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

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

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

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)

Virtual Learning Environment
Feedback
Student Interaction Student
Fe
tiv k
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Ad d b
bap ac
ac
e
ap
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tiv k
Fe
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Ad
F ee
Technology
Inte
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on
Int dbac
24/7 online
db
cti
rac
ack
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Fe
tion
Teacher

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?

Methodology

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

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

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
k
on
Int dbac
24/7 online
db
cti
rac
ack
era
e
Fe
tion
Teacher

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 …

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 (%)

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)

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)

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

Social Network Analysis
CS
CL
Macro

Social Network Analysis
CS
CL
Hierarchy A
Hierarchy B Mezzo

Social Network Analysis
CS
CL
Type A
Type B
Micro

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