Student dialogue during the group problem-solving activity is analyzed to reveal the nature of student interaction. Meanwhile, group knowledge exchange is revealed via a communication network (Milson, 1973). The network contains directed lines linking two students, indicating that one proposed an issue and the other offered a suggestion in response. The number in the line indicates the number of issues answered. Figure 9 displays the communication network for each group.
1
Figure 9. Communication network of knowledge exchange Keyword
Peer Review Mid-term Final exam Students
All 2.14/1.08 159.47/48.59 393.36/142.03 13.89/3.87 3.59/1.20 74.47/13.79 57
Type 1 1.93/0.36 157/40.27 442.00/90.48 15.4/2.05 3.44/0.44 77/13.79 10 Type 2 0.21/0.35 80/80.40 213.00/196.65 11.4/5.31 3.20/1.39 61/21.16 7 Type 3 1.81/0.36 138/55.86 352.00/130.10 14.4/2.61 4.75/0.35 80/4.94 2 Type 4 2.45/0.33 180/8.82 459.00/152.57 12.7/1.85 2.31/0.84 66/11.97 8 Type 5 3.06/0.38 182/5.63 429.00/59.44 14.4/2.40 4.28/0.40 81/9.21 18 Type 6 3.52/0.17 183/3.54 569/93.35 11.9/6.77 4.21/0.55 80.4/18.11 5 Type 7 1.1/0.289 117/65.30 244.00/133.28 13.7/1.44 4.87/0.13 76/9.16 3 Outlier 1.13/1.32 177/2.5 382.00/22.33 10.6/7.36 1.72/0.00 68/11.31 4
Table 7. Student portfolio clusters and characteristics (Mean/Standard Deviation)
Students ’ portfolios are analyzed to reveal how students with different statuses differ in exchanging knowledge. The portfolios include students ’ development levels of keywords self-reflected during browsing learning dictionary, the number of keywords evaluated by students, the frequency of logging in to the learning system, homework and peer review scores, and mid-term and final exam scores.
K-means cluster analysis identified seven different clusters of students , and Table 7 lists the characteristics of each cluster and the mean and standard deviation for each measure.
The characteristics of each cluster of students are described as follows:
l Cluster 1 students participated diligently in homework and peer review. Their mid-term and final exam score were around the middle of the range compared to the rest of the students, while their self-reflection on keywords status was lower than average.
l Cluster 2 students obtained relatively low scores in the mid-term and final exams. Furthermore, they seldom logged on to the learning system and evaluated their keyword development levels themselves.
l Cluster 3 students obtained relatively high score s in the mid-term and final exams. Despite this however, they tended not to self-reflect their keyword status in the learning system.
l Cluster 4 students rated their keyword development levels highly, but their mid-term exam results did not correspond with this evaluation.
l Cluster 5 students rated their keyword status highly and obtain ed corresponding high scores in the mid-term and final exams.
l Cluster 6 students resemble cluster 5 students, but tended to log on to the system very frequently.
l Cluster 7 students obtained middling scores in the final exam and higher scores in the mid -term exam. Meanwhile, they seldom logged on to the system and self-reflected their keyword status.
Students from clusters 3, 5, 6, and 7 are more able than those from clusters 2 and 4. Students in cluster 1 are characterized by active participation in learning activities such as homework and peer review.
Each student in the communication network is tagged to the node corresponding to the cluster number of the student’s portfolio in Fig. 9. Analysis of the communication network and students ’ portfolios indicates that the communication network of a group may differ according to the distribution of students’ ability levels in the group. Communication network formats and ability level distribution are described below:
l Centralized knowledge exchange: where a student became the center of knowledge exchange in a
group. This student suggested many responses to issues raised by others, and other students frequently requested this student ’s support. In such group, the student occupying the position of knowledge center has well-established abilities to solve related problems , while other students have middling or above average problem solving abilities. Group 1 in Fig. 9 is an example of this sort of group.
l Distributive knowledge exchange: where knowledge exchange did not converge on a single
student. In such a group all of the students exchange knowledge with each other, and usually have middling or above average problem solving abilities. However, some individuals were unable to solve the proble m assigned. Consequently, students had numerous very distributive knowledge exchanges during the group problem-solving activity. Groups 2, 7, and 12 are examples of such a knowledge exchange group.
l Group development impediment: Groups 3, 4, 6, and 10 are examples of this sort of knowledge
exchange group, which involves little group development, provision of responses , and acceptance
of responses among group members. In such a group, students generally have middling or above average problem solving abilities. However, students did not converge on a common approach to solving the assigned problem. Consequently, knowledge exchange to develop the ZPD of others.
was very limited in such groups.
l Ability impediment: Group 13 is an example of this sort of knowledge exchange group. In such a
group, students generally have very limited problem solving abilities. Conflicts among members are frequent in such groups, generally occurring when a student was unable to obtain support from others. Therefore, knowledge exchange is also very limited in such groups.
l Partial knowledge exchange: Groups 8, 9, 11, and 14 are examples of this sort of knowledge
exchange group. In such groups, the range of problem solving abilities is diverse. Knowledge exchange occurred among students with middling or above abilities, while students with limited abilities were unable to participate. Another example of such a group was group 5. This group also contained students with diverse levels of problem solving abilities, but they failed to converge on a common process for solving the assigned problem. Students in this group tended to object to the responses of others rather than to accept them. In this case, students with middling or above abilities suffered from limited knowledge exchange.
