Educational Technology & Society

12 downloads 196081 Views 384KB Size Report
Technology, both in Higher Education and in the corporate learning sector. She studies how .... All components of the personal disposition interact with the ...
Published as Allison Littlejohn, Colin Milligan, Anoush Margaryan, (2012) "Charting collective knowledge: supporting selfregulated learning in the workplace", Journal of Workplace Learning, Vol. 24 Iss: 3, pp.226 – 238

http://emeraldinsight.com/journals.htm?issn=1366-5626&volume=24&issue=3&articleid=17024902&show=abstract Post-print archived according to published policy: http://www.sherpa.ac.uk/romeo/search.php?issn=1366-5626

Charting Collective Knowledge: Supporting Self-regulated Learning in the Workplace Author Details: Professor Allison Littlejohn Caledonian Academy, Glasgow Caledonian University; Glasgow; UK Dr Colin Milligan Caledonian Academy, Glasgow Caledonian University; Glasgow; UK

Dr Anoush Margaryan Caledonian Academy, Glasgow Caledonian University; Glasgow; UK Corresponding author: Allison Littlejohn Allison,[email protected] Acknowledgments (if applicable): This work was supported by an industry-academic partnership research grant. Biographical Details (if applicable): Professor Allison Littlejohn is a Chair of Learning Technology and the Director of the Caledonian Academy, a Research Centre at Glasgow Caledonian University in the UK. She has almost twenty years’ experience leading research and innovation in Technology-Enhanced Learning, working with a wide range of international academic and industry partners, most notably Royal Dutch Shell, for whom she was Senior Researcher 2008-2010. Allison has over 80 academic publications, including two books, and is founding Series Editor for the Routledge ‘Connecting with eLearning’ book series http://www.academy.gcal.ac.uk/people/littlejohn.html Dr. Colin Milligan is a Research Fellow with the Caledonian Academy at Glasgow Caledonian University. He has worked in the area of educational development for around eighteen years. His research interests centre on the role of the collective in learning in the workplace, and the role social technologies can play in supporting knowledge workers. http://www.academy.gcal.ac.uk/people/milligan.html Dr. Anoush Margaryan is a Lecturer in the Caledonian Academy, Glasgow Caledonian University, where she leads a research programme on work-related learning (WRL). She has sixteen years’ experience in research, development and teaching in the field of Learning Sciences and Technology, both in Higher Education and in the corporate learning sector. She studies how professionals in knowledge-intensive domains learn and develop their expertise in the context of daily work and how digital technology can be used to support knowledge work and learning. Anoush has over 50 publications, including a book on work-based learning. http://www.academy.gcal.ac.uk/people/margaryan.html

Structured Abstract: Purpose – This study outlines an approach to improving the effectiveness of work based learning through knowledge creation and enhancing self-regulated learning. The paper presents a case example of a novel approach to learning through knowledge creation in the workplace. This case example is based on empirical data collected through a study of the learning practices of knowledge workers employed within a large, multinational organisation. Design/methodology – The case example is presented in this article is based on a study of the learning practices of knowledge workers employed within a large, multinational organisation. Participants were members of a number of global, online knowledge sharing networks focused around the core technical and commercial disciplines of the company. Membership of each network ranged from a few hundred to a few thousand professionals at various stages of their career. The survey is available online at http://dl.dropbox.com/u/6017514/survey.pdf. The case study reported in this paper is based on 462 survey respondents, including 211 (45.7%) experts, 128 (27.7%) mid-career professionals and 123 (26.6%) novices. 29 interviews were conducted with nine novices, and twenty experts. Findings – The study proposes a mechanism to enhance goal actuation processes for selfregulated learning in the workplace. We term this mechanism ‘charting’ and provide a scenario illustrating how it might work in practice. Drawing upon social cognitive theory of self-regulated learning, we argue that individualised conceptualisations of self-regulated learning should be reexamined. These contradict the interactional and collaborative nature of the workplace where goal actuation is socially mediated, structured by and closely integrated within work tasks. Research limitations - The case example is based on a previous study. It is not a real-life example because this paper aims to predict a likely case example to enhance learning performance in the workplace, based on empirical evidence. The. study on which this case example is based was limited in scope, examining a small group of workers in one multinational organisation. Quantitative studies, as well as studies in related contexts would complement and validate these findings. Originality –This article extends our understanding of the relationship between the individual learner and the collective knowledge and how this relationship can be enhanced through self regulated learning in the workplace. Keywords: learning processes, knowledge sharing, workplace learning, knowledge workers, collaboration, networks,

