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Opportunity to Learn: Understanding why the Achievement Gap Exists

Opportunity to Learn: Understanding why the Achievement Gap Exists. Jamila Jones Kennedy EDUC 872 April 7, 2011. Background/context.

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Opportunity to Learn: Understanding why the Achievement Gap Exists

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  1. Opportunity to Learn: Understanding why the Achievement Gap Exists Jamila Jones Kennedy EDUC 872 April 7, 2011

  2. Background/context • Schools are intended to provide distinct learning opportunities in multiple academic disciplines. Yet, the opportunities to learn the academic content are not equally distributed among districts, schools, classrooms or students. • Related to this, students’ performance varies across the country in ways that suggest certain regions and demographics lead to higher performance than others (achievement gap). • Typically lost within the achievement gap debate is the “opportunity to learn” (OTL) concept.

  3. Background/context • OTL as a concept: • What students learn in school is related to what is taught in school • Whether students are provided the opportunity to learn what is expected of them, especially the information for which they will be held accountable • The capacity of schools to provide adequate learning opportunities for all students • OTL used to encompass various ideas, including: 1)equal/equitable per-pupil funding, 2)adequate school organization, 3)equal access to qualified teachers, challenging curricula, and resources

  4. Background/context • As a research concept, OTL was first introduced in the 1960s • Carroll’s model (OTL as a function of time) • FIMS (OTL as content coverage w/o regard to time) • By the mid-1980s, the notion of OTL had gone through revisions • SIMS (OTL in terms of curriculum) • More current research (in the 1990s and 2000s) focuses on OTL in terms of teacher qualifications, access to resources, funding, teaching method, etc.

  5. School Finance and opportunities to learn:does money well spent Enhance students’ achievement? Author: Marta Elliott Source: Sociology of Education, Vol. 71, No. 3 (July 1998), pp. 223-245

  6. Study #1: School Finance & OTL • Research Questions: • Do educational expenditures affect students’ achievement? • What components of OTL affect math and science achievement? • If funds are allocated for the most critical components of OTL, do students learn more? • Focused on effects of expenditures on: • Teacher effectiveness • Classroom resources

  7. Study #1: School Finance & OTL • Data: • U.S. Census Survey of Local Government Finances for School Systems and NELS:88 • Dependent variable – 10th grade math/science item response theory (IRT) theta score • Independent variables: • Student-level controls – SES, race, gender • School expenditures – cost of instruction, staff and student support services • School-level controls – student composition, school size, urban or not • OTL – teacher qualifications, class size, pedagogy, classroom resources

  8. Study #1: School Finance & OTL • Sample: • N=6,318 for math analysis; N=5,343 for science • Treatment of missing data with mean values and dummy variables • Method: • Hierarchical linear modeling (HLM) • 8th-grade achievement and student-level controls • Expenditures and school-level controls • Teacher qualifications and class size • Teaching emphasis and classroom resources

  9. Study #1: School Finance & OTL • Results – Math Achievement: • Expenditures are significantly related to higher achievement in math (p=.018) • Students who achieve higher math scores have more educated(p=.000) and experienced (p=.007) teachers and tend to be in larger math classes (p=.000). • Math scores were higher when teachers emphasized higher-order thinking (p=.000). • The use of calculators appeared to improve math achievement (p=.000), whereas the use of computers tended to decrease math achievement (p=.034).

  10. Study #1: School Finance & OTL • Results – Science Achievement: • Expenditures are significantly related to higher achievement in science(p=.018) • Only teacher’s education is related to student’s science achievement (p=.000) • Teachers’ emphasis on inquiry skills (p=.000) and the condition of science equipment are significantly related to science achievement (p=.038) • No interactions between expenditures and student controls (SES, gender, etc) on math or science achievement were significant

  11. Study #1: School Finance & OTL

  12. Study #1: School Finance & OTL • Policy Implications: • A primary concern is to identify the mechanisms through which money can effectively enhance learning. • Financial data collected at the school level is needed to further study the effects of expenditures on achievement. District level data are not the best estimate since it is differentially allocated across schools.

