Attendance in Early Childhood Programs as a
Transkript
Attendance in Early Childhood Programs as a
Attendance in Early Childhood Programs as a Key Facet of Dosage Presenters Hedy Chang, Attendance Works Stacy Ehrlich University of Chicago Consortium on Chicago School Research Faith Connolly Baltimore Education Research Consortium, Johns Hopkins University Cheri Vogel, Mathematica Policy Research Discussant Amanda Bryans Office of Head Start Organization of This Session Presentations I. Building on the work on attendance in K-12 Measurement of attendance in k-12, implications for achievement, and understanding roots of attendance issues as key starting points for consideration of attendance in ECE II. Emerging research in ECE: Attendance patterns and child outcomes A. Findings from Chicago Public Schools B. Findings from Baltimore Public Schools C. Findings from Baby FACES III. Exploring the predictors of attendance in ECE A. Findings from Chicago Public Schools B. Findings from Baltimore Public Schools C. Findings from Baby FACES IV. What research is needed to pursue this set of issues further? Discussant Comments Implications for practice and policy in early care and education I. BUILDING ON THE WORK ON ATTENDANCE IN K-12 Hedy Chang Attendance Works Unpacking Attendance Terms • The % of enrolled students who attend school each day. It is used in some states for allocating funding. Average Daily Attendance Truancy Chronic Absence • Typically refers only to unexcused absences and is defined by each state under No Child Left Behind. It signals the potential need for legal intervention under state compulsory education laws. • Missing 10% or more of school for any reason -- excused, unexcused, etc. It is an indication that a student is academically at risk due to missing too much school 4 High Levels of Average Daily Attendance (ADA) Can Mask Chronic Absence (CA) CA in schools with 95% and 90% ADA Chronic Absence For 6 Elementary Schools in Oakland, CA with @ 95% ADA in 2012 Chronic Absence for 6 Schools in New York City with 90% ADA in 2011-12 30% 30% 25% 25% 20% 20% 15% 10% 12% 13% 13% 15% 16% 26% 20% 20% 20% A B C 21% 23% 15% 7% 10% 5% 5% 0% 0% A B C D E F % Chronic Absence 98% ADA = little chronic absence 95% ADA = don’t know 93% ADA = significant chronic absence D E F % Chronic Absence 5 Sporadic – Not Just Consecutive – Absences Matter New York City Schools (2008) • A 407 alert is issued when a student misses 10 consecutive days or 20 days over a 40 day period. It misses more sporadic absence. • 1 out of 5 elementary school children were chronically absent. Source: Nauer, K. et al, Strengthening Schools by Strengthening Families, Center for New York City Affairs New School, Oct 2008 6 Students Chronically Absent in Kindergarten and 1st Grade are Much Less Likely to Read Proficiently in 3rd Grade Percent Students Scoring Proficient or Advanced on 3rd Grade ELA Based on Attendance in Kindergarten and in 1st Grade 100% 80% 64% 60% 43% 41% 40% 17% 20% 0% No attendance risks No risk Small risk Moderate risk High risk Small attendance risks Moderate attendance risks High attendance risks Missed less than 5% of school in K & 1st Missed 5-9% of days in both K & 1st Missed 5-9% of days in 1 year &10 % in 1 year Missed 10% or more in K & 1st Source: Applied Survey Research & Attendance Works (April 2011) 7 The Long-Term Impact of Chronic Kindergarten Absence is Most Troubling for Poor Children 5th Grade Math and Reading performance by K attendance for children living In poverty. Academic performance was lower even if attendance had improved in 3rd grade. Average Academic Performance 52 50 48 46 Reading Math 44 42 40 0-3.3% in K 3.3 - 6.6% in K 6.6-10.0% in K >=10.0% in K Absence Rate in Kindergarten Source: ECLS-K data analyzed by National Center for Children in Poverty (NCCP) Note: Average academic performance reflects results of direct cognitive assessments conducted for ECLS-K. 8 Multiple Years of Elementary Chronic Absence = Worse Middle School Outcomes Each year of chronic absence in elementary school is associated with a substantially higher probability of chronic absence in 6th grade 18.0x Increase in probability of 6th grade chronic absence Chronic absence in 1st grade is also associated with: 7.8x 5.9x • • Lower 6th grade test scores Higher levels of suspension Years of Chronic Absence in Grades 1-5 Source: Oakland Unified School District SY 2006-2012, Analysis By Attendance Works 9 Suggested Framework For Unpacking Absences Myths Absences are only a problem if they are unexcused Sporadic versus