Construction of a digital learning environment based on cloud computing

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  • Construction of a digital learning environment based oncloud computing

    Jihong Ding, Caiping Xiong and Huazhong Liu

    Jihong Ding is a PhD candidate at School of Educational Information Technology of Central China Normal Univer-sity. Her main research interests are technology-enhanced learning and automatic recommendation. Caiping Xiongis a professor at School of Educational Information Technology of Central China Normal University. His mainresearch interests are technology enriched education, education resources optimal allocation and ubiquitous learning.Huazhong Liu is an on-job PhD candidate at Huazhong University of Science and Technology, and he is also a lecturerof Jiujiang University. His research interests are cloud computing and big data. Address for correspondence: DrCaiping Xiong, School of Educational Information Technology, Room 730, No. 9 Building, Central China NormalUniversity, No. 152 Luoyu Road, Wuhan 430079, China. Email:

    AbstractConstructing the digital learning environment for ubiquitous learning and asynchro-nous distributed learning has opened up immense amounts of concrete research.However, current digital learning environments do not fully fulfill the expectations onsupporting interactive group learning, shared understanding and social construction ofknowledge. This paper introduces cloud computing to the construction of the digitallearning environment for its on-demand services with high reliability, scalability andavailability in the distributed environment. Then a digital learning environment basedon cloud computing (DLECC) is proposed, including the architecture, co-constructionand sharing model, and incentive mechanism of DLECC. Finally, an Educational Tech-nology Space (ETS) platform under the concept of DLECC is constructed and applied tothe educational technology training for 110 teachers from primary and secondaryschools. The experimental results demonstrate that the co-construction and sharingmodel and incentive mechanism of DLECC may provide meaningful learning supportand interactive communities and promote the co-construction of befitting educationalresources.

    IntroductionThe construction of digital educational resources is the focus of lifelong learning in the new age.According to the Sloan Consortiums report on online education in USA, online courses areincreasingly becoming a common experience for students (Allen & Seaman, 2010).

    The digital learning environment is a cooperative and investigative learning system based onInternet resources. It is an open-learning space that contains abundant, diverse resources andinteractive and nonlinear organization information resources in line with human cognitive char-acteristics (Kadne, 2010). In such an environment, learners can decide when to learn, where tolearn and what to learn. Scilicet, learners can choose learning tasks and determine learningcontents, learning objectives and learning time. Meanwhile, they can customize their own per-sonalized learning tasks according to their own cognitive styles, learning ability and personalitycharacteristics. Besides, learning feedback can be obtained through network examination,assignment submission, group evaluation or teacher evaluation.

    British Journal of Educational Technology (2014)doi:10.1111/bjet.12208

    2014 British Educational Research Association

  • There is amount of positive research on digital learning environments. However, contemporarydigital learning environments are hard to fulfill expectations of fully supporting interactive grouplearning, shared understanding, social construction of knowledge and acquisition of competen-cies. The existing teaching platforms have abundant teaching resources and interactive tools butare deficient in information flow control and coordination mechanisms to ensure that learnerswould have a balanced information access opportunity (Guo, 2011).

    Thereby, Yang and Yu (2013) built ubiquitous learning ecosystem by integrating the wholeelements of learning environments from the perspective of ecology. Although Yang refers to usinga kind of mechanism to ensure that the ubiquitous learning environments run effectively, nodetailed measures are proposed.

    Oliver (2013) holds that technology can be used in learning. With the appearance of newtechnologies such as web 2.0, Internet of Things and cloud computing in succession, cloudcomputing has greatly triggered educators interests in applying cloud computing to education.Kadne (2010) argues that constructing digital learning environments under cloud computingmay be an economical way for resources co-construction and knowledge sharing. Cloud com-puting systems fundamentally provide access to large pools of data and computational resources(Kreijns, Kirschner & Jochems, 2002). Cloud computing has been applied to the field of educationsince 2009, and concepts such as cloud computing assisted teaching and education based oncloud computing appear in succession (Zhu & Guan, 2011).

    Practitioner NotesWhat is already known about this topic

    Cloud computing technology is widely spread and is integrated with education. Current digital learning environments based on cloud computing seldom consider

    designing the incentive mechanism to motivate learners learning initiative. Few empirical studies have shown the concrete design, implementation, evaluation of

    a digital learning environment based on cloud computing.

    What this paper adds

    Introduced cloud computing to construct the digital learning environment due to itson-demand services with high reliability, scalability and availability.

    Proposed a digital learning environment based on cloud computing, including thearchitecture, co-construction and sharing model, and incentive mechanism.

