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Math 20
       Mathematical Online Placement Exam
The ALM in Mathematics for Teaching Program




                 Gilligan, MOPE, and TiVo
                     Teaching activities at Harvard


                              Matthew Leingang

                                Harvard University
                            Department of Mathematics


                     University of California, Irvine
                             April 4, 2007




                          Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
              Mathematical Online Placement Exam
       The ALM in Mathematics for Teaching Program


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Current syllabus
       The ALM in Mathematics for Teaching Program   Examples


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
             Mathematical Online Placement Exam     Current syllabus
      The ALM in Mathematics for Teaching Program   Examples


Math 20: Introduction to linear algebra and
multivariable calculus



          Taught since 2004
          Original idea: stick
          to the title
          Almost no
          applications
          originally




                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
             Mathematical Online Placement Exam     Current syllabus
      The ALM in Mathematics for Teaching Program   Examples


Math 20: Introduction to linear algebra and
multivariable calculus



          Taught since 2004
          Original idea: stick
          to the title
          Almost no
          applications
          originally




                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Current syllabus
       The ALM in Mathematics for Teaching Program   Examples


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
                Mathematical Online Placement Exam     Current syllabus
         The ALM in Mathematics for Teaching Program   Examples


Syllabus for Math 20, Spring 2007
Foundational material



                                                           Systems of linear equations
                          Algebra

                                                          Gauss elim                     Inversion
        Dot product
                                        Vector           Matrix                   Determinants

                                                                          Eigenstuff

                                               Function
                Quad approx                                          Partial derivative

                                                               Lin approx
                           Differentials

                                   Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
                Mathematical Online Placement Exam     Current syllabus
         The ALM in Mathematics for Teaching Program   Examples


Syllabus for Math 20, Spring 2007
Applications



                                                              Stationary points
                Linear programming

                                                                            Lag mult
                                               Optimization
         Game theory
                                                Problems
                                                                          Least squares
               Assignment problem

                                                                Markov chains

                                                   Leontief

                                   Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
                Mathematical Online Placement Exam     Current syllabus
         The ALM in Mathematics for Teaching Program   Examples


Syllabus for Math 20, Spring 2007
Applications



                                                              Stationary points
                Linear programming

                                                                            Lag mult
                                               Optimization
         Game theory
                                                Problems
                                                                          Least squares
               Assignment problem

                                                                Markov chains

                                                   Leontief

                                   Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Current syllabus
       The ALM in Mathematics for Teaching Program   Examples


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Current syllabus
     The ALM in Mathematics for Teaching Program   Examples


Some fun problems you can solve


      (Economics) which is better: sales tax or income tax?
      (Linear programming) can you eat a healthy meal at
      McDonald’s?
      (Assignment problem) Match teaching fellows to time slots
      to maximize TF satisfaction
      (Game theory) What percentage of the time should you say
      “Merry Christmas” versus “Happy Holidays” to strangers?
      (Markov chains) Will Detroit become an annular city?




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
             Mathematical Online Placement Exam     Current syllabus
      The ALM in Mathematics for Teaching Program   Examples


A closed Leontief input-output system


   Problem from Fall 2006 Final
   Consider an island with a four-person economy:
       Gilligan (agriculture) produces coconuts, palm fronds, and
       bamboo poles by collecting them.
       The Professor (manufacturing) produces shelter and
       equipment by consuming raw materials and with the help
       of the Skipper.
       Mary Ann (service) takes coconuts and bakes delicious
       coconut cream pies, upon which the entire island subsists.
       The Skipper (labor) helps the professor with his projects.


                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
           Mathematical Online Placement Exam     Current syllabus
    The ALM in Mathematics for Teaching Program   Examples




Problem continued
The distribution of products works like this:
    Three-fourths of Gilligan’s raw materials go to the
    Professor for his creations and the rest go to Maryann for
    her pies.
    Gilligan and the Skipper each use a sixth of the Professor’s
    inventions. Mary Ann and the Professor himself use a third
    apiece.
    Everyone shares Mary Ann’s pies equally.
    All of the Skipper’s labor goes to the Professor.
Find the equilibrium prices each should charge for their
products.


                              Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Current syllabus
       The ALM in Mathematics for Teaching Program   Examples


Solution


   Find a solution to Ap = p, where

                    Gilligan Professor Mary Ann Skipper
                                                       
          Gilligan       0       1/6       1/4      0
     A = Professor  3/4         1/3       1/4      0
                                                      
         Mary Ann  1/4          1/3       1/4      0
          Skipper        0       1/6       1/4      1




                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Current syllabus
       The ALM in Mathematics for Teaching Program   Examples


Solution


   Find a solution to Ap = p, where

                    Gilligan Professor Mary Ann Skipper
                                                       
          Gilligan       0       1/6       1/4      0
     A = Professor  3/4         1/3       1/4      0
                                                      
         Mary Ann  1/4          1/3       1/4      0
          Skipper        0       1/6       1/4      1
                                   T
   p = 1 3.3 1.8 1                     works.




