A Collaborative Learning System Based on Cloud and E-commerce

Jian Liao School of Online and Continuing Education

Southwest University Chong Qing, China

leojames123@gmail.com

Minhong Wang Faculty of Education

The University of Hong Kong Hong Kong, China magwang@hku.hk

Abstract—The number of learners using e-learning has been explosively increasing in the past decade by virtue of easy access to preferable educational resources at Internet. On the other hand, the number of teachers in schools or universities is growing slowly. As a result, instructional problems have emerged due to lack of sufficient support to learners in their e-learning process. Collaborative learning is suggested as a solution to this problem. However, current collaborative learning has focused on teamwork or group discussion without sufficient support to each individual. This paper presents a new model for collaborative e-learning called Collaborative Cloud, in which knowledge modelling and market economic mechanism are utilized to optimize the collaborative use of e-learning resources including teachers, students, and artifacts in collaboration. To implement the approach, cloud computing and electronic commerce are applied to connect learners and coordinate the resources in e-learning in a more effective way.

Keywords-collaboartive learning; cloud computing; grid

I. BACKGROUND With the increase of on-line learners, the star-shaped

topological structure in traditional learning or e-learning, i.e., one or few teacher is in the centre and all students are connected to the teacher, as is shown in first picture of figure 1, results in the tremendous contradiction. For example, the number of distance education students in China has risen from 500727 to 3558950 since 2003[1]. However, the number of teachers still maintains unchanged approximately. Some common courses like advanced mathematics even contains thousands of students but just one teacher. Students almost have no chance to communicate with teacher.

Figure 1. Topological structure of different learning forms

One form of collaboration is learning in team, which specific tasks are given to a team and will be completed by its members collaboratively or co-operatively [2][3]. The structure of this learning form in topology becomes

hierarchy, in which the teacher is on the top to assign the task for the team, the team leaders are in the middle to manage the team and other students are at the bottom, as is shown in second picture of figure 1. The shortage of this form is that finitude of team size and the number of members has limited a wider range of interaction among students.

Another form of collaborative learning is discussing in an online learning community by text, audio, video or other internet-supported synchronously or asynchronously [4] which the teacher and students connect freely, as is shown in the third picture of figure 1. Although it permits making collaboration in a broader scope, the participation and contribution of members are out of balance. Some members participate in interaction actively. By contrast, some of them barely communicate with others, even isolated.

In a word, online learners in current mass scale cannot collaborate effectively and efficiently in current learning forms. It needs a new collaborative form of learning according to newest learning theories and technologies.

II. RELATED THEORIES AND TECHNOLOGIES

A. Related Learning Theoretical Trends Recently, connectivism is beginning to get more attention

in the area of learning science. Different from traditional learning theories, connectivism proposes that learning not only takes place in the process of personal interaction with others, but also occurred in the connections among learners[5][6][7]. In other words, the relations in a group is same important as the outcome of learning.

Moreover, Web 2.0 also advocates the idea that all members in group share and create the knowledge together [8][9]. Refer to the idea of “Deconstruction” in Post-modernism [10], the old order in the relations among the learners in a group, such as class, grade, or school, should be broken up, and more reasonable order should be rebuilt according to personal demands and the degree of mastering knowledge. Not only the teacher but also learners who master certain knowledge or capability can tutor other learners in this knowledge unit.

In fact, the research of PALS (Peer Assisted Learning Support) [11] also shows learners can tutor learners themselves. In the case study of PALS, some students in a higher grade are organized to tutor the lower grade students and the results show they improve their learning performance significantly.

2011 Eighth IEEE International Conference on e-Business Engineering

978-0-7695-4518-9/11 $26.00 © 2011 IEEE

DOI 10.1109/ICEBE.2011.14

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To sum up, it will get to be important tendency of collaborative learning that exploring how to optimize the relations among the learners to make maximum performance based on their knowledge distribution and degree of mastering in a mass collaborative group.

B. Cloud Technology By using the cloud technology, users can get different

services based on their demands and pay it in real-time according to the quantity and quality of service, just like consuming the electric power or water for life. These services come from the background of the cloud system, which is constructed by a large number of virtual resources. As for the narrow definition of cloud, the virtual resources just consist of IT infrastructures, and the services are merely related to IT, software or Internet. However, in the general definition of cloud, the resources include people who possess certain professional knowledge and can provide tutorial services for other people [12].

