The document discusses an online remedial teaching model for a summer course. It presents the framework, model, research questions, methodology, and results of the study. The study found that an online summer course using collaborative learning helped improve students' mastery of economics and increased their passing rate, with 92% passing rate for one course. However, further research is needed to determine if collaborative learning has temporary or structural effects and on the motivation of participants.

1. Remedial Online Teaching
on a Summer Course
… and more
Martin Rehm
MGSoG Young Talent Day
29th November, 2007

2. Outline
• Framework
• Online Remedial Teaching Model
• Research Questions
• Methodology
• Results
• More Results
• Outlook

3. Framework
• Increasing internationalization of enrollment
(~ 70% from abroad)
•‘ New’ accreditation procedures (Treaty of
Bologna)
• Differences in prior knowledge
• Incentive problems of physical summer course

4. Online Remedial Teaching Model
1. Online Availability 24/7
(Vrasides & Zembylas, 2003)
2. Adaptive
(Falmange et al., 2004)
3. Rapid feedback
(Draaijer, 2004, Vrasides & Zembylas, 2003)
4. Interactive
(Bryant et al. 2005, Ronteltap & Van der Veen, 2002)
5. Flexible Learning Methods & Assessment
(Marshall, 2003, Segers, 2004)

5. Virtual Learning Environment
Feedback
Student Interaction Student
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6. Research Questions
1. How can students assess their current level of
mastery before joining a (Bachelor’ programme?
s)
2. If the level of mastery of an individual student
seems too low, how can an online summer course
help to tackle this deficiency?
3. How can online summer courses be designed to
increase the completion rates of students who enroll
to them?

7. Methodology

8. Prior Knowledge Test
• Diagnostic Test
• Self-Assessment
• Feedback via email
• ~ 70 % of incoming students are below
predefined threshold level

9. Online Course Economics
• 6 weeks
• 10 –15 hours per week
• e-PBL approach
• ~ 15 participants per group
• Online Course Materials
• EleUM (POLARIS)
• Checkup Tests (formative)
• End Assessment (summative)
• Peer Evaluation

10. Virtual Learning Environment
Feedback
Student Interaction Student
Fe
tiv k
e
Ad d b
bap ac
ac
e
ap
ed
tiv k
Fe
e
Ad
F ee
Technology
Inte
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Int dbac
24/7 online
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Teacher

11. Results
• Pre-Evaluation:
– dissatisfied with their level of mastery in economics
– appreciated:
•online nature of the course
•collaborative learning
• Passing Rate:
– 1st Version: 50 %
– OSCE 2007 (MGSoG): 92 %
• End Evaluation:
– Café-Talk Forum
– very good …

14. More Results (1)
100
95
90
85
80
75
70
65
60
55
50
EconPrior NoEconPrior SC-pass SC-fail
Figure 1: Passing rates EcBus (%)

15. More Results (2)
7,4
7,2
7
6,8
6,6
6,4
6,2
6
5,8
5,6
5,4
EconPrior NoEconPrior SC-pass SC-fail
Figure 2: Average grades final exam EcBus (0-10)

16. More Results (3)
• Selection Bias?
– if any, it is very small & statistically insignificant
è Suggests a true learning effect
• Impact of CSCL on the quality of the learning
process of novice students?
– Based on Schellens & Valcke (2005)
– Opposite results (different type of sample)

17. Outlook
1. Does CSCL have a temporary or structural effect on
the (prior) knowledge level and competencies of
students?
2. Research is needed on the motivation of
participants.
3. More specified and detailed information about the
subgroups.
Bachelor è Master è Professionals

18. Social Network Analysis
CS
CL
Macro

19. Social Network Analysis
CS
CL
Hierarchy A
Hierarchy B Mezzo

20. Social Network Analysis
CS
CL
Type A
Type B
Micro

21. Remedial Online Teaching
on a Summer Course
… and more
Martin Rehm
MGSoG Young Talent Day
29th November, 2007