                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
             Mathematical Online Placement Exam     Current syllabus
      The ALM in Mathematics for Teaching Program   Examples


Results so far




     Very happy students
     Very high scores
     Possible book in the
     works someday




                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Implementation
       The ALM in Mathematics for Teaching Program   Lessons learned


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Status Quo




    Pencil-and-paper exam
    given on first day of
    Freshman week
    Grade Report is three
    numbers and a course
    code: Math Xa, 1a, 1b,
    or 21a




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa
     AP Calculus BC: 5




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa
     AP Calculus BC: 5
     Recommendation:
     Math 21a




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa
     AP Calculus BC: 5
     Recommendation:
     Math 21a
     Could be same person!




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa
     AP Calculus BC: 5
     Recommendation:
     Math 21a
     Could be same person!




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Example placement information


     HMPT1: 19 HMPT2: 10
     HMPT3: 6
     Recommendation:
     Math Xa
     AP Calculus BC: 5
     Recommendation:
     Math 21a
     Could be same person!




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Disadvantages of Status Quo




      Students descend upon advisors to interpret these
      numbers and give further guidance
      Somewhat unnecessarily intimidating and impersonal
      HMPT was designed in an era when high school student
      exposure to calculus was limited




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Mathematical Online Placement Exam (MOPE)




     Funded by Innovation Grant from the Provost’s Fund for
     Instructional Technology
     Goals
             Give entering students more personal, more detailed
             information for choosing a math course
             Form part of a student-friendly web presence




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Implementation
       The ALM in Mathematics for Teaching Program   Lessons learned


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Features



     Question database organized by mathematical topic and
     type of question
     A multitude of tests for qualification or mastery
     Can be taken any time
     Topic-specific feedback, with granularity
     Retakes after refreshing are allowed




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20              History
              Mathematical Online Placement Exam                Implementation
       The ALM in Mathematics for Teaching Program              Lessons learned


Portion of MOPE’s topic tree
                                                                                                            composing
                                                                                                                                  evaluating


                                                                                                        inverse trig

                                                         evaluting trig fns (radians)
                                                                                                                   arc length; sector a
           simplifying
                                            evaluting trig fns (degrees)                and circles
                                                                                                           radian measure
     sin2 + cos2 = 1

                          trig identities
                                                                                                 sign and range of trig fns
     angle-addition


                                                       Trigonometry
        double-angle


                 law of sines
                                       and triangles                                                                   sinusoidal
                law of cosines
                                                                                               graphs

                                                      trig fns from right triangles                                     tan/cot


                                    Matthew Leingang            Gilligan, MOPE, and TiVo
Math 20                       History
            Mathematical Online Placement Exam                         Implementation
     The ALM in Mathematics for Teaching Program                       Lessons learned


Screenshot of sample question
                                                                                                          https://mope.dce.harvard.edu:10000/authentication/index


                                                                                                           MATH PLACEMENT TEST
                                                   TECHNICAL REQUIREMENTS                   FAQ
                                                   NAVIGATION HELP                          LOGOUT



                                          TIME REMAINING: 43:56

        QUESTION 9                                                          22 questions left to answer         SELECT YOUR ANSWER


          Ø Á5˜       Ø Á 2˜
                      ø                                         Øø  Ø
       If v = Ë ¯ and w = Ë ¯, what is the length of the vector v - w ?
              ˯          ˯
              ˯          ˯
              È1˘         È-3˘


           A. 2

           B. 5


           C. 3                                                                                                    TEST NAVIGATION


             0    0
                                                                                                                CLEAR YOUR ANSWER
                                                                                                                NEXT QUESTION
           D. 26 - 13                                                                                           PREVIOUS QUESTION
                                                                                                                NEXT BLANK
           E. 7                                                                                                 FIRST QUESTION

                                                                                                                GO TO QUESTION
                                                                                                                10


                                                                                                                SUBMIT YOUR ANSWERS
                                                                                                                and end the test




      Answers: 0=>1 1=>4 2=>0 3=>2 4=>3
      Correct answer: 1
      Question index: 779
                                          Matthew Leingang             Gilligan, MOPE, and TiVo
      Question topic: 308
Math 20                                                                     History
              Mathematical Online Placement Exam                                                                       Implementation
       The ALM in Mathematics for Teaching Program                                                                     Lessons learned


Screenshot of sample question
                                                                                                                              https://mope.dce.harvard.edu:10000/authentication/index.php?school=fas


                                                                                                                                 MATH PLACEMENT TEST
                                                              TECHNICAL REQUIREMENTS                             FAQ
                                                              NAVIGATION HELP                                    LOGOUT




     Your Receipt

            Last Name: Strozek

           First Name: Lukasz

      Email address: strozek@fas.harvard.edu

            Test taken: Math-21a mastery

            Test score: Your score is 7 out of 30
            Placement: Placement not issued (test incomplete)

     You can take the test again in 1 hours. In the meanhile you may want to review: Analytic geometry, Vectors and planes, Parametrization and vector fields, Optimization and extrema, Directional
     derivatives, Double integrals, Differentiating functions of several variables, Gradients in the plane, Gradient and path-independent fields, Line integrals, and Applications of multiple integrals.



