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Engaging Students in a Large Lecture: An Experiment using Sudoku Puzzles

Engaging Students in a Large Lecture: An Experiment using Sudoku Puzzles. Caroline Brophy National University of Ireland Maynooth. Collaborator. Lukas Hahn Ulm University, Germany, 2008-13 Erasmus student at NUI Maynooth , Ireland, 2010-11

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Engaging Students in a Large Lecture: An Experiment using Sudoku Puzzles

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  1. Engaging Students in a Large Lecture: An Experiment using Sudoku Puzzles Caroline Brophy National University of Ireland Maynooth

  2. Collaborator Lukas Hahn Ulm University, Germany, 2008-13 Erasmus student at NUI Maynooth, Ireland, 2010-11 Current: Masters of Mathematics program in Statistics at the University of Waterloo, Canada

  3. Teaching large groups • First year undergraduate classes • First Science at NUIM currently  450 • Range of previous statistical experience • Range of ability • Data in class • Textbook data sets • Record personal information on students • Hypothesis testing

  4. The Sudoku experiment 1

  5. The Sudoku experiment • 9x9 grids would take too long to complete • Mini 6x6 grids used instead

  6. The Sudoku experiment Greek Letters Numbers Letters Random symbols

  7. Handout for students • Instructions on Sudoku puzzles • One of the four Sudoku puzzles • Space for recording completion time • Additional question Have you ever played Sudoku before today? Yes  No 

  8. Logistics • First lecture of the course • Printed handouts interleaved • Stopwatch on screen • Explain that students will need to • Read instructions • Complete puzzle • Record the time it took to complete their puzzle • Answer the question at end • Maintain exam like conditions throughout

  9. Logistics contd. • Give out the handouts but instruct to keep facedown • Start the stopwatch and instruct all students to start at the same time • Collect the handouts when finished • Manually record the data after class

  10. The data Three categorical variables One quantitative variable, right censored

  11. The data Explanatory variables Response variables

  12. Teaching opportunities • Discussions on • types of data • hypotheses to address • ideas on how to analyse the data • Descriptive statistics • Chi-square test for independence • ANOVA • Logistic regression • Survival analysis

  13. Hypotheses • Do Sudoku type and experience affect ability to get the Sudoku correct? • Do Sudoku type and experience affect the length of time it takes to complete the Sudoku?

  14. First hypothesis • Do Sudoku type and experience affect ability to get the Sudoku correct?

  15. Sudoku type Chi-square test for independence: 2= 4.62 df=3 p=0.2

  16. Sudoku experience Chi-square test for independence: 2= 43.5 df=1 p<0.001

  17. Logistic regression Model probability of Sudoku being correct Explanatory variables Sudoku type Sudoku experience Interaction LRT=4.8, df=3, p=0.189 LRT=36.1, df=1, p<0.001 LRT=4, df=3, p=0.262

  18. First hypothesis Do Sudoku type and experience affect the probability of getting Sudoku correct? In summary: • No evidence of interaction between Sudoku type and experience • No evidence of Sudoku type effect • Sudoku experience has a strong effect • No experience: • With previous experience:

  19. Second hypothesis • Do Sudoku type and experience affect the length of time it takes to complete the Sudoku? • ANOVA analysis on the correct Sudoku only time to completion values (limited inference) • Survival analysis on all completion times • Details in paper

  20. Concluding remarks • Easy to implement with large groups • Can illustrate the testing of real hypotheses • Downsides • Manual recording • Analysis on the subset of correct Sudokus only has inferential limitations that might be misunderstood • Fun in-class activity and appears to help students in an introductory class to engage with Statistics

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