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Collaborative cloud: a new model for e-learningJian Liaoa, Minhong Wangb, Weijia Ranc & Stephen J. H. Yangd

a School of E-Learning, South West University, Chong Qing, China.b Faculty of Education, The University of Hong Kong, Hong Kong,Hong Kong.c College of Computing and Information, State University of NewYork, Albany, USA.d Department of Computer Science and Information Engineering,National Central University, Jhongli, Taiwan.Published online: 14 May 2013.

To cite this article: Jian Liao, Minhong Wang, Weijia Ran & Stephen J. H. Yang (2014) Collaborativecloud: a new model for e-learning, Innovations in Education and Teaching International, 51:3,338-351, DOI: 10.1080/14703297.2013.791554

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Collaborative cloud: a new model for e-learning

Jian Liaoa, Minhong Wangb*, Weijia Ranc and Stephen J. H. Yangd

aSchool of E-Learning, South West University, Chong Qing, China; bFaculty of Education,The University of Hong Kong, Hong Kong, Hong Kong; cCollege of Computing andInformation, State University of New York, Albany, USA; dDepartment of Computer Scienceand Information Engineering, National Central University, Jhongli, Taiwan

The number of learners using e-learning has been increasing at an enormous ratein the past decade due to easy access to higher educational resources via theInternet. On the other hand, the number of teachers in most universities is grow-ing slowly. As a result, instructional problems have emerged due to the lack ofsufficient support to learners in the e-learning process. Collaborative learninghas been suggested as a solution to this problem. Current collaborative learningputs a heavy emphasis on teamwork and group discussion, but provides inade-quate support for individual learners during the whole learning process. Thispaper presents a new model of collaborative e-learning using cloud computingtechnology to solve this problem. A prototype system has been developed withempirical evaluations to demonstrate the effectiveness of the proposed approach.

Keywords: collaborative learning; e-learning; cloud computing; knowledge grid

Background

E-learning has become an important trend in educational reform. With the help ofinformation technologies, learners can access teachers and instructional resourcesover time and space more efficiently. Especially in China, a developing countrywith an urgent need for adult education and continuation education, instructionalresources for higher education available online have opened the door to college forthose who are at the bottom of society or fail to pass the college entrance exam. Asshown in China Education Statistics (2010), the number of distance education stu-dents in China had risen continuously from 500,727 to 3558,950 since 2003, whilethe number of university teachers remained unchanged, thus resulting in the sharpincrease in student–teacher ratio. For example, a lecture session for some generaleducation courses such as Calculus involves more than a thousand students.Although learning resources such as courseware, video resources and instructionaldocuments can be copied unlimitedly and shared via the Internet, learning supportservices (Sewart, 1978, 1993, 2001) such as individual tutorials provided by instruc-tors are limited and irreplaceable. As shown in Figure 1, the above problem withcurrent e-learning practices in higher education arises from the star-shaped topologi-cal relationship between the instructor and the student, that is, a number of students

*Corresponding author. Email: magwang@hku.hk

Innovations in Education and Teaching International, 2014Vol. 51, No. 3, 338–351, http://dx.doi.org/10.1080/14703297.2013.791554

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are connected to a single instructor or a few teaching assistants in the centre. As aresult, students cannot get timely tutoring when they need help in learning, andinstructors are overwhelmed with instructional work.

Collaborative learning, which is defined as a situation in which two or morepeople learn or attempt to learn together (Dillenbourg, 1999; Lee & Smagorinsky,2000; Mitnik, Recabarren, Nussbaum, & Soto, 2009), is considered to be a work-able solution to the problem. Current collaborative forms, however, are limited inoptimally using the ability of each individual in the learning group and sharingavailable learning resources among learners.

In one form of contemporary collaborative learning, people are asked to learn inteams, and members in each team are asked to complete specifically assigned taskscollaboratively (Huang, 2003; Liao, Wang, Li, & Huang, 2009; Stahl, Koschmann,& Suthers, 2006). The topological structure of this form of learning is a hierarchy,as shown in Figure 2. The top level of the hierarchy is the instructor, who assignstasks; the middle level consists of team leaders who manage their teams; and thebottom level consists of students. This form of collaborative learning has its ownlimitations. First, constraints on team size limit the total number of learners in alearning environment. For instance, if the maximum team size is 10, and more than1000 people need to learn, an instructor will need to serve 100 teams. In addition,this form of collaborative learning does not provide mechanisms for participation

Figure 2. Topological structure of team-based collaborative learning.