          PRINT
          CONTINUE
     Results of this pilot version of the Online Placement Examination provide only one of several pieces of information to help you with course selection. The Mathematics Department is always eager
     to meet you, to talk over your individual experience and goals, and to help formulate a plan that works for you. Please bring your scores on this and other tests (the pencil-and-paper placement
     examination, SAT, AP, etc.) to any of the times and places specifically listed when advisors will be waiting to speak with you.
     Anyone considering courses like Math 23 or Math 25 should especially plan on consulting with Professor Taubes during his office hours.



     Aug 22 2005 21:22:30                                                                        #58791-60547-10506-01628




                                                                   Matthew Leingang                                    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Screenshot of sample question



    You can take the test again in 1 hour. In the meanwhile
    you may want to review: Analytic geometry, Vectors and
    planes, Parametrization and vector fields, Optimization
    and extrema, Directional derivatives, Double integrals,
    Differentiating functions of several variables, Gradients
    in the plane, Gradient and path-independent fields, Line
    integrals, and Applications of multiple integrals.




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
             Mathematical Online Placement Exam     Implementation
      The ALM in Mathematics for Teaching Program   Lessons learned


“Result”



                   Average of Math 1a First Midterm
                               HMPT1 HMPT1
                                                    all
                                        passed
                               failed
                 MOPE failed     73.00    78.67 75.43
                MOPE passed      89.50      N/A 89.50
                          all    78.50    78.67 78.56




                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Implementation
       The ALM in Mathematics for Teaching Program   Lessons learned


“Result”



                    Average of Math 1a First Midterm
                                HMPT1 HMPT1
                                                     all
                                         passed
                                failed
                  MOPE failed     73.00    78.67 75.43
                 MOPE passed      89.50      N/A 89.50
                           all    78.50    78.67 78.56

   Unfortunately, N = 2 here




                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
              Mathematical Online Placement Exam     Implementation
       The ALM in Mathematics for Teaching Program   Lessons learned


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Math on the Web




     Very challenging problem!
     Originally we converted TEX to MathML
     Later went to images (no MathML support)




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20   History
            Mathematical Online Placement Exam     Implementation
     The ALM in Mathematics for Teaching Program   Lessons learned


Chicken-and-egg problem




     can’t be more widely adopted without greater credibility
     can’t be more credible without better calibration
     can’t be calibrated without more data
     can’t get more data without more people taking it
     can’t get more to take it without being more widely adopted




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                     History
              Mathematical Online Placement Exam
                                                     Example: Bayesian Decision Making
       The ALM in Mathematics for Teaching Program


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                   History
            Mathematical Online Placement Exam
                                                   Example: Bayesian Decision Making
     The ALM in Mathematics for Teaching Program


Background of the ALM program


    Goal: better K-12
    teachers in BPS and
    area
    Started in 2001 by
    D. Goroff and P. Sally
    Degree program since
    2003
    35 participants and
    soon to graduate first
    Master’s class



                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                   History
            Mathematical Online Placement Exam
                                                   Example: Bayesian Decision Making
     The ALM in Mathematics for Teaching Program


Objectives of the ALM program




      Teach teachers the mathematics behind the rules, e.g.:
             0.9999.... = 1
             Division by zero is undefined
      Give resources to challenge their students
      Demonstrate fun math learning activities




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                     History
              Mathematical Online Placement Exam
                                                     Example: Bayesian Decision Making
       The ALM in Mathematics for Teaching Program


Outline

       Math 20
   1
         History
         Current syllabus
         Examples

       Mathematical Online Placement Exam
   2
         History
         Implementation
         Lessons learned

       The ALM in Mathematics for Teaching Program
   3
         History
         Example: Bayesian Decision Making


                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


Bayes’s Theorem

 Theorem (Bayes)
 Let Ω be a probability space
 with probability measure P.
 If A and B are events, then
                  P(A | B)P(B)
  P(B | A) =
                     P(A)

  Proof.
                 P(B | A)P(A) = P(A ∩ B) = P(A | B)P(B)




                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                     History
              Mathematical Online Placement Exam
                                                     Example: Bayesian Decision Making
       The ALM in Mathematics for Teaching Program


Bayes and partitions



   If Ω = H1 ∪ H2 ∪ . . . ∪ Hn is a partition, and E is any event, then

                           P(E | Hi )P(Hi )
         P(Hi | E) =
                               P(E)
                                       P(E | Hi )P(Hi )
                         =
                           P(E | H1 )P(H1 ) + · · · + P(E | Hn )P(Hn )




                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                     History
              Mathematical Online Placement Exam
                                                     Example: Bayesian Decision Making
       The ALM in Mathematics for Teaching Program


Bayes and partitions



   If Ω = H1 ∪ H2 ∪ . . . ∪ Hn is a partition, and E is any event, then

                           P(E | Hi )P(Hi )
         P(Hi | E) =
                               P(E)
                                       P(E | Hi )P(Hi )
                         =
                           P(E | H1 )P(H1 ) + · · · + P(E | Hn )P(Hn )

   If P(E) and P(E | Hj ) can be estimated, then so can P(Hi | E).




