Session Information
09 SES 05 A, Towards Understanding Academic Resilience: Exploring the Roles of Individual and Group Level Factors
Paper Session
Contribution
General description on research questions, objectives and theoretical or conceptual framework
For all the economies participating in OECD’s sponsored Programme for International Student Assessment (PISA), achieving highly in terms of educational quality and equity in basic education is an ultimate educational goal. In the PISA 2012 mathematical literacy study, Macao, Hong Kong, Korea and Japan are four such high-achieving Eastern educational systems (OECD, 2013). Likewise, a number of Western countries (e.g. Canada, Estonia, and Finland) are also achieving with flying colors. An examination of factors predicting academic resilience of the students in these seven Eastern and Western educational systems is considered worthwhile so as to unveil the secrets of the “High-Quality-High-Equity” (HQHE) phenomenon from a comparative education perspective. Conceptually, the definition of academic resilience entails the important idea that higher equity is attained through the higher achievement of the ESCS (Economics, Social, and Cultural Status) disadvantaged students (OECD, 2010a, 2010b, 2011). From the PISA 2012 perspective, it is interesting to make a contrast between the Eastern and Western cultures with regard to how factors pertaining to mathematics learning and problem solving predict classification of academic resilience in mathematical literacy of the secondary students.
From the PISA perspective, academic resilient students are those who come from a disadvantaged ESCS background but somehow perform much higher than that predicted by their home background. Operationally, there are three steps in the identification of resilient students (Cheung, Sit, Soh, Ieong, & Mak, 2013). First, students located at the bottom quarter of the PISA’s index of ESCS within their own economies are identified as disadvantaged students. Second, literacy performance scores as assessed in PISA are regressed on students’ ESCS across all participating economies to find out the international ESCS-performance relationship. Third, student’s residual performance is obtained by comparing the actual performance of each student with the performance predicted by the international ESCS-performance relationship. Resilient student is identified as those whose residual performance is amongst the top quarter of students’ residual performance from all economies. In PISA 2012, percentages of disadvantaged resilient students of Macao, Hong Kong, Korea, Japan, Canada, Estonia and Finland are 17.0, 18.3, 12.8, 11.4, 8.4, 9.7 and 8.3 % respectively.
Hypothesis of the study
This study examines links between academic resilience (and non-resilience) in mathematical literacy of the ESCS disadvantaged students with pertinent mathematics learning and problem solving variables while controlling for a number of putative demographic characteristics. The following hypothesis is postulated for statistical significance testing:
The mathematics learning and problem solving variables, after controlling for the effects of the demographic covariates, have differential effects on the classification of academic resilience in mathematical literacy within the group of disadvantaged students in the seven economies high both in educational quality and equity in PISA 2012.
In sum, the overall research objectives of the present study lies with the academic resilience in mathematical literacy within the subgroup of ESCS disadvantaged students in connection with the resilience classification by the sets of mathematics learning and problem solving variables, using the demographic variables as covariates.
Method
Expected Outcomes
References
Cheung, K.C., Sit, P.S., Soh, K.C., Ieong, M.K., & Mak, S.K. (2013). Predicting academic resilience with reading engagement and demographic variables: Comparing Shanghai, Hong Kong, Korea and Singapore from the PISA perspective. The Asia-Pacific Education Researcher. doi: 10.1007/s40299-013-0143-4. OECD (2005). PISA 2003 data analysis manual: SPSS users. Paris: OECD. OECD (2010a). PISA in Focus: How do some students overcome their socio-economic background? Paris: OECD Publishing. OECD (2010b). PISA 2009 results: Overcoming social background: Equity in learning opportunities and outcomes (Volume II). Paris: OECD Publishing. OECD (2011). Against the odds: Disadvantaged students who succeed in school. Paris: OECD Publishing. OECD (2013). PISA 2012 results: What students know and can do: Student performance in mathematics, reading and science. Paris: OECD Publishing. WESTAT. (2007). WesVar® 4.3 User's guide. Rockville: WESTAT.
Search the ECER Programme
- Search for keywords and phrases in "Text Search"
- Restrict in which part of the abstracts to search in "Where to search"
- Search for authors and in the respective field.
- For planning your conference attendance you may want to use the conference app, which will be issued some weeks before the conference
- If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.