Keywords

1 Introduction

The progress in Collaborative Networks (CNs) and the increasing demand for pervasive networked communities have given rise to an emerging new trend and powerful models of collaboration involving large numbers of participants. Away from hierarchy and control, this new method of collective action shifts towards self-organizing and autonomy that, per se, shapes mass collaboration. We are now entering an age of collaboration explosion towards massive contribution where reaping the benefits of diverse minds in solving complex problems becomes a major goal. When this fascinating phenomenon is applied to social learning contexts, standing for limitless public contribution, and benefiting from collective knowledge building and sharing, the notion of Mass Collaborative Learning (MCL) evolves. Under the umbrella of CNs [1], MCL occurs “when a large number of scattered and self-directed contributors share their partial knowledge, information, data, and experiences with each other (typically by means of ICT platforms) in order to learn something new. In this collective action, knowledge is jointly and continually created, shared, and developed” [2].

This evolving phenomenon is altering the boundaries and basic mechanisms of both collaboration and learning at an unprecedented rate. MCL moves for example, from funneling all learning programs through instructors (consumer culture) towards proactive public engagement (culture of participation), from confrontation in traditional learning to collaboration in online environment, from a formalized and centralized form to an informal and decentralized form of learning, from a passive role of knowledge acquisition (at individual level) to an active participation in knowledge creation (at community level) [3]. On this basis, a learning ecosystem can and should take the advantages of the unique opportunity that mass collaboration has brought today where plenty of contributors collectively, proactively, and positively engage in the process of knowledge acquisition, building, sharing, and developing.

However, despite notable progresses in understanding the MCL and achievements gained in this context, not all its aspects, characteristics, and components have explicitly defined yet. For instance, different researchers have different viewpoints about this approach, so there is not yet an integrative view about the concept and we are still far from having a common understanding and unified definition of MCL. The boundaries of MCL have not been precisely determined, the processes of formation, organization, and development of MCL communities are still vague [4]. All these points show that this field of study is still evolving and requires further investigation and contribution to provide better clarification.

To fill part of this gap, we believe that MCL requires a proper reference model for some reasons: to provide an abstract representation of the system, to address the environment characteristics, to guide the process of foundation and operation, and last but not least, to elucidate its inherited complexity. Given that, by inspiration from the ARCON (A Reference model for Collaborative Networks) [5], this study proposes a contribution to reference model in order to comprehensively and systematically cover different aspects of MCL. The overarching goal of developing this reference model for MCL communities is to enhance the understanding of the related concepts, environments, entities, relationships, and interactions. Therefore, the main contribution of this study is proposing a preliminary reference model for MCL (based on ARCON reference model framework) aiming to facilitate the understanding of related concepts and underlying the main internal components and external interactions with the surrounding environment.

The remainder of this paper is structured as follows: the relationship between the topic of this work with technological innovation for life improvement is explained in Sect. 2, the research directions and plans are addressed in Sect. 3, our proposed reference model for MCL is presented in Sect. 4, a discussion around the main findings of this study is developed in Sect. 5, and the paper ends with some concluding remarks and a brief look into possible future work.

2 Relationship to Technological Innovation for Life Improvement

Learning is one of the world’s largest and fastest-growing fields of study (or even industry), and a major contributor to societies’ growth. Traditionally learning was driven by instructors, contained planned curriculum, followed by strict timetable of the academic year, occurred in a physical location, and stand on face-to-face interactions. Despite, traditional learning is still a predominant method for training, innovative methods in meaningful ways are now reshaping the learning process and creating radical or incremental changes in learning ecosystems. Innovative methods of learning mainly focus on benefiting of new technologies, pedagogies/methods, and environments in alignment with learners’ expectations. These methods are trying to move beyond existing routines. That is, they are not necessarily led by an instructor, nor do they follow a structured curriculum, or result in formal certification (particularly in informal method of direction) [6].

MCL as a holistic concept and an innovative learning approach introduces a social climate that stimulates interested learners who might be dispersed through time and space to work and learn together, and to grow up as an individual and community in the shadow of autonomy and flexibility. From the MCL point of view, learning is ubiquitous, it can take place over the lifetime, anywhere and anytime and in different formats (specifically informal). MCL provides concrete cases of innovative learning environments that “people acquire the intellectual heritage of their community” [7] where they can also create a bridge between educational contents and the issues that matter to their lives.