                                 Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                   History
            Mathematical Online Placement Exam
                                                   Example: Bayesian Decision Making
     The ALM in Mathematics for Teaching Program


Observations and Observables



     Suppose O ⊂ Ω is a “representative” sample:
     P(E | O) ≈ P(E) for all events E.
     Suppose we know what P(Hj | O) are.
     Suppose also we have sets {Cα } and we know
     P(Hj | Cα ∩ O), too.
     Given a a “new” ω ∈ Ω  O, if we can find its observables
     {Cαi }, what is the likelihood of ω being in any particular
     state?




                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                      History
              Mathematical Online Placement Exam
                                                      Example: Bayesian Decision Making
       The ALM in Mathematics for Teaching Program


Don’t look at this all at once

           P(Hi | Cα1 ∩ Cα2 ∩ . . . ∩ Cαm )


                              P(Cα1 ∩ Cα2 ∩ . . . ∩ Cαm | Hi )P(Hi )
                    =       n
                            k =1 P(Cα1 ∩ Cα2 ∩ . . . ∩ Cαm | Hk )P(Hk )
                                  m
                                  j=1 P(Cαj | Hi ) P(Hi )
                     !
                    ≈
                            n           m
                                                       | Hk ) P(Hk )
                                        j=1 P(Cαj
                            k =1



                                    m
                                                     | Hi ∩ O) P(Hi | O)
                                    j=1 P(Cαj
                    ≈
                            n           m
                                                       | Hk ∩ O) P(Hk | O)
                                        j=1 P(Cαj
                            k =1

   But everything at this stage is known.
                                 Matthew Leingang     Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


Which brings us to TiVo


     Ω is the set of all programs on
     television
     States Hj are your attitudes
     toward programs
     Observables {Cα } are
     metadata about the programs
     O is the set of shows you
     have marked with thumbs
     up/thumbs down.



                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                   History
            Mathematical Online Placement Exam
                                                   Example: Bayesian Decision Making
     The ALM in Mathematics for Teaching Program


Preference Data from Math E-304 on March 6, 2006
   Title                                   Like         Dislike          Neutral         Total
   King of Queens                            4             5                7             16
   How I Met your Mother                     5             0                11            16
   2 and a half Men                          3             3                10            16
   Courting Alex                             1             0                15            16
   CSI: Miami                                4             2                10            16
   Wife Swap                                 3             3                10            16
   Supernanny                                3             4                9             16
   Miracle Worker                            0             0                16            16
   Deal or no Deal                           4             3                9             16
   Apprentice                                6             4                6             16
   Medium                                    3             1                12            16
   24                                        5             1                10            16
   Total                                    41            26               125           192
   Prob(each preference)                  21.35%        13.54%           65.10%        100.00%
                               Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


Probability of class attitudes for each show (P(Hk | O))
    Title                                   P(like)      P(dislike)            P(neutral)     Total
    King of Queens                         25.00%         31.25%                43.75%      100.00%
    How I Met your Mother                  31.25%          0.00%                68.75%      100.00%
    2 and a half Men                       18.75%         18.75%                62.50%      100.00%
    Courting Alex                           6.25%          0.00%                93.75%      100.00%
    CSI: Miami                             25.00%         12.50%                62.50%      100.00%
    Wife Swap                              18.75%         18.75%                62.50%      100.00%
    Supernanny                             18.75%         25.00%                56.25%      100.00%
    Miracle Worker                          0.00%          0.00%               100.00%      100.00%
    Deal or no Deal                        25.00%         18.75%                56.25%      100.00%
    Apprentice                             37.50%         25.00%                37.50%      100.00%
    Medium                                 18.75%          6.25%                75.00%      100.00%
    24                                     31.25%          6.25%                62.50%      100.00%
    Prob(each attitude)                    21.35%         13.54%                65.10%      100.00%

                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


Frequency of attitude for each characteristic

    Characteristic          Like       Dislike      Neutral         Total
    Drama                    12           4           32             48
    Comedy                   13           8           43             64
    Reality                  12          11           41             64
    Game Show                 4           3            9             16
    Male Lead                22          16           42             80
    Female Lead              22          16           42             80
    Ensemble                  9           2           21             32
    TV-PG                    26          18           84            128
    TV-14                    15           8           41             64
    Totals                  135          86          355            576



