Dick Larson MIT Department of Civil and Environmental Engineering (CEE)

1. Emerging Opportunities for Operations Research in Educational ServicesHarold Larnder Memorial Lecture Dick Larson MIT Department of Civil and Environmental Engineering (CEE) Engineering Systems Division (ESD) And Operations Research Center May 17, 2004

2. Today we live in a knowledge age

3. The Most Valuable Natural Resources of a Country Lie Buried... http://www.mountain-environments.ndirect.co.uk/

4. Between the Ears of Its Citizens! • taken from: http://www.meritweb.com/spiritual/images/african_girl.jpg

5. What’s the Primary Technology For Teaching Today?

6. Chalk and blackboard... from: http://home.earthlink.net/~mariko-usa/graphics/chalk2.jpg

7. What’s the Difference Between the Blackboard and Cave Drawings? http://www.culture.fr/culture/arcnat/chauvet/en/gvpda-d.htm

8. The Eraser!! Eraser dust marks from: http://www.leednet.com/eraserdust/

9. And the dutiful student sits passively listening to her lecturer http://www.images.bham.ac.uk/im2011.jpg http://www.millennium.scps.k12.fl.us/bariosstudent.jpg

10. Tomorrow’s Learning, Yesterday’s Teaching Passive Listening Lectures Internet, PCs, Multimedia

11. But, -- with technology -- the delivery and pedagogy of education are changing rapidly

12. http://www.outreach.usf.edu/catalog/spring02/macgirls.jpg

13. Distance Learning Even Occurs on Campuses! http://www.outreach.usf.edu/catalog/spring02/macgirls.jpg

14. Education • Roughly 10% of the USA GDP • Under-represented as a contextual area for OR study • Opportunities in • (1) Operations of educational institutions, • (2) OR education per se and • (3) In design of new learning environments

15. There’s a time to reap and a time to sow In OR, there’s a time to frame, a time to formulate and a time to solve.

16. Operations of Educational Institutions

17. OR and Educational Operations • Library Effectiveness, Philip M. Morse, MIT Press, 1968. • Planning Models for Colleges and Universities, David S. P. Hopkins and William F. Massy, Stanford University Press, 1981. • Do you know what these books have in common? • They both won the Lanchester Prize!

18. What is a Services Industry Application of OR for Which We are Famous? SCHEDULING!

19. In Our Universities, we do this Typically in an Ad hoc, Locally Myopic, Sub-Optimal Manner

20. Need: “It has come to my attention that a couple of subjects have scheduled exams on the same night, which have created conflicts for many students. I want to remind you that I do keep track of out of classroom quizzes and exams and will do my best to inform instructors of potential conflicts, but it should be the responsibility of the instructor or teaching assistant to inquire whether or not there will be an exam scheduled for the same date and time that they do not want to conflict with. If you have any questions, please let me know.” Signed, MIT EECS Admin Person

21. Student Stress Minimizerwith Hernan Alperin, Ashok Sivakumar http://www.britishcouncil.or.th/graphics/330x226-edu-student-in-exam-room.jpg http://www.iesbarcelona.org/images/about/pics/03.jpg

22. Motivation • Cooperating faculty give “preferences” for quizzes and assignment dates • Algorithm • maximizes faculty preferences • limits number of quizzes student has in 1 day • uses mixed integer and linear programming

23. What This Means • An efficient solution to help students feel comfortable and less “stressed out” • A mechanism to show students that faculty and departments care for their welfare • An opportunity to use technology and operations research to benefit all of MIT

24. Freshman Course Registrations: Conflict Matrix

25. Switching Gears http://www.stockschatz.com/gears.jpg

26. OR Education http://www.dragontrove.com/LE426.jpg http://www.cs.sunysb.edu/~algorith/files/linear-programming-L.gif http://www.letsmakeadeal.com/images/70s-Doors.jpg

27. Mission INFORMS Transactions on Education (ITE) is a peer-reviewed electronic journal of INFORMS. The mission of ITE is to advance OR/MS education at all levels worldwide. ITE fulfills its mission by encouraging creation and facilitating dissemination of information, ideas, software, data sets, and other educational materials that are useful to OR/MS teachers.

