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TEACHING FOR LEARNING PAST, PRESENT, AND FUTURE

• Judith S. Liebman
• Professor Emerita
• University of Illinois at Urbana-Champaign
• jliebman_at_uiuc.edu

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Todays Topics

• Increased national interest in improving
  engineering and professional education
• Contributions from INFORMS, ORSA, and TIMS
• Knowledge acquisition
• Lessons from cognitive psychology
• Potential from neuroscience

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National Academy of Engineering

• Bernard M. Gordon Prize for Innovation in
  Engineering and Technology Education
• Biennial, cash award of 500,000
• Half to individual and half to institution to
  support continued development and dissemination
  of the individuals innovation

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(NAE continued)

• Center for the Advancement of Scholarship on
  Engineering Education
• Goal improvement in engineering education
  through increased attention to what is taught and
  how it is taught
• NAE Research Fellows to extend and apply their
  research on teaching and learning in engineering

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The Association to Advance Collegiate Schools of
Business

• Accreditation agency for business schools
• Every accredited business school now has to have
  a portfolio that includes development of
  educational tools

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ORSA Education Committee

• Created in 1952 in original ORSA bylaws
• To encourage and advise in the preparation of
  training programs in operations research
• Maintained and printed list of OR and MS
  educational programs with contact information

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Current activities of the INFORMS Education
Committee

• Expanded education tracks at national meetings
• Summer teaching workshops for teaching management
  science
• Started INFORM-ED
• Workshops, meeting sessions, electronic
  newsletter
• Annual case study competition award, in
  successful 4th year

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(continued)

• The INFORMS Transactions on Education
• Electronic journal
• First issue in September, 2000
• Published three times a year
• The Teaching Effectiveness Colloquia
• Held before national meetings
• Fifth colloquium at this meeting
• Attendees nominated by their departments

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Annual Doctoral Colloquia

• First held in 1983 at Northwestern
• Content split between research and teaching
• Focus on preparing doctoral students for academic
  and industrial careers
• Doctoral students nominated by their departments

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High school teacher workshops

• For math teachers in middle schools, high
  schools, or community colleges
• Provides course materials and lesson plans for
  learning operations research
• Initiated by Frank Trippi in 1990
• Now includes innovative computer modules by Ken
  Chelst
• 24th workshop being held tomorrow

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The Annual INFORMS Prize for the Teaching of
OR/MS Practice

• Recognizes a teacher who has succeeded in helping
  his or her students to acquire the knowledge and
  skills necessary to be effective practitioners of
  OR/MS
• First awarded in Spring, 1998

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Why Teaching for Learning?

• Most of us are disappointed when we grade exams
  the students have learned far less than we
  expected.
• Whose fault is it?
• The students?
• The textbook?
• The teacher?

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Information Overload

• Consider how OR introductory texts have changed
• 1950s text ½ inch thick, 1 lb, large print
• 1990s text 3 inches, 4 lbs, small print
• Growing trend one semester introductory OR
  course
• Our lectures often a brain dump of all we know
• Why are we surprised that students arent
  learning much!

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The Process of Acquiring Knowledge

• Sensory perception
• Arrival of that perception in working memory
• Concurrent arrival of related information from
  long-term (repository) memory
• Processing of the new information during which
  semantic links with the previously known
  information are built and previous links updated
• Depositing the new and updated information into
  long-term memory

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To increase learning we should

• Provide material that is well organized and
  clearly presented
• Cover a reasonable amount of material
• Identify for the students what is to be learned
• Use appropriate strategies to help students
  cognitively process the material
• Use active learning
• Use cooperative learning

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Teaching Methods from the Past

• Standing in front of class with chalk in hand for
  50 minutes, or the allotted class time
• Using overhead transparencies
• Occasionally asking the class questions and
  rarely getting volunteered responses

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Course Content from the Past

• In 1950s and early 60s, we taught solving LPs
  by hand
• Next we taught solving LPs by computer
• Focus moved from worrying about making numerical
  errors to making modeling errors
• We even asked students to program the algorithms
• Result we had to worry about programming errors!

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Lessons from Cognitive Psychology

• Poorly organized lectures and poorly written
  texts make learning unnecessarily difficult
• Effective instructional design significantly
  increases student learning

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Set Explicit Learning Goals

• Set goals for the course as a whole and each
  class session
• Keeps your learning plan focused instead of being
  a brain dump
• Convey these goals to the students
• Students then focus on the important information

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Some of my goals for an introductory OR course

• Students should be able to
• undertake a systems analysis (define decision
  variables, objectives, constraints) for a word
  problem and for a real life situation
• identify or construct the appropriate model to
  apply to a word problem and a real life situation
• identify the appropriate solution algorithm or
  computer program to use

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Learning objectives for two-hour first lecture

• The basic phases of an operations research study
• Defining the problem
• Gathering data
• Formulating and solving a model
• Basic definitions
• Decision variables
• Objective function
• Constraints and feasible region
• Algorithm
• (continued)

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(continued)

• The algebraic representation of a linear
  programming model
• The translation of a LP word problem into an
  algebraic model
• The graphical solution of a two variable LP
  problem

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How else can we create an effective learning
environment?