                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


Conditional probability of each characteristic, given
attitude and observed (P(Cα | Hk ∩ O))

    Characteristic             Like             Dislike         Neutral             Total
    Drama                     8.89%              4.65%           9.01%             8.33%
    Comedy                    9.63%              9.30%          12.11%            11.11%
    Reality                   8.89%             12.79%          11.55%            11.11%
    Game Show                 2.96%              3.49%           2.54%             2.78%
    Male Lead                16.30%             18.60%          11.83%             13.89%
    Female Lead              16.30%             18.60%          11.83%             13.89%
    Ensemble                  6.67%              2.33%           5.92%             5.56%
    TV-PG                    19.26%             20.93%          23.66%             22.22%
    TV-14                    11.11%              9.30%          11.55%             11.11%
    Totals                  100.00%            100.00%         100.00%            100.00%


                                Matthew Leingang    Gilligan, MOPE, and TiVo
Math 20
                                                    History
             Mathematical Online Placement Exam
                                                    Example: Bayesian Decision Making
      The ALM in Mathematics for Teaching Program


(Posterior) probability of class attitudes for shows
airing March 7, 2006
    Title                           P(Like)         P(Dislike)         P(Neutral)         Total
    NCIS                            23.98%            9.87%             66.14%          100.00%
    The Unit                        22.24%            2.80%             74.96%          100.00%
    Amazing Race                    14.58%           14.46%             70.96%          100.00%
    According to Jim                19.30%           14.67%             66.03%          100.00%
    Sons & Daughters                18.47%            4.29%             77.24%          100.00%
    Boston Legal                    25.33%            2.45%             72.22%          100.00%
    Joey                            19.30%           14.67%             66.03%          100.00%
    Scrubs                          21.20%            3.79%             75.00%          100.00%
    Law & Order: SVU                25.33%            2.45%             72.22%          100.00%
    American Idol                   20.69%            6.59%             72.72%          100.00%
    House                           27.40%            8.69%             63.92%          100.00%