consecutive absences aren’t a problem Attendance only matters in the older grades Barriers Aversion Child struggling academically Lack of access to health care Lack of engaging instruction Poor transportation Poor school climate and ineffective school discipline No safe path to school Parents had negative school experience 10 Hypothesis: Going to School Every Day Reflects When Families Have … Hope for a better future + Faith that school will help their child succeed + Capacity Resources, skills, knowledge needed to get to school 11 Recommended Site Level Practice Associated with Reduced Chronic Absence 12 II. EMERGING RESEARCH IN EARLY CARE AND EDUCATION Attendance Patterns and Child Outcomes II. A. PRESCHOOL ATTENDANCE IN CHICAGO PUBLIC SCHOOLS (CPS) Stacy Ehrlich University of Chicago Consortium on Chicago School Research Preschool programs and children included in Chicago study © CCSR Children enrolled in school-based preschool programs in Chicago Public Schools Excluded Montessori and children in non-inclusive special education programs Programs: • Serve 3- and 4-year-olds • Most are ½-day Students: • 88% qualify for free and reduced lunch • 29.4% qualify for English Language Learners (ELL) services 50% 42% 40% 40% 30% 20% 10% 10% 4% 6% 0% Latino African American White Asian Data retrieved from Office of Early Childhood Education, CPS (updated June, 2012): http://www.ecechicago.org/about/glance.html Other Percent preschool students in each absence category Preschool students have very high rates of chronic absenteeism 50% 45% 45% 40% 36% 35% 30% 25% 20% 20% 14% 15% 12% 10% 5% 0% Age 3 (PreK) Age 4 (PreK) Age 5 (K) Age 6 (1st) Age 7 (2nd) Age © CCSR 10% Absence rate: 10% < 15% 15% < 20% 20%+ Age 8 (3rd) 16 17 Students with lower preschool attendance have lower kindergarten readiness scores on all subtests Not controlling for prior knowledge 3.5 3.0 * ** Logits 2.5 2.0 1.5 *** ** ** ** *** *** 1.0 * ** ** ** *** 0.5 © CCSR Math Letter Recognition Not Chronically Absent Pre-Literacy Social-Emotional Development Chronically Absent * Indicates that scores are significantly different from scores of students who absent 0<3.3%, at p<.05 level; **p<.01; ***p<.001 20%+ 15<20% 10<15% 6.6<10% 3.3<6.6% 0<3.3% 20%+ 15<20% 10<15% 6.6<10% 3.3<6.6% 0<3.3% 20%+ 15<20% 10<15% 6.6<10% 3.3<6.6% 0<3.3% 20%+ 15<20% 10<15% 6.6<10% 3.3<6.6% 0<3.3% 0.0 18 In math and letter recognition, the relationship between absences and outcomes is stronger for students with lower prior skills than for those with higher prior skills Math 4.0 4.0 Letter Recognition 3.5 3.5 3.5 3.0 3.0 3.0 2.5 2.5 2.5 2.0 2.0 2.0 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 25% 0% 0.0 © CCSR 0% 5% 10% 15% Absence Rate 20% 5% 10% 15% Absence Rate Social-Emotional Development 4.0 20% 25% 0% 5% 10% 15% Absence Rate 20% Analyses control for prior preschool experience, race, gender, neighborhood poverty and social status, special education status, ELL status, and program type. Missing data points represent values with fewer than 30 students. 25% 19 © CCSR Roughly 1/3 of chronically absent 4-year-olds continue to be chronically absent in kindergarten Students who are chronically absent in preschool are 5 times more likely to be chronically absent in 2nd grade than other students 20% Percent CA in Second Grade 18% 16% 15.5% 14% 12% 10% 8% 6% 3.1% 4% 2% 0% © CCSR Chronically Absent in Preschool NOT Chrocnially Absent in Preschool 20 Multiple years of chronic absenteeism puts students at risk of needing academic intervention before 3rd grade Average second grade DIBELS Oral Reading Fluency score 105 100 95 90 85 80 75 70 65 60 55 98.8 Risk for intervention+ 94.6*** 88.9*** 81.8*** 72.9*** Not chronically absent (n=4,073) © CCSR 21 Chr in PreK (n=1,381) Risk for substantial intervention+ Chr in PreK + K Chr in PreK, K, Chr in PreK, K, (n=423) and 1st grade 1st, and 2nd (n=255) grade (n=306) * Indicates that scores are significantly different from scores of students who are never chronically absent, at p<.05 level; **p<.01; ***p<.001 + In the DIBELS 6th Edition Assessment and Scoring Guide (Good & Kaminksi, 2002), these are labeled as “Some Risk,” indicating the need for additional intervention and “At Risk,” indicating the need for substantial interventions. II. B. ATTENDANCE IN BALTIMORE CITY SCHOOLS’ PRE-KINDERGARTEN AND KINDERGARGEN Faith Connolly Linda S. Olson Baltimore Education Research Consortium (BERC) Johns Hopkins University Methodology – Data Source Cohort 1-Enter PreK in 2006-07 • (n= 3,364) - 77% remained through school year 2010-11 Cohort 