Figure 1. Topological structure of traditional e-learning.

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and performance assessment. As a result, active students may do all the work and‘take over’ the whole group, and some students would become “free riders”(Huang, 2003; Zhao, 2006).

Another form of collaborative learning is group discussion or online communitylearning via text (synchronous or asynchronous), audio, video or other Internet-supported devices (Gan, 2005). The topological structure of this learning form is anet, in which the instructor and students are connected freely, as is shown inFigure 3. In contrast with the team size limit in a team-based learning environment,this form of collaborative learning allows a large number of learners to participate.Nevertheless, it cannot guarantee that each individual learner receives sufficientlearning support. This problem is due to a lack of management control over learnerparticipation. Without enforcement or other incentives, learners are motivated toparticipate mainly by personal interests. If learners are interested in discussed top-ics, they will probably be actively involved in or lead the discussion and becomecore players in the learning community. Learners who are not interested in thetopics will perhaps respond passively, act as lurkers, or not participate at all. A sim-ilar situation exists when learners have questions and need others’ comments andfeedback. Responses from other learners or timely responses from the instructor arenot guaranteed. Therefore, this form of collaborative learning cannot optimally usethe ability of each learner, and thus fails to maximise the use of existing resourcesin a learning environment.

To summarise, current collaborative forms of e-learning cannot effectively meetstudents’ needs in an e-learning environment short of instructors. We have designeda collaborative e-learning environment based on cloud computing architecture andtechnology for the purpose of solving this problem. In our collaborative e-learningenvironment, students and instructors are treated as virtual learning resources andare connected to each other, forming a network, which is called a collaborativecloud. Learning support services are delivered to students who send requests to thecollaborative cloud. Besides virtual learning resources, the collaborative cloudembraces a knowledge modelling mechanism to process and organise these learningresources in optimally reasonable and effective forms based upon the economicmodel. This approach maximises the use of existing resources in a collaborative

Figure 3. Topological structure of discussion-based collaborative learning.

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learning environment. A prototype system has been developed with empirical evalu-ations to demonstrate the effectiveness of the proposed approach.

Related work in cloud computing

Cloud technology is a computing technology that allows web users to order and payfor real-time computer-based services in the same way as people consume and payfor electricity and water in daily life. Computer-based services are stored as a largenumber of self-managed and configurable virtual resources at the back end of cloudsystems (Antonopoulos & Gillam, 2010). Cloud technology enables these resourcesto be organised in such a way that the right resources can be delivered to the rightusers efficiently on-demand (Vaquerol, Roderos, Caceres, & Lindner, 2009).

Cloud computing consists of three different functional layers: infrastructure as aservice (IaaS), platform as a service (PaaS) and software as a service (SaaS)(Creeger, 2009; Vaquerol et al., 2009).

IaaS includes storage, hardware, servers and networking components. Virtualresources are stored in this layer and maintained by resource providers. Users orderresources and pay on a per-use basis. According to the total demand, suppliersadjust the resource amount in a timely manner.

PaaS provides a development environment and associated services that areaccessible from the Internet (cloud). PaaS enables building, testing and deliveringapplications, software and services directly via the cloud.

SaaS is a software distribution model in which software such as word processorsare hosted by a service provider and made available to users via the cloud.

Integrating cloud computing and e-learning to deliver students learningsupport services

Although cloud computing has many potential applications in the field of education,there is no ready-made solution to resolve the problem of instructor–student imbal-ance in e-learning. On the one hand, changes or improvements need to be made tocloud computing so that it can be applied in e-learning. On the other hand, themodel of e-learning should also be adjusted. The reasons are presented as follows.