    Constructed the co-construction and sharing model to promote the knowledge aggre-gation, knowledge regeneration and collaborative editing.

    Designed the incentive mechanism to motivate learners learning initiative andstrengthens community interaction.

    Implications for practice and/or policy

    Co-construction and sharing model may enrich the types and quantity of educationalresources, thus radically reform the static resource construction pattern.

    Incentive mechanisms may motivate students learning initiative, strengthen commu-nity interaction and promote the aggregation of collective intelligence.

    Ubiquitous learning and adaptive learning is realized due to abundant cloud servicesand the accessibility of different terminals, which may meet different users individualpreferences.

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  • This study applies cloud computing to the construction of digital learning environments, becauseit can provide on-demand services with high reliability, scalability and availability in the distrib-uted environments. The main objective of this study is to design a digital learning environmentbased on cloud computing (DLECC), and the research issues can be stated as follows: (1) thearchitecture of DLECC, (2) the co-construction and sharing model of DLECC, (3) the incentivemechanism of DLECC, and (4) the effect of applying DLECC into practice.

    Theoretical foundations of constructing DLECCIn the construction of DLECC, many disciplinary approaches are involved, such as social con-structivism theory, knowledge management and collective intelligence theory and complex learn-ing theory, which also constitute the theoretical foundations of constructing DLECC.

    Social constructivismSocial constructivism emphasizes the importance of culture and context in understandingwhat occurs in society and constructing knowledge based on this understanding (Ernest, 1999;McMahon, 1997). Knowledge is deemed to a human product and is socially and culturallyconstructed (Bloom, 2006). The social constructivist Ernest (1999) views learning as a socialprocess, which takes place not only within an individual, nor is a passive development ofbehaviors that can be shaped by external forces. Learning is a social process in which learnerscollaboratively construct knowledge through interactive processes of information sharing, nego-tiation and modification (Wang, 2009).

    Therefore, the acquisition of knowledge is self-constructed in the process of interacting with thesurroundings rather than passively accepted from teachers. Learning is closely connected withcognitive activities, and valuable learning activities and meaningful social interactions play afundamental role in the learning process. In DLECC, various available learning resources andpartners are provided to support learners self-directed learning and collaborative exploration inthe learning process. Ultimately, learners can create knowledge through their surroundings andtheir interactions with each other.

    Knowledge management (KM) and collective intelligenceKM is the process of capturing, developing, sharing and effectively using organizational knowl-edge (Davenport, 1994). It refers to a multidisciplined approach to achieving organizationalobjectives by making the best use of knowledge (Groff & Jones, 2003). KM may result in improvedcommunication, better decision making, greater creativity and innovation (Gurteen, 2012).Frappaolo (1998) argues that KM with collective intelligence can enhance the innovation capac-ity and emergency ability of enterprises. Collective intelligence is groups of individuals doingthings collectively that seem intelligent (Malone, 2008). It is the capacity of networked Informa-tion Communication Technology (ICT) to enhance the collective pool of social knowledge bysimultaneously expanding the extent of human interactions (Flew, 2005).

    In the construction of DLECC, ICT, KM and collective intelligence are integrated to achieve amaximum range of resource sharing and knowledge regeneration and promote the transfer,exchange and sharing of tacit knowledge. All the participants co-construct knowledge in ashared intelligence space. Valuable views are generated through discussions, exchanges,debates, analysis and negotiations. In such a collaborative learning environment with massiveresources and barrier-free communication, learners group thinking and wisdom can be sharedby the entire group.

    Complex learningComplex learning is always involved with achieving integrated sets of learning goalsmultipleperformance objectives (Van Merrinboer, Clark & De Croock, 2002). It aims at: (1) the integration

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  • of knowledge, skills and attitudes; (2) the coordination of different skills; (3) the transfer of what islearned to daily life or work settings (Van Merrinboer, Kirschner & Kester, 2003). Van Merrinboerand Sweller (2005) state that complex learning means that students must learn how to deal withmaterials incorporating an enormous number of interacting elements.

    DLECC would provide a favorable atmosphere for students and teachers to realize the educationalobjective of knowledge and skills, processes and methods, emotional attitudes and the values.DLECC provides student-centered learning experiences in which learners acquire knowledge,skills and attitudes through practice and reflection.