C. Electronic commerce Electronic commerce consists of buying and selling of

products or services over electronic systems like the Internet. A large percentage of electronic commerce is conducted entirely electronically for virtual items such as access to premium content on a website. The sale and purchase transaction can be completed electronically and interactively in real-time. Electronic commerce can provide a mechanism of payment between buyer and seller with genuine or virtual concurrency [13].

III. PROPOSED SOLUTION OF COLLABORATIVECLOUD

A. Abbreviations and Acronyms According to the analysis of current learning forms and

related theories, new collaborative learning solution should include four aspects as follow.

1) Learners can not only acquire support from teachers but also from other learners who have mastered the related knowledge or skills.

2) The collaborative group allows mass users who come from anyplace to join in at any time. There is little influence for learners if some of them quit from the group.

3) The whole group is a mass complicated system of knowledge delivery and construction. The building of collaborative connection is flexible. It should not be built based on the traditional organization such as team, class, grade or school but on knowledge status of each member. Learners even do not take care of whom they collaborate with but what they can get from others.

4) It needs a mechanism to stimulate participation of members and balance the contribution of every member. The effort and performance of collaboration also be recorded and scaled in detail. On the one hand, learners request what they need from group with least costs. On the other hand, they provide what they can provide to obtain most benefits.

Based on these requirements, a new collaborative learning solution called collaborative cloud (CL) based on cloud computing and electronic commerce can be represented in figure 2. In this form, a learner can access the cloud management system in which other learners and teachers are connected to provide support upon requested.

Figure 2. Topological structure of collaborative cloud

B. Definition of Related Conception in Collaborative Cloud To describe the definition of collaborative cloud, it is

necessary to define the key elements in cloud as follows: • Collaborators: All users in the cloud including

learners and teachers. They are treated as advance collaborators who master more knowledge to a greater extent.

• Artifacts: Learning resources produced in the process of collaboration or provided by collaborators.

• Resource: All what collaborators can obtain from the cloud, including collaborators and artifacts.

• Collaborative Connection: A kind of relationship among collaborators. It is built among two or more collaborators when they prepare to interact with each other.

• Collaborative Service: Collaboration among collaborators can be regarded as a collaboration service from one collaborator to another. As a kind of service, it can be recorded or assessed, and it costs some virtual concurrency.

• Service Requestor & Service Provider: Each collaborator has two roles in the cloud. When he (or she) asks for help or wants to collaborate with others , the collaborator plays the role of service requestor. On the contrary, when he (or she) provides service to others, the collaborator is service provider.

• Virtual Account: It is a virtual concurrency deposited for checking, savings, or brokerage use by collaborators. It also records the balance and the history of saving or payment.

• Collaborative Cloud: It is the further application of the idea of cloud in collaborative learning. The foreground of collaborative cloud provides learning support services through the internet and the users pay for the services according to their quantity and quality. The background of the collaborative cloud is constructed by a large number of organized collaborators. And they form a virtual resource pool with redundancy and high availability.

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IV. THE FRAMEWORK OF COLLABORATIVECLOUD

According to the definition of collaborative cloud and its key elements above, the framework of collaborative cloud is shown in figure 3:

Figure 3. Framework of collaborative cloud

In this picture, there are four sub systems in cloud which are resource pool, connection router, interaction platform and service transaction. In these sub systems, resource pool contains mass collaborator agents which represent collaborators’ behaviors. Connection router will establish collaborative connections based on service requestors’ demands. Interaction platform includes all kind of synchronous or asynchronous instructional services such as video conference, electronic whiteboard, remote assistance and so on. The sub system of Service transaction mainly deals with related transactions in each collaboration.

There is a typical flow in the cloud. First of all, collaborator needs to register their basic information and the degree of mastering knowledge or skill roughly. After this, the collaborator can request collaborative service. The cloud will understand the demands of service requestors and dispatch it to related collaborator agents. By contrast, the service provider will receive the request and acknowledge it if it is suitable. After that, the requestor will select a service provider according to the knowledge status, service quality and service price. Thus, the connection between or among the collaborators will be built and the tutorial or other learning support service will take place by the interaction platform. When the service finishes, requestor will pay for

the service and assess this service. Then, the knowledge status of both service requestor and provider will be updated in detail. If there are any issues or contradictions during the collaborative process, they can ask for managers of the cloud of which are also played the role by certain collaborators to arbitrate.

