Presentation on theme: "Statement of Problem Kindergarten Readiness Act- 2010 Phase in Change of Cut off Date Currently Dec 2 nd. Students can begin school as early as four years."— Presentation transcript:
Statement of Problem Kindergarten Readiness Act Phase in Change of Cut off Date Currently Dec 2 nd. Students can begin school as early as four years nine months 24 month age span in kinder Research suggests enter when age eligible Age eligible where? Must study age of entry versus age eligible
Purpose of Study Examine impact of chronological age on academic achievement through 4 th gr. Support or combat red shirting and examine possible influences to retention Examine optimal school age entry in a large urban school district in southern California Investigate fall entry students in terms of academic, social and behavior school progress through grade four
Research Questions How does entry age of kindergarten students impact student achievement scores in reading and math, retention rates and placement into special education programs? Do students who begin kindergarten at age four years nine months (who do not turn five until they have been in kindergarten) have lower achievement scores on reading and math benchmark assessments in grade two, three or four? Are younger entrants more likely to be retained in kindergarten or later? Do younger entrants have a higher probability of being classified as special education?
Theoretical Framework Inconsistent research results Younger entrants not perform as well as older in kinder and first (Apollini, McClure, Vaughan & Vaughan, 1997). ECLS-K found almost all kindergarten students were when they began only 9% not yet 5. (2001) National Institute of Child Health and Human Development (2007) examined entry age and academic and social achievement found age of entry impacts small
Background of Study Previous research focus on summer vs. winter Small performance gap did not last past 5 th grade (Oshima and Domaleski, 2006) Warder (1999)-literacy and birth date- 64% older students at grade level, younger decreased in test scores Lincove and Painter (2006) young children more likely to repeat a grade. Gender accounts for small part in variation of skills (ECLS-K data, Crosser, 1991).
Literature Review Summary of Literature Review Includes: ◦ Understanding kindergarten policies Entrance age and cutoff dates California Kindergarten specifics ◦ School readiness Language Acquisition skills related to academic success Developmental levels Prior school experiences Readiness skills ◦ Academic Red-Shirting and Retention ◦ Special Education and age of entry ◦ Entrance Age and Achievement studies-multiple opinions
Summary of Literature Review Inconclusive research First experiences shape educational future Questions that are still unanswered include: Is there an optimal school entry age? Will children who are older outperform their younger counterparts? Necessary research in this area of school entry age and achievement in a large urban school district to support decisions.
Methodology Research Design Quantitative- statistical data analysis Longitudinal correlation study Non-experimental- no control group-data in natural environment Pre-existing data base- large urban school district Secondary source of data collection Explanatory in nature in that its primary purpose is to explain the phenomenon of age of entry related to academic achievement
Sampling and Data Collection Convenience sampling Large urban school district Over five years through current year data available elementary schools 77% free and reduced lunch 46.7% ELL 10.2% special education Approx. 12,000 students
Sample and Data Collection Divided into three cohorts Cohort 1 enrolled in through (K-4) Cohort 2 enrolled in through (K-3) Cohort 3 enrolled in through (K-2) Younger Entrants- August, September, October, November, December 1 st and 2 nd Older Entrants- December 3 rd and on, January, February, and March Middle Entrants- April, May, June, and July
Measures Independent Variables: Entrance age Gender Current EL level Dependent Variables: Kindergarten through fourth grade ELA benchmark scores (fluency and reading comprehension); Math benchmark (overall percentage); 2-4 grade California State Assessment scores in ELA and Math Retention Rates Special Education Classification
Data Collection and Procedures Request to school district Permission granted Technology provided data set-from Zangle and Oars- demographics, birth date, school entry date, gender, ethnicity, El level, academic achievement scores on benchmark, CST scores, retention information and special education enrollment data. No identifying information was provided for confidentiality IRB request from APU for expedited review and approved.
Analytical Strategy Inferential Statistics: Logistic Regression- significant predictors for each criterion variable Chi Square – Chi Square and odds ratio examined to reveal nature of relationship between categorical variables
Analytical Strategy Each variable re-coded to new variables Gender –male 0; female 1 English Learner- ELL 0; EO 1 Retention- no 0; yes 1 Special education- no 0 ; yes 1 Birth date- younger 1; middle 2; older 3 Academic achievement- benchmark at risk 0 and at benchmark 1 K-4; CST below proficiency 0 and proficient and above 1 grades 2-4. Employed in same manner for each cohort
Null Hypothesis H Ɵ 1: There is no difference in CST and benchmark scores in ELA of students in grades K, 1, 2, 3, and 4 based on students' entrance age, gender, and ELL level. H Ɵ 2: There is no difference in CST and benchmark scores in Math of students in grades K, 1, 2, 3, and 4 based on students' entrance age, gender, and ELL level. H Ɵ 3: There is no difference in retention rates of students in grades K, 1, 2, 3, and 4 based on students' entrance age. H Ɵ 4: There is no difference in special education classification of students in grades K, 1, 2, 3, and 4 based on students' entrance age.