Article Classification: Professional Practice Paper

For internal production use only Running Heads:

2

The changing workplace Self-regulated learning is an essential capability for individuals in contemporary workplaces (IBM, 2008). Its rising importance is due to socio-political and technological changes sustaining new means of knowledge creation (Jakupec and Garrick, 2000; OECD, 2004). Labour and production in contemporary workplaces are being transformed under the hegemony of ‘immaterial labour’ that generates products such as information, knowledge and networks (Hardt and Negri, 2008, p65). Consequently, production and practices have transformed into knowledge-driven work practices. Production units are smaller, more mobile and flexible. Individuals have to adapt to working within newly structured, distributed, dynamic environments that are technologically-mediated. Not only do these structures employ networked technologies, but they adopt such technologies as a model for organizational structures (ibid, p82). Examples of emerging work practices include “bricolage”localization, through which knowledge resources (from information resources or people) are sourced, selected and re-combined to help solve novel problems (Fiedler and Pata, 2009). Another set of emerging work practice involve crowdsourcing (Tapscott and Williams, 2007). For example companies seeking solutions to difficult problems now post questions that are answered by experts across a network. Alternatively some companies post their Intellectual Property in an open innovation form (for example InnoCentive www.innocentive.com) as a solution in search of a problem. This can lead to innovative applications and development of new products. To function effectively within these practices, knowledge workers must develop a diverse range of competences including the ability to operate in ill-defined, non-hierarchical environments; the capacity to work within expanding geographical and time horizons; the ability to collaborate in diverse and distributed teams. These new conditions mean that learning goals are likely to be more complex. Learning goals are individually set, with influence from the collective, workplace, or organization and from other people’s goals. Individuals may draw upon networks to set or attain these goals. Therefore goals may be shared with or related to the goals of other network members. Consequently goals are likely to be emergent rather than predefined. Siemens (2006) emphasizes the centrality of networks in learning. He describes learning as ‘the process of creating networks’ that connect people, organisations, libraries, books, databases, websites and other information sources . While internal networks (neural structures within individuals’ minds) support the creation of understanding, external networks enable individuals to tap into the collective to continually consume, create, and connect new knowledge. We define collective knowledge as the aggregation of knowledge residing in people, practices, and machines both in and beyond the organization, including social agents, social objects, tools, artefacts, information and practices. To have the capacity to use collective knowledge and create the new knowledge, individuals must be able to set and attain their learning and development goals in a selfregulated way. Self-regulated learning in the workplace Self-regulated learning can be defined as “self-generated thoughts, feelings and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 2005). Zimmerman’s influential model of self-regulated learning provides a framework for analysis of the ways in which individuals set and attain learning and development goals (Zimmerman, 2006). The model suggests three stages of self-regulated learning: forethought, performance and self-reflection. 3

