Cultural Differences in e-Learning Behaviour and Overall

Assessment

Clemens Bechter, Fredric W. Swierczek

and Jeerawan Chankiew

Thammasat Business School, Thammasat University, Tha Prachan, Bangkok, Thailand

Keywords: e-Learning, Cultural Adaptation, Assessment Criteria, Peer Assessment, Course Evaluation.

Abstract: The article analyses the influence of culture on e-learning behaviour in form of LMS tool usage, assessment

of peers, and post-graduate student’s grades. E-learning behaviour in this research relates to tool usage such

as email, discussion board, number of sessions, time spent etc. The analysis suggests adapting e-learning to

participants based on their culture as well as making students aware that there may be a cultural bias in

assessing their peer’s contributions. Especially European students rate their Asian peers more than 10%

lower than their European ones although the overall GPA does not differ. Europeans do better in group

assignments than Asian students especially South Asians who perform better at individual assignments in a

culturally diverse setting. The qualitative findings provide additional evidence that cultural features do have

an impact on e-learning behaviours.

1 INTRODUCTION

Hofstede’s (1991, p. 89) definition of culture as “the

collective programming of the mind that

distinguishes one group or category of people from

another” and more recently, the GLOBE project

defining culture as “shared motives, values, beliefs,

identities, and interpretations or meanings of

significant events that result from common

experiences of members of collectives that are

transmitted across generations” (House et al., 2004,

p. 230) suggest that experiences and shared values

constitute a cultural group. Researchers generally

agree that variations between groups can exist on

multiple dimensions (cognitions, behaviours, and

values). However, cross-cultural research is mainly

focused on cultural values. In contrast, this paper

focuses on behaviours in the context of e-learning.

In our paper we investigated the influence of cultural

context on online learning behaviour of executive

MBA students at an online university based in

Singapore. Seven post-graduate elective business

simulation courses with a total of 206 students from

2006 to 2010 were analysed. The average age of

students was 38 years with predominantly

engineering background who want to pursue an

MBA to further increase their management

competence. Although based in Singapore the online

university attracts a large number of South Asian

(e.g. India, Indonesia) as well East Asians (e.g

Japan, China, Taiwan) and Europeans. The seven

selected business simulation courses were taught by

the same tutor, using the same software (Markstrat

from Insead), time span of 12 weeks each, and same

weight for assignments. A mix of nationalities was

encouraged and sometimes directed by the tutor.

2 RESEARCH OBJECTIVES AND

THEORETICAL FRAMEWORK

For cross-cultural theory of E-learning at the

national level the major issue is measurement. There

are five major perspectives. The first is Hofstede

(1991) which has been the most widely used and

criticised (Hofstede et al., 2010). Related to this

perspective is Project Globe (House et al., 2004)

which followed a different approach in methodology

and sampling but with similar categories.

Trompenaars & Hampden-Turner (2004) represents

a third approach which is based on executive

participants in management development programs

answering questions about value dilemmas. The

fourth approach is the Schwartz Value Survey

(1992) which covers many countries based on

respondents who are students and teachers. Finally,

527

Bechter C., W. Swierczek F. and Chankiew J..

Cultural Differences in e-Learning Behaviour and Overall Assessment.

DOI: 10.5220/0004337805270535

In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 527-535

ISBN: 978-989-8565-53-2

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Triandis (2001) uses an individualism-collectivism

spectrum. Each approach has advantages and

disadvantages. Typically, an approach is used as a

standard measurement of a particular culture. A

more qualitative approach is the distinction between

high and low context cultures. According to Hall

(1976) East-Asia (EA, e.g. Japan) would represent a

high context culture whereas Europe (EU) a rather

low-context culture with India somewhere in

between. Higher context cultures generally have a

stronger sense of group orientation, seniority,

unspoken rules, and tradition. In Hofstede’s (1991)

and Trompenaars (2004) classification systems,

South-Asia (SA) would be somewhere between EA

and EU.

Swierczek and Bechter (2008) amalgamated and

applied these approaches to e-learning, see Table 1,

demonstrating that High Context cultures show a

more a ‘wait and see’ reactive mode. Low Context

learner cultures show higher volume, less depth and

can be considered as more provocative and

innovative.

Table 1: Features of High vs. Low e-learning cultures.

