Deliverables Year 2
This document provides an overview of the results produced by WP1 during year 2. WP1‟s objective is to explore theories and models that help understand and build a common knowledge base about knowledge maturing, to explore current knowledge maturing practices empirically and to develop a reference model for knowledge maturing.
Three main areas of acting were part of year two‟s activities: (1) the planning, performing, analysis and reflection of an empirical study involving 126 representatives of medium-sized and large European organisations and employing a complementary set of quantitative, statistical methods and qualitative, interpretive methods, (2) to contribute to the software design and development activities and to impact on the evaluation activities in MATURE and (3) the reconciliation and revision of the knowledge maturing model.
The deliverable reports the findings of the second in a series of three MATURE empirical studies about knowledge maturing phases, activities and indicators which the consortium agreed as most relevant for broadening the scope of studied organisations to get a more varied picture of perceptions held in companies and to further explore corresponding assumptions underlying the knowledge maturing model and MATURE design activities. Moreover, WPs 2, 3 and 4 have been interested in the current software support and barriers for fostering knowledge maturing to discuss which software tools MATURE tools need to extend, replace or with which they need to interact. Also, knowledge maturing activities and indicators have been used as boundary concepts between engineering-oriented design activities in WPs 2, 3 and 4 and interpretive empirical activities in this WP. Results build a much more detailed conceptual basis for evaluation (WP6) and help prioritise prototype development for Year 3. Interviews with representatives of organisations that had no previous exposure to MATURE partners helped dissemination of the project‟s concepts and activities.
The fostering of knowledge maturing phases was evaluated to be equal across all organisations regardless of size, sector or knowledge-intensity. The barriers which may hinder the maturing of knowledge were very much in line with those expected, e.g., lack of time, lack of usability and low awareness of the value and benefit. Some barriers, such as fear of disgrace affect earlier phases of the KMM more than later phases. Although perception of success is very similar between sectors, the phase „distribution in communities‟ is perceived more successful in service-based and in medium-sized organisations than others. The phase „appropriating ideas‟ is perceived more successful in knowledge/technology-intensive organisations. Portfolios contrasting importance/success and support/success concerning knowledge maturing activities revealed that the KM activities “reflect on and refine work practices or processes” and “find people with particular knowledge or expertise” are most interesting for the MATURE project. Both are deemed to be important, whereas less supported and less successfully performed activities. The evaluation of the KM indicators revealed that process-related indicators had a higher rate of agreement than indicators of the dimensions digital resources or persons. In addition to indicators having been fed continuously into parallel activities in demonstrator and evaluation teams factors extracted by a factor analysis aid in restructuring indicators for further take-up in Year 3 developments of the KMM and Demonstrators. Three types of organizations were identified by clustering organizations according to their success of performing knowledge maturing: “best performing maturers”, “people- and awareness-oriented maturers” and “hesitant formalists”. Knowledge maturing stories supported the analysis of additional barriers for accepting collaborative knowledge maturing at all and further contextual factors and complementary initiatives, e.g., the innovation management “regime” within or the innovation “ecosystem” beyond the organisation, that also need to be taken seriously when further developing tools and concepts in the MATURE project.
The results have been taken on board of parallel activities performed in MATURE, most notably in the demonstrator teams (WPs 2, 3 and 4), the definition of maturing services (WP 4) and the process of formative evaluation of demonstrators (WP 6). The take up of the results was supported not only by frequent presentations of interim results in personal and Flashmeetings, early release of work-in-progress within the consortium, transferred by email and particularly in the Wiki, and discussions of their impact, but also by the fact that we continuously kept the close cooperation between those MATURE members that did empirical work and those who performed design and development activities with several persons belonging to both groups.
This document is the joint deliverable of Work Package 2 (PLME) and Work Package 3 (OLME). During our second year activities we recognised the necessity to jointly develop the software for personal learning and maturing environments and organisational learning and maturing environments since we realised that the distinction between both had become blurry. As software is mostly used by individuals it always has an impact on the personal perspective of learning and maturing, even if the focus of an application scenario is organisationally driven. Vice versa, software components used for individually oriented work-integrated learning can have at least a long-term effect on the organisational knowledge maturing.
These aspects became clear during the development and pre-evaluation of the software packages in year 2, the demonstrators. These demonstrators are further developments of the first year’s design studies that had aimed at the support of knowledge maturing on the three knowledge manifestations: artefacts, sociofacts, and cognifacts, respectively.
