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Phenomenology. Research Question. How do students in a freshman engineering fundamentals class describe the experience of “academic cheating”? Context bound Personal experience based Structure or ‘essence’ of term. Data Collection. Most common: depth interviews

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research question
Research Question
  • How do students in a freshman engineering fundamentals class describe the experience of “academic cheating”?
    • Context bound
    • Personal experience based
    • Structure or ‘essence’ of term
data collection
Data Collection
  • Most common: depth interviews
  • Other possible approaches: reflective essays, open-ended survey items, projective techniques such as posing scenarios, showing videotape clips to elicit responses
sampling
Sampling
  • All students in class
  • If sample, based on applicable strategy
    • Students who are “information rich” cases (students who have cheated)
    • Maximum variation (gender, GPA, etc.)
    • Snowball or other strategy
    • Purposeful
    • Small Ns
analysis
Analysis
  • Search for patterns in statements across cases that lead to characteristics that are not part of the phenomenon and those that are, distinctions with other related terms
  • Recursive coding until essence is captured across cases
    • Specialized text analysis software: Nvivo or others
question
Question
  • What happened in
    • the case of Mickey Mouse’s plagiarized term paper?
    • the cases of the Seven Dwarfs’ plagiarized term papers?
      • Specific instance(s) or context
      • Multiple lenses, holistic view
data collection1
Data Collection
  • Variety of choices, including
    • Document analysis (Disney U policies on cheating, drafts of papers turned in, etc.)
    • Interviews (with Mickey, the Dwarfs, their teachers, administrators at Disney U, etc.)
    • Observation -if event has not occurred yet-
      • (go to Disneyworld and watch as Mickey and the Dwarfs compose their term papers)
    • All focus on as many aspects of the case, or cases, that will give the story
sampling1
Sampling
  • Those documents, people, places, etc. that are likely to be relevant to the question
data analysis
Data Analysis
  • Pattern analysis, usually first by organizer such as chronology, viewpoint of specific actors, etc.; then by theme
  • Single case(s) (Dopey, Sneezy, etc.)
  • Also, cross-case analysis, if multiple (Patterns across the Dwarfs)
question1
Question
  • Is there a relationship between characteristics of the instructional activities in our curriculum and the amount and kind of cheating that occurs?
    • Relationship between variables
    • Operational definition of terms
data collection2
Data Collection
  • Thick description of instances of cheating
  • Thick description of instructional activities that were associated with them
  • Obtained from documents, interviews, observation
sampling2
Sampling
  • Cases that had been reported to Student Misconduct last year
  • Or, other cases that can be accessed
  • Within Engineering School
  • Related to instructional activities of school (as opposed to external exams, parking tickets, etc.)
data analysis1
Data Analysis
  • Open coding (data that answer, “What is this about?”)
  • Axial coding: relationships between codes
  • Search for characteristics that are associated across cases
  • Selection of core category and testing of patterns and refining to accommodate data
  • Statement that captures cases and describes context