(1) Unlike resources in a typical cloud, resources in a collaborative learningcloud should embrace a wide range of resource types including not onlyinfrastructure resources that consist of storage, hardware, servers and net-working components, but also other resource types such as human resources.Human resources refer to collaborators, including students, instructors andteaching assistants, and are the most important type of resource in a collabo-rative e-learning environment. This enlarged definition of resource alsoresults in a significant change in the e-learning model. As in Web 2.0 (Shu-en, 2008; Wang, 2011), collaborators in a collaborative e-learning environ-ment play not only the role of service consumers, but also the role of serviceproviders. Not only instructors but also learners who have certain knowledgeor capabilities can function as tutors. In Parkinson’s peer-assisted learningsupport study, students with higher grades were asked to tutor students withlower grades, and the findings showed that some learners were competent toprovide learning support to other learners (Parkinson, 2009).

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(2) Unlike services in a typical cloud, besides IaaS, PaaS and SaaS (Vaquerolet al., 2009), a collaborative learning cloud should also provide learningsupport such as tutorials and discussions as a service (LSaaS). Learningsupport services are more important than learning materials in an e-learningenvironment (Sewart, 1978, 1993).

(3) Unlike the dispatch decision in a typical cloud, which is solely based on theinformation about how much time and how many resources each supplierhas, the dispatch decision in a collaborative learning cloud should alsodepend on the information about each supplier’s (collaborator’s) knowledgestructure and knowledge status. Artificial intelligence (AI) technologies havebeen widely used in computer-supported learning environment, with a focuson profile modelling and reasoning of users’ behaviour and resources. AI ispowerful in identification, allocation and assessment of these peer learningactivities. However, mental states are not just computational states such asthe ‘Chinese room’ (Searle, 1980). A collaborative learning cloud cannotfunction smoothly without the support of knowledge modelling, knowledgediscovery and reasoning techniques. Ontology is a knowledge modellingtechnique, which can be described as a set of concepts within a domain andthe relationships between those concepts (Gruber, 1993; Knight, Gašević, &Richards, 2006; Mahmood & Ferneley, 2006). Ontology enables effectiveknowledge presentation, and is thus the basis for knowledge discovery andreasoning. A knowledge grid is considered as the implementation of cloudcomputing with the expansion of knowledge discovery and reasoning (Ber-man, 2001; Cannataro & Talia, 2003; Smarrand & Catlett, 1992; Zhuge,2004). It is applicable to the implementation of a collaborative learningcloud.

(4) Unlike traditional forms of learning, in a collaborative learning cloud, market-place rules should be applied in order to stimulate students’ participation andmore reasonably dispatch virtual resources among collaborators. This meansthat service consumers will choose the most appropriate service provider forthemselves, considering factors such as the consumer’s knowledge status (thedemands), the quality of service (the supply) and the service price, rather thansimply receive the service with the best quality from the best service provider.Thus, the form of learning will shift from being task-driven or interest-drivento being benefit-driven. Consumers can pay services either by using realmoney as in e-commerce transactions (Guan, 2006) or by using a money-equivalent such as the user’s credit score in online communities.

(5) Unlike traditional learning in a school, learning in a collaborative cloudwould crosses the boundaries of classes and schools, and challenge the exist-ing curriculum and grading system. A significant benefit of collaborativelearning across boundaries is that there would be more collaborators, whichmeans more available resources and services. A learning group would enableknowledge to be shared to a greater extent. In a collaborative cloud, thecourse learning and course evaluation period would go beyond semesterboundaries. In addition, students with higher grades could tutor their peerswith lower grades. Both groups of students would benefit from this practice.

Based on the above discussion, we propose an e-learning solution that combinescloud-computing technology and an improved collaborative learning model.

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Proposed collaborative learning based on cloud computing

The topological structure of the proposed collaborative e-learning solution based oncloud computing is represented in Figure 4. In this form of learning, a student canaccess the cloud management system in which other students and instructors areavailable to provide support upon request.

The definition of the proposed collaborative learning cloud is described in thefollowing.

(1) The output of the collaborative learning cloud is learning support serviceslike tutoring and collaborative discussions.

(2) The collaborative learning cloud records and evaluates the knowledge andservice supply information of every collaborator, detects the demands forlearning support services through enquiring and reasoning, and then estab-lishes the match between supply and demand for learning support servicesthrough evaluating and reasoning.

