ECUE MEETING

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E-CUE MEETING

• Tuesday, January 27, 2004

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E-CUE AGENDA January 27, 2004Workload and
Learning Understanding the Issues in MIT
Engineering Education

• Understanding and quantifying student workload
  and learning
• Recent literature on workload and learning
• Recent SoE activities to examine workload and
  learning Preliminary findings of workload
  learning survey and focus groups at MIT
• Brief review of relevant MIT data
• Making sense of data to date
• Discussion for next E-CUE next steps/ additional
  study
• SoE engineering education website
• http//web.mit.edu/engineering/ecue
• Member suggestions to encourage faculty use

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Workload and learning Recent literature

• Student perceptions of workload are related to
  (Hounsell 84, Ramsden 92)
• Teaching approaches (didactic/ active)
• Assessment (grades) of exams and assignments
  (frequency)
• Relevance of coursework to student interests
• Work hours (class independent study hours)
• Perceived workload can shape students learning
  approaches (Ramsden and Entwistle 81, Entwhistle
  and Ramsden 83)
• High workloads can lead to students adopting
  reproducing learning approach over meaning
  learning approach
• Reproducing learning approach
• A study of a mechanical engineering program
  supports this finding (Kember and Leung 98) can
  particularly be the case in high workload
• Methods for studying student workload perceptions
  among engineering students
• Study process inventory permits some
  identification of student learning approaches
  when faced with high workload (Biggs and Leung
  01)
• Workload diary of class and independent study
  hours (Kember et al., 95)
• Interview or focus groups to examine study
  strategies and workload (Gow and Kember 95)

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Recent SoE activities to examine workload and
learning

• Develop pilot written survey and focus group
  protocol to examine / clarify workload and
  learning issues
• Pilot written survey (see handout)
• Establish IF there is a high workload issue to be
  addressed
• Clarify factors related to perceptions of high
  workload
• Course major
• Teaching methods
• Assignments
• Grading/ feedback
• Required versus elective subjects
• Establish study process patterns using Biggs
  Study Process Questionnaire
• Modified 21 question written survey that permits
  some identification of student study habits
  (reproducing or surface learner versus deep or
  meaning learner)
• Pilot focus group
• Semi-structured focus group questions elaborate
  on written survey questions

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Pilot student group

• Pilot group participants
• Course 6 undergraduates (juniors-M.Eng.) as pilot
  group
• Email request to participate in written survey
  and focus group sent to all Course 6
  undergraduates and M.Eng. Students
• 61 completed written survey (representative of
  6-2 and 6-3 student population, but few 6-1
  sophomore and freshman surveys not counted)
• 18 participated in 3 focus groups

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Workload and learning study Preliminary findings
from Course 6 respondents

• Academic workload is high (N52, 4.1/5 where
  1workload very low and 5workload very high)
• Satisfaction with workload relative to what is
  learned (N52 where 1very dissatisfied and
  5very satisfied)

Table 1. Course 6 student satisfaction with
workload relative to What is learned (All
students and by program)

• Prefer assignments are not graded so that I can
  focus on learning
• (N52, 2.5/5 where 1strongly disagree,
  5strongly agree)

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Table 2. Factors determining perception of high
workload (All Course 6 students and by program)
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Preliminary findings 1 Focus group general
impression and comments on problem sets and
learning

• High workload is OK with us buttweak the system
  to improve the connection between workload,
  learning, and demonstrated performance.

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Preliminary findings 2 Focus group comments on
teaching and going to lecture
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Preliminary findings 3 Focus group comments on
assigned units, grading, assignment scheduling
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Factors listed by students as related to problem
set learning experience

• Tweak these factors toward the student ideal
  and learning improves, according to students
• Teaching clarity
• Teaching so that the book doesnt teach more
  than the lecturer
• Student relationship with lecturer, recitation
  instructor, not just TA (particularly for women
  students)
• Relationship of lecture content to assignments
• Clarity of assignments
• Aid in using tools assigned for use with
  assignments
• Length and frequency of assignments (especially
  end of term)
• Busywork, grody math and pset length
• Grading of assignments (fairness, students put
  lots of time into psets and often theyre not
  graded. They should be graded to permit student
  to demonstrate mastery of material)
• Timely and comprehensive feedback so students
  know all that work was reviewed thoroughly and
  for improvement in performance

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Student study process questionnaire results

• No time to tabulate!
• Quick perusal of responses shows students prefer
  deep learning methods, but it can not be used as
  much as they would like. Hence, they find that
  they must shift to surface learning to keep up.
• Since students prefer to learn as deep learners,
  but dont have the time, their frustration is
  higher.

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6.004 was considered as nearly the ideal
learning experience by many. Why?

• Lecture clarity/ students wanted to go to lecture
  even though they were tired
• Psets were well-written and organized. Since they
  were not too long, there was less incentive to
  use bibles (prior year pset solutions).
• If you used bibles, you end up losing out in
  doing the labs since psets were structured to
  teach what you needed for the lab projects.
• Psets online were great.You could do them at your
  own pace.
• Lab assignments were clear, carefully structured
  and not too long.
• Lab grading was fair if a student carefully
  demonstrated mastery for each lab, then you got
  an A.
• Five exams was good if no pset grade. Even those
  with test-taking fear liked this system.
• No tests that really counted compared to labs.
  Designing a working processor in your lab project
  got you and A.
• But workload seemed low relative to other
  subjects since factors related to high perceived
  workload were missing
• Students felt guilty about this learning
  experience since they didnt suffer!

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Summary Other useful MIT data to date

• MIT engineering students work more hours on
  average per week than students at peer
  engineering schools (Enrolled Student Survey
  2003)
• Lower MIT engineering senior satisfaction with
  workload, workload relative to amount learned,
  and grades as reflection of performance compared
  to peer engineering school seniors (EBI Senior
  Survey 2003)

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(No Transcript)
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Table 3. Comparison of MIT engineering and other
peer engineering student satisfaction with
workload, workload and learning, and feedback
From EBI Senior Exit Survey 2003
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Making sense of the data Relationship of student
workload to assessment and curriculum

• Perceptions of workload are connected to all
  aspects of the subject learning experience
• Given the preliminary data, which suggests that
  students identify challenging workload with being
  at MIT, how can we tweak curricula and subjects
  to ensure that our highly motivated students
  maximize learning?
• How can we create learning experiences that are
  appropriate to our diverse community of students?
  

Curriculum/ Teaching quality/ assignments
Student workload perception/ deep versus
reproducing learning
Assessment methods/ grading/ rewards
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Next steps.

• Preliminary data is exciting! Student comments
  begin to suggest ways for individual instructors
  to tweak subjects.
• Preliminary data gathered is just that, however,
  preliminary.
• Small pilot student group in Course 6 identified
  important factors in high perceived workload and
  its detrimental impact on solid learning. The
  group was small, limited to 1 department.
• It would be great to nail down issues of surface
  and deep learning and its relationship to
  workload (class structure, assignments), clarity
  of learning goals, grading, and rewards.
• Expand survey questions?
• It would be great to expand to other departments.
  
• Expand student groups to other departments?
• Ultimate goal develop a comprehensive teaching /
  learning model that identifies key factors for
  faculty and that is appropriate to MIT
  engineering students.

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SoE education website If we build it, they will
come

• http//web.mit.edu/engineering/ecue
• What do faculty want to support teaching/
  learning?
• According to DUE, instructors rarely use learning
  objectives as part of surveys
• Do faculty actually write any learning
  objectives?
• How might the SoE website be used to improve
  writing and use of learning objectives as a
  learning tool?
• If we build Workload findings into site?