We view goal setting as largely occurring in the forethought phase, goal attainment during performance, while goal refinement is at the point of self-reflection. The process of self-regulated learning is the autonomous actions learners take in planning, carrying out and evaluating their learning. As a product self-regulated learning is the disposition of learners to direct their own learning (Brookfield, 1986). Both the process and product aspects of selfregulated learning are related; as an individual develops a representation of the scope of his/her learning goals and related challenges, he or she will become gradually more ready to set goals for his or her further development. The connection between process and product views is articulated in Zimmerman’s Social Cognitive View of Self-Regulated Academic Learning (Zimmerman, 1989) in which there are mutual interactions between learners’ personal disposition and the environment (community, tools, rules), mediated by behaviour (enacted outcomes). The personal disposition itself contains components of commitment to goals, strategies, and self-efficacy perceptions (the individual’s belief that they can act effectively). All components of the personal disposition interact with the externally revealed behaviour and environment and are affected by these. For example, self-efficacy may be affected by four factors to which it is linked: commitment to goals, knowledge of strategies, behaviour (e.g. successful past outcomes) and environment (e.g. peer encouragement). Similarly, commitment to goals is not just an internal disposition, but is linked to the external community. We propose these components represent processes, and afford the possibility of intervention, through a set of technology tools, to enhance self-regulated learning. There are deficiencies within SRL research when applied within workplace settings. Zimmerman’s model implies that these phases occur sequentially and that goal actuation is linear. There is a growing body of evidence that, in the workplace, adults acquire a significant part of their competences through transformations with open objectives in which goals and motivations are continually reviewed (Fiedler & Kieslinger, 2006). A second limiting factor is that self-regulation research has historically focused on an individual perspective and has been conducted largely within laboratory or formal learning settings with disconnected individuals. Although social cognitive theories of self-regulated learning recognise that the social context plays a role in learning, the impact of the collective is often assumed to be less significant than that of factors related to the individual (Jackson, Mackenzie and Hobfoll, 2005). There is an increasing interest in considering self-regulated learning at the social level with reference to concepts such as social or shared regulation (Hadwin, Boutara, Knoetze and Thompson, 2004). Thirdly, although. a number of recent studies have explored the use of technologies to support process and product features of self-regulated learning, very little research has contributed to knowledge of SRL in informal contexts. Winne’s (2009) studies of self-regulated learning explore learners engagement in and modulation of cognitive processes in relation to a range of learning tasks supported by technology tools. However, these studies are within the context of school-based, formal learning. A number of projects have investigated ways in which self-regulation be supported by a range of technology environments. For example Pata (2009) developed a theoretical framework for modeling learning spaces for self-directed learning in formal learning settings. Türker and Zingel (2008) explored ways in which adults’ self-regulated learning could be scaffolded within Personal Learning Environments (PLEs). Similarly, iClass, a largescale European FP6 project, developed support tools for self-regulated personalisation within mainstream Virtual Learning 4

Environments (VLEs) used in universities across a range of countries (Aviram et al, 2008). iCamp (Innovative, Inclusive, Interactive & Intercultural Learning Campus http://www.icamp-project.org/) extended support for SRL beyond university-regulated environments, developing new learning environments and design models to support formal learning through structured, self-directed projects, drawing upon social networks. Although iClass and iCamp studies were carried out in adult learning contexts, the focus has been on formal educational systems, which is fundamentally different from informal, workplace learning. Two other projects, TenCompetence (http://www.tencompetence.org/) and PROLIX (Process-oriented Learning and Information exchange: http://www.prolixproject.org/) investigated competence-based approaches to adult learning through identifying, tracking and fulfilling users’ competence development needs. However, these competence-based approaches did not focus on the product features of SRL, such as personal motivations and goal rationalities. Observation of learning behaviours in practice In earlier empirical studies we investigated how experts and novices in a global multinational company self-regulated their learning (Margaryan, Milligan, Littlejohn, Hendrix and GraebKonneker, 2009; Margaryan, Littlejohn and Milligan, 2009). The participants were members of a range of online networks focused around the core technical and commercial disciplines of the company. Membership of each network ranged from a few hundred to a few thousand professionals at various stages of their career. Members used the online networks to exchange knowledge, experience, discuss problems and solutions. We compared experts’ and novices’ goal setting and learning practices, analysing similarities and differences in their behaviours, observing the ways in which individuals networked with others, drawing upon and contributing to the collective. Our studies concur with previous findings that self-regulated learning in knowledge intensive workplaces appears to be a highly social process, structured by and deeply integrated with work tasks (Billet, 2001). Our findings confirm that Zimmermann’s three stages of self-regulated learning, forethought, performance and self-refection, are carried out simultaneously by experts performing their work tasks. Even though goals are set by and centred around individuals, goal setting and attainment takes place at the intersection of the individual and the collective. Learning and development goals were linked with work tasks. Goals often were shared by individuals engaged in collaborative work. Also goals sometimes overlapped. During the process of setting and attaining their learning goals, individuals draw from and contribute to collective knowledge, through interactions with others (mentors, experts, peers and other people outside their organization). One of the major differences between experts and novices was that most novices did not engage in deliberate and systematic self-reflection to the same degree as experts. This may be because reflections are tacit and bound to actions, therefore it may be difficult for respondents to explicate their strategies, or it may be that experts find it easier to relate new events to past experience. We propose that self-regulated learning in the workplace could be enhanced through mechanisms that allow experts and novices to create and share knowledge by connecting with each other and the broader collective. This could be achieved by enabling each individual to draw upon and contribute to the collective. Developing such mechanisms requires an understanding of how individuals interact with the collective through discovering information, establishing and maintaining networks, and generating new knowledge to offer back to the collective. We propose a meta-level process to enhance self regulated learning which we call ‘charting’ 5