(3) In each collaboration transaction, those who receive learning support servicespay a certain amount of virtual money (credit score) to the provider accord-ing to the knowledge status of the provider, service quality and service time.To accumulate their virtual money (credit score), collaborators have to pro-vide services to other collaborators who need learning support.

(4) Instructors are also collaborators. They provide initial and high-quality sup-port services. They act as the ‘primary promoter’ throughout collaborativeprocesses in the collaborative learning cloud. A relatively larger proportionof collaborations should take place among other collaborators. In addition,teachers are in charge of managerial tasks including defining teaching objec-tives, and coordinating or arbitrating conflicts in collaborations.

(5) Learning occurs in the process of establishing collaborative connexions andcollaborative interactions. Both learning support service providers andconsumers are able to build their own and new knowledge in this process.

Typical flow of the collaborative learning cloud

According to the above definition of the collaborative learning cloud, a typical col-laborative flow includes seven steps, which are presented in Figure 5:

Figure 4. Topological structure of collaborative learning based on cloud computing.

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The collaboration flow starts with learners’ sending their requests forinstructional support service to the collaborative learning cloud. The cloud collectsinformation about service demand and then lists service providers who are mostlikely to match the demand. Learners then select their preferred service providersaccording to the provider’s knowledge status, service quality and service price. Inthis way, the connection between or among learners is established, and appropriatetutorials or learning support services in other forms are delivered to service request-ers. After consuming the services, learners make the payment and evaluate theservice quality. Learners can ask for teachers or administrators to arbitrate whenproblems or conflicts occur.

Architecture of the collaborative learning cloud

As discussed in the third section, a knowledge grid is applicable to the implementa-tion of a collaborative learning cloud. In this study, we build an architecture basedon a knowledge grid to implement the proposed collaborative learning cloud.

The architecture has five layers, as shown in Figure 6. The lowest layer is theresource layer, storing collaborators’ personal information including their accountregistration information, knowledge levels and services. The second layer is theknowledge layer, which describes the knowledge allocation structure among collab-orators, defines learning objectives of each learner and sets the knowledge status ofeach learner. The second layer consists of knowledge reasoning functions that sup-port upper layers. The third layer is the connection layer. This layer mainly uses theinformation about service requests to look for suitable service providers among col-laborators with the help of knowledge reasoning functions in the second layer. Thefourth layer, which is the service layer, delivers corresponding services through anappropriate application platform after collaborative links among collaborators havebeen established. The delivered services could be tutorials in various formsincluding multi-user video conference, test-based knowledge assessment,discussions, Q&A sessions, etc. The last layer is the transaction layer, which deals

Figure 5. Collaboration flow in the collaborative learning cloud.

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with service-delivery-related affairs including paying credit scores, service evalua-tion, dispute resolution, etc.

Knowledge model in the collaborative learning cloud

To enable more reasonable dispatching of virtual resources, ontology technology isused for knowledge modelling, conceptualising the learning environment into amachine-readable format. The machine-readable format is a knowledge structurethat consists of concepts and relationships among these concepts. In the constructedontology for this study, these concepts are called knowledge components. Basicrelationships between knowledge components are presented in Table 1. It should be

Figure 6. Architecture of collaborative learning cloud based on a knowledge grid.

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noted that BelongTo and AncestorOf relations are defined as recursive relations.‘Bel(A,B)’ indicates two possible situations: A is part of B; or A is part of C, andC is a part of B, so A is a part of B. Similarly, ‘AncestorOf’ also indicates twopossible situations.

In this study, an e-learning course in software testing is used as the study settingin order to build a prototype that is implemented using the proposed architectureand knowledge modelling technique (Wang, Ran, Liao, & Yang, 2010). Softwaretesting is an important component of software development. It is essential for soft-ware quality control in identifying defects and problems. The ontology that concep-tualises the knowledge structure in a software-testing course is presented in

Table 1. Basic relations between knowledge components in the collaborative learningcloud.

Name Notation Description

PartOf Par(A,B) Knowledge component A is a part of knowledgecomponent B.