28. January 2003 Issue: ARMACOST and LOWE Operations Research Capstone Course: A Project-Based Process of Discovery and Application RICHETTA Risk Economies of Scale in the Finance and Insurance Industries SNIEDOVICH OR/MS Games: 3. Counterfeit Coin Problem TARAS and GROSSMAN Stay or Switch: An Organizational Behavior and Management Science Joint Classroom Exercise

29. 1 Figure 1 1 With Sam Chiu of Stanford, we are Working on a Submission Bertrand’s Paradox

30. Designing New Learning Environments http://www.xmaonline.co.uk/Images/Dorrington.jpg http://web.mit.edu/newsoffice/tt/2001/dec12/teal.jpg

31. Real Virtual Labs: Web LabBring the Laboratory Experience to Those Without Labs Professor Jesus del Alamo, EECS Microelectronics WebLab An MIT I-Campus Project

33. Question: Are there Web Labs in OR?

34. Professor David Pritchard • CyberTutor is a software agent that monitors a student's progress on solving a problem http://cybertutor.mit.edu/ • It detects all mistakes immediately, arithmetic and conceptual • Can give hints or corrections • Can grade the final work

35. MIT’s PIVoT Project PhysicsInteractiveVideoTutor

36. MIT’s PIVoT Project PhysicsInteractiveVideoTutor

37. Another Pedagogy: On Line Video Tutors • Fundamental Hypothesis • PIVoT can increase the equivalent face-to-face contact between learner and mentor by an order of magnitude or more.

38. CECI: Center for Educational Computing Initiatives (Professor Steve Lerman, Director) • Doing research on the design of the learning environment from a software point of view • Steve Niemczyk: Completed doctoral dissertation creating a software agent for students to navigate through large nonlinear learning spaces. (PIVoT: Physics Interactive VideoTutor)

39. CECI, continued • Ralph Rabbat: • -developing a Bayesian assessment tool that tries to uncover students' individual learning styles; • -constructing multi-agent systems that use assessed learning styles to provide recommendations on what content from PIVoT would be most useful to a student • Bassam Chaptini: • working on discrete choice modelsto predict probabilities that students' will choose specific course options. His case study is in EECS, where he is looking at undergraduates' choices of courses.

40. Guided Learning Pathways • Idea: different students start a subject with different backgrounds, interests and learning styles. These facts should be reflected in the ‘design of the subject.” • Allow students to take different paths through learning space • Active vs. passive learning • Contextually relevant homework problems • All attempt to reach same end state • Directed graph, Markovian decisions

41. Simulating Physician Decision Making:Fungal Infections Grand Rounds Website Lincoln Chandler http://www.figrandrounds.org

42. Background • Website developed in 2001 as part of a joint venture between MIT and Pfizer • Site serves as a free educational resource for early clinicians as well as the general public • Current project focuses on the “Case Studies” component of the site

43. Uses of OR Tools and Methodology • Decision Analysis: • Provides a more apt decision tree case model, which alternates between the user’s choice of treatment and the patient’s (non-deterministic) response • Hidden Markov Models: • Provide a means to depict a patient status when pertinent information may not be available to the user • Data Mining Tools: • Provide a platform for tracking users’ decisions to find possible gaps in comprehension and/or available site material

44. End States Decision Node Choice Node Choice Arc Outcome Arc As the tree grows… • The number of potential states becomes exponentially large; how do we keep the problem tractable? • One proposed method: Node pruning, with appropriate pedagogical teaching feedback

45. Designing Student Teams, Local or Virtual http://www.haltonrc.edu.on.ca/news/may00/news5.jpg

46. Designing Student Teams • Can we do better than random? • Wide and diffuse literature without clear results • Issue of objective function: • Try to make teams 'compatible' according to some score • Try to 'cover' required attributes in each team

47. Student compatibilityKendell Timmers • Each pair of students is assigned a score with a value from zero (completely incompatible) to one (perfectly compatible). • The score is determined by the desirability of their differences in personality, demographics, motivation, and skills. Each of these categories are weighted depending on the importance of the category to the particular learning context.

48. Objective function • Primary goal: maximize the minimum compatibility score between any two students on a team. • Secondary goal: maximize the sum of the pairwise compatibilties.

49. Preliminary results • Initial tests show strong evidence that all methods beat random team generation, the method used by most large classrooms. • In tests with 36 students, the best random team out of 20 million was still only 80% as good as the best solution found with other heuristics • In tests of 100 students, the best random team out of 5 million was on average only 64% as good as the best solution found with other heuristics

50. Probabilistic Coverage Formulation • Applicable to teams in which content or thinking attributes are more important than personal one-to-one ‘compatibilities.’ • Assume that an M-digit binary vector of attributes characterizes each student. • For a particular student a binary 1 for attribute a indicates that the student ‘possesses attribute a’ and a binary 0 indicates that the student does not possess that attribute.