                                Matthew Leingang    Gilligan, MOPE, and TiVo

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  • 5. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Math 20: Introduction to linear algebra and multivariable calculus Taught since 2004 Original idea: stick to the title Almost no applications originally Matthew Leingang Gilligan, MOPE, and TiVo
  • 6. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 7. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Syllabus for Math 20, Spring 2007 Foundational material Systems of linear equations Algebra Gauss elim Inversion Dot product Vector Matrix Determinants Eigenstuff Function Quad approx Partial derivative Lin approx Differentials Matthew Leingang Gilligan, MOPE, and TiVo
  • 8. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Syllabus for Math 20, Spring 2007 Applications Stationary points Linear programming Lag mult Optimization Game theory Problems Least squares Assignment problem Markov chains Leontief Matthew Leingang Gilligan, MOPE, and TiVo
  • 9. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Syllabus for Math 20, Spring 2007 Applications Stationary points Linear programming Lag mult Optimization Game theory Problems Least squares Assignment problem Markov chains Leontief Matthew Leingang Gilligan, MOPE, and TiVo
  • 10. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 11. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Some fun problems you can solve (Economics) which is better: sales tax or income tax? (Linear programming) can you eat a healthy meal at McDonald’s? (Assignment problem) Match teaching fellows to time slots to maximize TF satisfaction (Game theory) What percentage of the time should you say “Merry Christmas” versus “Happy Holidays” to strangers? (Markov chains) Will Detroit become an annular city? Matthew Leingang Gilligan, MOPE, and TiVo
  • 12. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples A closed Leontief input-output system Problem from Fall 2006 Final Consider an island with a four-person economy: Gilligan (agriculture) produces coconuts, palm fronds, and bamboo poles by collecting them. The Professor (manufacturing) produces shelter and equipment by consuming raw materials and with the help of the Skipper. Mary Ann (service) takes coconuts and bakes delicious coconut cream pies, upon which the entire island subsists. The Skipper (labor) helps the professor with his projects. Matthew Leingang Gilligan, MOPE, and TiVo
  • 13. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Problem continued The distribution of products works like this: Three-fourths of Gilligan’s raw materials go to the Professor for his creations and the rest go to Maryann for her pies. Gilligan and the Skipper each use a sixth of the Professor’s inventions. Mary Ann and the Professor himself use a third apiece. Everyone shares Mary Ann’s pies equally. All of the Skipper’s labor goes to the Professor. Find the equilibrium prices each should charge for their products. Matthew Leingang Gilligan, MOPE, and TiVo
  • 14. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Solution Find a solution to Ap = p, where  Gilligan Professor Mary Ann Skipper  Gilligan 0 1/6 1/4 0 A = Professor  3/4 1/3 1/4 0   Mary Ann  1/4 1/3 1/4 0 Skipper 0 1/6 1/4 1 Matthew Leingang Gilligan, MOPE, and TiVo
  • 15. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Solution Find a solution to Ap = p, where  Gilligan Professor Mary Ann Skipper  Gilligan 0 1/6 1/4 0 A = Professor  3/4 1/3 1/4 0   Mary Ann  1/4 1/3 1/4 0 Skipper 0 1/6 1/4 1 T p = 1 3.3 1.8 1 works. Matthew Leingang Gilligan, MOPE, and TiVo
  • 16. Math 20 History Mathematical Online Placement Exam Current syllabus The ALM in Mathematics for Teaching Program Examples Results so far Very happy students Very high scores Possible book in the works someday Matthew Leingang Gilligan, MOPE, and TiVo
  • 17. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 18. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Status Quo Pencil-and-paper exam given on first day of Freshman week Grade Report is three numbers and a course code: Math Xa, 1a, 1b, or 21a Matthew Leingang Gilligan, MOPE, and TiVo
  • 19. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Matthew Leingang Gilligan, MOPE, and TiVo
  • 20. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa Matthew Leingang Gilligan, MOPE, and TiVo
  • 21. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa AP Calculus BC: 5 Matthew Leingang Gilligan, MOPE, and TiVo
  • 22. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa AP Calculus BC: 5 Recommendation: Math 21a Matthew Leingang Gilligan, MOPE, and TiVo
  • 23. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa AP Calculus BC: 5 Recommendation: Math 21a Could be same person! Matthew Leingang Gilligan, MOPE, and TiVo
  • 24. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa AP Calculus BC: 5 Recommendation: Math 21a Could be same person! Matthew Leingang Gilligan, MOPE, and TiVo
  • 25. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Example placement information HMPT1: 19 HMPT2: 10 HMPT3: 6 Recommendation: Math Xa AP Calculus BC: 5 Recommendation: Math 21a Could be same person! Matthew Leingang Gilligan, MOPE, and TiVo
  • 26. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Disadvantages of Status Quo Students descend upon advisors to interpret these numbers and give further guidance Somewhat unnecessarily intimidating and impersonal HMPT was designed in an era when high school student exposure to calculus was limited Matthew Leingang Gilligan, MOPE, and TiVo
