Remedial Online Teachingon a Summer Course

Martin Rehm

MGSoG Young Talent Day29th November, 2007

… and more

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 ofBologna)

• 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)

Technology24/7 online

Student Student

Teacher

Feedback

Interaction Feed

back

Inte

ract

ion

Feedback

Interaction

FeedbackAdaptive Feed

back

Adapti

ve

Virtual Learning Environment

Research Questions

1. How can students assess their current level ofmastery before joining a (Bachelor’s) programme?

2. If the level of mastery of an individual studentseems too low, how can an online summer coursehelp to tackle this deficiency?

3. How can online summer courses be designed toincrease the completion rates of students who enrollto them?

Methodology

Prior Knowledge Test

• Diagnostic Test• Self-Assessment• Feedback via email

• ~ 70 % of incoming students are belowpredefined 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

Technology24/7 online

Student Student

Teacher

Feedback

Interaction Feed

back

Inte

ract

ion

Feedback

Interaction

FeedbackAdaptive Feed

back

Adapti

ve

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)

50

55

60

65

70

75

80

85

90

95

100

EconPrior NoEconPrior SC-pass SC-fail

Figure 1: Passing rates EcBus (%)

More Results (2)

5,4

5,6

5,8

6

6,2

6,4

6,6

6,8

7

7,2

7,4

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 insignificantè 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 onthe (prior) knowledge level and competencies ofstudents?

2. Research is needed on the motivation ofparticipants.

3. More specified and detailed information about thesubgroups.

Bachelor è Master è Professionals

Social Network Analysis

CSCL

Macro

MezzoHierarchy A

Hierarchy B

Social Network Analysis

CSCL

MicroType A

Type B

Social Network Analysis

CSCL

Remedial Online Teachingon a Summer Course

Martin Rehm

MGSoG Young Talent Day29th November, 2007

… and more