High Context (East-Asian) Low Context (European)

Introvert

Modest

Reactive

Reflective

Natural

Reads First

Data Focused

High Frequency

Group oriented

Team Harmony

Deduction

Share knowledge within

group

Tutor as Leader

Extrovert

Superior

Active

Thinks outloud

Exaggerated

Posts First

Monologue Dominant

High Involvement

Individual Achievement

oriented

Critical Peer evaluation

Induction

Share knowledge openly

Tutor as Facilitator

The purpose of the study is to analyse culture

related e–learning behaviour and its outcome.

The research questions are:

What is the relationship between a culture like

South-Asia (SA), Europe (EU) or East-Asia

(EA) on e-learning behaviours?

Does culture influence e-learning?

Is it possible to design an e-learning approach

which is compatible with different cultures?

The objectives of this research:

1. To assess e-learning behaviours of post-

graduate students.

2. To determine the influence of cultural values

on e–learning behaviours.

3. To identify the impact of culture on e-learning

activities.

4. To compare peer assessments of participants

working together with student colleagues from

different cultures.

5. To propose a multi-culturally compatible

approach to e-learning design.

Figure 1: Research Framework.

The paper is structured in the way that overall

assessments for students and tutor will be analysed

in a first step. In a second step student online

behaviour (input) and grades (output) split by

cultural group will be compared. Included in the

grade analysis is a comparison of peer assessment

grades followed by relative success of cultural

regions in group vs. individual assignments. In line

with a mixed method approach (Creswell, 2009)

some quotes from online discussions are provided.

To test the hypothesis that there are cultural

differences a second, purely quantitative, approach

was used by grouping of students (clustering) along

major e-learning behavioural dimensions (factors).

Finally, results of both approaches were compared

and recommendations given.

3 FINDINGS

3.1 Overall Assessment

The overall assessment grade (GPA) consisted of

seven components. Discussion Board (DB)

contributions accounting for 30%, two case studies

(GA) 30%, final project (FP) 15% and final exam

25%. The case studies did not differ in complexity

but were replaced by more current ones every two

years. Case studies and final project (summary of the

key learning points of the simulation game) were

group assignments with team sizes ranging from five

to seven participants.

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Table 2: Overall assessment and its components.

N Mean

Std.

Deviation

GA_1 205 82.93 14.525

GA_2 206 83.04 14.581

FP 206 82.71 12.708

DB_1 206 84.76 11.565

DB_2 206 84.41 12.942

DB_3 206 81.85 14.089

Final Exam 206 77.13 13.596

GPA 206 81.54 10.085

Valid N 205

Grades ranged from 0 to 100, average see Table

2. Final exam score is below other assessment

criteria which could be related to exam phobia or

time pressure or tutor related by not advising

students what was expected. The grades for online

participation on the Discussion Board (DB) declined

slightly toward the end of the course: DB_1: week 1-

4; DB_2: week 5-8; DB_3 week 9-12. The two

group assignments (GA_1 & GA_2) took place in

the first eight weeks whereas the final project (FP)

was due at the end of the course, shortly before the

final exam. The GPA is relatively high which may

be due to the fact that this was an elective course.

Whereas individual assignments were solely graded

by the tutor, the group assignments had a peer

assessment component whereby each student was

asked to rate team colleague’s contributions on a

scale 1-5.

Students were asked to evaluate subject content

(25 questions) and tutor (22 questions) upon

completion of the course. Table 3 shows the

averages of five selected questions on a scale 1-5:

S_A4. The various learning tools were used

effectively (e.g. discussion boards, self-assessment

exercises, instant messenger, webinar).

S_B7. The case studies and final project selected

for this subject were useful for my learning needs.

S_C3. The ratio of individual to team

assignments was appropriate.

S_E1. Overall, how would you rate the quality of

your learning in this subject?

T_D1: Overall, how would you rate the

performance of the professor in this subject?

The four subject related (S_) items as well as

overall tutor satisfaction (T_) was high. For obvious

reasons it is not possible to make the link between

individual evaluation and a particular student;

otherwise the tutor may penalise that student in

courses to come (Table 3).

Table 3: Course evaluation by students.

N Mean Std. Deviation

S_A4 206 4.30 0.881

S_B7 206 4.47 0.689

S_C3 206 4.36 0.751

S_E1 206 4.46 0.645

T_D1 206 4.51 0.703

Valid N 206

There is a significant high correlation between

perceived quality of learning and tutor performance

which indicates that a student who ‘likes’ the tutor

may also like the subject and vice versa, see Table 4.

Table 4: Correlation between Subject and Tutor

satisfaction.

