The document discusses how the Open University uses Moodle quizzes and how they help students. It presents data from several modules that shows completing quizzes and interactive computer-marked assignments (iCMAs) correlates with higher exam scores and module completion rates. The university has also found that computers can accurately automatically grade questions beyond just multiple choice through advances in computational linguistics and pattern matching.

1. The Moodle Quiz at the Open University:
how we use it & how that helps students
Tim Hunt, Information Technology
MoodleMoot Dublin 2015

2. The Moodle Quiz at the Open University:
how we use it & how that helps students
Tim Hunt, Information Technology
MoodleMoot Dublin 2015
many dedicated
OU teaching staff

3. Alternate titles
Lots of interesting graphs
The scholarship of teaching and learning in practice

4. Overall use

5. It matters

6. End of module survey results
0%
20%
40%
60%
80%
100%
KPI 01: Overall, I am satisfied with the quality of this module.
Q33 Clear understanding of what was required to complete the assessed work
Q34 the assessment activities supported my learning

7. It makes a difference

8. Level 3 physics (SM358)

9. SM358 assessment tasks
0 TMAs 1 TMA 2 TMAs 3 TMAs 4 TMAs
0 iCMAs
1 iCMA
2 iCMAs
3 iCMAs
4 iCMAs
5 iCMAs
6 iCMAs

10. Students completing tasks
0 TMAs 1 TMA 2 TMAs 3 TMAs 4 TMAs
0 iCMAs 11.6% 3.4% 1.5% 0.5% 0.5%
1 iCMA 1.5% 1.0%
2 iCMAs 1.5% 2.4% 1.5%
3 iCMAs 1.5%
4 iCMAs 5.3% 2.4%
5 iCMAs 0.5% 3.9% 5.8% 8.2%
6 iCMAs 0.5% 0.5% 5.8% 5.8% 34.3%

11. Average exam scores
0 TMAs 1 TMA 2 TMAs 3 TMAs 4 TMAs
0 iCMAs 6.0
1 iCMA
2 iCMAs 17.0 24.0
3 iCMAs 60.0
4 iCMAs 43.7 62.0
5 iCMAs 23.0 46.0 62.6 69.5
6 iCMAs 35.3 60.8 77.5

12. Exam scores vs prediction
0 TMAs 1 TMA 2 TMAs 3 TMAs 4 TMAs
0 iCMAs −20.8
1 iCMA
2 iCMAs −43.9 −27.5
3 iCMAs −9.0
4 iCMAs −15.6 +1.8
5 iCMAs −3.8 −11.1 +1.4 +2.4
6 iCMAs −17.1 +3.4 +4.6

13. Changing assessment type

14. T184 robotics & meaning of life
Before
10% mid-course iCMA
90% final written EMA
part 1 short-answer
part 2 programming
& essays
After
10% mid-course iCMA
30% final iCMA
60% final written EMA

15. Module completion rates
T184 completion rates
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011
May
Oct
Introduction of
CME

16. T184 completion rates
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011
May
Oct
Introduction of
CME
Module completion rates

17. T184 completion rates
60%
65%
70%
75%
80%
85%
90%
95%
100%
2004 2005 2006 2007 2008 2009 2010 2011
May
Oct
Introduction of
CME
Module completion rates

18. Deadlines

19. SM358 iCMA51 submit date
2010 advisory deadline

20. SM358 iCMA51 submit date
2010 advisory deadline
2012 hard deadline

21. Grades

22. Optional quizzes

23. Compulsory quizzes

24. Do students read feedback?

25. Interactive with multiple tries

26. Same question in two quizzes

27. Can computers grade sentences?

28. Spoiler: Yes!

29. How good are humans?
Question Number of
responses in
analysis
Percentage of responses where
the human markers were in
agreement with question author
Percentage of
responses where
computer marking
was in agreement
with question
author
Range for the
6 human
markers
Mean of the 6
human
markers
A 189 97.4 – 100. 98.9 99.5
B 248 83.9 – 97.2 91.9 97.6
C 150 80.7 – 94.0 86.9 94.7
D 129 91.5 – 98.4 96.7 97.6
E 92 92.4 – 97.8 95.1 98.9
F 129 86.0 – 97.7 90.8 97.7
G 132 66.7 – 90.2 83.2 89.4

30. Comparing three algorithms
Question Number of
responses in
analysis
Percentage of responses where computer marking
was in agreement with question author
Computational
linguistics
Algorithmic manipulation of
keywords
IAT Pattern-match Regular
Expressions
A 189 99.5 99.5 98.9
B 248 97.6 98.8 98.0
C 150 94.7 94.7 90.7
D 129 97.6 96.1 97.7
E 92 98.9 96.7 96.7
F 129 97.7 88.4 89.2
G 132 89.4 87.9 88.6

31. Summary

32. References
• Overall iCMA usage numbers collated by Phil Butcher.
• End of module server results from Student Analytics in the Institute of Educational
Technology via Linda Price.
• SM358 data from John Bolton
• T184 data from Jon Rosewell
• SDK125, S141, S151 & S240 data from Sally Jordan
• Jordan, Sally (2014). Using e-assessment to learn about students and learning.
International Journal of e-Assessment, 4(1)
• Jordan, Sally (2014). Adult science learners’ mathematical mistakes: an analysis
of responses to computer-marked questions. European Journal of Science and
Mathematics Education, 2(2) pp. 63–86.
• Jordan, Sally (2012). Short-answer e-assessment questions : five years on. In:
2012 International Computer Assisted Assessment Conference, 10-11 July 2012,
Southampton.
• Pattern-match question type for Moodle
https://moodle.org/plugins/view/qtype_pmatch

33. Key points
Getting the assessment right is important
Online quizzes can be powerful learning tools
Dig into the data and you can learn
• Is this quiz working?
• What are my students’ misconceptions?
Computers can grade much more than multi-choice
but only on behalf of a teacher
Want more? I’m giving this talk again at #iMoot15