Collective knowledge and charting We abstracted the observed behaviours of experts and novices in setting and attaining their learning and development goals as part of their work practice (Margaryan, Milligan, Littlejohn, Hendrix and Graeb-Koenneker, 2009; Margaryan, Littlejohn and Milligan, 2009). Our findings indicate that when learning in the context of work tasks individuals consume, connect and contribute to collective knowledge. These three ways of interacting with collective knowledge have been emphasised in contemporary approaches to learning (Dron, 2007; Siemens, 2004; Collis and Moonen, 2001). In consuming collective knowledge, individuals need to be able to identify and source knowledge residing within the collective. To enable them to find relevant knowledge, the knowledge base must be transparent and accessible. The individual continually elaborates and refines their view of the collective knowledge by connecting resources people, discussions and reflective notes. He or she contributes to the collective knowledge, through creating, sharing and feeding knowledge back into the collective. These three components represent a set of intertwined activities rather than discrete linear steps. They represent the primary mechanisms by which an individual interacts with the collective to attain their goals. Charting is a process, which can potentially be implemented as a set of web-based tools, to support an individual in dynamically mapping and managing their own view of the collective knowledge, configuring the components of the collective to suit his/her personal needs at any given time. The individual brings personalised collective knowledge to bear upon his/her learning goals, and importantly feeds the outcomes of his/her learning and charting back to the collective, for others to learn from, consume and build on. Charting supports self-regulated learning by guiding the individual in defining, sequencing and reflecting upon personal goals. It can be viewed as an action above the generic operations (consuming, connecting and contributing) that individuals perform in order to find, make sense of, use and share collective knowledge. Charting connects individuals to others with similar goals and development needs, creating networks of people who may support each other in work and learning. Imagine if a new employee setting her learning goals could dynamically look up another individual’s plan and see how they reached their learning goals. Charting facilitates this since it is both individually focused and collaboratively enabled allowing individuals to use other peoples’ knowledge and experience to refine and achieve their personal goals. Goals and motivations are continually reviewed as the stages of self-regulated learning (forethought, performance and self-reflection) are carried out simultaneously. Individuals benefit from seeing how others with similar goals achieved them and their reflections on the process. Charting enhances an individual’s experience of the collective whilst at the same time enabling integration and alignment of the activities and goals of all members of the collective. Charting draws upon a metaphor of the ‘wisdom of the crowds’ (Surowiecki, 2004) the idea that large groups of connected people are better able to solve problems and foster innovation. Within this metaphor the consumption and creation of collective knowledge is the responsibility of each individual. Although this metaphor has been contested (Keen, 2007), it offers potential for an individuals’ learning to be supported by greater diversity of knowledge and independent thinking. The individual is recognised as a key contributor to the wealth of collective knowledge – not just in terms of discrete resources, but also through reflection, gaining experience, developing reputation, 6

forming trust based relationships, and benefitting from emergent patterns and information in the system such as ratings and usage data, to provide additional cues as to quality and utility of resources. Over time, the knowledge held by the collective is enriched by the contributions of the collective, and individual members learn from each other’s reflective practice; and benefit from seeing how other’s solved problems, the resources they used and the routes they took to learn. Although charting is individually-driven, it is not an individualistic process, since the worker both draws heavily from the collective, when needed and in ways that are most suitable to his personal context, and contributes back, through deliberate actions as well as through the machine analysing, modelling and aggregating individual behaviours into the collective (Baker, 2009). The notion of charting extends self-regulated learning beyond traditional competence-based approaches, by exploring personal motivations, learning relationships and individual goal rationalities, rather than focusing only on competences, which tend to be organised around formalised business, learning and development processes. It also extends the notion of personal development planning (PDP) in that it facilitates a much greater agency for the collective than in PDP which is predominantly limited to the individual. Since charting extends beyond existing processes, current business process management tools, such as e-portfolios, are not able to support the complex interplay across the components of charting. Figure 1 illustrates how an individual might consume knowledge from various sources, connects with others within and beyond their workplace and creates new knowledge how they might contribute back to the collective. Alongside these activities, each individual can chart the collective knowledge needed to attain their learning goals. Charting involves the combined operations of consuming, connecting and contributing to collective knowledge. The individuals’ goals are an organizing principle for charting and are, therefore, at a different level from charting. Although goals are individual, goal setting and actuation takes place at the intersection of the individual and the collective. This means that individuals can work towards their goals in parallel, benefitting from the support of their network and strengthening the value of the collective.

