Clickers 26 th March 2010

1. Clickers 26th March 2010 Richard Jardine, Learning Technologist, EPS e Learning Team Clickers Mini Conference, 26 March 2010, University of Manchester

2. Developing a hierarchy of clicker use for teaching and learning from models of dialogue analysis • Michael O’Donoghue, School of Education, University of Manchester • Richard Jardine, Learning Technologist, Faculty of Engineering and Physical Sciences, University of Manchester Using Clickers with Physics Students • Marion Birch, School of Physics and Astronomy, University of Manchester Clickers Mini Conference, 26 March 2010, University of Manchester

3. Clicker Information You do not need to point the keypad at anything, just press your answer. You do not need to press Enter or YES, just your choice. Your answer will appear on the handset screen to indicate your answer has been registered. If you wish to change your mind, just press a different number. Only your last answer will be recorded for each question. Clickers Mini Conference, 26 March 2010, University of Manchester

4. Channel Setting Press and release the orange “Menu” button. (If asked if you wish to “leave presentation mode”, select the “YES” button). Use the orange Down button (also the YES button) to highlight “Change Channel” in the options. Press the Enter button Enter the 2 digit channel code “41” Press the Enter button The handset should display a confirmation message to confirm communication with the receiver. Clickers Mini Conference, 26 March 2010, University of Manchester

5. No A little A lot 0% 0% 0% Do you know anything about clickers? Clickers Mini Conference, 26 March 2010, University of Manchester

6. T_A • A • U • O • E Clickers Mini Conference, 26 March 2010, University of Manchester

7. S_A_I_T_C_ • T O B T D • T S S I T • U L L D B • H Q O K N Clickers Mini Conference, 26 March 2010, University of Manchester

8. To get to this session I came On foot By bus By car By train By Helicopter

9. Which Chocolate would you choose? 1 3 • Almond Delight • Almond Crunch • Caramel Crème • Orange Sensation • None 4 2 5 None, do not like chocolate Clickers Mini Conference, 26 March 2010, University of Manchester

10. How you add Clicker Slides Clickers Mini Conference, 26 March 2010, University of Manchester

11. 30 0 15 What is your favourite drink Tea Coffee Beer Water *

12. What is your favourite subject • Chemistry • Physics • Mathematics • English • French • Art • Computer Science • Art History • Electronics • PE Clickers Mini Conference, 26 March 2010, University of Manchester

13. Lessons should finish by 4pm do you agree? • Yes • No Clickers Mini Conference, 26 March 2010, University of Manchester

14. 10 Which textile do you like to wear • Denim • Cotton • Wool • Polyester • Linen * Clickers Mini Conference, 26 March 2010, University of Manchester

15. 0 of 30 These lessons are fun? • Strongly Agree • Agree • Neutral • Disagree • Strongly Disagree Clickers Mini Conference, 26 March 2010, University of Manchester

16. What is most important to do in the morning • Brush teeth • Have breakfast • Put Cat out • Read newspaper • Make cup of tea • Make cup of coffee Clickers Mini Conference, 26 March 2010, University of Manchester

17. Do you like Mathematics • Yes to Maths • No to Maths Clickers Mini Conference, 26 March 2010, University of Manchester

18. Do you want more Physics? • Yes to more Physics • No to more Physics Clickers Mini Conference, 26 March 2010, University of Manchester

19. Of those that liked Maths all these light blue ones wanted more Physics Clickers Mini Conference, 26 March 2010, University of Manchester

20. Do you agree? • Yes • No • Abstain Clickers Mini Conference, 26 March 2010, University of Manchester

21. Do you agree 2? • Yes 2 • No 2 • Abstain 2 Clickers Mini Conference, 26 March 2010, University of Manchester

22. Clickers Mini Conference, 26 March 2010, University of Manchester

23. There are exactly 52! (about 8 × 1067) possible ways to order the cards in a 52-card deck. The magnitude of this number means that it is exceedingly improbable that two randomly selected, truly randomized decks, will ever, in the history of cards, be the same. However, while the exact sequence of all cards in a randomized deck is unpredictable, it may be possible to make some probabilistic predictions about a deck that is not sufficiently randomized. http://en.wikipedia.org/wiki/Shuffle Clickers Mini Conference, 26 March 2010, University of Manchester

