Kooken, Janice , Welsh, Megan E., Mccoach, D. Betsy, Johnston-Wilder, Sue and Lee, Clare (2012) Measuring mathematical resilience : an application of the construct of resilience to the study of mathematics. In: AERA 2013, San Francisco, California, 27 Apr-1 May 2013 (Submitted)

Text (Submitted conference paper) WRAP_Johnston_aera paper.pdf - Accepted Version Restricted to Repository staff only until 1 May 2013. Download (444Kb)

Abstract

To meet the challenge of accelerating demands for quantitative literacy in the work force, improvements are needed in mathematics education. Student skill must be increased at all ability levels while also reducing the achievement gap across gender, racial and ethnic groups to increase their participation in advanced mathematics coursework and representation in mathematics related careers (National Mathematics Advisory Panel, 2008). Research has shown that affective traits such as motivation and attitude are linked to increased likelihood of taking advanced mathematics courses (Ma, 2006) and are significant predictors of improved cognitive activity and achievement (Buff, Reusser, Rakoczy,& Pauli, 2011; Ethington & Wolfe, 1986). In addition, males generally score more favorably than females on affective variables related to mathematics achievement and persistence (McGraw, Lubienski, & Strutchens, 2006; Sherman & Fennema, 1977; Wilkins and Ma, 2003). Although psychological resilience has been researched extensively (Luthar, Cicchetti, & Becker, 2000; Luthar, 2007) the study of mathematical resilience, defined as a positive adaptive stance to mathematics which allows students to continue learning despite adversity, represents a new approach (Johnston-Wilder & Lee, 2010; Rivera & Waxman, 2011). Math anxiety looks at maladaptive response to learning mathematics and is well-studied (Hembree, 1990; Richardson & Suinn, 1977; Tobias, 1978). In contrast, resilience incorporates factors associated with optimal functioning. Although mathematical resilience has been identified as important for success (Johnston-Wilder & Lee, 2010; Rivera & Waxman, 2011), little consensus exists around its definition and no measures of resilience have been rigorously developed and/or validated. Rivera & Waxman (2011) identified the use of teacher nomination of resilient students as a limitation of their study, further motivating development of an instrument. This presentation will report on efforts to develop and validate an instrument measuring mathematical resilience. Ultimately, the measure will aid in developing and testing models that gauge the role of mathematical resilience in student achievement and persistence in advanced coursework. These models can be used to develop interventions to improve mathematical resilience, achievement, and quantitative literacy (Johnston-Wilder & Lee, 2010).

Item Type:	Conference Item (Paper)
Subjects:	B Philosophy. Psychology. Religion > BF Psychology L Education > LB Theory and practice of education
Divisions:	Faculty of Social Sciences > Institute of Education
Library of Congress Subject Headings (LCSH):	Mathematics -- Study and teaching, Resilience (Personality trait)
Date:	2012
Status:	Not Peer Reviewed
Publication Status:	Submitted
Conference Paper Type:	Paper
Title of Event:	AERA 2013
Type of Event:	Conference
Location of Event:	San Francisco, California
Date(s) of Event:	27 Apr-1 May 2013
Related URLs:	Organisation
References:	Arbuckle, J. L. (2009). Amos (Version 18.0.0) [Computer Program]. Chicago: SPSS. Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44 (9), 1175-1184. Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9 (3), 75-78. Buff, A., Reusser, K., Rakoczy, K. & Pauli, C. (2011) Activating positive affective experiences in the classroom: “Nice to have” or something more? Learning and Instruction, 21, pp. 452-466. Chouinard, R., & Roy, N. (2007). Relations among competence beliefs, utility value, achievement goals, and effort in mathematics. British Journal of Educational Psychology, 77, 501–517. Dweck, C. (2000). Self-theories: Their role in motivation, personality and development. Lillington NC: Psychology Press, Taylor & Francis. Ethington, C.A. & Wolfe, L.M. (1986). A structural model of mathematics achievement for men and women. American Educational Research Journal, 23, pp. 65-75. Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21 (1), 33-46. Johnston-Wilder, S & Lee, C. (2010). Mathematical resilience. Mathematics Teaching, 218, 38-41. Johnston-Wilder, S & Lee, C. (2010). “Developing mathematical resilience.” Berea Conference Paper, University of Warwick, UK. Karairmak, O. (2010). Establishing the psychometric qualities of the Connor-Davidson Resilience scale (CD_RISC) using exploratory and confirmatory factor analysis in a trauma survivor sample. Psychiatry Research. 179, 350-356. Luthar, S. (2007). Resilience in development: A synthesis of research across five decades. Developmental Psychopathology, 739-783. Luthar, S., Cicchetti, D., & Becker, B. (2000). The Construct of Resilience: A critical evaluation and guidelines for future work. Child Development, 71, 543-562. Ma, X. (2006). Cognitive and affective changes as determinants for taking advanced mathematics courses in high school. American Journal of Education, 113, pp. 123-149. McGraw, R., Luebienski, S., & Strutchens, M. (2006). A Closer Look at Gender in NAEP Mathematics Achievement and Affect Data: Intersections with Achievement, Race/Ethnicity, and Socioeconomic Status. Journal for Research in Mathematics Education, 37, 129-150. McKenzie, J. F., Wood, M. L., Kotecki, J. E., Clark, J. K., & Brey, R. A. (1999). Establishing content validity: using qualitative and quantitative steps. American Journal of Human Behavior, 23, 311-318. National Mathematics Advisory Panel (2008). Foundations for Success: The Final Report of the National Mathematics Advisory Panel, U.S. Department of Education: Washington, DC. Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks, CA: Sage. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage. Richardson, F. C. and Suinn, Richard M. (1972). The mathematics anxiety rating scale: psychometric data. Journal of Counseling Psychology, 19, 551-554. Rivera, H, & Waxman, H. (2011). Resilience and nonresilient Hispanic English language learners’ attitudes towards their classroom learning environment in mathematics. Journal for Education for Students Place at Risk, 16, 185-200. Sherman, J & Fennema, E. (1977). The study of mathematics by high school girls and boys: related variables. American Educational Research Journal, 14 (2), 159-169. SPSS Inc. (2009). PASW STATISTICS 18.0 [Computer Program]. SPSS Inc., Chicago. Thompson, B. (2010). Exploratory and confirmatory factor analysis. Washington, DC: American Psychological Association. Tobias, S. (1978). Overcoming math anxiety. New York: Norton Wilkins, J. & Ma, X. (2003). Modeling change in student attitude toward and beliefs about mathematics. The Journal of Educational Research, 97, 52-64.
URI:	http://wrap.warwick.ac.uk/id/eprint/51559

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