Figure 1: Charting and collective learning 7

Our ideas of Charting are still at a conceptual stage. It may not seem a natural approach to learning for an archetypal employee, who might choose to learn through formal courses or by sharing knowledge with a limited number of colleagues. However, aligning new approaches to selfregulated learning to long established workplace practice might lead to very limited solutions. Charting should be contextualized within authentic challenges observed and emerging from current workplace settings, as outlined in the case example in the following section. How might charting work in practice? Our empirical data helped build an initial understanding of how the components of consume, connect and contribute could be linked through ‘charting’ to support socially-mediated processes by which individuals accomplish their learning and development goals. This empirical data form the basis of a scenario of how charting might work in practice, presented below. Sally is an experienced product design engineer working with a large engineering company where she has worked for several years. Typically, Sally works in multi-disciplinary project based teams where she is the expert from her particular discipline. Over her time with the company Sally has developed a strong network of contacts with different skills and experience. This network comprises other employees in her company, contractors who are affiliated to the company on a project by project basis, and professional contacts who work for external organizations (for example product suppliers, who will have precise technical knowledge of their own products). Sally also has a network of her own professional contacts drawn from colleagues from past projects, along with external colleagues from her membership of other communities. Sally’s work is heavily knowledge based and a large proportion of her time is spent accessing and interpreting existing knowledge held within and outside her company, as well as working in project teams to create new knowledge in the form of design specifications and research reports. As her work is heavily dependent on collaborating with others, the tools she uses must not only fit her own needs, but also interface with tools used by others. For the whole team, the range of tools used should fit closely with each individual’s own working habits, to ensure that sharing within the group does not become an extra, unnatural task. 8

A key component of Sally’s work environment is the ‘Charting System’ she uses to organize her work and learning. The charting system uses a goals metaphor to help an individual organise and interact with their work and learning, without stipulating how those goals are articulated. As teams come together to collaborate, goals can be negotiated and refined, and shared amongst all interested parties. The Charting system helps Sally to organize the activities she is involved in and interact with other people, tools and resources. The system allows Sally to structure her work around her current work tasks, linking to others who share those task, and the resources she creates to achieve them. By organizing information and activity relative to these transient goals, Sally can constantly refine and re-prioritise her actions to ensure she effectively achieves her goals. As time passes, completed tasks are lost from view, replaced by current tasks – in this way her view of her network’s collective knowledge (people and resources) is always matched to Sally’s current interests. At work, Sally also uses the Charting tools to agree a set of personal learning goals for the year with her manager. Some of these goals will relate to explicit tasks and projects and may be clearly defined. Others will relate to longer term career development goals and will be (initially) less well defined. Sally and her manager identify an initial set of resources and people that will be relevant to achieving these goals and these are recorded within the charting tools. Sally continues to engage with this process throughout the year, utilising internal and external resources (websites, wikis, directories, indexes and knowledge sharing fora) to assist her in achieving his goals. The Charting system enables Sally to engage with these personal learning goals alongside her work activities. By situating them together, Sally is able to draw inter-relate them and ensure that her effort serves both her personal goals, and those of her employer. The charting tools become an organising focus for Sally’s planning and learning throughout the year, allowing her to develop a personal view of her knowledge base structured around her own work and learning goals which in turn relate to those shared with her colleagues and peers. The Charting system allows Sally to manage her interaction and learning with the people and resources that are important for her work. These interactions can be thought of as comprising three complementary activities: 1. Consuming knowledge smartly - Sally uses search tools to find resources which have been created and used by others who were involved in similar tasks. Recommender tools can be used to identify new resources (those who read x, also found y useful) whilst rating tools can be used to fine-tune these recommendations (did you find this resource helpful? please rate the resource between 1 and 5). 2. Connecting to others with whom Sally shares task goals or similar skills and interests. The charting tool allows comparison of Sally’s own skills (recorded initially via a skills audit then dynamically updated as new skills are acquired) and task goals with those of her peers and colleagues. For her own personal development, Sally can identify the next steps for her own development by seeing how others have achieved similar goals. 3. Contributing new knowledge to the collective - create evidence which is relevant to specific tasks and (in the future) to the whole collective. As Sally works, her outputs automatically 9

