Motivating Urban Minority Students Through Error Analysis An Action Research Study

1. Motivating Urban Minority Students Through Error AnalysisAn Action Research Study Serigne Gningue (Co-PI) & Julissa Soriano (Noyce Scholar) NSF Robert Noyce Teacher Scholarship Program Conference, Washington, DC May 31, 2013

2. Noyce Program at Lehman College • Funds senior undergraduate year and master’s degree. • Mathematics and science teachers from the Bronx area commit to 6 years in high needs middle schools. • Full-year pre-service internship in Bronx middle schools. • Graduate courses co-taught by science, mathematics, and education faculty. • Emphasis on formative assessment strategies.

3. Noyce Program Study • What strategies have the Scholars employed to improve their effectiveness? • How might the pre-service portion of our program be modified to better prepare teachers? • What challenges and concerns have the Noyce Scholars experienced during their first year in high needs classrooms?

4. New Teacher Challenges • Classroom Issues • Lack of preparedness for classroom management • Lack of preparedness for students’ level of poverty • Administrative Tensions • Chaotic nature of administration priorities/directives • Parental Involvement • Assessment • Chronic Absenteeism • 34% of secondary students miss at least 1 month of school (NY Times, 7/16/11)

5. Introduced the Following Action Research Model

6. Background • Power in learning through discovery. • Students are not capable to correct their misconceptions through the coaching or assistance of someone else.

7. Context • FDA • 81% free lunch • 7% Limited English Proficiency (LEP). • 41% Hispanic, 57% Black

8. What I Found During My First Year of Teaching There is a problem of student achievement, interest, motivation, and confidence, and overall attitude towards math. Students have difficulty mastering higher-level mathematical skills. Students perform poorly on assessments of critical thinking skills, formal deduction, and proof writing.

9. The Process of S.E.A. • Allows students to discover their own mistakes and misconceptions; • Requires the learner to fix the mistake, thus forcing the student to dig deeper into the subject matter and move onto the next level of knowledge; • Gets students to learn to justify their reasoning; • Allows students to question the reasoning of others thus allowing the classroom to become a stage for mathematical discourse and student-centered instruction.

10. Group Work Instructions • 1) Look at student’s work. • 2) Identify at least one error. • 3) Complete table on page #2.

11. Implementation-Meaningful Groups • Color-Coded Cards • Cards Represent Ability on Learning Goal • Data Tracker • Online Resource: LearnBop

12. Data Tracker

13. Teacher Error Analysis • Student’s Error • Student’s misconception • Common Core Standard addressed by question • Intervention (activity) to address the misconception

14. Student Error Analysis • Group students based on the common error • Give samples of the work • Have them identify the mistake(s) • Correct the mistake(s) • Support their reasoning

15. Differentiation • Ability • Product • Scaffolds and Multiple Entry Points • Groupings • Homogenously • Heterogeneously

16. Common Core Standards • Data driven instruction • Data driven student groupings, differentiation , and scaffolding • Each playlist is Common Core aligned • Promotes the mathematical practices – Construct viable arguments and critique the reasoning of others • Fosters teacher-student and student-student discourse

17. The gift that keeps on giving… • Data can be used in school inquiry teams • Future classroom action research

18. Purpose Problem Statement The impact of discussion integrated instruction on student achievement. Decreasing number of students pursuing advanced courses or careers in the field of mathematics. • To measure the impact of a discourse-integrated teaching strategy utilizing Student Error Analysis on student achievement and students’ attitude in the field of mathematics.

19. Research Questions 1 - To what extent does the use of student error analysis improve students’ attitude and motivation in the mathematics classroom? 2 - To what extent does the use of student error analysis improve students’ academic performance in the mathematics classroom?

20. Literature Review • The use of incorrect answers and misconceptions. • Student Engagement • Discourse-based instruction

21. Participants • Both groups were given a baseline assessment; • The experimental group had an overall mean of 29% of correct responses with 94% of students scoring in the 0-74 % range and 6% scoring in the 80-89% range. • The control group had an overall mean of 30% of correct responses with 95% of the students scoring in the 0-74% range, 2.5% scoring in the 80-89 % range; and 2.5% scoring in the 90-100% range. • Limited study of 54 students in two ninth grade classes; • The sample is of convenience • one 9th grade class -- the experimental group • Asecond 9th grade class --the control group. • 27 female students • 27 male students.

22. Intervention Plan • In between the pre- and post-survey, the experimental group received two student error analysis activities. • One more error analysis activity was carried out in between the pre- and post-achievement test. • The experimental group: student error analysis activities were used as a strategy. • The control group was instructed through a traditional method of teacher’s whole class lectures. • Pre and Post Attitude Surveys were also given

23. The first time, students analyzed one exercise; • The incorrect answer and procedure were chosen from their previous homework and/or class work. • The second time, two exercises were analyzed. • Each time, common errors in students’ homework were copied onto the smart board. • students were then asked to identify the errors and explain why they thought there was a mistake.

24. Data Collection • Four different instruments were used for data collection: a math attitude survey, a performance task, an achievement test, and classroom observations. • The data obtained from the pre and post tools were averaged and analyzed;

25. Data Collection Cont. • Other instruments were also used as formative assessment, such as exit cards and concept attainment activities during the experimental period. • These assessments, however, were used to identify patterns in students ‘ misconceptions.

26. RESULTS Research question #1: To what extent does the use of student error analysis improve students’ attitude and motivation in the mathematics classroom? • The overall average of students’ attitude and motivation towards math in the control group decreased by 0.02 from pre-to post-survey. • there was an increase of 0.37, from pre- to post-survey, in students’ attitudes and motivation towards math in the experimental group.

27. SURVEY RESULTSCONTROL GROUP

28. SURVEY RESULTS EXPERIMENTAL GROUP

29. Performance Tasks Results Research question #2: To what extent does the use of student error analysis improve students’ academic performance in the mathematics classroom? • The overall average of students’ performance task in the controlled group decreased by 0.33 from the pre- to the post-task. • Conversely, the data shows an increase of 1.14 in students’ overall performance in the experimental group between the pre- and the post-task.

30. PERFORMANCE TASKS

31. PRE- AND POST-TESTS. • Both control and the experimental groups showed improvement from pretest to the posttest. • The control group had a 25.39 increase; • The experimental group showed a 31.43 increase.

32. TESTS

33. Conclusions –Control Group • Data suggest a decrease in students’ attitude and motivation towards math when whole-class lecturing was used as a teaching strategy. • Students performance on higher order thinking tasks seemed to decrease. • Their test performance slightly increased.

34. Conclusions _Experimental • The error analysis as an instructional intervention tool seems to have: • made a significant difference in student’s attitude and motivation towards math; • improved students mathematical performance on higher order thinking tasks • improved students performance on regular achievement tests.

35. Recommendations - Use of more precise data analysis methods to determine if the differences found are of enough significance. - Investigate whether error analysis is appropriate as an effective strategy regardless of the content being taught.

36. Share Out • Any thoughts?