Sequential Seq(A,B) Knowledge component B is the prerequisite of knowledgecomponent A.

Relevant Rel(A,B) Knowledge component A and knowledge component Bare co-relevant.

BelongTo Bel(A,B) Par(A,B) or (Par(A,C) and BelongTo(C,B))AncestorOf Anc(A,B) Seq(A,B) or (Seq(A,C) and AncestorOf(C,B))

Figure 7. Ontology of an e-learning course in software testing.

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Figure 7. It is constructed according to IEEE standards for software testing intro-duced by Bertolino (2001).

Based on the ontology, the e-learning prototype identifies a set of knowledgecomponents that relates to learning objectives, and then generates a customised quizto test the learner’s knowledge. The assessment result is used to update the learner’sknowledge status, which in turn affects the learner’s connexions with other collabo-rators. In addition, after the consumption of the service, the service consumer’sevaluation of the service provider also changes the provider’s knowledge status.

Implementation of the proposed collaborative learning cloud prototype

Based on above framework, a prototype using SQL Server and J2EE, which con-tains jsp, struts, hibernate, java applet and so on, has been developed, as is shownin Figure 8.

Users are required to register as collaborators before they log into the e-learningprototype. Upon registration, users are asked to complete a quiz and self-assessment, which collect collaborators’ basic information and initial knowledgestatus. In the prototype, the knowledge structure and collaborators’ knowledgestatus are virtually presented by using a tool called JGraph, as shown in Figure 8.

Once they log into the prototype, collaborators can request instructional servicesrelated to certain knowledge components. The cloud will dispatch appropriateresources including text, audio, video files or a list of service providers to requestersbased on requesters’ knowledge statuses and characteristics of the provider or theresource.

Figure 8. KGCC prototype and the audio-video conference system.

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Then, service requesters pick appropriate resources or provider according totheir knowledge statuses, service price and historical quality information. Thisaction of the service requester starts a collaborative transaction. In the prototype, amultiple-user video conference tool is used to support the collaborative process.

After the collaboration, the requester pays for the collaboration and assesses theservice provider on service quality and knowledge status. Both their knowledge sta-tuses change after a collaboration transaction. Governed by rules in the marketplace,resources in the collaborative learning cloud are fairly and maximally used.

Evaluation

We put forward three questions for evaluation of the effectiveness of the proposedcollaborative learning cloud model:

(1) Can the learners teach themselves to bring about better performance orgreater improvement with respect to the whole group? In terms of serviceproviders, can learners be considered the equivalent of instructors in the col-laborative learning cloud?

(2) Does the collaborative learning cloud recommend appropriate resources, espe-cially service providers whom learners really need and are satisfied with?

(3) Is the economic model of a free market in the collaborative learning cloudeffective in maximizing the use of available resources in the learningenvironment?

In order to answer these questions, a pilot evaluation was conducted. The datafor evaluation were collected using both quantitative and qualitative methods. A fullevaluation using an experimental approach is in progress. In the pilot evaluation,we invited 286 students, majoring in computer science from the e-learning schoolof South West University of China, to use the prototype for three months. At theend of the third month, we conducted an online survey to collect their feedback.257 students responded to the survey. In addition, 10 students were randomlyselected for the purpose of interview. The interview was designed as a semi-struc-tured interview and was conducted online to collect more substantial feedback onthe prototype.

For the first question, the findings from the pilot evaluation showed an obviouspositive attitude towards the prototype. About 60.7% of the students felt that learn-ing support from students with good academic performance had the same effect asthe support from the instructor in the prototype, although they preferred to betutored by the instructor. About 12.1% of the students felt that there was no differ-ence between the support from the teacher and the support from peer students withgood academic performance. Only 18.3 and 8.9% of the students felt that there wasa large and small difference between these two, respectively. Interview data indi-cated that although students preferred instructors’ tutoring when they needed helpin studying, if they could obtain similar help from students with better academicperformance, they would also be satisfied. Furthermore, when learning with theirpeers, students felt more relaxed, which could facilitate deep discussion. However,in some situations, the instructor was required. For example, if learners receiveddifferent answers from different higher-grade students, they would expect an author-itative answer from the instructor.

