1. Utilizing PBS “Cyberchase” Math Computer Games with Struggling Math Students Michelle Brennan Education 7202T Seminar in Applied Theory and Research II Fall 2013 Dr. O’Connor-Petruso

2. Table of Contents Research Design ……………………………………..………………….3 Threats to Internal Validity ………………………….……………..4 Threats to External Validity …………………………….………….5 Sample Survey Questions ………………………………….……....6 Sample Assessment Questions …………………………….…....7 Proposed Data Analysis ………………………...……………..8-10 References ………………………………………....…………..........11

3. Rationale & Research Design Rationale: I observed many students struggling in math in my student teaching in a second grade class in Brooklyn, NY and decided to look into ways to help them. At the same time I found it very hard to separate children at this age from their electronic games. I decided to use their love for electronic games to teach these struggling students math. Therefore my treatment is to have these students play “Cyberchase” math games on the computer through the PBS Kids website. I took the bottom third of the class in math scores and randomly assigned them to two groups. The treatment will be given to the treatment group (X 1 ), three times a week for 40 minutes, during extended stay in the computer lab, for an eight week period. The research will be focusing on the impact of these math computer games on student attitudes towards math and student assessment scores in math. Research Design: Quasi-Experimental – Students are not randomly assigned as they are all from the same class. – Two-Groups: Designated treatment group (X 1 ) Control group (X 2 ) – Nonequivalent Control Group Design: – Two groups are pretested (O), exposed to a treatment (X) and posttested (O). – Symbolic Design: OX 1 O OX 2 O

4. Internal Threats History: Can’t control environment/classroom-fire alarm during testing Maturation: “Over time, lose interest” Testing/Pre-Test Sensitization: “Pretesting can affect posttest” Instrumentation: Survey/Test design has not been tested Mortality: “Drop out rate-results will become inflated” Differential Selection of Subjects: Not studying clones-all different Selection-Maturation Interaction: varying maturity of participants

5. External Threats Generalizable Conditions: another researcher may not get same results with different children Pretest-Treatment: Student reaction to treatment because of pretest Treatment Diffusion: Groups share information Experimenter Effects: My biases Reactive Arrangements/Participants Effects: – Hawthorne Effect: Students will know they are being singled out – Compensatory Rivalry Effect: Control group change behavior by working really hard or giving up. – Novelty Effect: new technology

6. Sample Questions Pre/Post Survey Preference: 1)Strongly Disagree2)Disagree3)Agree4)Strongly Agree Math is my favorite subject in school. English Language Arts is my favorite subject in school. I like to do math. I am good in math. Math is easy. Math is hard. Frequencies: 4) 5-7 days per week3) 2-4 days per week2) 1 day per week1) Never How often do you use the computer outside of school? How often do you use the computer at school? How often do you play games on the computer? How often do you play Math Games on the computer? How often do you play Math Games on the PBS Cyberchase site outside of Math class?

7. Sample Questions Pre/Post Assessment Directions: Solve the following equations. 8 + 9 = 7 + 8 = 30 + 9 = 25 – 13 = 134 – 39 = Directions: What number is missing in the pattern? 24__8 51015__ 108__4 10 __3040

8. Proposed Data Analysis Sample Data Set I am good in math (pre survey)I like math (pre survey) X - VariableY-Variable 21 43 32 44 43 32 21 44 43 43 1= Strongly Disagree, 2=Disagree, 3=Agree, 4=Strongly Agree Correlation Coefficient:.932rxyAnalysis: Excellent positive correlation (.932rxy). As "X" increases so does "Y". I am predicting that as students belief in their math ability increases so does their attitude towards math increase.

9. Proposed Data Analysis Sample Data Set I like Math (Post Survey)Post Test Score X - VariableY-Variable 265 4100 380 490 385 380 140 495 385 4 Strongly Disagree=1Test Scores are out of 100 Disagrees=2Analysis: Excellent positive correlation (.946rxy). As "X" increases so does "Y". Agree=3 I am predicting that as students attitudes towards math increase so do their post test scores. Strongly Agree=4 Correlational Coefficient:.946rxy

10. Proposed Data Analysis Treatment Group X 1 Control Group X 2 Pre-TestPost-TestPre-TestPost-Test Student 17090Student 63035 Student 27595Student 77580 Student 36080Student 87075 Student 44060Student 95560 Student 53560Student 104045 Mean5677Mean5459 Pre-Test MeanPost-test Mean% Increase Treatment Group X 1 5677+37.5% Control Group X 2 5459+9.3% I predict that average test scores will increase overall but the increase will be more significant with the treatment group X 1. The treatment group improved their scores by 37.5% while the control group only improved by 9.3%

11. References O’Connor-Petruso, S. (2013). Descriptive Statistics Threats to Validity [PowerPoint slides]. Retrieved from https://bbhosted.cuny.edu/webapps/portal/frameset https://bbhosted.cuny.edu/webapps/portal/frameset