In order to support promoting the innovative methods of learning in MCL, it is essential to build and develop networks or Communities of Learning (CoL). Such type of virtual community creates a learning-centered environment in various shapes and sizes that in which group of interested learners actively and intentionally attempt to construct knowledge together. A CoL is, indeed, a dynamic and democratic learning society that shifts toward lifelong learning, rather than formal educational institution such as universities, schools, and colleges. It is predominantly generated by self-motivated voluntaries who individually and collectively not only share a range of values, beliefs, experiences, and knowledge, but also assist others in this process through developing heated discussions.

From the MCL perspective, a CoL embraces three major centered elements: (a) learners: the main asset of the community and contributor in learning process, (b) collaboration: the core process of performing activities, and (c) knowledge: the key concerning object. Even though communities of learning vary in form and context, an MCL community basically serves several significant purposes, from encouraging engagement in open collaboration to nurturing the culture of knowledge sharing, advancing the general knowledge of the domain, improving the shared body of knowledge developed in the community, sparking meaningful discussions, triggering self-reflection, reinforcing the links between participated entities, etc. [8].

A CoL can potentially benefit everyone involved in through diverse ways. It is also advocated that a strong CoL can “set the ambience for life-giving and uplifting experiences necessary to advance an individual and a whole society” [9]. Evidences show that CoL can positively influence the capacity, growth, and life of not only the participants, but also the community and society, directly or indirectly [10, 11]. Some of these benefits are separately listed below:

Participants

  • Participants will find the chance to actively learn even outside the conventional educational frameworks.

  • Participants can acquire useful information (that is generated and developed within the community), skills, talents, and potential (e.g., basic life management) that are applicable in any walk of life.

  • Participants can contribute to the process of collective knowledge building, sharing, and development.

  • Participants can choose and utilize the potential source(s) of information in the community that best suits their personal goals and aspirations.

  • It can help participants to become active and informed citizens.

  • It amplifies collaborative abilities and interpersonal relationships.

  • It can help participants to put learning at the center of everything.

  • It can help participants to enrich their education in unexpected ways.

  • It can assist participants to advance their careers.

  • It can assist participants to build relationships with new faces and minds.

Community

  • It gives chances to the community for long-term, deeper, and problem-driven learning.

  • It escalates the productivity of community with widespread availability of the various range of knowledge, information, and data.

  • It increases the capacities of community for openness, diversity, and difference.

  • It can address the learning needs of its locality.

  • It opens the opportunities for productive collaboration with others (e.g., similar communities, public, private, and non-profit organizations, partners, competitors).

  • It enables communities to create added value and social capital.

  • It may enable communities to evaluate the validity and reliability of the knowledge (both, received and created) by means of collective intelligence and wisdom.

Society

  • It creates in societies rare opportunities for inclusion in global and social learning.

  • It offers societies a free, accessible, and reliable source for casual learning.

  • It can promote the level of general knowledge and awareness of the societies.

  • It can help societies to find better solutions for their issues (e.g., social, economic, health, safety).

  • It helps societies to promote systematic societal change.

  • It opens some doors and breaks down walls to honoring diversity and embracing novelty.

  • It can promote social cohesion, culture, and economic.

In addition to these benefits, there are also risks if proper organizational structures and support mechanisms to guarantee quality of knowledge are not put in place.

3 Research Approach

This research work is part of a PhD thesis research about mass collaboration and learning. For the thesis, a systematic literature review was initially conducted to get an overview of the area, basic concepts, affecting factors, required organizational structure for MCL, and to identify the relations, contradictions, and gaps in related literature. In order properly guide the survey, a number of research questions were formulated. Inclusion and exclusion criteria were then identified. Next, relevant works were picked out and required data extracted from. Then the collected data were qualitatively and quantitatively assessed. Subsequently, all collected evidences were synthesized and summarized. Finally, after interpreting the findings of the study, they were published in the form of one survey [2] and two articles [3, 12] in recognized journals and conferences. In this process, the received comments and feedbacks from the reviewers have greatly helped improving the understanding of the area.