• Recognizing the type of knowledge that must be
  learned
• Using active learning in the classroom
• Turn to your neighbor discussions
• Joint pop quizzes
• (Both students sign their joint effort)
• Small group assignments
• Case studies

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Please turn to your neighbor and discuss the
following

• What are the advantages and disadvantages of
  using spreadsheet optimization software versus
  algebraic optimization software (such as GAMS or
  LINDO) when teaching operations research and
  management science?

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Three types of knowledge

• Procedural
• Declarative
• Conditional

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Procedural knowledge

• Examples include knowing how to
• Analyze a system
• Select a model
• Formulate a model
• Execute an algorithm
• Perform sensitivity analysis
• Use computer software
• Design an algorithm

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To promote learning procedural knowledge, for
example, you can

• Divide students into groups of three and having
  them do a systems analysis of a word problem and
  put their results on the blackboard
• Having students work with their seat neighbor to
  do a systems analysis of a word problem

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Declarative Knowledge

• Examples include
• Basic definitions
• Notation
• Classic models
• Properties
• Concepts
• Relationships
• Conditions
• Theorems

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To promote learning declarative knowledge, you
can

• Ask students to
• Classify a set of queuing models using Kendalls
  notation
• List the similarities and differences between an
  ergodic and an absorbing Markov chain
• Describe the structure of a linear programming
  model
• Identify the basic and non-basic variables at an
  extreme point

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Conditional Knowledge

• Examples include
• When and why to do a systems analysis
• When and why to use linear programming
• When and why to perform a simulation
• When and why to undertake a sensitivity analysis

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How to Promote Conditional Knowledge

• For example, ask students to
• List the advantages and disadvantages of
  developing a deterministic rather than a
  stochastic model
• Develop a causal-concept map describing the
  impact of an increase in machine-down time on the
  output rate of a production process

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Cognitive strategies for active learning include

• Organizing information ask students to
• Develop a taxonomy for optimization models
• List the similarities and differences between
  simple gradient search and Newton search
• Describe the structure of a decision tree
• List the advantages and disadvantages of using a
  spreadsheet optimizer instead of an algebraic
  modeling language such as GAMS or LINDO

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• Use of Framing
• Type 1 frame example a two-dimensional table
• Row labels different nonlinear optimization
  algorithms
• Column labels algorithm characteristics such as
  initialization, determining direction of
  movement, determining step size, termination
  criterion
• Students asked to fill in the cells with the
  appropriate procedures or criteria
• Type 2 frame example with three components
• Decision variables
• Objective function
• Constraints
• Students asked to fill in the frame based on
  information in a word problem

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• Concept mapping
• Ask students individually or as a group to
• Develop a hierarchy map representing managerial
  decision making
• lthttp//cmap.coginst.uwf.edu/cmaps/MDM2/Managerial
  20Decision.htmlgt
• Interactive software for concept mapping

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(No Transcript)
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A model A model is a simplified description of
reality. This simplification is usually achieved
through mathematical representations. Therefore
model and mathematical model usually means the
same concept. Reference E. and J.R. Meredith
(1985) Fundamentals of Management Science Third
Edition Business Publications, Inc.
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Metaphors and Analogies

• Comparing gradient search in constrained space to
  hikers on hilly terrain within fenced boundaries
  (metaphor)
• Understanding gradient search in higher
  dimensions by thinking of gradient search in two
  dimensions (analogy)

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Rehearsals

• Following a reading assignment discussing
  primal-dual relationships, asking students to
  identify the concept they understand best and the
  one they understand least
• Asking students to develop word problems to be
  solved by linear programming models
• Asking students to identify the three most
  important concepts covered in a reading assignment

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Experiences with Using Active Learning

• Two parallel sections of one semester intro to OR
  undergraduate course in industrial engineering
• One section lecture based with turn to your
  neighbor questions every 10 minutes or so
• Other section no lectures. Groups of three
  students and a worksheet asking them to develop
  material based on reading assignment
• Students self-selected into the two sections

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What Was the Result?

• Both sections performed equally well on final
  exam
• Course/instructor evaluations at end of course
• Both sections rated course highly on standard
  campus course evaluation form
• Focus group evaluations done by campus
  instructional resource staff

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(continued)

• Key strength of course identified
• No lecture section the development of their
  own learning skills and interest
• Lecture section interest of professor in
  student, professors knowledge of material,
  professors presentation skills, etc.
• Students in the no lecture section recognized
  that they had assumed the responsibility for
  learning and believed their own learning skills
  had improved

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Clickers Electronic Audience Feedback in the
classroom

• Clickers are magic-marker sized infra-red
  transmitters
• During lecture, students answer multiple-choice
  questions (Concept tests) with personal
  electronic transmitters
• The system records how each individual student
  voted

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(continued)

• During a typical 50-minute lecture, students are
  asked 4 to 6 multiple-choice questions and are
  given about 2 minutes to answer each question.
• Students are encouraged to discuss the question
  with their neighbors before answering.

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Example OR/MS Concept Question

• Feasible solutions must be basic.
• a True b False

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What Will the Future Bring?

• Neuroscientists are using functional magnetic
  resonance imaging to learn even more about
  cognitive processes.
• They can identify just where in our brains
  cognitive activity is taking place.
• Who knows where this will take us in our ability
  to improve student learning?

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But one thing we know now

• The key to student learning is active learning.