  13. Why Now, More than ever, we need to talk about opportunity to learn Author: Lisa Scherff, Carolyn Piazza Source: Journal of Adolescent & Adult Literacy, 52(4), Dec 2008/Jan 2009, pp. 343-352

  14. Study #2: Let’s Talk about OTL • Purpose: • To examine OTL from the perspective of students, the one, group of voices seldom heard from in discussions of quality literacy education. • Sample: • More than 3,000 students in grades 9-12 in four public high schools throughout Florida. • Method: • Surveys administered by English teachers during last 3 weeks of the school year • 56% response rate, n = 1,801

  15. Study #2: Let’s Talk about OTL • Instrument: • Based on Standards for the English Language Arts, co-written by IRA and NCTE • Items measured: • Perceived Access – the right to participate • Exposure (the number of opportunities to participate) to: • Content (reading, writing, speaking) • Curricular tasks (collaborative work) • Materials (novels, technology)

  16. Study #2: Let’s Talk about OTL • Results: • Survey uncovered 3 problems: • Systems – OTL is largely an individual student phenomenon rather than a collective and systematic one • Offerings – OTL is often constrained by course and experiential offerings for students & the amount of time spent on certain literacy activities • Acknowledgment - If opportunity is something defined for students, and not with them, then decisions are made and goals are set without them having a say in the matter

  17. Study #2: Let’s Talk about OTL

  18. Study #2: Let’s Talk about OTL • Policy Implications: • Student opinions and perceptions can contribute relevant and necessary information • For curricular standards to be accurately measured by mandated assessments, educators must ensure that students have more choice, ownership, and commitment to education

  19. Relationship between opportunity to learn and student performance on English and Algebra Assessments Authors: Christy Kim Boscardin, Zenaida Aguirre-Munoz, Ginger Stoker, Jinok Kim, Mikyung Kim, and Janet Lee Source: Educational Assessment, 2005, 10(4), 307-332

  20. Study #3: OTL, English, algebra • Purpose: • To examine whether and how various OTL variables significantly impact outcomes and whether these effects are consistent across different content areas • Researchable Questions: • What are various OTL variables that impact student achievement? How are they differentially impacting student performance? • Is the impact of OTL variables consistent across different subject areas? • What student background characteristics are related to student achievement?

  21. Study #3: OTL, English, algebra • Sample: • Tests administered to all students enrolled in an English and/or Algebra course in schools within a large urban school district in the Midwest during the spring semester of the 2000-01 school year • Data used: • Scores from the 4,715 students taught by 118 English teachers who completed the survey • Scores from the 4,724 students taught by 124 Algebra teachers who completed the survey

  22. Study #3: OTL, English, algebra • Instrument: • Teacher OTL Survey, developed by the UCLA Natl Center for Research on Evaluation, Standards, and Student Testing (CRESST) & content experts • 5 major sections related to aspects of OTL • Teaching experience • Teaching expertise in content topics • Topic coverage • Classroom activities • Assessment strategies and preparation

  23. Study #3: OTL, English, algebra • Method: • Two-level Hierarchical Linear Model • Student level – explores relationships between test scores and course grades, gender within teachers • Vars: course grade, gender, minority status, lunch status • Teacher level – explores relationships between avg test scores from each class, teacher expertise, time spent covering content, and free/reduced lunch status of the class • Vars: teacher expertise, time spent on subject area-specfic topics, average class free/reduced lunch • The other three OTL variables – teacher experience, classroom activities, assessment strategies – were not significant predictors and not considered in the final model

  24. Study #3: OTL, English, algebra • Results: • Gender and course grade were significant predictors of Algebra and English test scores, whereas ethnicity and free/reduced lunch status were not. • Course grade positively related to Algebra and English test scores (p<.00) • Students earning As in Algebra and English were expected to answer 7 and 4 more items correctly, respectively • Males outperformed females in Algebra; females outperformed males in English (p<.00) • Average differences in Algebra scores - 0.42 points; English scores - 0.77 points