Results Cohort One: ( thru ) Descriptive Statistics 4772 students 36.5% younger, 31.4% middle, 31.9% older 61.8% EO and 38.2% ELL 11% retention students 9% special education
Cohort One- Hypothesis 1 Significant relationship between entrance age and academic performance defined by reading comprehension 1-4 Not a significant relationship with reading fluency in all grades Significant relationship between entrance age and upper and lower case letter naming fluency and high frequency words in kindergarten. Significant relationship between entrance age and academic performance on ELA-CST grades 2-4 ◦ Grade % of younger entrants were at risk while 54.9% older entrants at risk ◦ Grade % of the younger entrants at risk 65% older at risk– 72.4% of younger entrants below statewide proficiency; 58.2% older entrants ◦ Older entrants in kindergarten 1.6 times more likely to meet benchmark standards ◦ Older entrants in grade 4 were 2.3 times more likely to score proficient 4 th gr. CST ELA. ELL a factor; Gender not a significant factor
ELA Chi Square Values of Entrance Age and Benchmark/CST Proficiency by Grade Level Grade/BenchmarkN % young % old χ 2 p__ Kinder Upper-Case Kinder Lower-Case Kinder High Frequency First Avg. Fluency First Reading Comp Sec Avg. Fluency Sec Reading Comp Third Avg. Fluency Third Rdng Comp Fourth Avg. Fluency Fourth Rdng Comp Second CST Third CST Fourth CST
% of younger entrants vs. older entrants at risk on benchmark or CST for ELA
Cohort 1- Hypothesis 2 Significant relationship between entrance age and academic performance as defined by math benchmark in first, third and fourth grade. Not second grade Significant relationship between entrance age and academic performance on Math-CST grades 2-4 ◦ Grade % of younger entrants at risk with 46% older entrants at risk ◦ Grade 2 CST 59.4% of younger entrants were below proficiency with 48.4% older entrants below proficiency. ◦ Older entrants 2 times more likely to pass third grade math benchmark. ◦ Grades 2,3, and 4 older entrants 1.5 times more likely to score proficient on CST Gender not significant; ELL a factor
Math Chi Square Values of Entrance Age and Benchmark/CST Proficiency by Grade Level Grade/BenchmarkN%young %oldχ 2 p First Math Second Math Third Math Fourth Math Second CST Math Third CST Math Fourth CST Math
% of younger entrants vs. older entrants at risk on benchmark or CST for Math
Cohort 1-Hypotheses 3 and 4 Significant relationship between entrance age and retention rates. 13.8% younger entrants more likely to be retained—7.9% of older entrants No significant relationship between entrance age and special education qualification.
Pattern of Cohorts Odds ratio of cohort one described how older entrants have a higher likelihood of being successful on grade level benchmarks and CST ◦ Cohort two and three odds ratio presented similar results. ◦ Confirmed the model Entrance age, and ELL were a significant model in predicting performance proficiency in ELA and Math over time and multiple assessments.
Grade05-06 cohort06-07 cohort07-08 cohort K Lower Case Reading Comp Reading Comp Reading Comp N/A 4 Reading Comp1.20N/A 2 CST CST N/A 4 CST1.53N/A Odds Ratio for entrance age and ELA performance
Odds Ratio for entrance age and Math performance Grade05-06 cohort06-07 cohort07-08 cohort K math benchmarkN/A math benchmark math benchmarkP> math benchmark N/A 4 math benchmark1.10N/A 2 CST CST N/A 4 CST1.22N/A
Odds ratio for ELL and ELA and Math performance Larger than entrance age-indicative of the current achievement gap between ELL and EO Impact of ELL stronger for language arts assessment than math. For each cohort the likelihood of proficient performance for older entrants repeats for each cohort and increases as the students move through the grade levels.
Summary of Findings Entrance age and ELL has a significant impact on the area of academic achievement in reading comprehension benchmark, math benchmark and proficiency on CST ELA and Math. Two areas not impacted by entrance age ◦ Average end of year fluency ◦ Second grade math for cohort one Gender was not found to be a contributing factor to the model Entrance age and retention rates were significant in all three cohorts Entrance age and special education not significant in all three cohorts
Conclusions Findings suggest that younger entrants (in this study- fall entrants) have a higher likelihood of being at-risk as measured by benchmark and CST Students should turn five prior to starting school (Younger entrants not yet five upon starting school) Unlike past research did not find entrance age impact became less significant over time (de Cos, 1997 & Lin, Freeman, & Chu, 2009), Supports previous inconsistencies regarding gender impacts
Discussion Younger entrants more likely to score below proficiency Age impact lasts over time Younger entrants higher likelihood to be below grade level standards over time Age becomes a risk factor ELL younger entrant more likely to score below proficiency than an EO younger entrant. ELL strong factor along with entrance age
Discussion/Recommendations Entrance age and ELL proficiency significantly impact academic achievement scores in reading and math. Students who begin school prior to turning five, the younger entrants, are more likely to be at risk on benchmark assessments and state assessments. The younger entrants are more likely to be retained in kindergarten through 4 th grade. The younger entrants do not have a higher probability of being classified as special education students. Beginning school after turning five would be considered a significant factor in determining school success.
Significance of Study Understand age gap in kindergarten Support continued implementation for SB 1381 and preschool programs- empirical evidence for support Add research data when developing and adopting common kindergarten standards Data in determining school entry and decisions in regards to retention and at-risk younger students. Guide decisions in regards to transitional pre-k/k programs Consistency of entrance age across states could promote educational opportunity equity
Recommendation for Further Research Additional research with students from various SES, preschool or no preschool attendance and more diverse populations Draw samples from various school districts ◦ i.e. suburban school district Additional grade levels and/or subject areas Path model using a Structural Equation Model could be used to determine the impact of school entrance age on academic achievement through a moderating variable such as earlier academic achievement. As law is implemented compare two groups those students who entered when cut off was December 2 nd and those entering kindergarten with cut off September 1 st