  • 27. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Mathematical Online Placement Exam (MOPE) Funded by Innovation Grant from the Provost’s Fund for Instructional Technology Goals Give entering students more personal, more detailed information for choosing a math course Form part of a student-friendly web presence Matthew Leingang Gilligan, MOPE, and TiVo
  • 28. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 29. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Features Question database organized by mathematical topic and type of question A multitude of tests for qualification or mastery Can be taken any time Topic-specific feedback, with granularity Retakes after refreshing are allowed Matthew Leingang Gilligan, MOPE, and TiVo
  • 30. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Portion of MOPE’s topic tree composing evaluating inverse trig evaluting trig fns (radians) arc length; sector a simplifying evaluting trig fns (degrees) and circles radian measure sin2 + cos2 = 1 trig identities sign and range of trig fns angle-addition Trigonometry double-angle law of sines and triangles sinusoidal law of cosines graphs trig fns from right triangles tan/cot Matthew Leingang Gilligan, MOPE, and TiVo
  • 31. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Screenshot of sample question https://mope.dce.harvard.edu:10000/authentication/index MATH PLACEMENT TEST TECHNICAL REQUIREMENTS FAQ NAVIGATION HELP LOGOUT TIME REMAINING: 43:56 QUESTION 9 22 questions left to answer SELECT YOUR ANSWER Ø Á5˜ Ø Á 2˜ ø Øø Ø If v = Ë ¯ and w = Ë ¯, what is the length of the vector v - w ? ˯ ˯ ˯ ˯ È1˘ È-3˘ A. 2 B. 5 C. 3 TEST NAVIGATION 0 0 CLEAR YOUR ANSWER NEXT QUESTION D. 26 - 13 PREVIOUS QUESTION NEXT BLANK E. 7 FIRST QUESTION GO TO QUESTION 10 SUBMIT YOUR ANSWERS and end the test Answers: 0=>1 1=>4 2=>0 3=>2 4=>3 Correct answer: 1 Question index: 779 Matthew Leingang Gilligan, MOPE, and TiVo Question topic: 308
  • 32. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Screenshot of sample question https://mope.dce.harvard.edu:10000/authentication/index.php?school=fas MATH PLACEMENT TEST TECHNICAL REQUIREMENTS FAQ NAVIGATION HELP LOGOUT Your Receipt Last Name: Strozek First Name: Lukasz Email address: strozek@fas.harvard.edu Test taken: Math-21a mastery Test score: Your score is 7 out of 30 Placement: Placement not issued (test incomplete) You can take the test again in 1 hours. In the meanhile you may want to review: Analytic geometry, Vectors and planes, Parametrization and vector fields, Optimization and extrema, Directional derivatives, Double integrals, Differentiating functions of several variables, Gradients in the plane, Gradient and path-independent fields, Line integrals, and Applications of multiple integrals. PRINT CONTINUE Results of this pilot version of the Online Placement Examination provide only one of several pieces of information to help you with course selection. The Mathematics Department is always eager to meet you, to talk over your individual experience and goals, and to help formulate a plan that works for you. Please bring your scores on this and other tests (the pencil-and-paper placement examination, SAT, AP, etc.) to any of the times and places specifically listed when advisors will be waiting to speak with you. Anyone considering courses like Math 23 or Math 25 should especially plan on consulting with Professor Taubes during his office hours. Aug 22 2005 21:22:30 #58791-60547-10506-01628 Matthew Leingang Gilligan, MOPE, and TiVo
  • 33. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Screenshot of sample question You can take the test again in 1 hour. In the meanwhile you may want to review: Analytic geometry, Vectors and planes, Parametrization and vector fields, Optimization and extrema, Directional derivatives, Double integrals, Differentiating functions of several variables, Gradients in the plane, Gradient and path-independent fields, Line integrals, and Applications of multiple integrals. Matthew Leingang Gilligan, MOPE, and TiVo
  • 34. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned “Result” Average of Math 1a First Midterm HMPT1 HMPT1 all passed failed MOPE failed 73.00 78.67 75.43 MOPE passed 89.50 N/A 89.50 all 78.50 78.67 78.56 Matthew Leingang Gilligan, MOPE, and TiVo
  • 35. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned “Result” Average of Math 1a First Midterm HMPT1 HMPT1 all passed failed MOPE failed 73.00 78.67 75.43 MOPE passed 89.50 N/A 89.50 all 78.50 78.67 78.56 Unfortunately, N = 2 here Matthew Leingang Gilligan, MOPE, and TiVo
  • 36. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 37. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Math on the Web Very challenging problem! Originally we converted TEX to MathML Later went to images (no MathML support) Matthew Leingang Gilligan, MOPE, and TiVo
  • 38. Math 20 History Mathematical Online Placement Exam Implementation The ALM in Mathematics for Teaching Program Lessons learned Chicken-and-egg problem can’t be more widely adopted without greater credibility can’t be more credible without better calibration can’t be calibrated without more data can’t get more data without more people taking it can’t get more to take it without being more widely adopted Matthew Leingang Gilligan, MOPE, and TiVo
  • 39. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 40. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Background of the ALM program Goal: better K-12 teachers in BPS and area Started in 2001 by D. Goroff and P. Sally Degree program since 2003 35 participants and soon to graduate first Master’s class Matthew Leingang Gilligan, MOPE, and TiVo