become part of the knowledge held by the collective. Newly created resources are automatically tagged and augmented with secondary usage metadata as the resources are viewed and utilised by others. One of Sally’s current projects is to design the housing for a new Personal Video Recorder. The project team must design a functional and stylish case which can be made using existing production systems and to a pre-defined budget. A group of experts is established to work on the project the team has been assembled based on the skills they possess and their availability for the duration of the project, as well as other factors such as whether individuals have previously worked together or whether some members have worked on similar projects. The team comprises some people from Sally’s existing contacts, and some new colleagues. The ‘My Network’ section of her Charting system shows all the members of the team and how they are connected to her current network (using degrees of separation similar to LinkedIn), inviting her to make ‘trusted connections’ with new contacts. The team can be expanded at any time should they feel new expertise is needed. In this case, the ‘My Network’ tools can be helpful in identifying the right skills mix. The group begins work on their ideas, with individual members assuming lead responsibility for specific tasks. All members share the overall goals for the project but may be involved in only a subset of the activities defined. The Charting system reflects this, showing each team member a personal view of the key activities they are involved in. Activity workspaces provide immediate access to shared documents and resources, other team members and communication tools. Explicit goal setting by the group encourages all members to take ownership of constituent tasks, helps to clarify overlapping aims and provides transparency of structure. Goals can be refined and renegotiated as necessary. New goals emerge as ideas are put forward and knowledge is constructed; these then become the focus of new knowledge structures. For each individual, the Charting system automatically organizes new knowledge being created by members of the team with a minimum of extra effort. When Sally does some work related to a particular task the resources she creates are automatically tagged by project and task, and automatically made available to the other members of her sub-team. Sally can choose whether to share new resources with individuals or with all members of a task, by dropping files onto the appropriate part of the workspace. Tightly integrated collaborative spaces provide a locus for coworking which creates new resources at a specific point in the growing knowledge structure. ‘Idea tools’ encourage Sally, and other members of the team to capture ideas as notes attached to resources: capturing new thoughts (tacit knowledge) and integrating them with established (explicit) knowledge. Integrated ‘Communications tools’ capture asynchronous and synchronous conversations in the context of the resources that they relate to. These discussions can be searched or viewed at any time. ‘Value boxes’ allow each viewer a simple way of highlighting useful resources for themselves or others. System ‘Search tools’ enable discovery of new ideas using an algorithm which takes into account these ratings, the tags, and temporal indicators (how long since a resource was created or last viewed) to promote the key content for retention whilst allowing content which has been superseded to be ignored or de-prioritised. Sally can store her own resources in the system without sharing them, though the expectation is that all resources in the system will be accessible at least among colleagues with common goals. The 10

overall priority of the system is that new knowledge is structured automatically as it is added to the system with rich interconnections to related knowledge. The system encourages exploration, suggesting similarities with previously completed tasks (by semantic analysis of the wording of tasks for example). Keeping formal resources (reports, documents), alongside less formal sources (asynchronous discussions, video clips etc.) enriches the knowledge base for future use. The Charting system ensures that each individual maintains a view of the team’s collective knowledge which is unique to them – each member of the team can choose to prioritise specific goals and activities and is shown a view of the knowledge in the system which reflects the tasks they are part of and the tags they use to describe their work. The system can alert team members to new items which have been added and can suggest new additions which may be relevant based on tagging, origin etc. The system can also highlight people who may possess knowledge to solve problems based on their previous experience. This stimulates creativity within the team by widening perspectives beyond the current task. When working on a project, Sally can easily look back at her work (and that of her colleagues) on previous projects by viewing her completed tasks. This gives a view of all the ideas, resources, discussions and outputs from any project in a structure that will be instantly familiar to her. The evolution of ideas can be replayed to see how they developed. This scenario illustrates how dynamic linking of individual and collective learning, enables networked participants to draw upon and contribute to the collective knowledge, configuring the components of the collective to suit their learning needs.