    Construction of DLECCArchitecture of DLECCThe architecture of DLECC is composed of three layers: cloud equipment, cloud learning envi-ronment components and cloud services, as shown in Figure 1. The cloud equipment layermainly consists of physical hardware, system software and network devices. The layer locates atthe bottom of the model, and it should ensure the security of DLECC. The cloud learning envi-ronment components layer provides students with abundant learning resources, such as learningcontent, learning support and learning community, and these components can be combinedaccording to students imagination. The cloud services layer mainly provides public or individualon-demand services. Users can utilize these available services just expend lower upfront costs,capital expenses and operating expenses. They need not to possess their own infrastructure,software and platform, nor are they concerned as to how servers and networks run in the cloud.

    Co-construction and sharing model of DLECCThe aggregation of the collective wisdom is seldom considered in most paradigms of resourcesco-construction model. Each institution and university develops respective educational resources,and the lack of complementary advantages leads to the repeated development of some low-quality resources. Worse still, many resources libraries are seldom updated and optimized afterconstruction.

    In contrast, DLECC may co-construct and share these resources across time and space, as shownin Figure 2. Teachers, students and experts can upload, construct and share various educationalresources, they can also download, utilize and evaluate these resources, the evaluation andfeedback can impel the revision and evolution of these resources. The co-construction of

    Figure 1: Architecture of DLECC

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  • resources emphasizes collaborative learning because it may promote the coordinated develop-ment of students learning ability, team cooperation, emotion and cognition. Different studentsinvolved in the collaborative learning possess different cognitive styles, thinking patterns andpersonal values; these individual differences may arouse reciprocal thinking and collisionsthrough communication and cooperation. Obviously, the resources provided by different peopleare various, and the diverse resources may generate diverse cultures. Accordingly, creativity willbe sparked through the thinking collisions from different cultures.

    The co-construction and sharing model promote knowledge sharing and knowledge regenera-tion. For example, the high-quality multimedia coursewares shared on the platform can betransformed into different versions and become more adaptable to different teaching modes afterparticipants collaborative editing. Compared with single-user editing, the collaborative editingmay shorten editing time, reduce editing cost, extend resource types and thus improve resourcequality. The goal of the model is to enable every user to consume and construct high-qualityresources.

    Mechanism of DLECCMost online communities suffer from insufficient user participation in their initial phase of devel-opment, and effective incentive mechanism is very important to encourage participation (Cheng& Vassileva, 2006). In DLECC, virtual credits (VC) is exploited to motivate participants initiative.VC can be acquired from the following modules: (1) resources co-construction and sharing,(2) questions and answers, and (3) learning activities.

    First, in the resources co-construction and sharing module, if the contributor successfullyuploads a resource, his or her VC would be increased by two. In addition, if the resource is givenaccurate annotation and passes a quality audit by means of some control parameters such asformat, size and readability, the contributors VC would be increased by two. If the resource isdownloaded by others once, then the contributors VC would also be increased by one. The VCwould be increased by two if the resource is given a positive comment. Meanwhile, the evaluatorsVC would be increased by one if he or she rates others resource once, but it is limited to five everyday.

    Second, in questions and answers module, if the user initiates a discussion, his or her VC wouldbe increased by two, and every participants VC in the discussion group would be increased byone. Meanwhile, the initiators VC would be added by one every five followers participating in thediscussion, but the upper threshold is limited to ten in every discussion. If the participantsanswer is considered as the best in the discussion, his or her VC would be increased by ten.





    Platform of co-construction and sharing



    Mechanism ofconstruction and sharing

    Resources sharing








    Figure 2: Co-construction and sharing model of DLECC

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  • Third, in learning activities module, if the user finishes learning the resource or the learning timeexceeds the preset threshold, his or her VC would be increased by two if the user passes an onlineexamination. If his or her work is posted as a demonstration, his or her VC would be increasedby 20.

    Meanwhile, active degree (AD) is used in DLECC to evaluate students learning initiative. For everystudent i, their AD can be expressed as ADi and calculated according to equation 1. The expla-nation of notations in the equation is shown in Table 1. Finally, every students total VC and ADwould account for a certain proportion in final grades, and the respective proportion could be setby teachers according to different situations.

    ADi w Di D w Ti T w Ri R w RSi RS w TOi TO= + + + + 1 2 3 4 5 (1)

    Application of DLECC: Educational Technology Space (ETS)In accordance with the architecture and mechanism of DLECC, and integrated with Gleasy cloudservices platform and full-time cloud conference system, we construct ETS platform, as shown inFigure 3. The Gleasy cloud services platform can support cloud storage, and the full-time cloudconference system can assist the conference to be held ubiquitously.

