Motivating Urban Minority Students Through Error Analysis An Action Research Study

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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

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Noyce Program at Lehman College

• Funds senior undergraduate year and masters
  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.

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Noyce Program Study

• What challenges and concerns have the Noyce
  Scholars experienced during their first year in
  high needs classrooms?

• 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?

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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)

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Introduced the Following Action Research Model
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• Background

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

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Context

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

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• 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.

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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.

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Group Work Instructions

• 1) Look at students work.
• 2) Identify at least one error.
• 3) Complete table on page 2.

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Implementation-Meaningful Groups

• Color-Coded Cards
• Cards Represent Ability on Learning Goal
• Data Tracker
• Online Resource LearnBop

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Data Tracker
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Teacher Error Analysis

• Students Error
• Students misconception
• Common Core Standard addressed by question
• Intervention (activity) to address the
  misconception

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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

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Differentiation

• Ability
• Product
• Scaffolds and Multiple Entry Points

• Groupings
• Homogenously
• Heterogeneously

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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

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The gift that keeps on giving

• Data can be used in school inquiry teams
• Future classroom action research

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• Purpose

• Problem Statement

• 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.

• The impact of discussion integrated instruction
  on student achievement.
• Decreasing number of students pursuing advanced
  courses or careers in the field of mathematics.

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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?
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Literature Review

• The use of incorrect answers and misconceptions.

• Student Engagement

• Discourse-based instruction

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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.

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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 teachers whole class
  lectures.

• Pre and Post Attitude Surveys were also given

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• 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.

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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

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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.

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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.

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SURVEY RESULTSCONTROL GROUP
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SURVEY RESULTS EXPERIMENTAL GROUP
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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.

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PERFORMANCE TASKS
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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.

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TESTS
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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.

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Conclusions _Experimental

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

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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.
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Share Out

• Any thoughts?