The future relevance of charting Charting addresses the challenge of how to simultaneously support personal and collaborative (informal) learning by enabling networked individuals to make better use of the collective knowledge emerging through work practices. In this conception of learning, the individual is recognised as a key contributor to the wealth of collective knowledge – not just in terms of discrete resources, but also through reflection, gaining experience, developing reputation, forming trust based relationships, and benefitting from emergent patterns and information in the system such as ratings and usage data, to provide additional cues as to quality and utility of resources. Over time, the knowledge held by the collective is enriched by the contributions of the collective, and individual members learn from each other’s reflective practice; and benefit from seeing how other’s solved problems, the resources they used and the routes they took to learn. Charting tools bring the individual and the collective aspects of self-regulated learning closer together, supporting individuals in managing and optimising their learning and development process. A Charting environment requires an open architecture connecting advanced Web 2.0 services including charting services, accessible via a variety of interconnected devices. A prototype interface in which charting tools can be accessed through a web page, and also through widgets to provide integration with existing work practices is currently being developed (Littlejohn, Margaryan and Milligan, 2009). This work sees charting as a service which enables the individual to manage their interaction with the collective. Further research is required to study, develop and deploy social and technical infrastructures that enhance self-regulated learning. Facilitating networked knowledge-building and knowledge sharing for collective learning management requires not only a set of advanced, integrated tools, but well-defined processes for effective management of learning process. This research could abstract current behaviours that form the basis of charting practices from real-world testbeds. This research 11

could underpin the design of a charting system that would allow the individual to define and manage their goals through a web-based interface, and permit continuous engagement with and refinement of these goals through widgets embedded in their web browser environment: The tools would encourage reflection, and allow the individual to personally structure resources and knowledge around specific goals and activities. A charting system comprising these core tools for managing an individual’s participation in their personal networks would exist as part of a wider ecology of tools supporting their interaction with this collective. These tools could include: Discovery tools: Finding and linking to new people and resources is vital to the concept of charting. Recommender systems can extract value form the way resources are used and identify common interests between individuals who share common associates. Sharing tools: Once an individual finds and creates resources, it is essential that they are able to share them efficiently with others in their personal network. Enhancements to services such as delicious would enable rich sharing and recommending within and between users and groups of users. Drop-boxes would allow quick sharing of files with other users, automating upload and notification tasks. Shell extensions: the secret of a successful charting system is to integrate closely with the existing systems that an individual uses. Therefore, close integration with word processing and communication tools is as important as web browser integration. For instance, within a word processor, when a file is saved, an additional dialogue box could prompt the user to provide tags, share with specific users, specify a level of privacy (and automatically upload and hare non-private files) and supply a cover note ‘this document outlines …’ all of which will improve the future value and utility of the resources. These charting tools and processes could be important in enhancing self-regulated learning in ways that enable contemporary, networked professionals to leverage collective knowledge in order to remain competitive. References Aviram, A., Ronen, Y., Somekh, S., Winer, A. And Sarid, B. (2008) Self-Regulated Personalized Learning (SRPL): Developing iClass’s pedagogical model, eLearning Papers, 14 Nº 9 (July 2008) ISSN 1887-1542 Available at: http://www.elearningpapers.eu/index.php?page=doc&doc_id=11941&doclng=6

Baker, S. (2009). The Numerati. New York: Hougthon Mifflin. Billett, S. (2001). Learning in the workplace: Strategies for effective practice. Crows Nest: Allen & Unwin. Brookfield, S. (1986). Understanding and facilitating adult learning. A comprehensive analysis of principles and effective practice. Milton Keynes: Open University Press. Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and expectations. London: Routledge.