V. KNOWLEDGE MODEL IN COLLABORATIVECLOUD

A. Knowledge Structure How to model knowledge or skill is crucial for collaborative cloud. Ontology should be established in the cloud. Based on this, learning objective and knowledge status of collaborators can be described formally. The artifact also can be related to proper knowledge units. As a result, the collaborative connection can built more reasonably according to this. An example of knowledge structure is shown in figure 4. Each node is a knowledge unit. There are some relations among the knowledge unit such as part-of , sequence, sub and so on. Highlight sub diagram is the objective which the collaborator needs to master.

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Figure 4. An example of knowledge structure in collaborative cloud

Table 1 lists basic relationships between two concepts in the constructed ontology, used in the prototype of the proposed collaborative cloud model.

TABLE I. THE BASIC RELATIONS AMONG KNOWLEDGE

Name Present Description

PartOf Par(A,B) Knowledge unitUnit A is a part of Knowledge Unit B

Sequential Seq(A,B) Knowledge Unit B is the prerequisite of Knowledge Unit A

Relevant Rel(A,B) Knowledge Unit A and Knowledge Unit B are co-relevant.

In fact, more types of relations of knowledge units can be listed and described in an ontology. In the ontology conceptualizing the learning environment, knowledge structure is decomposed into sub-concepts that are called knowledge units and their relationships. The prototype of the proposed model is constructed using the context of an e-learning course in software testing [12]. Software testing is an important and mandatory part of software development. It is essential for evaluating the quality of software products by identifying defects and problems. Figure 4 also depicts the ontology which conceptualizes the knowledge structure in a software-testing course. It is constructed based on IEEE standards for software testing.

B. Knowledge Status Based on the knowledge structure ontology, the e-

learning prototype identifies a set of Knowledge units that relate to learning objective, and then generate a customized objective test to exam the learner’s knowledge roughly.

Another way to obtain initial knowledge status is self-assessment. Users can evaluate the degree of all or the top level of knowledge unit.

]1,0[,,1,1 ∈=++= =ijijkij

j

N

kijk

ij STSN

TK

j

βαβα (1)

A user’s initial knowledge status over a certain knowledge unit is calculated using equation 1. The equation defines the knowledge status of the jth knowledge unit of the ith collaborator. Nj is the number of questions in the test relevant to knowledge unit j. Tijk is the score of the kth question of the ith collaborator on knowledge unit j. Sij is the score of self-assessment of user i on knowledge unit j.

, are the weight of test and self-assessment. After this, the knowledge status of users can be updated

dynamically during the process of using collaborative cloud. Service provider will receive the assessment result Khijfrom service requestor in the hth collaboration, as is shown in equation 2.

]1,1[,' −∈ΔΔ×

+= hijhijij

ijij KhKK

KK (2)

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C. Knowledge Reasoning Table 2 lists two hidden relations. More comprehensive

inference of the knowledge status can be conducted based on these two relations.

In Table 2, the relations of BelongTo and AncestorOf are both defined in the way of recursion. For example, “Bel(A,B)” can either mean if A belongs to B, then A is part of B, or A is part of C and C belongs to B. In this way, all knowledge units that belong to one knowledge unit can be found according to the relations of PartOf. The definition of AncestorOf is also defined in this way.

TABLE II. REFERENTIAL RELATIONS AMONG KNOWLEDGEUNITS

The algorithm used to update the knowledge status is described updating as follows. By using this algorithm, when knowledge status upon one certain knowledge unit is changed, all knowledge statuses upon other knowledge units can be updated synchronically based on the relations of BelongTo, AncesterOf and RelevantTo.

Algorithm Update_KnowledgeStatus( i , j ){ If (Updated(Kij)) Then For Each ( k which KCj BelongTo KCk){ Kik = Average(All(Kil which KCl BelongTo KCk)) } For Each ( k which KCk is AncesterOf KCj){ Kik = Kik + Kij } For Each ( k which KCj is Relevant to KCk){ Kik = Kik + Kij } End If

}

D. Service QualityAfter each service, the quality of this service should be

also assessed by the service requestor according to the process of learning support service. Then the value of service quality on certain knowledge unit on certain learner can be acquired and calculated.

