LCSA - June 8, 2012

Continue the use of data for dialogue and decision-making Support compliance reporting for Comprehensive Needs Assessment and School Improvement planning

Major Question Data Representations Major Question Data Representations Dialogue Questions – Observations – Inferences Dialogue Questions – Observations – Inferences

Predictions Observations Inferences Adapted from Deb Clancy, Washtenaw ISD, 2008, based upon the work of Nancy Love, “Using Data/Getting Results” (2002)

Observations – What percentage of our students were at levels 1 and 2? – At which level of performance do we have the most students? Inferences – What school processes by adults might explain the students’ achievement? – What next steps should be taken to address this achievement? Observations – What percentage of our students were at levels 1 and 2? – At which level of performance do we have the most students? Inferences – What school processes by adults might explain the students’ achievement? – What next steps should be taken to address this achievement?

Observations – Which strands were our strengths on the test? – Which strands were our weaknesses on the test? Inferences – What school processes by adults might explain the students’ achievement? – What next steps should be taken to address this achievement? Observations – Which strands were our strengths on the test? – Which strands were our weaknesses on the test? Inferences – What school processes by adults might explain the students’ achievement? – What next steps should be taken to address this achievement?

Summary Assessments with scores –pre/post, unit tests, literacy scores Item Bank Assessments with standards –tests created with DataDirector items Answer Sheet Assessments with standards –tests created with items outside of DataDirector

Using EXPLORE Scores to Predict Future PLAN Scores Highest Probability High Medium Low Lowest Probability 10-11Expected10-11Expected10-11Expected10-11Expected EXPLOREPLANEXPLOREPLANEXPLOREPLANEXPLOREPLAN LastnameFirstnameEnglish Reading Mathematics Science

Student Group # Students Nonsense Word FluencyOral Reading Fluency Phoneme Segmentation Fluency All Students 63 Deficit00.00%At Risk23.17%Deficit00.00% Emerging23.17% Some Risk %Emerging11.59% Established % Low Risk %Established %

Performance Level Scaled Score Domain/ Standard Score Benchmark/ GLCE Score Written Curriculum Alignment Analysis of Performance Task Analysis of Student Learning VALIDITYVALIDITY

“X” represents opportunities Fall 2009 Fall 2010 Fall 2011 Statewide Assessment (MEAP) XXX Interim Assessment ( NWEA, DIBELS, DRA, STAR ) XXXXXXXXX Classroom Assessment (Unit tests, common writings with rubrics) XXXXXX XXXXXX XXXXXX XXXXXX XXXXXX XXXXXX XXXXXX XXXXXX XXXXXX

Performance Level data with MEAP/MME –“On Track” designation with PLAN or EXPLORE –Threshold designation on interim assessments

LastFirst STAR EOY Grade Equivalency Fall 2011 MEAP PL

Scaled Scores data with MEAP/MME –Scale Scores with PLAN or EXPLORE –Scale Scores on Interim Assessments

LastFirst Fall 2011 MEAP SS Spring Test RIT Score

Standards data with MEAP/MME –Subarea scores with PLAN or EXPLORE –Goal scores on Interim Assessments –Standards data on local assessments

Fall 2011 PreTestSpring 2012 MME MATH A1MATH A2MATH L2 Math A1 Math A2 Math L2 Number of Questions FirstLast 20%0%20% %100%80% %100%20% %67%60% %0% %67%0% %100%20% %33%20% %0%20% %0% %67%40% %67%20% %67%60% %67%20% %67%80% %67%20%364038

Expectations data with MEAP –Item Analysis scores with EXPLORE –Item Analysis on local assessments

Classroom TestMEAP WHG WHG WHG WHG 3.2.3W1.2.1W2.1.4W3.1.9W3.2.3 Q1Q2Q3Q4Q8Q9Q11Q13 Name YNNNYNNN YYYYYNYY YYYYYYYY YYYYYNYN YYYYNYNN NNNYYNYY YYYYYYYY YYYYYNYY YYYYYYYY YYYYYYYN YYYYYNNN NNNNYYYY YNNNYYNY

Source: Presentation by Dr. Victoria Bernhardt, April 2007