As an extension of this study, at this stage, it is essential to identify an appropriate reference model for foundation and designing of the proposed MCL. It is believed that such reference model should provide an abstract representation with a high-level view of the MCL environment and related components. This model should also form the conceptual basis to derive more concrete models from which implementations could be developed. Prior to definition of such reference model, it is significant to consider the previous contributions from related works in the context of CNs. Although the current literature still lacks a well-developed and validated reference model for CNs, the investigation of relevant studies shows that the ARCON (A Reference model for Collaborative Networks) modeling framework is a promising proposal for this purpose. According to [13], ARCON can provide a generic abstract framework and representation for understanding of base concepts, involved entities, significant relationships, interfaces and data flow among the entities of CNs. As such, it can be used for the development of specifications supporting CN environments. The positive features that can be attributed mostly to the ARCON include:

  • Simplicity: it is a simple, easy to understand and explicit model.

  • Comprehensiveness: it tries to cover and involve the main relevant components of the environment characteristics of CNs.

  • Neutrality: it tries to address different aspects of CNs from a neutral point of view.

In addition to these specific characteristics of ARCON, in comparison with other relevant previous approaches (e.g. Zachman, VERAM, CIMOSA, GERAM, IFIP-IFA TFAEI, GERAM, FEA, EGA, and SCOR) that contributed to related areas, it has less limitation when a holistic modeling is pursued, being focused on networked organizations [14]. The literature shows that ARCON has potential applications in variety of domains. It has, for example, been applied to the PROVE initiative (a Portuguese network in the agri-food sector that enables small farmers to sell their goods directly to consumers) [15]. ARCON has also been applied for different purposes including but not limited to, e-government and e-services [16], trust management [17], decomposing value for the customer [18], and learning in on-line and local University of the Third Age (U3A) in Australia [19].

It is note taking that defining a reference model for a new system like MCL is not an easy task. Since, from one side, the MCL is an emerging paradigm and not all its aspects are well understood and developed yet, and from another side, very few inputs are available in the literature regarding to reference models for CNs. In this context, our findings from reviewing previous studies along with our understanding from ARCON modeling framework are complementarily used in the current study as a basis to propose a reference model for MCL. This development, as a contribution to the area, is presented in Fig. 1. In addition to literature review, an analysis of emerging cases of mass collaboration was done in order to identify their relevant characteristics [12]. Since, identifying the positive and negative factors in existing and emerging successful examples of mass collaboration is one possible way of supporting community learning through mass collaboration. The 14 reviewed case studies of mass collaboration along with a short explanation are presented in Table 1.

Fig. 1.
figure 1

Approach towards building a MCL reference model.

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Table 1. 14 reviewed case studies.
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In our previous research study [12] the organizational structures of the above-mentioned 14 case studies were evaluated aiming to derive a general organizational structure for MCL through the analysis of their most significant features. The developed general organizational structure provides us helpful guidelines and directions in this work to help proposing a reference model for MCL.

4 Mass Collaborative Learning Reference Model

The ARCON modeling framework for CNs represents the involved environment features and specifications namely, internal aspects and external interactions. Internal aspects mainly concentrate on controllable entities, properties, function, and features of the network and thus address network’s Endogenous elements, whereas external aspects focus on external interactions between the network and its surrounding area and thus address network’s Exogenous interactions [14].

Endogenous elements comprise four dimensions, including:

  • Structural dimension – refers to participants in the network, and their relationships and roles. This dimension also deals with compositional characteristics of the network (e.g. typology).

  • Componential dimension – refers to all tangible resources (e.g. technologies) and intangible resources (e.g. knowledge) of the network.

  • Functional dimension – refers to all those functions, operations, processes, procedures, and methods that are related to the network.

  • Behavioral dimension – refers to the principles, policies, and governance rules that drive the behavior of the network.

Exogenous interactions also include four dimensions, as follows:

  • Market dimension – refers to issues that are related to interactions between the network and its customers, competitors, and potential partners. Part of this dimension embraces the mission of the network, its value proposition, joint identity, etc.

  • Support dimension – refers to interactions with those support services (e.g. financial, technical) that are provided by third-party entities outside the network.

  • Societal dimension – refers to general interactions between the network and the society (e.g. public and private organizations).

  • Constituency dimension – refers to interactions between the network and its potential new members (e.g. attracting and recruiting).

Given the above-mentioned environment characteristics of the ARCON and considering the basic requirements of mass learning communities, we accordingly adapt a general reference model for MCL (MCL-RM). See Tables 2 and 3.