    The ETS consists of five main components: learning resources, learning support, learning termi-nals, learning communities and cloud computing platform. Learning resources include texts,images, audios, videos, animations or their combination, which can be accessed and recombinedaccording to users locations and social context. Learning support, such as learning tools, inter-active tools, evaluation strategies and recommendation services, are available online. They can berecombined according to users different situations and requirements to support efficient andinstant services. Learning terminals vary from PC to iPad to smartphone. Learning communitiescan be established on demands, and corresponding learning communities would be recom-mended to users according to their learning context and learning preferences. The cloud com-puting platform is composed of cloud hardware, cloud software as well as numerous ready-madecloud services, and it should guarantee system and data security.

    In ETS, every registered user can upload, utilize and download various resources, initiate onlinediscussions, join in interactive communities, etc. As learning is relaxed, engaged, exciting andeffective, students have greater courage to express themselves in the virtual space. Therefore,formal learning and informal learning, school situations and social situations are integratedorganically.

    Research designThe ETS platform could be used for different projects, such as some professional courses learningduring regular teaching process, the online training for on-job teachers and blended learning for

    Table 1: Description of notations in equation 1

    Symbol Description Symbol Description

    ADi Active degree of student i wi Weight of part i (i = 1 . . . 5, wi = 1),set by teachers

    Ti Total topics posted by student i T Total topics posted in DLECCDi Total discussions participated in by student i D Total online discussions in DLECCRi Total replies made by student i R Total replies made in DLECCRSi Total resources contributed by student i RS Total resources contributed in DLECCTOi Accumulative online time of student i TO Accumulative total online time of

    all students

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  • college students, and so forth. In this study, we took the summer courses for teachers fromprimary and secondary schools from Jiujiang city in China as a case to illustrate the study andvalidate ETS.

    Context and participantsTraining in educational technology is an important part of continuing education for primary andsecondary school teachers, and its purpose is to improve teachers information literacy and theability to integrate ICT into curricula. Previously, the training had been conducted by a tradi-tional face-to-face approach without online learning, and training effect demonstrated that train-ees had low enthusiasm in traditional approach. Since 2013, ETS had just been published andused as the online learning platform, and the training was renewed by blended learning. Face-to-face learning focused on instructional design and project-based learning, whereas onlineinteractive learning is concerned with pedagogical theory and ICT application.

    In 2013 summer, 110 primary and secondary school teachers from Jiujiang city participatedin the training at Jiujiang University from July 11th to 30th. They came from different schoolsand various majors, including 67 females and 43 males, their age was between 22 and 45, andaverage age was about 28. Meanwhile, five professional teachers and two experts from the edu-cational technology major in Jiujiang University were invited to provide professional guidance.

    Research procedureThe research was carried out on the ETS platform, and the procedure was divided into four stages:(1) preparation stage: trainees accounts had been created by system automatically before thefirst lesson, and trainees could log into ETS and perfect their detailed information. Meanwhile,guides could upload some basic instructional materials, tools and activities related to the train-ing course in advance. (2) Utilization stage: trainees began to learn on ETS; they could utilizevarious services on ETS, for instance, upload and download learning resources, select andstudy recommended content. They could also take part in the learning activities and launch or

    Figure 3: Construction of ETS

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  • participate in the online discussions through full-time cloud conference system. Certainly, tutorscould observe trainees learning status at any time. (3) Evaluation stage: in order to evaluate theeffect of ETS platform, a survey was conducted, and every trainee should answer some questionscarefully before completing the training. (4) Data analysis stage: quantitatively analyzed theserelevant data recorded in backend database and data log files and discovered the hidden learningpatterns.

    At the beginning, trainees were informed that online learning process on ETS would be trackedand evaluated, but the trainees could not adapt to ETS. Whereas, with the help of the tutors, theywould experience more pleasure from ETS and actively participate in the learning. For instance,trainees might feel a sense of being taken seriously when the educators gave individual instruc-tion through video conversation. Besides, online discussion closely linked the trainees and pro-vided abundant expression opportunities for every trainee, so that they could know more abouteach other.

    MeasurementTo evaluate and validate ETS based on DLECC, some evaluation indexes are taken into considera-tion. These are shown in Table 2.

    Results and discussionData collectionEvery users access information was recorded in the backend database and log data files of ETS,including access time, access frequency, duration of learning, records of online discussion andquantities of shared resources, etc, which could be tracked daily. In this study, we sampled somestatistical data every day from July 11th to 30th concerning the number of participants, partici-pants online learning time and interaction time and the size of total shared resources.

