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Gabriela Gómez Carmen Sotomayor Juan Pablo Valenzuela

High achievement in poverty conditions in South America T he case of resilient students in PISA 2009 Argentina , Chile and Uruguay. Gabriela Gómez Carmen Sotomayor Juan Pablo Valenzuela Center for Advanced Research in Education UNIVERSITY OF CHILE. Overview. Theoretical Framework

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Gabriela Gómez Carmen Sotomayor Juan Pablo Valenzuela

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  1. High achievement in poverty conditions in South AmericaThe case of resilient students in PISA 2009 Argentina, Chile and Uruguay Gabriela Gómez Carmen Sotomayor Juan Pablo Valenzuela Center for Advanced Research in Education UNIVERSITY OF CHILE

  2. Overview Theoretical Framework • What is Resilience? • Resilience in PISA 2009 • Questions and objectives Procedures: • Descriptive analisys • Multilevel logit-model Results: Factors related to resilience Conclusions

  3. What is resilience? …positive outcomes in the face of adversity. (Kolar 2011, p. 430) …resilience is an interactive concept that is concerned with the combination of serious risk experiences and a relatively positive psychological outcome despite those experiences. (Rutter 2006, p. 2) Resilience is the art of navigating in torrents. (Cyrulnick 2002, p. 213)

  4. Key concepts: Adversity Risk factors as • Poverty (Werner, 1996) • Family dysfunctions (Cyrulnick, 2002) • Discrimination or racism (Spencer-Rodgers & Collins, 2006) Outcome Positive response to risk • Academic resilience:achievement as outcome (Capella y Weinstein, 2001) Students who, despite a set of risk factors, attain normal or outstanding academic outcomes through a range of protective factors.

  5. Key concepts: Protective factors Resilience depends on factors that can be controlled: • Autonomy, positive self-concept (Martin and Marsh 2006) • Protective figures (Baruch y Stutman, 2006) • Supportive environment (Suárez-Ojeda & Autler 2006)

  6. Defining our research interest: Risk factor low socioeconomic status, poverty condition Outcome normal or high reading comprehension despite a low socioeconomic status Protective factors a comprehensive number of factors related to the students and schools.

  7. Resilience in PISA 2009 OECD (Ed.). (2010). Pisa 2009 results: Overcoming social background – equity in learning opportunities and outcomes (volume II). Paris: OECD Publishing. OECD (Ed.). (2011). Against the odds: Disadvantaged students who succeed in school. Paris: OECD Publishing. Our definition: students who achieve a score equal or superior to the national average and who belong to the lowest 25% in the PISA index of socioeconomic and cultural status (ESCS), will be consider resilient.

  8. Objectives General objective: To identify which factors determine the odds of being resilient for those students coming from disadvantaged socioeconomic contexts in the PISA 2009 for the reading skills in Argentina, Chile and Uruguay. • Identify and describe who and how many are the resilient students in PISA reading survey 2009. • Determine which individual features are risk or protective factors related to resilience in reading comprehension. • Identify features of the school which are protective or risk factors. • Fit a model quantifying the influence of the different factors in the probability of being resilient.

  9. ESCS quarters and reading achievement

  10. Vulnerable students in the sample

  11. Factors Related to individuals: • Girls • 10 grade and above • Mother at home • Parents’ education (years) • Cultural possessions (PISA index) • Home wealth (PISA index) • Enjoy reading (index) • Fiction reading • Memorization • Elaboration strategies Related to Schools • Peers effect (peers’ parent education) • Public (administered by the states) • Academic selection • Bullying • Class climate • Class size (n of students)

  12. Regression Model Logit model • Fit a model to quantified the relevance of different factor in the probability of being resilient • Compare the role of these factor in the different educational systems Multilevel model • Estimate how much the probability of being resilient varies from one school to another • Calculate how much of that variance is accounted for individual and school factors

  13. Example: Enjoy reading index

  14. Estimatedmodels Nullmodel Total model (4) 01

  15. Interpretation of the models • Coefficients: Sign, tests of significance and intensity. • Odds ratios: measure of the size of the effect on probability () • Variance partition coefficient (ICC): • Likelihood ratio: testto compare the fit of models.

  16. MultilevelLogitRegression: NullModel

  17. Multilevel-logitmodel

  18. Multilevel-logitmodel

  19. Results: Factors that have an impact in the probability of being resilient • Female gender: factor that is linked to both resilience and good reading achievement. • The grade those students are in (normal 10th grade) • Reading for pleasure: key factor as a motivational component of resilience, and also for its incidence over reading comprehension. • The educational level of peers’ parents: indicator of the quality of the academic environment among students at each schools

  20. How to foster resilience in Argentina, Chile and Uruguay • Positive use of gender differences. • Avoid grade retention. • Foster a better attitude towards books and reading. • Heterogeneity in school enrollment.

  21. Gabriela Gomez Vera gabriela.gomez@ciae.uchile.cl CIAE, Center for Advanced Research in Education University of Chile

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