]1,0[,1 ∈= =ijk

j

M

kijk

ij SKM

SKS

j

(3)

Equation 3 defines the Service Quality of the jth knowledge unit of the ith collaborator. Mj is the number of

service in the past on knowledge unit j. SKijk is the score of kth service on knowledge unit j.

E. Transaction Agent and Collaborative Connection To create reasonable collaborative connection in cloud, it

is necessary to make the price of service of each qualified collaborator according to their knowledge status and service quality. It also needs to select the service from candidates who can provide service on the required time according to their price, knowledge status, service quality, the number or time of providing services, and so on. These transactions can be made by learners themselves manually, also can be made by the transaction agent based on knowledge inference automatically.

There are two kind of transaction agent. One is Price Setting Agent and another is Service Selection Agent.

The price of service can be assessed by the price setting agent as follow.

]1,0[,1, ∈=++++= ijijijijij MMSKP γβαγβα (4) Equation 4 defines the integrated assessment of the jth

knowledge unit of the ith collaborator. Mij is market factor that acquire according to the supply and demands of service.

, , are the weight of knowledge status, service quality and market factor.

Service selection agent mainly selects the service according to the learner’s strategy that includes price priority, knowledge status priority and service quality priority. Then the collaborative connections between learners are created.

VI. ECONOMIC MECHANISM INCOLLABORATIVE CLOUD

Market-economic mechanism can optimize all kinds of resources in the group and stimulate collaborators’ participation. There are three principles to realize this as follow:

1) Each action of collaborator in the cloud will obtain certain number of virtual currency as rewards for collaborators’ participation like providing an artifact, communicating with other collaborators, even assessing the collaboration or relating artifact. The total income of all collaborators can be counted as GDP of collaborative cloud and represent activity of whole group. The total number of virtual currency should always increase.

2) If users want to request service, they need to pay certain virtual currency to the provider. It includes obtaining tutorial from other collaborators synchronized or reading the artifacts asynchronous. Then, collaborators should always provide service for other to keep their account balance. Otherwise, they should use real money to supple their virtual account. There are also certain benefits for the organizer of collaborative cloud.

3) The greater the service provider masters the knowledge unit, the higher the costs of collaboration with him (or her) will be. Service requestor can compare all service providers and select one who costs least price. Thus, the resources in cloud will be got used in a most reasonable way.

Name Present Description

BelongTo Bel(A,B) PartOf(A C) or (PartOf(A B) and belongTO(B C))

AncestorOf Anc(A,B) Sequential (A C) or (Sequential (AB) and ancestorOf(B C))

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VII. AN PROTOTYPE OF COLLABORATIVE CLOUD Based on above framework, a prototype using SQL

Server and J2EE, which contains jsp, struts, hibernate, java applet and so on, has been developed, as is shown in figure 5.

In this system, users can apply for registration as a collaborator in the cloud. Then they can log in the learning system, as is shown at the top left page in figure 5.

When the users are registering, some basic information and initial knowledge status need to be set up roughly, by completing the test or self-assessment, shown in two bottom pages of figure 5. A graphic page is developed by JGraph to indicate the knowledge structure. Also, knowledge status can be distinguished by the different colors on nodes.

After this, collaborator can request instructional service related to certain knowledge units. The cloud system will dispatch appropriate resources including text, audio, video

files or the candidate list of service provider to requester based on the relations among requestor’ knowledge status and providers’ or resources’, as is shown in the top right page of figure 5.

Thus, the service requestor can pick satisfied resources or provider, according to their degree on relative knowledge, price and historic service quality, to begin a collaborative activity, for example, explaining a concept or elaborating how to solve a mathematic problem. In this prototype, a multiple-user video conference is used to support the collaborative process as is shown in the middle of figure 5.

When collaboration has finished, the requestor needs to assess the service provider about service quality and the degree of mastering related knowledge of the provider. Then, both their knowledge status are updated accordingly. Also, the fee of this collaboration will be paid for the provider. Under the mechanism of free market economy, the resources in whole cloud system will be utilized to the most extent.

Figure 5. Prototype of collaborative cloud

VIII. EVALUATION To evaluate the effectiveness of the model, three

questions should be discussed as follow. 1. Can the learners rather than teachers tutor learners

themselves to make more performance or improvement in the scale of whole group?