2-Enter K in 2007-08 • (n= 6,374), -81% were still enrolled 2010-11 Cohort 3-Enter PreK in 2008-09 • (n=4,057), 85% remained in 2010-11 Methodology – Regression Models • Attendance – Average Daily Attendance (ADA) and Chronic Absence (CA) • Suspension • Retention • Later identification for Special Ed Services • Grades 1 and 2 SAT10 • Grade 3 MSA (State Assessment) Methodology – Regression Models • Covariates - primary • Gender/Race/Ethnicity • Free/Reduced Price Meals • Being overage • MMSR • Receipt of Special Education services in K CA in Baltimore City Grade 2010-11 2009-10 2008-09 2007-08 2006-07 Pre-K 26.5% 27.4% 19.5% 21.6% 21.7% K 22.9% 22.5% 17.8% 19.4% 20.6% Grade 1 21.0% 19.5% 15.6% 16.4% 18.7% Grade 2 17.9% 18.2% 13.6% 14.5% 15.2% Grade 3 17.6% 16.1% 12.2% 12.8% 14.4% CA Patterns by Neighborhood Pre K 2006-07 K in 2007-08 Both PreK and K Later Chronic Absence That Year Only Percent CA in PreK (2006-07) PreK (n=505) 36.4% 21.8% 20.2% 12.1% 9.5% Once More Twice More Percent CA in K (2007-08) K (n=903) 29.5% 24.8% 24.1% Three Times More 21.6% Four More Times (only PreK) 0% 25% 50% 75% 100% CA in PreK & K and Attendance • Significant predictor of ADA and CA in later grades • Two to 3 times more likely to be retained before they reached third grade • Lower achievement scores in reading and math in G1 and G2, and lower math in G3 Head Start Outcomes • Head Start graduates had highest rates of attendance compared to all other groups • By Grade 3 students had caught up to their peers on state assessments • More often identified to receive Spec Ed services after K than peers independent of their attendance. II. C. ATTENDANCE IN CENTER-BASED EARLY HEAD START CLASSROOMS AT AGES 1-2 Cheri Vogel, Pia Caronongan, Jaime Thomas, and Kimberly Boller Mathematica Policy Research Acknowledgments OPRE, OHS, and ACF Amy Madigan, our Baby FACES Federal Project Officer 89 Early Head Start programs, their staff, and nearly 1,000 families and children What Is Baby FACES? Descriptive study of a nationally representative sample of 89 Early Head Start programs Followed newborns and 1-year-olds through their experience in the program Rich data from multiple sources: – – – – – – – Direct in-home child assessments at ages 2 and 3 Weekly services offered and received (first time available) Program director interviews Parent interviews Staff interviews (home visitors and teachers) Staff-child reports Classroom and home visit observations Data Sources Direct in-home child assessments at age 3 – Language: PPVT-4, PLS-4 Auditory Comprehension – Social Emotional: BRS Emotion Regulation; BITSEA Problem and Competence – Parent/child interaction: Parental Synchronicity (twobags) Weekly Family Service Tracking (FST) – Teacher (or home visitor) completed weekly • Number of center days scheduled (offered) • Number of center days attended by children and percentage taken up • Reasons for missed days (due to program or due to family) Methods Descriptive information: Multiple imputation to account for missing data in FST reports Predicting age 3 outcomes from attendance over two years (age 1 to 3) – Continuous: total center days attended – Binary: total days attended (240+ vs. <240 days) • Equates to about 2.3 days per week or 50% of the amount recommended by OHS – Far lower standard than definition of CA in other presentations – No analogous measure of average daily attendance Things to Keep in Mind Early Head Start programs typically operate year round (not on a school year) – Some variability in summer closures Focusing on center-based today – Early Head Start offers home-based and combinations of service options in addition to center-based services • About half of the Baby FACES sample was in the home-based option • Changes occur between options (although are relatively uncommon) Infants/toddlers may face challenges to attend center regularly (longer program year, illness, etc.) Services Offered and Received Varies Seasonally Number of Center Days Offered and Received per Week, Monthly Averages Center days per week 5 4 3 Center days offered 2 Center days attended 1 0 Source: FST data July 2009-June 2010. Sample includes 1-year-old Cohort only. Range of Early Head Start Attendance Policies Number of Center Days Missed Before Disenrollment 3–5 consecutive 6–10 consecutive 15–30 consecutive 3–5 days in one month 10 days in six months 20 days in nine months 5–10 days in a year Sample Size Weighted Percentage of Programs (Std. Error) 19 (7.02) 33 (9.58) 21 (8.72) 11 (7.46) 3 (3.29) 3 (2.79) 10 (6.75) 30 Source: 2010 Program Director