1. Investigating the Core Group
Effect in Usage of Resources with
Analytics
Agathe Merceron
Beuth University of Applied Sciences
Berlin, Germany
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2. Motivation
 Learning Management
Systems save usage
data.
 Reports, statistics or
mining have no great
support.
 Exactly these functions
are necessary to gain
useful information from
the collected data.
 This information can be
used to enhance the
learning experience.
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3. Aim
 Develop a tool to analyse usage data stored by LMS.
 Analyse Tool independent of any LMS (own Data Model).
 For different kind of users: content providers, teachers,
researchers, etc. all non computer science specialists.
 Analysis techniques provided by the tool have to be
well understood.
 Results have to be easily interpretable.
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4. Access to exercises: observing the drop
 View, attempt, finished
60
50
40
30
20
10
0
ex1 ex2 ex3 ex4 ex5 ex6 ex7
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5. Analysing the drop: other works
 Hwang, W. -Y., & Wang, C. -Y. (2004).
 A. Hershkovitz, R. Nachmias. (2011).
 Low-extent users
 Late users
 Online quitters
 Accelerating users
 Decelerating users
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6. Investigating the core group effect
 Core group: do the students who attempt exercise_k
form a sub-group of the students who attempt exercise_i,
i <k?
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7. Investigating the core group effect
 |X| number of students who consulted resource X.
 |X, Y| number of students who consulted X and Y.
 Perfect core group: all students who consulted X
consulted also the previous resource Y:
 |X, Y| = |X| or |X, Y| / |X| = 1.
 Confidence of the association rule X → Y is 1, the
probability of having consulted Y, knowing the
consultation of X, is 1.
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8. Investigating the core group effect
 Perfect core group: all students who consulted X
consulted also the previous resource Y.
 Case of n resources X_1, X_2, ..., X_n: a deluge of
rules have confidence 1.
 X_k → X_i with 1 ≤ i < k ≤ n,
 X_k → S with S a set of resources with indexes
smaller than k.
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9. Investigating the core group effect
 No perfect core group: some students consulted X
without consulting the previous resource Y:
 |X, Y| < |X| or |X, Y| / |X| < 1.
 Confidence of X → Y is smaller than 1, could be 0.
 Confidence measures the probability that Y has been
consulted knowing than the consultation of X.
 Case of n resources X_1, X_2, ..., X_n: a deluge of
rules has a non predictable confidence, X_k → S with
S a set of resources with indexes smaller than k.
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10. Investigating the core group effect
 Pragmatic hypothesis: no need to check a deluge of
rules! (No real mathematical foundation for that.)
 Checking local rules then perhaps global rules will be
enough to understand the trend.
 Local rules: a resource and the preceding one
 X_i → X_(i-1).
 Global rules: a resource and all preceding ones
 X_i → X_(i-1), … , X_1.
 By the anti-monotony property confidence of global
rules give a lower bound for the rules X_i → S with S
a set of resources with indexes smaller than i.
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11. Testing the Hypothesis
 Course Introductory Programming with Java, 65 students
 Course Formal Basics of Computer Science, 57 students
 46 students enrolled in both – Consider those here.
 7 non compulsory self-tests.
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12. Number of Students Attempting the Self-Tests
40
35
30
25
20 Formal B.
Java
15
10
5
0
ex1 ex2 ex3 ex4 ex5 ex6 ex7
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13. Confidence of local rules
 Roughly speaking: 80% versus 60% of the students persist
locally (last rule excepted).
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
2→1 3→2 4→3 5→4 6→5 7→6
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14. Confidence of local rules ( 4 → 5) = 0.6
 Roughly speaking: 80% versus 60% of the students persist
locally (last rule excepted).
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
2→1 3→2 4→3 5→4 6→5 7→6
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15. Confidence of global rules
 Roughly speaking: 75% versus 30% of the students persist
(last rule excepted).
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
3→1:2 4→1:3 5→1:4 6→1:5 7→1:6
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16. Confidence of other rules X_j → S
 Java:
 never below 0.77.
 Well above 0.8 if S contains only 1 resource.
 Formal basics:
 Varies between 0.18 and 0.65.
 Many rules with a confidence around 0.30.
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17. Conclusion
 Local and global rules as representative seem to work.
 Local and global rules extracted with queries, not with
some Data Mining Tool.
 Similar results when taking all students in both courses
for local and global rules.
 Students seem to adapt some of their learning style to
the course: state not trait similar to Hershkovitz &
Nachmias.
 When should the teacher intervene? My experience:
local and global rules are not enough. Look also for the
impact of using the resources on success in learning.
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18. Thank you for your attention. Questions ?
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19. References
 Hwang, W. -Y., & Wang, C. -Y. (2004). A study of
learning time patterns in asynchronous learning
environments. Journal of Computer Assisted Learning,
20(4), 292−304.
 A. Hershkovitz, R. Nachmias. (2011). Online persistence
in higher education web-supported courses. Journal of
Internet and Higher Education 14 (2011) 98 106
–
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