2. Does the cloud system recommend the appropriate resources, especially the service providers which the learners really need or are satisfied with?

3. Is the free market economy mechanism in cloud effective to improve the utility of resources in the whole group to the greatest extent?

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Based on these questions, a pilot test of investigation and interview had been conducted qualitatively and quantitatively. A full experimental validation is under progress. In the pilot test, we invited a number of students, majoring in computer science from the e-learning school of South West University to use the prototype approximately three months. After this, we conducted an online survey to collect their feedback. 257 students responded to the survey. In addition, 10 students who are picked up randomly had been interviewed online to get more information about the model.

For the first question, the initial findings from the pilot test show an obviously positive attitude towards the solution. 60.7% students felt that learning support from the students of advanced academic capabilities had the same effect with the support from the teacher in the prototype, although they prefer to be tutored by the teacher. 12.1% students felt that there was no significant difference between the support from the teacher and the support from peer students of advanced academic abilities. Only 18.3% and 8.9% students felt that there was significant difference between the two. Interview data indicate that although when the learners face the problems in study, they prefer to ask for the teacher, if the higher grade student can provide the similar reply, they will also be satisfied. Furthermore, talking with classmates or higher grade students is subject to make the learners feel free, and then, discussing the problem more deeply. However, a problem should be emphasized that is for some opening questions, if learners received different answers from different higher grade students, they still hope to appeal to teacher to provide an authoritative answer.

For the second question, the survey indicates slightly positive results. 36.8% students consider the system recommends the right persons they want. 51.4% students think the result can be accepted but maybe it is not the best choice. 11.8% students don’t believe the choice made by the system. However, concerning the prototype is limited to smaller group, rougher ontology and shorter time to adjust the knowledge status on the knowledge units, if the model is applied to greater extent and the algorism get to refined continuously, the result will be much better. Otherwise, one of the interviewee suggests, if the cloud system is prior to recommend the familiar persons of the user, it will also make better experience and result. So, there is still an interesting and challenging area to explore in the future.

For the last question, most of users have positive attitude. 55.7% users think the virtual money and market mechanism will be very helpful to stimulate users’ participation and to improve the quality of service. 26.5% users think that there are some problems in it, but it is still useful. Only 17.8% users consider it useless and trouble. In the interview, someone considers that it is a little embarrassed when collecting money or paying for the service from the familiar. Sometimes, it is better to change virtual currency to virtual gift as the fee of service. Another comment is for the novice, it is hard to play the role of service provider. In most time, they need to get

services from others. So, there is an economic unbalance for them. They need a sum of initial virtual capital to take over this period.

Based on the pilot evaluation results, we will make relevant modification and improvement on the system for further experiment and evaluation.

IX. CONCLUSION Compared to the traditional collaborative learning

organization in team and in community, the collaborative cloud has more features and benefits as follow:

1) Maximizing the use of the students themselves to provide learning support services and freeing the teachers from the role of knowledge evaluation to alleviate or even solve the core contradiction of e-learning.

2) Teachers’ work load will be decreased significantly. Teachers could provide superlative collaborative support service. Nevertheless, more collaboration are taken place among other collaborators and they provide support service by themselves.

3) Collaborative cloud can help learners set up the best connection with other learners in the completely collaborative group. Combined with other more knowledge management techniques, it makes learning objectives more specific and could evaluate the knowledge structure of students in a smaller granularity.

4) Benefit-driven learning. The market economic mechanism will optimize collaborative resources of teachers, students and other resources in platforms.

5) The connection in the cloud among the members can break the boundaries of classes, grades even schools of the traditional education. Cross-organizational collaboration has become feasible. And it provides a chance to realize the maximized share of teaching resources particularly teacher resources in different educational institutions.

Technologies have been enhancing education all the time, at same time, educational theoretical ideas are updating continuously, especially with the emergence of connectivism, Web2.0. Current collaborative learning focuses on teamwork or group discussion without attention to sufficient support for each individual during the whole learning process. This paper presents a new model of collaborative e-learning called Collaborative Cloud, in which knowledge modelling and market economic mechanism are utilized to optimize the collaborative use of the e-learning resources including teachers, students and artifacts in collaboration. To implement the approach, cloud computing and electronic commerce are applied to connect learners and coordinate the resources in e-learning in a more effective way.

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