Interview. Sample restricted to programs that offered centerbased services and had an attendance policy Center Attendance On average, children were offered over 4 center days per week and attended over 3 days per week – Total days offered was higher between ages 1-2 (225) vs. ages 2-3 (194) – Total days attended was also higher between ages 1-2 (190 days) vs. ages 2-3 (167 days) Take-up rate was similar across ages/years at around 85 percent. Average days attended over 2 years is 261 days (57% had 240 or more days) Attendance Predicting Outcomes at Age 3 Comparing attending 240 days or more vs. fewer, age 1 to 3 Outcome Multivariate Linear Regression (coefficient) PPVT-4 5.9 (p<.10) PLS-4 (English) 9.1 (p<.05) Sample size 190-219 • Some evidence for better language development at age 3, although associations are modest, especially for the PPVT-4. • Sensitivity tests were not significant. • No associations with social emotional or parent-child interaction outcomes. III. EXPLORING FACTORS RELATED TO ATTENDANCE IN EARLY CARE AND EDUCATION III. A. EXPLORING FACTORS RELATED TO PRESCHOOL ATTENDANCE IN CHICAGO PUBLIC SCHOOLS (CPS) Stacy Ehrlich University of Chicago Consortium on Chicago School Research Health, logistics, and family-related reasons account for 80 percent of why preschool children miss school Reasons for Absences Sick 12% Wellness Appointment 5% Chronic Illness 3% Transportation 10% 54% Child Care Family-related 3% 5% Vacation © CCSR 3% Other 4% Don't Know Data source: Attendance logs Note: "Other" includes school phobia, lack of sleep, religious observances, weather, safety issues, and a general other category. 43 African American and Latino children miss more school due to being sick, and African American families face more logistical obstacles Reasons for Absences, by Race 18% 15.7% 16% 14% 12% 10.7% 10% 1.3% 8% 6.6% 6% 0.9% 4% 2% 0.9% 1.9% Don't Know 1.0% Other 2.0% Vacation 0.6% 1.3% 0.6% Family-related 0.6% Transportation Chronic Illness 0.6% 6.4% 7.5% 3.9% © CCSR Data source: Attendance Logs Wellness Appointment Sick 0% White (n=164) Child Care Latino (n=507) African American (n=485) 44 45 Most parents believe regularly attending preschool is important Almost 2/3 of parents believe that attending preschool regularly matters These beliefs are related to children’s attendance in preschool Attendance MATTERS, as much as later years 7.5% Attendance MATTERS, but less than later years 10.7% Attendance somewhat matters / doesn't matter 0% 2% 4% 6% 8% © CCSR Absence rate Data source: Parent interviews 10% 13.2% 12% 14% Schools with better climate also have higher preschool attendance School safety: Teachers report little/no disorder in hallways, physical conflict among students, vandalism, robbery or theft, and threats of violence against teachers. Teacher-parent trust: Teachers and parents are partners in improving student learning Parent involvement: Parents are active participants in their child's schooling School commitment: Teachers are deeply committed to the school. Teacher collective responsibility: Teachers share a strong sense of responsibility for student development, school improvement, and professional growth © CCSR Preschool inclusiveness: Preschool teachers report they feel a part of the larger elementary school and work with kindergarten teachers 46 III. B. PREDICTING ATTENANCE IN PRE-K AND K IN BALTIMORE PUBLIC SCHOOLS Faith Connolly Linda S. Olson Baltimore Education Research Consortium (BERC) Johns Hopkins University Who is CA in PreK and K Comparisons of CA students to higher attenders found: • No differences gender, race, or ethnicity • More likely to receive FARMS • More likely to receive special education services • More likely from some neighborhoods III. C. PREDICTING ATTENDANCE IN CENTERBASED EARLY HEAD START CLASSROOMS Cheri Vogel, Pia Caronongan, Jaime Thomas, and Kimberly Boller Mathematica Policy Research Methods Predicting attendance: multi-level models – Up to 2 years of data for each child/family – Children nested within programs – Models include child, family, staff, and program characteristics as predictors • Characteristics measured the previous spring predicts attendance over the following year Predicting Center Attendance Child Predictors Center Days Take-Up Race and Ethnicity (vs. white) African American ns Hispanic ns Other ns Boys 3 pct pts DLL ns Birth weight ns Excellent or very good health ns Family Predictors Center Days Take-Up Maternal Demographic Risks (vs. lower risk) Medium risk ns High risk ns Psychological Risks (vs. no risk) One risk ns Two