Table 2. Endogenous elements for MCL.
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Table 3. Exogenous interactions for MCL.
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As addressed in Table 3, three main groups of elements are considered for Exogenous Elements:

  • Network identity – that defines the environment in which a MCL is positioned in, shows the position of MCL in the environment, and addresses the way in which a MCL presents itself in the environment.

  • Interaction parties – identify the potential entities that MCL interacts with.

  • Interactions – list the type of transactions that a MCL can develop with its interlocutors.

A MCL network and community needs to deal, among the others, with the issue of how to prove the value and quality of created and shared knowledge. The fact is that the key success factor for effective evaluation of collaboratively generated content is the trustworthiness and reliability of the involved participants [3]. “As user-generated content is no more regarded as a second-class source of information, but rather a complex mine of valuable insights, it is critical to develop techniques to effectively filter and discern good and reliable content” [20]. In order for the community participants to efficiently evaluate the reliability and quality of the created and shared contents/knowledge, there are several proposed strategies. In this regards we believe that the integration of human and computer support can help reaching an optimal balance between simplicity and speed on one hand, and validity of result on the other. In this suggested method, the human part consists of two phases namely, individual phase and community phase. In the individual phase, a participant initially checks the created and shared content/knowledge based on a proposed check list, considering some criteria such as authority, accuracy, currency, accessibility, relevancy, purpose, and bias. Once a certain percentage of assurance upon the reliability of content or knowledge and its source is achieved, the content will be next evaluated by the community and benefit of collective intelligence through again completing the same checklist (but this time through collaboration), evidence-based reasoning, formal argumentation, and collective decision making. By means of a computer part, detecting tools (e.g. fact check extension, fake news detector, and other novel tools) can be envisaged to help the human part [3].

5 Discussion

In this study, the proposed MCL-RM aims to provide a generic representation and conceptual model which can enhance the knowledge and understanding of the main contributing elements and practices around the environments of a MCL community. It attempts adding some inputs to this field of study for the purpose of discussion among those dealing with this issue (e.g. researchers, educators, decision makers, developers, innovators, and the community stakeholders). It is expected that once a reference model is established, it could drive the process of developing, organizing, implementing, simulating and evaluating real cases of such type of community.

However, it is important to note that MCL not only involves a multidisciplinary nature, but also it is a highly complex system. Thus, it should be considered, described, and modeled from multiple perspectives in order to truly cover and reflect its different aspects and conditions. Thus, the findings of this study have to be seen in the light of some limitations. For example, there are lack of prior research studies on this topic, and neither CNs, nor learning areas have yet offered a suitable reference model for, or even developed considerable background around this particular topic. The complexity of MCL and the required reference model is another limiting factor that originally comes from, e.g. its nature, environment, multiple functions, stakeholders and applications.

Apart from these constraints, this study which relies on existing related models and also findings from reviewed literature, tries to propose a reference model for MCL to capture its complexity through identifying the core components that can directly or indirectly influence the internal environment and external interactions of MCL. It is our belief that this proposal can facilitate understanding the paradigm and provide the starting basis for future developments. However, we must take this fact into account that the proposed MCL-RM can only be considered as a first step towards defining a reference model for MCL, since this model is introduced for the first time. So that, it is quite clear that a complete model cannot be developed at this stage in time. On the other hand, this model, at the current stage, is proposed theoretically (although taking inputs from real cases) and undoubtedly it requires to be applied to a wider range of real cases (to determine its possible limits and weaknesses). Therefore, there is a need for further investigation, elaboration, development, dissemination actions, and feedback collection. In the next stage of development, this model should also be validated by some experts in this area.

6 Conclusion

Advances in knowledge discovery and management in the era of rapid expansion of collective activities has led to new emerging approaches for learning. MCL, as an example, is looking to solve a variety of complex problems by means of collective efforts and knowledge sharing. The developed communities from MCL will stand for collaborative knowledge construction and sharing through unlimited number of distributed but interested learners from around the world. Such communities, however, are still lacking a comprehensive refence model that can broadly and clearly elaborate the involved environment characteristics. This study, therefore, getting inspiration in the ARCON modeling framework, attempts to propose a general and appropriate reference model for MCL in order to develop a better understanding of related concepts, elements, and interactions. The preliminary findings of this work can be used for further investigation and development among interested and/or involved entities. Having reached this MCL-RM, we are then, as future work, going to apply it in furthers real case of learning communities.