention, and assessment. The key components of the RTI model include (1) high-quality instruction matched to the needs of students, (2) evidence-based interventions of increasing intensity, (3) ongoing progress monitoring, and (4) data-driven decision making. Components of the model, such as data-driven decision making and multitiered instruction, have been studied for the past few decades, but the model as an integrated whole has been developed more recently. One catalyst for increased research and interest in RTI has been a change in federal legislation in the United States. The most recent reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA) in 2004 permits the RTI model to be implemented as an alternative means to identify students with learning disabilities (LDs). These amendments to IDEA stipulate that the RTI process may be used to determine if a child is responding to research-based instruction and intervention as part of the special education evaluation process. Although driven by special education policy, RTI has been lauded as an instructional model that can improve general education overall and for special populations. However, critiques of the model argue that it has been implemented with limited research, resources, and funding and may not be valid for identifying LDs. Some experts question the psychometric validity of the model and promote using multiple forms of assessment, including more traditional standardized psycho-educational tests, in combination with RTI when evaluating students for possible LDs.


2010 ◽  
Vol 28 (2) ◽  
pp. 102-114 ◽  
Author(s):  
Matthew K. Burns ◽  
Sarah E. Scholin ◽  
Stacey Kosciolek ◽  
Judy Livingston

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.


2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.


2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.


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