or more risks ns Enrolled in pregnancy Left EHS before eligibility ended ns 15 pct pts 51 Predicting Center Attendance Staff Predictors Center Days Take-Up Race and Ethnicity (vs. white) African American ns Hispanic ns Other ns Speaks language other than English ns Has a BA or higher 5 pct pts Years of experience in EHS Program Predictors Multiple approach (vs. single) Center Days Take-Up ns Population served: Over 50% families with unsafe neighborhood/or experience violence ns 5 pct pts ns Over 50% families with MH or SA problems Has a degree in EC ns Over 50% families with multiple risks ns Has a CDA ns Fully implemented ns 52 Takeaways Evidence that children take up offered services at relatively high rates, despite frequent absences Weak positive associations between attendance and language outcomes Teacher education related to higher attendance Those who leave the program participate at lower levels while enrolled – Reach out to families who are not engaged 53 IV. WHAT RESEARCH IS NEEDED TO PURSUE THIS SET OF ISSUES FURTHER AND SUPPORT PRACTICE? Highest Priority Next Step for Research: Hedy Chang Attendance Works Examining the relationship between early childhood program quality and attendance Does ECE program quality predict attendance? Is program quality more important in predicting attendance in ECE programs than in k-12? How should program quality be taken into account in examining the linkages between attendance in early childhood programs and later achievement? 55 Highest Priority Next Step for Research: Stacy Ehrlich University of Chicago Consortium on Chicago School Research Working towards a shared understanding of how to measure attendance accurately and in timely, useful ways Clear definitions to ensure that programs, schools, and districts measure attendance in similar ways Setting up data systems that allow for accurate, ongoing measurement of student-level absences Develop data tools that allow for easy-to-understand, on-time data for use by practitioners and researchers 56 Highest Priority Next Step for Research: Faith Connolly Baltimore Education Research Consortium, Johns Hopkins University Understanding What Happens 0 to 5 Understanding parent and family perspective on Head Start, pK, and K enrollment and attendance More public reporting of attendance in public school PreK and K Development of data systems that capture and link early childhood data to pK-12 Data Systems 57 Highest Priority Next Step for Research: Cheri Vogel Mathematica Policy Research Programs need supports to collect data and use it to understand attendance patterns. Data are complex: Children may exit the program or change service options (in EHS) Requires staff diligence, data systems that can record the information, and some ability to manipulate the data We used multiple imputation because of evidence that data were not missing at random (complex to implement) 58 DISCUSSANT COMMENTS: IMPLICATIONS FOR PRACTICE AND POLICY IN EARLY CARE AND EDUCATION Amanda Bryans Office of Head Start QUESTION AND ANSWER 60 References and Resources Connolly, F. & Olson, L.S. (2012). Early elementary performance and attendance in Baltimore city schools’ pre-kindergarten and kindergarten. Baltimore: Baltimore Education Research Consortium. http://baltimore-berc.org/pdfs/PreKKAttendanceFullReport.pdf Ehrlich, S.B., Gwynne, J.A., Pareja, A.S., & Allensworth, E.M. with Moore, P., Jagesic, S. & Sorice, E. (2014). Preschool attendance in Chicago public schools: Relationships with learning outcomes and reasons for absences. Chicago: The University of Chicago Consortium on Chicago School Research. http://ccsr.uchicago.edu/sites/default/files/publications/Pre-K%20Attendance%20Report_0.pdf Vogel, C.A., Boller, K., Xue, Y., Blair, R., Aikens, N., Burwick, A., Shrago, Y., Carlton, B.L., Kalb, L, Mendenko, L., Cannon, J., Harrington, S. and Stein, J. (2011). Learning as we go: A first snapshot of Early Head Start programs, staff, families, and children. OPRE Report #2011-7, Washington, DC. Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. http://www.mathematica-mpr.com/publications/PDFs/earlychildhood/learning_vol1.pdf [Note that reports on children at ages 2 and 3 are forthcoming] 61 Contacts • Hedy Chang, Attendance Works hedy@attendanceworks.org • Faith Connolly, Baltimore Education Research Consortium, Johns Hopkins University faith.connolly@baltimore-berc.org • Stacy Ehrlich, University of Chicago Consortium on Chicago School Research sehrlich@uchicago.edu • Cheri Vogel, Mathematica Policy Research cvogel@mathematica-mpr.com 62