  • 41. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Objectives of the ALM program Teach teachers the mathematics behind the rules, e.g.: 0.9999.... = 1 Division by zero is undefined Give resources to challenge their students Demonstrate fun math learning activities Matthew Leingang Gilligan, MOPE, and TiVo
  • 42. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Outline Math 20 1 History Current syllabus Examples Mathematical Online Placement Exam 2 History Implementation Lessons learned The ALM in Mathematics for Teaching Program 3 History Example: Bayesian Decision Making Matthew Leingang Gilligan, MOPE, and TiVo
  • 43. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Bayes’s Theorem Theorem (Bayes) Let Ω be a probability space with probability measure P. If A and B are events, then P(A | B)P(B) P(B | A) = P(A) Proof. P(B | A)P(A) = P(A ∩ B) = P(A | B)P(B) Matthew Leingang Gilligan, MOPE, and TiVo
  • 44. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Bayes and partitions If Ω = H1 ∪ H2 ∪ . . . ∪ Hn is a partition, and E is any event, then P(E | Hi )P(Hi ) P(Hi | E) = P(E) P(E | Hi )P(Hi ) = P(E | H1 )P(H1 ) + · · · + P(E | Hn )P(Hn ) Matthew Leingang Gilligan, MOPE, and TiVo
  • 45. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Bayes and partitions If Ω = H1 ∪ H2 ∪ . . . ∪ Hn is a partition, and E is any event, then P(E | Hi )P(Hi ) P(Hi | E) = P(E) P(E | Hi )P(Hi ) = P(E | H1 )P(H1 ) + · · · + P(E | Hn )P(Hn ) If P(E) and P(E | Hj ) can be estimated, then so can P(Hi | E). Matthew Leingang Gilligan, MOPE, and TiVo
  • 46. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Observations and Observables Suppose O ⊂ Ω is a “representative” sample: P(E | O) ≈ P(E) for all events E. Suppose we know what P(Hj | O) are. Suppose also we have sets {Cα } and we know P(Hj | Cα ∩ O), too. Given a a “new” ω ∈ Ω O, if we can find its observables {Cαi }, what is the likelihood of ω being in any particular state? Matthew Leingang Gilligan, MOPE, and TiVo
  • 47. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Don’t look at this all at once P(Hi | Cα1 ∩ Cα2 ∩ . . . ∩ Cαm ) P(Cα1 ∩ Cα2 ∩ . . . ∩ Cαm | Hi )P(Hi ) = n k =1 P(Cα1 ∩ Cα2 ∩ . . . ∩ Cαm | Hk )P(Hk ) m j=1 P(Cαj | Hi ) P(Hi ) ! ≈ n m | Hk ) P(Hk ) j=1 P(Cαj k =1 m | Hi ∩ O) P(Hi | O) j=1 P(Cαj ≈ n m | Hk ∩ O) P(Hk | O) j=1 P(Cαj k =1 But everything at this stage is known. Matthew Leingang Gilligan, MOPE, and TiVo
  • 48. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Which brings us to TiVo Ω is the set of all programs on television States Hj are your attitudes toward programs Observables {Cα } are metadata about the programs O is the set of shows you have marked with thumbs up/thumbs down. Matthew Leingang Gilligan, MOPE, and TiVo
  • 49. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Preference Data from Math E-304 on March 6, 2006 Title Like Dislike Neutral Total King of Queens 4 5 7 16 How I Met your Mother 5 0 11 16 2 and a half Men 3 3 10 16 Courting Alex 1 0 15 16 CSI: Miami 4 2 10 16 Wife Swap 3 3 10 16 Supernanny 3 4 9 16 Miracle Worker 0 0 16 16 Deal or no Deal 4 3 9 16 Apprentice 6 4 6 16 Medium 3 1 12 16 24 5 1 10 16 Total 41 26 125 192 Prob(each preference) 21.35% 13.54% 65.10% 100.00% Matthew Leingang Gilligan, MOPE, and TiVo
  • 50. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Probability of class attitudes for each show (P(Hk | O)) Title P(like) P(dislike) P(neutral) Total King of Queens 25.00% 31.25% 43.75% 100.00% How I Met your Mother 31.25% 0.00% 68.75% 100.00% 2 and a half Men 18.75% 18.75% 62.50% 100.00% Courting Alex 6.25% 0.00% 93.75% 100.00% CSI: Miami 25.00% 12.50% 62.50% 100.00% Wife Swap 18.75% 18.75% 62.50% 100.00% Supernanny 18.75% 25.00% 56.25% 100.00% Miracle Worker 0.00% 0.00% 100.00% 100.00% Deal or no Deal 25.00% 18.75% 56.25% 100.00% Apprentice 37.50% 25.00% 37.50% 100.00% Medium 18.75% 6.25% 75.00% 100.00% 24 31.25% 6.25% 62.50% 100.00% Prob(each attitude) 21.35% 13.54% 65.10% 100.00% Matthew Leingang Gilligan, MOPE, and TiVo
  • 51. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Frequency of attitude for each characteristic Characteristic Like Dislike Neutral Total Drama 12 4 32 48 Comedy 13 8 43 64 Reality 12 11 41 64 Game Show 4 3 9 16 Male Lead 22 16 42 80 Female Lead 22 16 42 80 Ensemble 9 2 21 32 TV-PG 26 18 84 128 TV-14 15 8 41 64 Totals 135 86 355 576 Matthew Leingang Gilligan, MOPE, and TiVo
  • 52. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program Conditional probability of each characteristic, given attitude and observed (P(Cα | Hk ∩ O)) Characteristic Like Dislike Neutral Total Drama 8.89% 4.65% 9.01% 8.33% Comedy 9.63% 9.30% 12.11% 11.11% Reality 8.89% 12.79% 11.55% 11.11% Game Show 2.96% 3.49% 2.54% 2.78% Male Lead 16.30% 18.60% 11.83% 13.89% Female Lead 16.30% 18.60% 11.83% 13.89% Ensemble 6.67% 2.33% 5.92% 5.56% TV-PG 19.26% 20.93% 23.66% 22.22% TV-14 11.11% 9.30% 11.55% 11.11% Totals 100.00% 100.00% 100.00% 100.00% Matthew Leingang Gilligan, MOPE, and TiVo
  • 53. Math 20 History Mathematical Online Placement Exam Example: Bayesian Decision Making The ALM in Mathematics for Teaching Program (Posterior) probability of class attitudes for shows airing March 7, 2006 Title P(Like) P(Dislike) P(Neutral) Total NCIS 23.98% 9.87% 66.14% 100.00% The Unit 22.24% 2.80% 74.96% 100.00% Amazing Race 14.58% 14.46% 70.96% 100.00% According to Jim 19.30% 14.67% 66.03% 100.00% Sons & Daughters 18.47% 4.29% 77.24% 100.00% Boston Legal 25.33% 2.45% 72.22% 100.00% Joey 19.30% 14.67% 66.03% 100.00% Scrubs 21.20% 3.79% 75.00% 100.00% Law & Order: SVU 25.33% 2.45% 72.22% 100.00% American Idol 20.69% 6.59% 72.72% 100.00% House 27.40% 8.69% 63.92% 100.00% Matthew Leingang Gilligan, MOPE, and TiVo