This document discusses how the Open University uses Moodle quizzes and how they help students. It notes that quizzes are a major part of assessment and that analyzing quiz data provides insights into student learning. Completing quizzes and interactive computer-marked assignments is linked to higher completion rates and exam scores. While computers cannot fully replace teachers, they can effectively grade short answers, math problems, and code with high accuracy. The quizzes provide formative and summative assessment that support learning when combined with analysis of student performance.

1. The Moodle Quiz at the Open University:
how we use it & how that helps students
Tim Hunt, Senior Developer
5th MoodleMoot Croatia

2. The Moodle Quiz at the Open University:
how we use it & how that helps students
Tim Hunt, Senior Developer
5th MoodleMoot Croatia
many dedicated
OU teaching staff

3. The Open University

4. About the Open University
45 years old
All distance education
~200 000 students (part time)
Used Moodle since 2005

5. Where we are
Contains Ordnance Survey data © Crown copyright and database right. Licensed under CC BY-SA 3.0 via
Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Buckinghamshire_UK_location_map.svg

6. Where we are

7. Where we are

8. Moodle at the OU

9. A typical module website

10. Daily load

11. Lots of servers

12. The quiz at the OU

13. A typical quiz – Maths

14. A typical quiz – Languages

15. Overall use

16. It matters

17. 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

18. It makes a difference

19. Level 3 physics (SM358)

20. 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

21. 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%

22. 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

23. 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

24. Changing assessment type

25. 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

26. 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

27. 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

28. 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

29. Deadlines

30. SM358 iCMA51 submit date
2010 advisory deadline

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

32. Grades

33. Optional quizzes

34. Compulsory quizzes

35. Can computers grade sentences?

36. Spoiler: Yes!

37. 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

38. 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

39. Summary

40. 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 https://moodle.org/plugins/view/qtype_pmatch
• STACK question type (maths) https://moodle.org/plugins/view/qtype_stack
• Chris Sangwin (2013) Computer Aided Assessment of Mathematics

41. Key points
Getting the assessment right is important
Online quizzes can be powerful learning tools
Computers can grade much more than multi-
choice but only on behalf of a teacher
Analyse the data and you can learn
• Is this quiz working?
• What are my students’ misconceptions?