12

Dron, J. (2007). Control and constraint in e-Learning: Choosing when to choose. Hershey, PA: Information Science Publishing. Fiedler, S., & Kieslinger, B. (2006). iCamp pedagogical approach and theoretical background. Report (Deliverable 1.1.), iCamp project, EU FP6. Available at http://www.icamp.eu/wpcontent/ uploads/2007/05/d11___icamp___pedagogical-approach.pdf HT

Fiedler, S., & Pata, K. (2009). Distributed learning environments and social software: In search for a framework of design. In S. Hatzipanagos & S. Warburton (Eds.), Social software & developing community ontologies (pp. 145-158). Hershey, PA, USA: IGI Global. Hadwin, A. F., Boutara, L., Knoetze, T., and Thompson, S. (2004). Cross case study of selfregulation as a series of events. Educational Research and Evaluation, 10, 365-418. Hardt, M. and Negri, A. (2008). Multitude. London: Penguin, p82 IBM (2008) The IBM Global Human Capital Study, Available from http://www935.ibm.com/services/us/gbs/bus/html/2008ghcs.html Jackson, T., Mackenzie, J., & Hobfoll, S. (2005). Communal aspects of self-regulation. In Boekaerts, M., Pintrich, P., & Zeidner, M. (Eds.), Handbook of self-regulation (pp. 275-300). San Diego: Academic Press. Jakupec, V. & Garrick, J. (2000) (Eds.). Flexible learning, human resource and organisational development: Putting theory to work. London: Routledge. Keen, A. (2007) Cult of the Amateur. New York: Doubleday Littlejohn, A., Margaryan, A, and Milligan, C. (2009). Charting collective knowledge: Supporting self-regulated learning in the workplace. In Proceedings of the 9th IEEE International Conference on Advanced Learning Technologies (ICALT) 2009 Margaryan, A., Milligan, C., Littlejohn, A., Hendrix, D., and. Graeb-Koenneker, S., (2009) Selfregulated learning in the workplace: Enhancing knowledge flow between novices and experts. Paper accepted for the 4th International Conference on organisational learning, knowledge and capabilities (OLKC), Amsterdam, 26-28 April 2009. Margaryan, A., Littlejohn, A., and Milligan, C., (2009) Self-regulated learning in the workplace: Enhancing knowledge flow between novices and experts. Accepted for the 13th European Conference for Research on Learning and Instruction, Amsterdam 25-29 August 2009 Matuga, J. M. (2009). Self-Regulation, Goal Orientation, and Academic Achievement of Secondary Students in Online University Courses. Educational Technology & Society, 12 (3), 4–11. Pata, K. (2009). Modelling spaces for self-directed learning at university courses. Educational Technology & Society, 12 (3), 23–43.

13

Siemens, G (2004). Connectivism: A learning theory for the digital age. Available at http://www.elearnspace.org/Articles/connectivism.htm Siemens, G. (2006). Knowing knowledge. Available at http://www.knowingknowledge.com/book.php Surowiecki, J. (2004). The Wisdom of Crowds. New York: Random House Tapscott, S. and Williams, D. (2007) Don Wikinomics; How Mass Collaboration Changes Everything ISBN-10: 3-446-41219-0 Türker, M.A. and Zingel, S. (2008) Formative Interfaces for Scaffolding Self-Regulated Learning in PLEs , eLearning Papers, 14 Nº 9 (July 2008) ISSN 1887-1542 Available at: http://www.elearningpapers.eu/index.php?page=doc&doc_id=11942&doclng=6 Wine, P. (2009). The Learning Kit Project: Lessons Learned and Implications for Future Uses of Technology in Researching Self-Regulated Learning, Proceedings of EARLI 13th Bi-annual conference, Amsterdam, Netherlands Available at http://www.earli2009.org/nqcontent.cfm?a_name=public_proposal_view&abstractid=1802

Zimmerman, B. (2005). Attaining self-regulation: A social cognitive perspective. In Boekaerts, M.,Pintrich, P., & Zeidner, M. (Eds.), Handbook of Self-Regulation . San Diego: Academic Press. Zimmerman, B. (2006). Development and adaptation of expertise: The role of self-regulatory processes and beliefs. In Ericsson, A., Charness, n., Feltovich, p., & Hoffman, R. (Eds), the Cambridge handbook of expertise and expert development (pp. 705-722). Cambridge, MA: Cambridge University Press Zimmerman, B. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81 (3), 329-339.

14