Based on the state of the art analysis, the ethnographic study, and the results of the first evaluation activities (now called participatory design activities to avoid confusions), each demonstrator focused on a different aspect and stages of the Knowledge Maturing Model v2. Demonstrator 1: “Assuring quality for social learning in content networks” has a focus on artefact maturing and quality assurance, which fosters cognifact maturing. Demonstrator 2: “Developing Collaborative Understanding” and 3: “Collaborative Competence Management” emphasise more the sociofact level and organisational maturing in terms of a common ontology and competence management. The main research area regarded in Demonstrator 4: “KISSmir” is related to process artefact maturing but also to sociofact maturing in terms of sharing experience and provide awareness for context dependent guidance.
These Demonstrators were analysed by means of maturing activity systems (MAS), a projection of Engeström’s activity systems concentrating on aspects of knowledge maturing. This helped us in understanding the impact of activities on the three instances of knowledge and vice versa. Thus, it has become possible to identify patterns of knowledge maturing describing the coherence of dynamics of (social) activities, knowledge maturing and learning.
The description and research of maturing activity systems confirmed our initial decision for this joint deliverable of Work Package 2 and 3. It became clear that each of the demonstrators and probably most of learning and maturing systems do not support only one aspect of learning and maturing, but both the individual and the organisational perspective.
Moreover, the analysis of the MAS gave us feedback about strengths and weaknesses of the four demonstrators in terms of knowledge maturing. This has helped us shape further developments in year 3 and define integrated scenarios of learning and maturing environments in order to foster personal and organisational learning in certain application scenarios.
Concluding, the differentiation between work package 2 and work package 3 is still eligible as the different application scenarios and their context define the requirements towards an emphasis of supporting the individual or the organisation. Thus, WP2 will continue to research on supporting the individual knowledge worker in learning and knowledge maturing within its community. WP3 will continue and enhance the research on supporting the artefact maturing within organisations driven by its community.
This document gives an update on our work on Knowledge Maturing Services in project year 2. The major part of the work was concerned with designing and realizing a set of knowledge maturing services which we have put to use in various settings. These services have been implemented in the project by various partners to support knowledge maturing, mostly in the demonstrators presented in MATURE Deliverable D2.2/D3.2.
The services are split into three categories: (1) structure services which are based on an analysis of knowledge structures (semantics or processes), (2) content services which are based on an analysis of natural language texts, and (3) usage services which are based on an analysis of usage data generated in the interaction with knowledge artefacts and services. For each maturing service, we give some implementation details, such as details about the algorithms employed, the relationship with other services, and their status of development. We also discuss some future plans for each service and how they will be employed in the demonstrators. In a technical sense, the services have been developed as web services and are available in a distributed service environment. By providing the Maturing Services on the web all other project internal applications (Demonstrators/Design Studies) can access the services to use maturing functions or to share semantic data with other applications. Together, these services form the Knowledge Maturing Services Prototype V1.
As a second strand of research, we continued our theoretical and empirical work on which we base our services. From a theoretical point of view, this document presents two conceptual updates to our original conception (see Deliverable D4.1). First and to inform our structure services, we draw on both structural and subsymbolic aspects of information processing in semantic networks and relate this to cognitive categorization research. We are reporting two empirical studies which more closely examined some of the central assumptions. In an experimental study with a collaborative tagging environment, we examined the basic level effect known from categorization research and its impact in collaborative tagging. Subsymbolic aspects were examined with the use of an activation equation which takes into account the frequency and recency of prior exposure to the concept of a semantic network for predicting availability in memory. We used this activation equation to predict usage of tags in a CiteULike dataset.
Second and in an attempt to introduce a complementary perspective in content and usage services, we introduce a co-evolution model of collaborative knowledge construction recently presented by Cress and Kimmerle (2008). This theoretical perspective allows us to examine processes of assimilation and accommodation of internal and external knowledge representations. In our view, the latter present a key element in the maturing of knowledge. We experimentally tested some of the assumptions in a study of collaborative wiki editing. Specifically, it was our aim to automatically classify accommodation activities by observing user interactions with the wiki environment. It was possible to classify correctly almost 80% of the edits using only 4 extracted features of the user interaction context.
For each maturing service, we give some implementation details, such as details about the algorithms employed, the relationship with other services, and their status of development. We also discuss some future plans for each service and how they will be employed in the demonstrators.
We close this document with a discussion of the results obtained and from these we derive further research questions. Two areas will be the main areas for our future work. First, we would like to put a greater emphasis on reseeding services by providing support in the process of gardening of knowledge structures. Such explicit guidance in our view is a way to supplement the evolutionary growth typically to be found in social systems. Secondly, we will put a focus on exploiting contextual information for persons and artefacts, derived from the users’ interaction histories.