24. 1. How many times do you need to shuffle a pack of cards to make it random • 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 Clickers Mini Conference, 26 March 2010, University of Manchester

25. With the people close to you discuss why you gave the answer that you did. Clickers Mini Conference, 26 March 2010, University of Manchester

26. Discuss what you know about Markov Chains or how your experience of card playing influenced your answer Clickers Mini Conference, 26 March 2010, University of Manchester

27. 2. How many times do you need to shuffle a pack of cards to make it random • 1 • 2 • 3 • 4 • 5 • 6 • 7 • 8 Clickers Mini Conference, 26 March 2010, University of Manchester

28. Clickers Mini Conference, 26 March 2010, University of Manchester

29. What does this mean? First time around most voted 1 or 2 Second time around most voted 5 or 6 Clickers Mini Conference, 26 March 2010, University of Manchester

30. A famous paper by Diaconis, and Bayer, on the number of shuffles needed to randomize a deck, concluded that the deck did not start to become random until five good riffle shuffles, and was truly random after seven. In the precise sense of variation distance described in Markov chain mixing time; of course, you would need more shuffles if your shuffling technique is poor. Clickers Mini Conference, 26 March 2010, University of Manchester

31. Recently, the work of Trefethen et al. concluded that six shuffles are enough. The difference hinges on how each measured the randomness of the deck. The question of what measure is best for specific card games is still open. Diaconis released a response indicating that you only need four shuffles for un-suited games such as blackjack Clickers Mini Conference, 26 March 2010, University of Manchester

32. The Markov chain • An example of a Markov chain is a random walk on the number line which starts at zero and transitions +1 or −1 with equal probability at each step. The position reached in the next transitions only depends on the present position and not on the way this present position is reached. • http://en.wikipedia.org/wiki/Markov_chain Clickers Mini Conference, 26 March 2010, University of Manchester

33. Consider this creature Another example is the dietary habits of a creature who only eats grapes, cheese or lettuce, and whose dietary habits conform to the following (artificial) rules: Clickers Mini Conference, 26 March 2010, University of Manchester

34. Consider this creature • Another example is the dietary habits of a creature who only eats grapes, cheese or lettuce, and whose dietary habits conform to the following (artificial) rules: • It eats exactly once a day. If it ate cheese yesterday, it will eat lettuce or grapes today with equal probability for each, and zero chance of eating cheese. • If it ate grapes yesterday, it will eat grapes today with probability 1/10, cheese with probability 4/10 and lettuce with probability 5/10. • Finally, if it ate lettuce yesterday, it won't eat it again today, but will eat grapes with probability 4/10 or cheese with probability 6/10. Clickers Mini Conference, 26 March 2010, University of Manchester

35. Could you calculate is the expected percentage of the time the creature will eat cheese over a long period? • Yes • No • Don’t know Clickers Mini Conference, 26 March 2010, University of Manchester

36. Discuss with the person next to you why you gave the answer you gave? • The position reached in the next transitions only depends on the present position and not on the way this present position is reached. • Does what it ate 2 or 3 (or 4, etc...) days ago determine what it will eat the next day Consider the following: Clickers Mini Conference, 26 March 2010, University of Manchester

37. Could you calculate is the expected percentage of the time the creature will eat cheese over a long period? • Yes • No • Don’t know Clickers Mini Conference, 26 March 2010, University of Manchester

38. Yes • This creature's eating habits can be modeled with a Markov chain since its choice depends on what it ate yesterday, not additionally on what it ate 2 or 3 (or 4, etc...) days ago. One statistical property one could calculate is the expected percentage of the time the creature will eat cheese over a long period. Clickers Mini Conference, 26 March 2010, University of Manchester

39. Clickers Mini Conference, 26 March 2010, University of Manchester

40. Discussion on Clickers Are there any parts of your lectures where you think clickers could be used? How could they be used? Can you think of any questions that you could introduce with clickers and using Peer discussion help the students to deeper understanding? Clickers Mini Conference, 26 March 2010, University of Manchester

41. Can you think of any advantages Clickers may have over raising hands in a lecture? Any comments on Clickers? Clickers Mini Conference, 26 March 2010, University of Manchester

42. What is your opinion we should have a break? • Strongly Agree • Agree • Neutral • Disagree • Strongly Disagree Strongly Agree Strongly Disagree 1 5 Clickers Mini Conference, 26 March 2010, University of Manchester