    FindingsIn this study, quantitative result was collected through data analysis under the data trackingmechanism. Figure 4 shows the relationship between average learning time on ETS (ALTETS)/average interaction time on ETS (AITETS) and the dates. It demonstrates that ALTETS/AITETShas increased quickly since the opening of ETS, and then ALTETS maintains a stable growthafter about 1 week but grows again before they finish the training, whereas AITETS still keepsrelative stable. Because it takes time for trainees to accept ETS, when the online learning becamea habit for them, the length of learning time become fixed gradually. The social interactionsbetween trainees on ETS develop gradually and become more intense as they become morefamiliar with each other daily.

    Table 2: Description of evaluation indexes

    Index Abbreviation Definition Unit Description

    Average learningtime on ETS

    ALTETS Divide the total learningtime by the number ofparticipants per day

    Minute To analyze participants on-linetime on ETS per day

    Average interactiontime on ETS

    AITETS Divide the total interactiontime by the number ofparticipants per day

    Minute To analyze interaction intensityamong participants on ETSper day

    Total participationson ETS

    TPETS The number of participantson ETS per day

    Person To analyze the degree ofparticipation on ETS

    Total quantities ofshared resourceson ETS

    TQSRETS The total storage ofresources on ETS

    MB To measure the participantsenthusiasm to co-constructand share resources

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  • Figure 5 shows the relationship between total participations on ETS and the dates. It indicatesthat the number of participants increases rapidly as time goes on. Only a few users attempt to usethe space in the initial phase, but then more and more participants emerge, which is consistentwith the innovation diffusion theory.

    Figure 6 shows the relationship between total quantities of shared resources on ETS and thedates. It illustrates that the amount of shared resources reaches breakneck speed. Because anincreasing number of users are inclined to share resources after they are familiar with theplatform, especially, a large number of resources are uploaded at the end of the training, such asfinal assignments, learning notes and reviewing materials.

    Discussion and implicationsThe above data analysis only reflects trainees learning initiative and the quantity of sharedresources. In order to explore the quality of educational resources, pedagogical affordance, social

    Figure 4: Average learning/interactive time on ETS

    Figure 5: Total participations on ETS

    Figure 6: Total quantities of shared resources on ETS

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  • affordance and technical affordance of ETS, a survey was also conducted by using 5-point Likertscale items. Every trainee was required to answer five questions carefully before completing thetraining. The survey aimed to find out: (1) the degree of trainees satisfaction with the resources;(2) the usefulness of the incentive mechanism; (3) the enhancement of trainees knowledge andability; (4) interactivity; and (5) usability.

    The statistical data are shown in Table 3. It seems that the resources on ETS platform substantiallymeet trainees requirement; the incentive mechanism may motivate their initiative of sharingresources. Interaction among trainees is sufficient, and collaborative learning may improve train-ees knowledge and ability to some extent. Compared with traditional face-to-face training, theirsocial interaction becomes wider and relationship becomes closer.

    The results of the above study trigger our deep reflection, and some enlightenment is discussed asfollows: (1) ubiquitous learning is realized because of the implementation of cloud services andthe accessibility of different terminals. (2) The richness and diversity of learning resources andpresentation styles basically meet different users individual preferences. (3) The collaborativecommunities and the incentive mechanism may facilitate the construction of collective intelli-gence, interpersonal interaction, collaborative editing and knowledge regeneration. (4) Variouslearning support enables adaptive learning. Resources and communities can be recommended,and immediate online services are afforded on demand.

    ConclusionIn this paper, we present DLECC, a digital learning environment based on cloud computing,including the architecture, co-construction and sharing model, incentive mechanism and theeffect analysis. The analytical results show that the implementation of DLECC may (1) provideparticipants with befitting educational resources, meaningful learning support and interactivecommunities, (2) enrich the types and quantities of educational resources, thus radically reformthe static resource construction pattern and (3) strengthen interpersonal interaction. It impliesthat the co-construction and sharing model and incentive mechanism of VC is helpful. However,DLECC could continue to be improved. We will further strengthen recommendation services andpersonalized services. We also plan to validate the proposed platform across more large-scaleparticipants.

    AcknowledgementsThis work was supported by the National Natural Science Foundation of China (project number:71273108). The authors would like to thank Dr Qiyun Wang for his help during the study.

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    Table 3: Statistical table


    The number of people for each point

    Average (points)5 (points) 4 3 2 1

    Q1 18 (people) 59 31 2 0 3.85Q2 14 47 38 9 2 3.56Q3 21 61 25 3 0 3.91Q4 13 48 37 10 2 3.55Q5 17 56 36 1 0 3.81

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