S_E1 T_D1

S_E1 Pearson

Correlation

1.000 .851

Sig. (2-tailed) .000

N 206.000 206

T_D1 Pearson

Correlation

.851

1.000

Sig. (2-tailed) .000

N 206 206.000

**. Correlation is significant at the 0.01 level (2-tailed).

This should be taken into consideration when

assessing a tutor’s performance based on students’

evaluations as many universities nowadays do. For

example, the Singaporean university in question will

not renew contracts if the overall evaluation falls

short of 4.2 which may be caused by teaching a less

exiting subject or the pedagogical performance of

the tutor. As above evaluation shows, students were

generally satisfied with subject and tutor which may

be a result of their active learning behaviour.

3.2 Learning Behaviour

The business simulation course consisted of 160

SCORM modules. Blackboard served as LMS with

readily available statistics such as:

 Number of session during the 12 week course

 Total Time spent

 Number of eMails read

 Number of eMails sent

 Number of DB posts read

 Number of DB replies posted

 Number of times the (LMS internal) organizer

with upcoming events/deadline was viewed

 Peer assessment scores and Grades

Most of the behavioural input factors correlate

positively with the GPA. Students that engage via

email or discussion board are more successful. The

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Table 5: Correlations: Input vs. Overall Assessment Grade (GPA).

Pearson

Correlation Sessions

Total

Time Mail_Read Mail_Sent DB_Read DB_Posted SCORM Organiser GPA

ssions .467

.209

.312

.479

.453

.631

.146

.397

Total Time .467

.111 .144

.226

.386

.389

-.109 .330

Mail_Read .209

.111 .660

.055 -.022 .187

.056 .171

Mail_Sent .312

.144

.660

.165

.131 .166

-.001 .224

DB_Read .479

.226

.055 .165

.294

.161

.062 .178

DB_Posted .453

.386

-.022 .131 .294

.138

.026 .379

SCORM .631

.389

.187

.166

.161

.138

.194

.274

Organiser .146

-.109 .056 -.001 .062 .026 .194

-.042

GPA .397

.330

.171

.224

.178

.379

.274

-.042

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

only tool that did not contribute to the success was

the organizer; viewing deadlines doesn’t seem to

constitute a high performing student. On the

contrary, it correlates slightly (but non-significantly)

negatively, see Table 5. Organizer is a typical

‘uncertainty avoidance’ parameter.

Table 5 suggests that e-learning behaviour

component correlate i.e. a student spending a lot of

time online is also more active and gets a better

grade than not so active ones. There is a high

correlation between mail read and sent as well as DB

posted and read which may indicate a preference for

a specific communication tool.

3.2.1 Cultural Differences

At first sight there seem to be no significant

differences between East-Asian (EA) students,

Europeans (EU) and South-Asians (SA). Numbers of

sessions as well as average grades (GPA) are

similar, see Table 6, and do not differ significantly.

Table 6: Overall Sessions and GPA.

Nationality N Mean Std.

Deviation

EA Sessions 75 147.65 77.509

GPA 75 81.35 10.732

EU Sessions 32 144.41 93.429

GPA 32 81.70 11.541

SA Sessions 99 142.53 78.528

GPA 99 81.64 9.143

Given that a course lasts for 12 weeks it can be

concluded that on average a student logs in around 1

½ times per day; more realistically, once per

working day and 5 times over the weekend because

they were executive students.

Despite an overall similar picture, we see

behavioural differences when comparing cultural

regions in more detail. Differences between EA

(East-Asians) and SA (South-Asians) and EU

(Europeans) that were significant at 0.05 levels are

highlighted in italics, see Table 7.

Table 7: e-Learning behaviour by culture.

Nationality N Mean Sig.

Mail_

Read

EA 75 48.8

SA 99 50.47

Yes, vs.

EU 32 53.16

Yes, vs.

Mail_

Sent

EA 74 6.18 no

SA 99 7.36 no

EU 32 10.53 no

DB_

Read

EA 75 4707

SA 99 7264

Yes, vs.

EU 32 8240

Yes, vs.

DB_

Posted

EA 75 101

SA 99 102

EU 32 157

Yes, vs.

EA & SA

SCORM EA 75 204 no

SA 99 191 no

EU 32 181 no

Organiser EA 75 7.21 no

SA 99 7.51 no

EU 32 8.19 no

SA and EU read more mails and more DB posts.

