Design on Collaborative Virtual Learning Community 429 asynchronous communication. Besides, virtual learning community refers to the con- cept of visual process chart (VPC), so that it can visualize the learning process. 4.3 Visual Process Chart (VPC) VPC as a chart tool can display the learning process of group member or between the group members. According to the collaborative learning, factors of affecting learner’s learning involve group’s division, participation of group members, task distribution and teachers’ help in time, progress test and so on. The paper illustrates one of the factors named the participation of group members to design VPC. The measure of learning participation contains learner’s sending message, replying message, looking message, neglecting other factors. Each group members contribute to their group’s online communication in certain message. In VPC, red circle presents group member while grey circle presents a group [10]. The same group agglomerates together with dashed Circle. As shows in figure 4, it is assigned into 3 groups in a class. Fig. 4. Screenshot of the VPC For example, observing the encircled part we can see there are four red circles which stand for group members and one grey circle which presents a group. They are around the group tightly. In VPC, the distance between circle and group circle, named line segment (LS), points out the length of sending message. We called information content (IC) for short. If a circle is near the group, then its IC is more, compared to far away. Circle’s diameter implies the quantity of a learner’s average message, we called it information degree (ID) for short. If a circle is smaller, it means the message quan- tity is less, compared to those bigger ones. According to this, we can know the group circle in the same way. If the grey circle is bigger, in other word, the diameter of which is longer, the ID is bigger, compared to the smaller one. If LS between group and class is longer, the information content is less. Therefore, students can recognize 430 W. Tan et al. themselves clearly through VPC. The total information equals to the sum of IC and ID. Finally, the class circle could be drawn. Following is the algorithm of VPC. We use the kind of C pseudo code and Natural Language to describe the algorithm of VPC chart as follows: int totalMessage; // total information degree int totalInformation; // total information content int group_Count; // the amount of groups int stu_info; //student information content int stu_in; // student information in-degree int stu_out; // student information out-degree int group_info; // group information content int group_in; // group information in-degree Int group_out; // group information out-degree define p; // parameters if totalMessge>0 { if totalInformation>0 Class_R= (totalMessage+totalInformation)*p; Class_X=0; Class_Y=0; Take (Class_x, Class_Y) as circle center, Class R as radius to draw the class circle; if group_Count>0// Here starts to process the group { i=0; while i<group_Count { group_l=(group_info)*p; Confirm the length of line segment according to group IC; group_r=(group_in+group+out)*p; Comfirm the circle center based on the group ID; Take group_r as radius to draw circle; Link the group circle center and class circle cen- ter; j=0; while j<stu_count { stu_l=(stu_info)*p; Confirm the length of line segment according to learner’s IC; stu_r=(stu_in+stu_out)*p; Comfirm the circle center based on the learner’s ID; Take stu_r as radius to draw circle; Link the student circle center and group circle center; j++; } i++; } } Fig. 5. Pseudo code of VPC chart algorithm Design on Collaborative Virtual Learning Community 431 As figure 3 shows, there is a coordinate graph in down left position. It demon- strates the performance of group’s collaborative status when a task is finished, named collaborative performance. The result of collaborative performance is measured by some factors of each group member, like the total information quantity, learner’s online time and progress test. Fuzzy comprehensive evaluation is involved to calcu- late each group member’s dynamic performance. The brief step is as follows [11]: evaluation purpose and evaluation index should be determined firstly. Analyzing the group member real- time learning process, the purpose is to attain feedback in time and reach better learning way. Total information quantity, learner’s online time and progress test are chosen as evaluation index. Then, evaluation index weight and comment rate are confirmed to establish fuzzy relation matrix. After choosing fuzzy operator, we use mathematical model of fuzzy comprehensive evaluation to gain the evaluation result. Then, the result is utilized to measure the collaborative perform- ance, the concrete method refers to the book of “The Theories and Methods of Com- puter-Supported Cooperative Learning” [6]. (a). Evaluation of concentration magnitude. Arithmetic average, a method of evaluating concentration magnitude, is involved in the paper. Suppose the group scale is n, the group member’s score is respectively x1, x2,…, xn., x is the average score of group members(1). 