Gender Gap and Gendered Math Education: Myth or Reality?

1. Gender Gap and Gendered Math Education: Myth or Reality? Tatyana Sumner Fall 2012 ED.7202.T Action Research Final Presentation

2. Table of Contents Introduction – Slide 3 Research Design – Slide 4 Threats to Internal Validity – Slide 5 Threats to External Validity – Slide 6 Proposed Data – Slide 7 Proposed Correlations (Graphs) – Slide 8 – 9 Sample Survey Questions – Slide 10 References – Slide 11

3. Introduction This research will focus on possible differences in math attitudes between female and male students. The proposed intervention in a form of mixed-gender, peer-assisted math instruction aims to minimize the gender biases and implicit math-gender stereotypes to improve female students’ attitudes toward mathematics.

4. Research Design • Quasi-Experimental Design • Nonequivalent Control Group Design • Two groups, 9 Girls and 9 Boys, (4th grade – School X in Brooklyn, NY) will receive a pre-test (O), exposed to treatment (X1), (X2), and conclude with a post-test (O). • Symbolic Design Representation: O X1 O O X2 O • Pre-test (O) and Post-test (O) will consist of pre and post survey. • Treatment (X1) and (X2) will be administered simultaneously in a form of math instructions with mixed-gender peer-assisted learning pairs, over the period of 4 weeks, 3 times a week.

5. Threats to Internal Validity History – because of the length of treatment – events outside of the scope of the experiment are possible Maturation – duration of the treatment, may cause students to lose interest. Instrumentation – instruments are devised by researcher and used for the first time – validity and reliability in question. Mortality – students/parents may opt and cease participating. Students can be transferred or have long absences. Statistical Regression – small number of participants may yield results, which are statistically insignificant. Differential Selection of Subjects – non-random selection of participants does not guarantee similar educational or cognitive backgrounds.

6. Threats to External Validity Ecological Validity – because research collects subjective/opinion data, it may not be possible to replicate it with same results. Selection-Treatment Interaction – diversity ofselected participants may not be representative of greater population. Specificity of Variables – because research collects subjective/opinion data, it may be difficult to measure differences between pre and post treatment.

7. Proposed Data Analysis Proposed data displays female students having worse math attitude than male students. Both groups’ attitudes improved with lesser difference.

8. Correlations Post-Survey Correlation Brief Analysis: Very good, positive correlation (.556rxy) – The more students enjoy spending time working in peer-assisted pairs/groups, the higher their independent self-math confidence becomes. Correlation Coefficient = 0.556rxy

9. Correlations Brief Analysis: Fair Positive Correlation (.432rxy) – More comfortable students are about asking questions/for help in class, the more confident they are in their independent math work. Correlation Coefficient = 0.432rxy

10. Sample Questions Pre/Post Survey Preferences Frequencies

11. References O’Connor-Petruso, S. (2013). Descriptive Statistics Threats to Validity. PowerPoint slides. Retrieved from Blackboard Action Research site. Brock, S. E. (n.d.) Experimental Research PowerPoint. California State University, Sacramento. Retrieved from http://www.csus.edu/indiv/b/brocks/Courses/EDS%20250/EDS%20250/PowerPoint/PDFs/Presentation%209.pdf