  25. Study #3: OTL, English, algebra • Results (cont.): • Teacher expertise, time spent on content areas, & avg free/reduced lunch status were significant predictors of Algebra and English test scores • Students taught by “expert” teachers answered 3 and 4 more test items correctly, respectively • Students of teachers who spent more time on relevant content scored 0.85 points higher on Algebra and 1.59 points higher on English • Teachers with 10% more students on free/reduced lunch expected to have 1.14 point lower Algebra score and 1.20 point lower English score

  26. Study #3: OTL, English, algebra

  27. Study #3: OTL, English, algebra • Policy Implications: • Before teachers are held accountable, they need appropriate training and resources to ensure they are prepared to teach and see themselves as experts in their content areas • Recruit more qualified teachers. Local districts can require higher standards for teachers. • Content standards aligned with assessment, instructional goals, and professional development

  28. Optimizing Early Mathematics Experiences for Children from Low-income Families: A Study on Opportunity to Learn Mathematics Author: Aubrey H. Wang Source: Early Childhood Education Journal , 2010, 37, 295-302

  29. Study #4: Optimizing OTL • Research Questions: • Do African-American and Caucasian kindergartners from low-income families have differential opportunity to learn mathematics? • To what extent do opportunities to learn mathematics predict math achievement for African-American and Caucasian children from low-income families? • Data: • Early Childhood Longitudinal Study, Kindergarten cohort (ECLS-K) • Survey data from 48 items on math OTL

  30. Study #4: Optimizing OTL • Participants: • 1,721 first time kindergartners in fall 1998 who lived below the poverty line • Method of Analysis: • Factor analyses • instructional time, instructional method, instructional emphasis • Independent t-tests • differences at entry and exit • Simultaneous multiple regression • Dependent var. = math achievement • Independent var. = instructional time, method, emphasis (or OTL math)

  31. Study #4: Optimizing OTL • Results of t-tests: • Significant differences in math achievement at kindergarten entry (p<.001) and exit (p<.001) with AA scoring lower than Caucasians • Significant differences in instructional time (p<.05) with low-income AA students having more than Caucasians • Significant differences in 3 types of instructional methods • Use of manipulatives higher for AA (p<.001) • Use of math worksheets and textbooks more frequent for AA (p<.01) • Use of explanation and real-life math more frequent for AA (p<.001)

  32. Study #4: Optimizing OTL • Results of t-tests (cont.): • Significant differences in 2 aspects of instructional emphasis • Telling time and using measurement tools accurately higher for AA (p<.05) • Reading graphs and doing simple data collection lower for AA (p<.05) • Regression Results: • OTL predicts math achievement for: • Both races of students (p<.01, R2 = .62) • Low-income AA kindergartners (p<.001, R2 =.59) • Low-income Caucasian kindergartners (p<.001, R2=.60)

  33. Study #4: Optimizing OTL

  34. Study #4: Optimizing OTL • Policy Implications: • Kindergarten teachers of low-income students need to balance the math curriculum with more emphasis on higher order math content and less on manipulatives and math games • More current data collection efforts are needed; ECLS-K data are more than a decade old

  35. Overall Policy Implications • Results make a case for increasing funding, but funding alone doesn’t explain why some students have higher achievement than others. • Given high stakes, need to ensure equitable access to educational opportunities before holding teachers, schools, and students accountable • OTL is difficult to measure. It requires information on the day to day activities of teachers and students, which is costly. • Need a national data set that includes measures of school finance, OTL, and student achievement. We also need data on the proportion of funds allocated to various needs so we can determine directly the effects of how money is spent. • Use OTL as part of the accountability system (rather than just student performance as an indicator of progress); may lead to the types of instruction and reform efforts that will ultimately yield desirable outcomes for all students

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