Given the fact that each student posts around 100

replies on the DB and on average there are 40

students per class, we can expect around 4000 DB

postings per course. This means that EA view a post

more or less once whereas their SA counterparts

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view some post at least twice. EU were the most

active group in DB postings which didn’t translate

into a better DB grade, see Table 8.

Table 8: Grade Component Differences by Culture.

Nationality N Mean Sig.

GA_1 EA 75 82.79

SA 98 81.49

32 87.66 Yes, vs.

EA & SA

GA_2 EA 75 82.55

SA 99 82.24

32 86.66 Yes, vs.

EA & SA

FP EA 75 81.41 No

SA 99 83.48 No

EU 32 83.38 No

DB_1 EA 75 86.39 No

SA 99 84.10 No

EU 32 83.00 No

DB_2 EA 75 83.15 No

SA 99 84.68 No

EU 32 86.56 No

DB_3

75 81.67 Yes, vs.

99 83.63 Yes, vs.

EU 32 76.81

Exam EA 75 76.64 No

SA 99 77.99 No

EU 32 75.59 No

An explanation could be that EA’s and SA’s

postings show more substance and EU are more

frequent but shallower. Whereas emails do not form

part of the grade, DB contributions do. An extensive

list with evaluation criteria was provided prior to the

course to eliminate a subjective judgement as much

as possible.

After analysing the behavioural input (Table7),

what tools were used, we looked at the grade in

more detail (Table 8). We have seen in Table 6 that

the overall grade did not differ significantly between

EA, EU, and SA. Looking at the grade

components/criteria, however, there are three

differences, see Table 8.

EU students seem to take it relatively easier with

DB contributions toward the end of the term (DB_3:

week 9-12) whereas their Asian counterparts

maintain their high level of activity throughout the

course.

3.2.2 Peer Assessment

Peer assessment is essential part of collaborative

learning.

Table 9: Peer Assessment.

Assessment

Criteria

Name Team members (initials)

GJ FC BW JA MS

Collection of

data

Goh J

5 5 5 3 3

Foo C

3 3 4 4 4

Bob W

3 3 4 5 4

Joy A

4 4 5 5 5

Muthu S

4 4 5 5 5

Data analysis

Goh J

5 4 5 4 3

Foo C

3 2 3 4 2

Bob W

3 3 5 5 4

Joy A

4 4 5 5 5

Muthu S

4 4 5 5 5

Co-ordination

and writing of

submission

Goh J

5 5 4 3 3

Foo C

2 4 5 2 4

Bob W

3 3 5 4 4

Joy A

4 4 5 5 5

Muthu S

4 4 5 5 5

Overall quality

of input

(creative ideas,

insights)

Goh J

5 4 4 3 3

Foo C

3 3 4 5 4

Bob W

3 3 4 5 4

Joy A

4 4 5 5 5

Muthu S

4 4 5 5 5

Overall

contribution

to the efficient

functioning of

team

Goh J

5 5 5 3 3

Foo C

3 2 4 5 4

Bob W

4 4 5 5 4

Joy A

4 4 5 5 5

Muthu S

4 4 5 5 5

After each group assignment including the final

project students were asked to rate anonymously

their peers on 5 categories, see above Table 9, from

1-5. The peer assessment accounts for around 25%

of the group assignment grade. Because we

hypothesized that groups from the same culture will

rank their peers higher than from other cultures we

calculated three different peer scores.

In Table 9 we see peer scores of 5 students:

 Goh J: EA

 Foo C: EA

 Bob W: EU

 Joy A: SA

 Muthu S: SA

To calculate the Peer_other (culture) score for

Goh (the average score s/he gave to peers, not the

one s/he received) we will not consider Foo because

s/he is from the same culture, instead only the two

SA and one EU team member will be considered.

For Peer_own only Foo would qualify. Peer_score

gives the average score this person gave to all team

members.

We can see that EU and SA give similar scores

between 4.1 and 4.3 to students sharing the same

cultural background but drop if they evaluate

students from another cultures; especially the EU

gap is significantly high (4.289 vs. 3.747).

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Table 10: Peer Score.

Nationality N Mean Std.

Deviation

Peer_score EU 32 4.018 0.571

SA 99 4.006 0.522

EA 75 4.040 0.540

Peer_own EU 32 4.289 0.454

SA 99 4.113 0.566

EA 75 4.230 0.560

Peer_other EU 32 3.747 10.328

SA 99 3.899 0.517

EA 75 3.860 0.540

The results confirm that there is a cultural bias in

peer assessment which may be down to the fact that

one relates more easily to the own culture. A similar

pattern can be found on DB where students tend to

reply to postings made by same culture students

more frequently than others. One could argue that it

is difficult, for example, for an Indian to relate to

Haier as for a Chinese to Amul and more engaging

the other way round.