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For the second question, the survey indicated slightly positive results. About36.8% of the students said the prototype recommended the right service providersthey needed. About 51.4% of the students said the recommendation was acceptable,but it might not be the best choice. About 11.8% of the students did not think therecommendations made by the prototype were helpful. One of the interviewees sug-gested that the performance of the prototype would be better if it could recommendappropriate service providers whom students were familiar with in the first place. Inaddition, the algorism of dispatch would influence the effects of recommendation.All these considerations lead to interesting and challenging areas for exploration inour future research.

For the last question, most of the users had positive attitudes towards the proto-type. About 55.7% of the students thought virtual money and marketplace ruleswould be very helpful in stimulating users’ participation and in improving the qual-ity of service about 26.5% of the students thought that this model might be useful.Only 17.8% of the students considered it useless. In the interviews, some studentssaid that people would be embarrassed when collecting money or paying for theservice from someone with whom they were familiar. Sometimes, it would be betterto change virtual currency to virtual gifts as the service fee. Another comment wasthat novices were incapable of playing the role of service provider. Most of thetime, they would need to get services from others. So, in economic terms, theywould not have any income in the early stage and might need initial virtual capitalto overcome this period.

The current evaluation of the proposed collaborative learning cloud was limitedto the data concerning users’ perceptions. Further evaluation of the proposed systemin its resource utilisation and learning effectiveness is needed. After some modifica-tions and improvements of the prototype based on the pilot results, further studieswill be carried out to analyse the learning resource utilisation, collaborative learningprocess and learners’ achievement using the proposed system.

Conclusion

Technology innovations have been enabling new educational approaches all thetime. Educational theories are also being updated continually. Based on new cloudtechnology and related learning theories, this paper presents a new e-learning modelcalled the collaborative learning cloud to solve the problem of instructor–studentimbalance in current e-learning applications, especially in China. Students canreceive learning support services according to their needs from the collaborativelearning cloud in which other students and instructors are connected with each otheras a kind of virtual learning resources. By applying the knowledge modelling tech-nique and the economic model of free market in the collaborative learning cloud,virtual resources can be dispatched in the most reasonable and effective way. Thisdesign alleviates the tension between limited instructional resources and too manylearning support demands. A prototype system has been developed with initialempirical evaluations to demonstrate the effectiveness of the proposed approach.

Future work will include refining the algorithm of matching the servicerequester and the service provider and service pricing. More solid evaluation ofthe collaborative learning cloud model will be conducted before applying it toreal practice.

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AcknowledgementThis research is supported by a UGC GRF Grant (No. 717708) from the Hong Kong SARGovernment, and a Seeding Fund for Basic Research (No. 201011159210) and a Seed Fundfor Applied Research (No. 201002160030) from The University of Hong Kong. The authorsalso thank Professor Jonathan Michael Spector for his valuable support to this project.

Notes on contributorsJian Liao is a software engineer in the E-Learning school of South West University inChina. He was a member of the KM&EL Lab of the University of Hong Kong. His researchfocuses on computer-supported collaborative learning (CSCL), and online learning andinformation systems in organisations. He obtained best paper awards in ICCE 2006 andICALT 2007.

Minhong Wang is an associate professor in the Faculty of Education and director of theKM&EL Lab of the University of Hong Kong. Her current research interests include e-learning, web-based training, knowledge management, complex process management andinformation systems. She is the editor-in-chief of Knowledge Management & E-Learning: anInternational Journal (KM&EL).

Weijia Ran is currently a PhD student at the College of Computing and Information, StateUniversity of New York at Albany. She was a member of the KM&EL Lab of theUniversity of Hong Kong. Her research interests include e-learning in the workplace,knowledge management and information systems in organisations.

Stephen J.H. Yang is the distinguished professor of Computer Science & InformationEngineering, and the associate dean of Academic Affairs at the National Central University,Taiwan. He received his PhD degree in Electrical Engineering & Computer Science from theUniversity of Illinois at Chicago in 1995. His research interests include creative learning, 3Dvirtual worlds, App software and cloud services.

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