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RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP [email protected] Multiple Sources of Data 1

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Page 1: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

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RtI Documentation: Understanding, Interpreting and Connecting Data for Educational

Decision Making Day 2

Andrea Ogonosky, Ph.D., LSSP

[email protected]

Multiple Sources of Data

Page 2: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Agenda

• Quick Review• Converging Data

– Problem Identification– (Resources for organization of data: RIOT/ICEL)

• Professional Judgment• Case Activities

Page 3: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

QUICK REVIEW

Page 4: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

The National Center on Response to Intervention (NCRTI) proposes four essential components of RtI:

– A school-wide, multi-level instructional and behavioral system for preventing school failure

– Screening of all students to determine who is at risk for poor learning outcomes Progress monitoring during instruction

– Data-based decision making for instruction, movement with the multi-level system and identification of students with learning disabilities

Page 5: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

RtI: Problem Solving Assessment

80%

15%

5%

Interventions

Universal ScreeningProgress

Monitoring

Progress MonitoringDiagnostics

Progress MonitoringDiagnostics

Grade LevelInstruction/ Support

Student Instructional LevelSupplemental Interventions90 min per week additional

Student Instructional LevelSupplemental Interventions120 min per week additional

Page 6: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Fidelity

Intervention Well Checks Observe in Tiers I and

II/III (ICEL) Consult with Teacher Review data weekly in

PLC/ Planning meetings Check data collection Talk to parent

Page 7: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Successful Data Collection

• Use of naturally occurring data• Led by General Education, Supported by Special Education• Problem-Solving Model Implemented with Integrity• Systematic decision rules consistently implemented • Access to Technology to Manage and Document Data-Based

Decision Making• Evidence of Improved Academic and Behavior Outcomes for

All Students

Page 8: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Components Addressed When Using Multiple Data Sources

• The interrelationship between classroom achievement and cognitive processing criteria– Classroom achievement– Academic Deficit (RtI)– Cognitive Processing– Behavior

Page 9: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Balancing Assessments

-- Assessment systems-- Multiple measures-- Varied types -- Varied purposes-- Varied data sets-- Balanced with needs

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Page 10: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Multiple Data Sources Criterion Referenced Assessment

Formative Summative ScreenProgress Monitor

Norm Referenced AssessmentDiagnosticComparativeProgress Monitor

Curriculum Based MeasurementRate of ImprovementUniversal ScreenProgress Monitor

Page 11: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Linking Assessment: Type, Need, & Purpose

TYPE

• Data used to immediately inform instruction

• Data used to establish a starting point and/or monitor progress:

• Data used to evaluate cumulative learning

PURPOSE

To plan learning prior to instruction

To support learning during instruction

To monitor learning between instruction

To verify learning after instruction

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DATA NEED

Formative

Progress Monitor

Summative

Page 12: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

CONVERGING DATA

Page 13: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Data to Consider

Referral QuestionTest SelectionInterpretation

Diagnostics

PMSummative

Page 14: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Problem Identification

Is the Tier 1 Core curriculum effective? (District Data)

– The percentage of students (aggregated or sub-groups) meeting proficiency on the state standards as measured by the statewide assessment.

– Universal Screening Trends

Page 15: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Problem Identification• School level: The percentage of students who

are at benchmark on the fall, winter and spring screening assessment is not increasing.– Who are the students? – Do the data suggest a sub-group? – Has their risk level increased (benchmark to

strategic or strategic to intensive)? – Is a clear pattern of skill deficits evident?

Page 16: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Problem Identification• Grade level: Students in certain grades are not

making adequate progress. – Has the staff been provided adequate professional

development and training on the curriculum?– Has fidelity of implementation been addressed?– Can root causes be identified?

• Class level: Instructional groups are not making growth at the expected rate. – Are the interventions matched to student needs?

Page 17: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Problem Identification• Student level: The student is not making the

same amount of progress as other students in the instructional group. – What skills has the student not mastered? – Has a diagnostic assessment been administered?

Page 18: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

The two most common reasons for less than expected rate of student progress are:

1. a mismatch between instruction and learner needs

2. fidelity of implementation

Page 19: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

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Component Data Review:

Consider the impact of each domain relative to the

“problem” Formative

Progress MonitoringSummative

Environment

Learner

Instruction

Curriculum

Page 20: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

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Review - assessment information, curriculum, discipline referrals, cumulative & health files, etc.

Interview – teacher, parent, student, specialist, etc.Observation – instruction, student, curriculum use,

environment, etc.Test/Assess – research on curriculum, instructional

effectiveness, screening, diagnostic and outcome measures, etc.

ICEL/RIOT:

Page 21: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

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Instruction – Does the teacher use data to make instructional decisions?; Does the teacher provide differentiation to assist at-risk learners?

Curriculum - Is the curriculum research-based and completed with fidelity?

Environment – What factors in the environment impact the student’s learning?

Learner - What are the learners strengths and weaknesses?; What kind of learner is he/she?

ICEL/RIOT (continued):

Page 22: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

PROFESSIONAL JUDGMENTInterpretation Issues

Page 23: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Suspected Disability?ID: What to look for in the data:Screening: Below cut score across the boardDiagnostics: Focused Skill deficits and patterns across many areas (mostly pattern of weaknesses)Progress Monitoring: ROI would be slow and possible have a downward trend, not variable, slope is evident (not flat-line)Outcome: STAAR failure pervasive, Unit and District assessments in bottom percentile

Page 24: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Suspected ID/ Slower Cognitive Processing:• I: Student instructional level significantly below grade

level, often times manipulatives, graphic organizers needed, slow (not variable) progress, well below grade level expectations.