3.2.3 Group vs. Individual Assignments

Looking at the grades, SA performed 1.59% better

than average in the final project (FP) and EA 1.25%

better in the GAs. A minus sign indicates a tendency

to the left (GA) and a plus sign to the right (FP), see

Table 11. EA preferred group assignments, whereas

SA preferred Individual assignments. Both, GA and

FP are group assignments but at a different level.

GA covered case studies whereas the FP was far

more team oriented in form of a simulation game.

Quite often students split case study tasks getting

close to becoming an individual assignment.

GroupvsIndi measured the different performance

between team work (GA, FP) and truly individual

assignments (3 DBs, final exam). Only SA

performed better at individual assignments. Pramila

(2011) came to similar conclusions that Indian

students are more individualistic and less group-

oriented which would bring them closer to low

context cultures.

Table 11: Group vs. Individual Assignment.

Nationality N Mean Std.

Deviation

GAvsFP SA 99 1.586 11.466

EA 75 -1.253 11.517

EU 32 3.000 14.870

GroupvsIndi SA 99 0.210 5.536

EA 75 -0.290 7.657

EU 32 -5.400 16.440

Surprisingly, Europeans tend toward group

assignments. The values in above tables do not

represent perceptions or likeability, instead they

stand for the relative success, expressed as grade

points (range from 0 to 100), between various forms

of formal assessments. One reason could be that

Europeans take the initiative and volunteer to

become team leaders. A study by Klein (2012) has

shown that successful teams have a strong leadership

and delegate tasks; similar to this study a computer

game/simulation was analysed. Sometimes EU

students were frustrated by the slow pace of

reaching a (for high context cultures typical)

consensus and took things in their own hands (Table

12).

Table 12: Sample DB Postings.

EU: Lets get started with the activity, before we run

out of time.

EA_1: From a suggestion made by Prof, I have

purchased all the market research studies.

EA_2 Thanks! Please go ahead.

SA_1: My relocation got me really tied up. Moved from

Egypt to UAE after 9 years. Tough call. Ready to

contribute and will from now.

EU: Team, Any update... we have to make decision

by midnight today for next round.

EA_2: I doubt anyone has the time to do a thorough

analysis of the results from period 1. I have made

some simple observations:…

SA_2: Guys, We missed tdy's deadline to upload next

decision on Markstrat. Need to ensure that we do

our best fr next round..

EU: The way we have been doing this is quite

disorganized and without directions…

SA_1: Logging in.

EU: Hi team, I have initiated 2 R&D projects: …

Table 12 demonstrates that the EU student shows

leadership whereas EA_1 tries to avoid uncertainty

(another feature of high context cultures) and gets

reconfirmed by EA_2. Both EAs including the one

SA do not move things forward; they rather take a

‘wait and see’ attitude. Finding themselves in the

diver seat may challenge EU students resulting in

better grades in group than individual assignments.

Language proficiency could be another reason.

3.2.4 Grouping across Cultures

We have seen that culture has an impact on e-

learning behaviour and learning success. However,

even within a culture differences can exist; a person

from one culture can be closer to another culture

than its own. To analyse the proximity of students in

the sample a cluster analysis was conducted.

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To reduce the number of dimensions a factor

analysis was applied beforehand, resulting in 3

major dimensions, see Table 13.

Table 13: Rotated Component Matrix.

Factor

Time eMail Organizer

Sessions .798 .231 .341

Total Time .747 .093 -.133

DB_Posted .716 -.066 -.096

DB_Read .590 .048 .099

Mail_Read -.005 .915 .073

Mail_Sent .160 .887 -.021

Organiser -.086 -.034 .894

SCORM .523 .188 .527

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Factor 1 is the most important dimension and can

explain 29.5%, Factor 2 21.6% and Factor 3 14% of

variance. Along these 3 dimensions a subsequent

clustering of students was conducted. Whereas

Factor 1 and 2 correlate positively with the GPA,

Factor 3 does not, see Table 14.

Table 14: Correlations between Factors and GPA.

GPA

REGR factor score 1 Pearson

Correlation .440

REGR factor score 2 Pearson

Correlation .176

REGR factor score 3 Pearson

Correlation -0.004

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

A cluster analysis based on the three factors

resulted in 4 distinct groups (Table 15).

