Gilligan, MOPE, and TiVo
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A survey of teaching activities from the past few years

A survey of teaching activities from the past few years

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    Gilligan, MOPE, and TiVo Gilligan, MOPE, and TiVo Presentation Transcript

    • 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