12 1 () n xxx x n =+++" , 1 1 n i i xx n = = ∑ (1) (b).Evaluation of difference magnitude. Standard deviation is calculated: (2) 2 1 1 () n i i sxx n = =− ∑ (2) (c). Evaluation of the collaborative performance based on the collaborative per- formance formula (3). xs C xs − = + (,)Exc= , C < 1 (3) Where C presents collaboration degree, as well as E presents collaborative performance. The higher the collaboration degree is evaluated, the better the collaborative perform- ance is proved. Thus it illuminates high cohesion. For example, look at Fig. 6. 2 1 1 ( ) 3.6056 n i i sxx n = =−≈ ∑ , 81.3944 0.9186 88.6056 xs C xs − =≈ ≈ + , ( , ) (85,0.9186)Exc== (4) 432 W. Tan et al. Fig. 6. Score of group B By calculating (4), group B is proved better in collaborative performance. That is to say, the group members cooperate and communicate with each other well and achieve the task perfectly. Following is the algorithm of the histogram in VPC to present the collaborative performance shown in Fig. 4, using the kind of C pseudo code and Natural Language. drawing(x,y); // draw the Coordinate Graphs. Int i=1; Int S; // Group performance Int p,q; //parameters While(i<=group_count) { S= (group IC)*P+(group ID)*q; Draw columnar section in the interval of (x>5*I, x<5*(i+1), y>0, y<s); } Fig. 7. Pseudo code for implementing the collaborative performance histogram in VPC 4.4 Realization of Virtual Learning Community The development of virtual learning community involves many tools. Firework is used to design the foreground graphic interface. Dreamweaver and visual studio 2005 is combined to edit webpage and develop programs. C# is a programming language developing by Microsoft to fit .net. Modules designed with C# may easily transform to web service, may arbitrary transfer in random language and operating system. So the system uses C# to instantiate algorithms. Background database uses SQL server2000, while server adopts the production of IIS 6.0. 5 Conclusions and Perspectives Collaborative virtual learning community is a tendency of the future development. Constructing effective and suitable virtual learning community must integrate certain Design on Collaborative Virtual Learning Community 433 theory. Under the collaborative learning theory of constructivism and the standard architecture proposed by Chinese E-learning Technology Standardization Committee, this paper designs a “software engineering” collaborative virtual learning community to maximize each learner’s learning performance. Moreover, it is feasible to introduce a concept of learning process visualization in collaborative learning because of China’s traditional learning pattern. Information content and information degree is distilled as learner’s characteristics to design the visualization of learning process, which provides nicer feedback information. Nevertheless, we must emphasize that not only constructing learning environment is complex and comprehensive, but also visu- alizing learning process needs to be paid more attention. Some factors can affect the learning effect, such as learning style, group member’s organization, the quality of information communication and so on, which is not contained in the paper. This part needs to be studied farther in the coming future, and collaborative virtual learning community also will be improved. Acknowledgment This paper was supported by the Zhejiang provincial Natural Science Foundation of China (Grant No. Y106039), the Key Research Foundation of Zhejiang Education Department of China (Grant No. 20060491), and the Innovation Foundation of Zheji- ang Normal University Graduate School. References 1. Gates, B.: Future Tense. Beijing University Press, Beijing (1999) 2. Wang, L.: The Principle and Application of Virtual Learning Community. Higher Educa- tion Press, Beijing (2004) 3. Yu, S.Q.: A Model for Evaluation of Internet-based Distance Teaching. Open Education Research, Shanghai 4. Yang, Z.K., Lin, Q.T.: Research and development of web-based virtual online classroom. Computers & Education 48, 171–184 (2007) 5. Gan, Y.C.: Knowledge Construction and Collective Wisdom Development in Virtual Learning Community. 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Publishing House of Elec- tronics Industry, Beijing . int stu _in; // student information in- degree int stu_out; // student information out-degree int group_info; // group information content int group _in; // group information in- degree Int group_out;. follows: int totalMessage; // total information degree int totalInformation; // total information content int group_Count; // the amount of groups int stu_info; //student information content int. learner’s sending message, replying message, looking message, neglecting other factors. Each group members contribute to their group’s online communication in certain message. In VPC, red circle