Table 15: Cluster by Nationality.

Cluster EA EU SA

Freq. Percent Freq. Percent Freq. Percent

1 (SA) 1 1.4% 0 .0% 18 18.2%

2 (SA) 0 .0% 0 .0% 81 81.8%

3 (EU) 0 .0% 32 100.0% 0 .0%

4 (EA) 73 98.6% 0 .0% 0 .0%

Combined 74 100.0% 32 100.0% 99 100.0%

Cluster 1 is dominated by SA, cluster 2 also by

SA, cluster 3 by EU and cluster 4 by EA. This

confirms that there are cultural differences in e-

learning behaviour; Cluster centroids, see Table 16.

Table 16: Centroids.

Cluster

REGR factor

score 1

REGR factor

score 2

REGR factor

score 3

Mean

Std.

Deviatio

Mean

Std.

Deviation Mean

Std.

Deviatio

0.344 1.68 0.353 1.37 1.95 1.48

-0.19 0.793 -0.07 0.637 -0.34 0.429

0.241 1.21 0.226 1.23 -0.23 1

0.011 0.833 -0.11 1.08 -0.03 0.691

Because the standard deviations are very high the

confidence intervals are also very broad. Figure 2

illustrates the means and intervals for all 4 clusters

on Factor 1. Only Figure 4, the organizer dimension,

shows non-overlapping confidence intervals.

However when looking at Table 13 it shows that

Factor 3, viewing the organizer tool, does not

correlate with the GPA, confirming its non-

significance to overall assessment.

Figure 2: Factor 1 group means.

Figure 3: Factor 2 group means.

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533

Figure 4: Factor 3 group means.

Both, cluster 1 and 2 consisted mainly of South-

Asian students whereas cluster 1 was more active

than cluster 2. Since both share the same cultural

background other reasons must exist why there is a

difference. In a further step the executive student’s

educational background, age group, current location

(domestic or expat), and job were analysed. It was

found that in the more active cluster 1 there were

significantly more SA working abroad as expats or

either having their own business or planning to have

and were on average 4 years younger. In most cases

the first two attributes exclude each other; either a

person is working as expat for a company or is

running his/her own business domestically. Whereas

one can easily understand that a business owner’s

intrinsic motivation to study may be higher than a

normal employee, the fact that expats perform better

is not that obvious. Their job and family demands

are usually higher than those working in their home

country. Therefore, one would expect that they have

less time to study. Possible reasons why they

perform better could be: rigid time management,

high motivation, and fewer distractions. Many of

Cluster 1 (Indian) students were working in the

Middle East especially UAE. Combined with the

fact that they are younger and therefore likely to be

more career-oriented may make them better

performing students. Whether a student just started

or was close to the end of the programme made

another significant difference. Students had to take

16 courses plus a Master Thesis in form of a project.

Because the analysed course was an elective it could

be taken as 5

course earliest and as late as 16

More experienced SA students having at least done

10 courses performed significantly better than

students taking it as 5

until 9

. It seems that there is

a learning curve and maybe a motivational push

toward the end of the programme to improve the

final GPA.

4 CONCLUSIONS

We demonstrated that cultural differences in e-

learning behaviour, assessment grade components,

and peer assessment exist. A major issue in e-

learning is whether the trend will be to greater

convergence or more divergence (Edmundson,

2006). Greater convergence would mean e-learners

worldwide are becoming more similar. More

divergence would signify that e-learners are more

likely to be significantly different (Blanchard and

Allard, 2010). This study provided support for the

divergence trend. One size will not fit all. Course

design will need to be more adaptive not more

generalisable. Unfortunately most e-learning courses

have been designed by Westerners, including the

analysed courses at the Singaporean university.

However, the fastest-growing markets are non-

Western: China and India.

5 IMPLICATIONS

Make students aware that there is a cultural bias.

Encourage EA to take the lead in group assignments.

Encourage SA to pull their weight in group

assignments.

Make students aware that viewing the organizing

tool reflects uncertainty avoidance but does not give

a better grade.

Stress DB assessment criteria to EU to achieve

more substance and less quantity.

Encourage students working as expats or

planning to start their own business to share their

experiences with others and serve as role model.

Further research in form of longitudinal studies

(Goda & Mine, 2011) should take the behavioural

changes over time into account as well as the impact

of foreign exposure such as working as expat.

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