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

Clickers Mini Conference, 26 March 2010, University of Manchester

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Clickers 26th March 2010

Richard Jardine, Learning Technologist, EPS e Learning Team


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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


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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.


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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.


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Do you know anything about clickers?

1. No

2. A little

3. A lot

0%

0%

0%


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T_A

1 2 3 4

0% 0%0%0%

1. A

2. U

3. O

4. E


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S_A_I_T_C_

1 2 3 4

0% 0%0%0%

1. T O B T D

2. T S S I T

3. U L L D B

4. H Q O K N

To get to this session I came

1 2 3 4 5

0% 0% 0%0%0%

1. On foot

2. By bus

3. By car

4. By train

5. By Helicopter


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1

2

3

4

5 None, do not like chocolate

Which Chocolate would you choose?

1) Almond Delight

2) Almond Crunch

3) Caramel Crème

4) Orange Sensation

5) None


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How you add Clicker Slides

What is your favourite drink

1 2 3 4

0% 0%0%0%

1. Tea

2. Coffee

3. Beer

4. Water 150

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*


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What is your favourite subject

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%1. Chemistry

2. Physics

3. Mathematics

4. English

5. French

6. Art

7. Computer Science

8. Art History

9. Electronics

10. PE


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Lessons should finish by 4pm do you agree?

1. Yes

2. No


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Which textile do you like to wear

0%0%0%0%0%

Denim Cotton Wool Polyester Linen

1. Denim

2. Cotton

3. Wool

4. Polyester

5. Linen

10

*


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These lessons are fun?

1. Strongly Agree

2. Agree

3. Neutral

4. Disagree

5. Strongly Disagree

0of30


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What is most important to do in the morning

0%

0%

0%

0%

0%

0%1. Brush teeth

2. Have breakfast

3. Put Cat out

4. Read newspaper

5. Make cup of tea

6. Make cup of coffee


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Do you like Mathematics

Yes

to M

aths

No to

Mat

hs

0%0%

1. Yes to Maths

2. No to Maths


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Do you want more Physics?

Yes

to m

ore P

h...

No to

more

Phy.

..

0%0%

1. Yes to more Physics

2. No to more Physics


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Do you like Mathematics

50%

50%

50%

50%

No to Maths

Yes to Maths

Yes to more Physics No to more Physics

Of those that liked Maths all these light blue ones wanted more Physics


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Do you agree?

Yes N

o

Abst

ain

0% 0%0%

1. Yes

2. No

3. Abstain


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Do you agree 2?

Yes

2 N

o 2

Abst

ain 2

0% 0%0%

1. Yes 2

2. No 2

3. Abstain 2


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Do you agree 2?

33%

33%

33%

33%

33%

33%

33%

33%

33%

Abstain 2

No 2

Yes 2

Yes No Abstain


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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

http://en.wikipedia.org/wiki/Factorial

http://en.wikipedia.org/wiki/52-card_deck

http://en.wikipedia.org/wiki/Orders_of_magnitude_%28numbers%29#1045_to_10100


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1. How many times do you need to shuffle a pack of cards to make it

random1. 1

2. 2

3. 3

4. 4

5. 5

6. 6

7. 7

8. 8


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With the people close to you discuss why you gave the answer

that you did.


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Discuss what you know about Markov Chains or how your experience of card playing

influenced your answer


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2. How many times do you need to shuffle a pack of cards to make it

random1. 1

2. 2

3. 3

4. 4

5. 5

6. 6

7. 7

8. 8


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2. How many times do you need to shuffle a pack of cards to make it random

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

12%

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12%

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12%

12%

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12%

12%

8

7

6

5

4

3

2

1

1 2 3 4 5 6 7 8


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What does this mean?

First time around most voted 1 or 2

Second time around most voted 5 or 6


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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.

http://en.wikipedia.org/wiki/Persi_Diaconis

http://en.wikipedia.org/wiki/Dave_Bayer

http://en.wikipedia.org/wiki/Markov_chain_mixing_time


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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

http://en.wikipedia.org/wiki/Blackjack


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• 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

The Markov chain

http://en.wikipedia.org/wiki/Random_walk

http://en.wikipedia.org/wiki/Number_line


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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:


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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.


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Could you calculate is the expected percentage of the time the creature will

eat cheese over a long period?

1) Yes

2) No

3) Don’t know


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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:


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Could you calculate is the expected percentage of the time the creature will eat

cheese over a long period?

1) Yes

2) No

3) Don’t know


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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.


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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?


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Can you think of any advantages Clickers may have over raising hands in a lecture?

Any comments on Clickers?


42

What is your opinion we should have a break?

1 5

Strongly Agree Strongly Disagree

0%0%0%0%0%

1 2 3 4 5

1. Strongly Agree

2. Agree

3. Neutral

4. Disagree

5. Strongly Disagree

Documents

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