Collaborative Learning in Culturally Heterogeneous Student Pairs through a CSCL Environment
Conference:
ECER 2012
Format:
Paper

Session Information

20 SES 07, Using New Technologies for Intercultural Education

Parallel Paper Session

Time:
2012-09-19
17:15-18:45
Room:
ESI 3 - Aula 1
Chair:
John Willumsen

Contribution

Introduction of computer-supported collaborative learning (CSCL) in an intercultural learning environment creates both challenges - coordinating students’ different communication skills, behavioral patterns and intercultural competences - and potential benefits - sharing culturally diverse knowledge, increased access to other cultures, formation of learning communities regardless of physical and cultural distance between students. Previous research indicates that cultural differences should be taken into consideration when designing and implementing collaborative learning environments (Cox et al., 1991; Weinberger et al., 2007; Zhu, 2009; Popov et al., in press).

To investigate the impact of cultural diversity on cognition and behavior, the individualist-collectivist (I/C) dimension has proved to be one of the most robust concepts. Research replicating and supporting the robustness and validity of Hofstede's (2001) dimensions of culture is large in scope and number, exceeding more than 1500 published studies. The I/C dimension defines the extent to which a culture shapes an individuals’ dependence on the self or the group.

This paper examines differences between culturally homogeneous and heterogeneous students’ perceptions of collaborative learning, their online collaborative behavior, and learning outcomes in CSCL environments. Using data from surveys, interviews, transcripts of online discussion we explored the differences arising from individualistic and collectivistic cultural orientations. Our sample of 98 university students comprised 48 Dutch and 50 international students. The preliminary results from the surveys and interviews suggest that students' cultural background (the individualist-collectivist dimension) may affect their perceptions of the online group process and engagement in collaborative learning environment. Our results showed that collectivists who collaborated in dyads with individualists scored statistically significantly lower, in terms of domain-specific knowledge test than student dyads consist of only students with individualistic cultural orientation. Forthcoming results from analyses of the collaborative discourse will also illustrate differences in the ways culturally homogeneous and heterogeneous groups of students interact and manage actions related to their operations in collaborative learning.

Design

Culturally diverse groups of students (N = 98) were assigned a partner (see below), resulting in 49 culturally homogeneous or heterogeneous dyads. The participants were MSc students enrolled at a large research university in the Netherlands. The students were recruited from two different disciplinary backgrounds, international land and water management (environmental sciences) as well as international development studies (social sciences). Both types of expertise were necessary for accomplishing the specific learning task utilized in this study, which required students to develop a plan for fostering sustainable behavior among wheat farmers in a province of Iran. The study session took about 4 hours and consisted of five phases where students were seated at individual computers and had face-to-face contact with the study personnel

Research questions

RQ1. What are the differences between culturally homogeneous and heterogeneous students' perceptions of collaborative learning in CSCL environment?

RQ2. To what extent does the cultural composition of the student dyads affect students’ learning outcomes in CSCL environment?

RQ3. What are the differences between culturally homogeneous and heterogeneous groups of students' online collaborative behavior in CSCL environment?

RQ4. What is the relationship between students’ perception of collaborative learning and students’ actual online collaborative behavior in CSCL environment?

Method

To answer the RQ1, the data collected were analyzed utilizing the following two methods: a post-collaboration questionnaire about students’ perceptions of collaborative learning developed by So and Brush (2008) and Behavioral Event Interview (Getha-Taylor, 2008). The interview questions addressed students’ opinions, values and feelings with respect their most successful and challenging or problematic collaborative experiences during the study. In order to examine the extent to which students’ learning outcomes are affected by cultural composition of the dyad (based on Hofstede’s individualist-collectivist cultural dimension), a one-way analysis of variance was conducted. The learning outcome score was measured based on the individual, post-collaboration, solution. Two experts assessed individual domain specific knowledge of the learners based on their quality of knowledge construction using a 5-point grading scale. We assessed students’ individual analyses based on an expert solution, adequate and correct relations between theory and case information. Cohen's kappa, was 0.77. To answer the third research question, students’ online collaborative behavior in the CSCL environment, the chat log data will be analyzed using content analysis. To address the fourth research question, the relationship between the first and the second research questions, data from the discussion transcripts and interview transcripts will be triangulated.

Expected Outcomes

Concerning the RQ1, the results of a one-way analysis of variance showed statistically significant differences between students’ perceptions of the collaborative learning based on their cultural backgrounds (F= 5.68, p<0.05). Students from collectivist cultures (M=3.72, SD=0.67) scored significantly higher than students from individualist cultures (M=3.38, SD=0.55) with respect to their perceptions of the online collaborative learning. Six main categories emerged from the content analysis of the interview data: 1) Exposure to online collaborative learning; 2) Technical problems; 3) Explicit revelation of cross-cultural issues; 4) Cooperative versus competitive behavior; 5) Task-oriented versus socio-emotional features; 6) Important elements for learning together in online environment. Regarding the RQ2, there was a statistically significant difference between groups on the learning outcome measure as determined by one-way ANOVA (F = 2.84, p = .042). A Tukey post-hoc test revealed that collectivists who collaborated in dyads with individualists scored statistically significantly lower than student dyads consist of only students with individualistic cultural orientation (p = .032). There were no statistically significant differences between other groups. Addressing the RQ3 and RQ4 (data triangulation) will allow us to see how students’ perceptions and understanding of collaborative learning relate to students’ actual online collaborative behavior in CSCL environment.

References

1. Cox, T.H., Lobel, S.A. & McLeod, P.L. (1991). “Effects of Ethnic Group Cultural Differences on Cooperative and Competitive Behavior on a Group Task”, Academy of Management Journal, 34(4), 827-847. 2. Getha-Taylor, H. (2008). “Identifying Collaborative Competencies”, Review of Public Personnel Administration, 28(2), 103-119. 3. Hofstede, G. (2001). Cultures consequences. (2nd ed.). CA: Sage Publications. 4. Popov, V., Brinkman, D., Biemans, H.J.A., Mulder, M., Kuznetsov A., & Noroozi, O. (in press). Multicultural Student Group Work in Higher Education: An Explorative Case Study on Challenges as Perceived by Students. International Journal of Intercultural Relations (2011), doi:10.1016/j.ijintrel.2011.09.004 5. So, H.-J., & Brush, T. A. (2008). “Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors”, Computers & Education, 51(1), 318-336. 6. Weinberger, A., Clark, D.B., Hakkinen, P., Tamura, Y., & Fischer, F. (2007). “Argumentative Knowledge Construction in Online Learning Environments in and across Different Cultures: a collaboration script perspective”, Research in Comparative and International Education, 2(1). 7. Zhu, C. (2009). E-learning in higher education: student and teacher variables in the Chinese and Flemish cultural context. PhD dissertation, University of Ghent, Belgium.

Author Information

Vitaliy Popov (presenting / submitting)
Wageningen University and Research center
Education and Competence Studies
Wageningen
Wageningen University and Research center, Netherlands, The Netherlands
School of Information, University of Michigan, USA
Wageningen University
Education and Competence Studies
Wageningen

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