• C: Curricular mismatch is evident across academic areas

• E: Student performs best in environment that is highly structured, highly organized, rules posted, high degree of task analysis needed

• L: Student demonstrates adaptive skill weaknesses, difficulty with use of learning strategies independently, social skill weaknesses

Page 25: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Suspected ID“Intelligence is a complex system, but not all parts equally important to overall system functioning (Parts=Broad and Narrow; System=General IQ)”

…”Assumption - flat profiles (minimal variability), but now that tests have expanded to include more broad and narrow abilities, MR profiles show variability” (Cheramie, 2013)

Significantly subaverage cognitive functioning– Measured by standardized, individually administered test of cognitive

ability– Overall test score is at least two standard deviations below the mean,

when taking into consideration the standard error of measurement of the test

Overall cognitive score has to be at least 70, considering SEm.– If test has SEm of 4 or 5, could have overall score of 74 or 75 and be

ID

Page 26: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Additional Data• Concurrently exhibits deficits in at least two of

the following areas of adaptive behavior– Communication, Self-Care, Home Living– Social/Interpersonal Skills– Use of Community Resources, Self-Direction– Functional Academic Skills– Work, Leisure, Health, and Safety

Page 27: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Reminder (ID)

• Children with ID will not likely display a flat cognitive profile on comprehensive assessments of cognitive abilities

• ID is usually evident when data indicates there is one (or more) impaired cognitive ability with high centrality that lower the functioning of the whole system

• As a group, students identified with ID have lower scores on all CHC factors

Page 28: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Suspected DisabilitySLD: What to look for in the data:a. Data that shows appropriate instruction and data-based

documentation of progress in some academic areasb. Does not achieve adequately for age or meet state-approved

grade-level standards – Does not make sufficient progress …response to scientific, research-

based intervention…

Screening: District Cut Score on US (Should be above in some areas)Diagnostics: Reading, Math, WritingProgress Monitoring: Grades, formative assessments, unit tests, district common assessments, RtI CBM’s (ROI)- variable data results, however grade expectations in some areasOutcome: Summative Assessments, Report card grades, STAAR, Review objectives met/not met

Page 29: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Suspected SLD:• I: Grade level in some areas, below grade level in

others• C: Differentiated strategies based upon learning style

will vary depending on academic area• E: Student displays differing degrees of AE based

upon content and delivery, performs better in small group with instruction aligned to learning preferences

• L: Most often demonstrates increased off task behaviors in area of weaknesses, family history may include learning problems, medical history positive for certain “red flags”, development is positive for specific deficit and skill acquisition.

Page 30: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Professional Judgment: Test Selection Based Upon Multiple Sources

“Pick the battery that best fit the student and the referral concern” (Misak, 2013)

Focus selection of narrows dependent on data related to Tiered instruction on specific skill deficits.

Do you have enough fidelity to do this?Does RtI team give you enough data?What is sufficient for ROI data pts?Norms?Comparison to peers?

Page 31: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Reminder

• All G’s are involved in all learning – What is required for learning determines involvement of each and will differ.

• Some G’s (Gc, Gf) affect learning across all academic areas.

• Within each G, specific narrow abilities are more directly related to specific academic skills – these narrow abilities need to be measured for LD patterns.

Page 32: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

FIE Test Selection

• Review RIOT/ICEL and all RTI data- determine reason for referral

• Carefully select measures- watch for variance– Do not want to use too many measures – Need to measure the appropriate narrow abilities– Also may need to measure constructs such as

executive function, orthographic processing, etc.– Select a core battery and the relevant tests to give

and then supplement appropriately

Page 33: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Converge Data

Reason for Referral

Historical Data

RtI DataMultiple Sources

FIE Test Battery

ProfessionalJudgment

Recommendations

Page 34: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

FIE Language

Reason for ReferralStudent was referred for a comprehensive Full and Individual Evaluation by the campus RTI committee. Student has participated in Tiers 1, 2 and 3 intensive instruction and intervention in the area of basic reading skills and comprehension and continues to evidence poor progress within grade level and instructional level curriculum.

Page 35: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Achievement Data• In addition to reporting your review of

assessment data, include such data as:– US: Student participated in district-wide screening

on Aimsweb BOY scores indicate…. Or Scan and import data

• Scan and or report PM data:

Page 36: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Recommendations

Student’s weakness in his reading fluency and comprehension of concepts is evidenced in his difficulties with long term retrieval of information He would benefit from repeated practice of new information using multiple modalities of delivery of instruction within content areas.

Student would also benefit from vocabulary based instructional interventions to aid in fluency of retrieval of information.

Page 37: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

Student would benefit from added graphic organizers/ thinking maps to facilitate his weakness with short term memory deficits. Provide a variety of organzers and have student choose 1 or 2 to use.

Due to measured processing weaknesses in the area of auditory processing, student would benefit from additional support by using sight-word recognition when reading text….

Page 38: RtI Documentation: Understanding, Interpreting and Connecting Data for Educational Decision Making Day 2 Andrea Ogonosky, Ph.D., LSSP aogonosky@msn.com

CASE ACTIVITIES