Analysis of students ’ knowledge exchange behavior and their portfolios also confirms that the difficulty and the abilities required to solve the assigned problems may affect knowledge exchange among students. Communication network and student portfolio analysis confirms that s tudent cluster 1 tended to actively exchange knowledge with peers during the group problem-solving activity. Among ten such students, six actively exchanged knowledge with others (proposed issues or positions more than three times), and a fu rther t wo exchanged knowledge at least once. However, students in clusters 2 and 4 did not exchange knowledge with peers very actively. Among the seven students in cluster 2,
only two of them exchanged knowledge even once, while a mong the eight students in cluster 4, only one exchanged knowledge more than three times, and two did so once. The above results may arise partly because the experiment did not assign different problems to students according to their core abilities. The students in clusters 1, 5, and 6 had sufficient ability to exchange knowledge with others and develop their ZPDs. Figure 10 lists the complete results concerning the knowledge exchange for different clusters of students .
0
Figure 10. Knowledge exchange behavior for students of seven clusters
Ⅴ. Conclusion
As students learn and communicate on the World Wide Web, learning systems require mechanisms to mediate and include appropriate students in peer support activities to activate students’ ZPD. This study has described how teachers and learning systems should effectively mediate peer help and activate ZPD by managing knowledge exchange. This work proposed a decision tree methodology for discovering the rules of knowledge exchange concerning individual learning strategies and pair differences of students . The experiment and guidelines for utilizing the decision tree methodology in peer support is demonstrated by a group problem-solving example. The experiment revealed the format of dialogues of
group activities and communication networks. Cluster analysis of students’ portfolio revealed that the communication network of a group may differ according to the distribution of ability levels within the group, and the difficulty of solving the assigned problem. The teacher and the learning system can examine the effectiveness of the group problem-solving activity and mediate students to optimize that effectiveness. Therefore, teachers employing a web-based learning system can also extend the use of decision tree technology for other peer support settings with relative ease.
Acknowledgements
The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 90-2511-S-155-004.
Reference
Baker J. & Dillon G. (1999). Peer Support on The Web, Innovations in Education and Training International, 36:1, pp. 65-79.
Berger J., Cohen B. P. & Zelditch M. (1972) Status characteristics and social interaction, American Sociological Review, 37:241-56.
Blue Martini Software(1998), URL is http://www.bluemartini.com/.
Boud D. (1990). Assessment and the promotion of academic values, Studies in Higher Education, 17, 185-200.
Chiu M. M. (2000). Effect of status on solutions, leadership, and evaluations during group problem solving, Sociology Education, 73(3), pp. 175-195.
Clementine (2000) URL is http://www.isl.co.uk/.
Cohen E. G. (1994). Reconstructing the classroom: Conditions for productive small groups, Review of Educational Research, 63:1-35.
Conklin J., & Begeman M. L. (1988). gIBIS: A Hypertext Tool for Exploratory Policy Discussion, ACM Transactions on Information Systems, 6:4, pp.303-331.
Davis, F.D., Bagozzi R.P. and Wars haw P.R. (1989). User acceptance of computer technology：A comparison of two theoretical models, Management Science , 35( 8) , pp.982-1003.
Ellis C. A., Gibbs S. J., & Rein G. L. (1991). Groupware: some issues and experiences, Communication of the ACM, 34(1), 35-58.
Hiltz S. R. (1994) The virtual classroom: learning without limits via computer network , Alex
Publishing Corporation, Norwood NJ.
Kautz H., Selman B. & Milewski A. (1996). Agent Amplified Communication, in Proc. of the 13th National Conf. on Artificial Intelligence, pp. 3-9, Portland, Oregon.
Leont'ev, A. N. (1978). Activity, consciousness, and personality. Englewood Cliffs: Prentice-Hall.
Maria C. M. De Guerrero (2000). Activating the ZPD: Mutual Scaffolding in L2 Peer Revision, The Modern Language Journal, Vol 84 (1), p51-68.
McConnell D. (1994). Managing open learning in computer supported collaborative learning environments, Studies in Higher Education, Vol. 19 Issue 3, p341-359.
Meeker, B. F. (1981). Expectation states and interpersonal behavior, in Social Psychology: Social Perspectives, edited by M. Rosenberg and R. H. Turner, Pp. 290-319, New York: Basic Books.
Milson, F.(1973). An introduction to group work skill, London: Routledge and Kegan Paul.
Mitchell T. M.(1997). Decision tree learning in Machine Learning, McGraw-Hill Company, 1997.
Pauls on F. L., Paulson P. R., and Meyer C. A. (1991). What makes a portfolio a portfolio?, Educational Leadership, 48:5, pp. 60-63.
Quinlan J. R. (1993). C4.5 Programs for machine learning, Morgan Kaufmann Publishers, San Mateo, California.
Rumelhart D., Widrow B. & Lehr M.(1994). The basic ideas in neural networks, Communication of the ACM, 37:3, pp. 87-92.
Tsai C.C., Liu E. Z. F., Lin S. S. J., & Yuan S. M.(2001). A networked peer assessment system based on a Vee heuristic, Innovation in Education and Teaching International, 38-3, 220-230.
Vivacqua A. S. (1999), Agents for Expertise Location, in Proc. of AAAI Spring Symposium on Intelligent Agents in Cyberspace, Technical Report SS-99-03, Stanford, CA, USA.
Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes.
Cambridge: Harvard University Press.
Weinstein, C.E.,Learning and Study Strategies Inventory (LASSI), User's Manual, 1987.
Wilhelm K. H. (1997). Use of an expert system to predict language learning success, System: An International Journal of Educational Technology and Applied Linguistics, 25:3, pp. 317-334.