Deliverables Year 3
- D1.3 Results of In-depth Case Studies, Recommendations, and Final Knowledge Maturing Model
This document provides an overview of the results produced by WP1 during year 3. 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.
Year three's activities can be divided into three main strands of action: (1) the planning, performing, analysis and reflection of the in-depth study, involving six organisations and one network of professionals sharing knowledge about careers guidance, (2) to foster the take-up of results of WP1 and thereby to contribute to the software design and development activities and to impact on the evaluation activities in MATURE and (3) the revision and finalisation of the knowledge maturing model landscape.
The deliverable reports on the results of the final of a series of three empirical studies conducted in MATURE. Building upon the two previous empirical studies, i.e. the ethnographic study (reported in D1.1) and the representative study (reported in D1.2), the in-depth study is designed to deepen our knowledge and gain additional insights on the results we had achieved in the earlier studies. The in-depth study was particularly designed to complement and extend the insights we received from single interviewees in the representative study by multiple perspectives gained by several individuals within one organisation or network that together provided richer insights into the organisations. We relied on qualitative, interpretive methods, mainly based on observation and face-to-face interviews at work places of the interviewees. The in-depth study was conceptualized as a case study with multiple instances the investigation of which re-lied on a single, coordinated framework of study topics and design agreed in the consortium. Concretely, the in-depth study focused on reasons why organisations perceive themselves as performing better with respect to knowledge maturing than others they compare themselves to, what measures have been employed and are planned to be employed to foster knowledge maturing, what barriers have been overcome and how software is used. Also, each case study instance included an additional topic reflecting specific research questions, related to activities in work packages or demonstrator developments of the partners involved in the respective study instance. Thus, the in-depth study was always aligned to research interests in other MATURE activities. Results of case study instances were analysed individually and collectively in a cross-case analysis.
We collected nine reasons why organisations perceived themselves as performing knowledge maturing better, many of them related to the individual employees and their relationships. Another big role played the design of information systems for supporting these informal relationships and supporting the access to knowledge. We also collected 19 measures, organisations employed to support knowledge maturing and 11 measures which were planned to be implemented. These planned measures seemed to be of evolutionary nature and to aimed at improving existing approaches and tools rather than to revolutionize processes. We also collected data on 13 barriers to knowledge maturing that were existent in organisations and ways how they were (partly) overcome. These ways partly overlap with general measures for improving knowledge maturing that were named. Although generally in line with results from the representative study from year 2, it became evident that the organisational culture spans and subsumes other barriers. We also focused on software used for knowledge maturing in organisations. It became apparent, that there was a wide variety of heterogeneous software applications in place that typically not had been designed with knowledge maturing in mind. Employees in organisations seemed to struggle with these software applications to appropriate them to fit the purpose of developing knowledge and keeping track of their contributions in the collaborative learning zone. Especially the transition between individual phases was unsupported. Based on these results, we developed a view integrating measures, reasons and ways to overcome barriers for knowledge maturing into a causal model building on levers and two levels of effects.
This deliverable also presents the results of WP1 activities to collect and analyse the parallel conceptual activities in the MATURE project as well as the take-up of conceptual artefacts in other WPs, in this year with a special focus on knowledge maturing indicators and guidance activities. These and the results of the series of empirical studies are taken on board of the knowledge maturing landscape which is presented in this deliverable in its final form. The model is considered final as an instrument to influence activities in year 4 of the MATURE project, but we are certainly more than happy to continue to submit our findings to the discourse in the scientific community and intend to develop it further. The deliverable is concluded by a list of recommendations for organisations intending to engage in an initiative to improve knowledge maturing.
- D2.3/3.3 Design and Delivery of Prototype Version V2 of PLME / OLME
This document describes the further development of MATURE demonstrators, undertaken in the third year of the project, especially partial integrations of demonstrators. Moreover we present the theoretical foundation of how the MATURE software components support Knowledge Maturing and how this can be observed.
The primary focus of Year 3 was the (partial) integration of the demonstrator software and the development of MATURE building blocks, which allow configuring the MATURE software platform depending on the context specific application scenarios. Functionalities of existing software were further developed taking into account the results of the formative evaluation.
Based on application partners' requirements, we have developed different instantiations of MATURE software components. This document presents these instantiations, elaborating their particular contextual requirements and explaining the specific differences among individual application cases. Furthermore, along the content, people and processes dimensions, we describe how knowledge maturing is being supported by the different software tools. More specifically, we concentrate on the mappings between intended tool functionalities and the different knowledge maturing phases.
The software instantiations developed will be used during the summative evaluation where their support to fostering knowledge maturing at the workplace will be investigated. There are two main foci of evaluation:
1. How does the software actually support knowledge maturing, in opposition to the intended way?
2. Can MATURE software increase the organisational and personal performance of knowledge maturing?
Apart from software development, theoretical research on possible measurements of Knowledge Maturing effects has been undertaken, resulting in the concept of Transition Indicators (TIs). TIs provide an instrument to identify transitions between knowledge maturing phases based on the traces of user behaviour. They help to observe the influence of our tools on personal or organisational knowledge space.
- D4.3 Maturing Services Prototype V2
This document gives an update on our work on Knowledge Maturing Services in project year 3. The major part of the work was concerned with extending and further developing our existing set of 17 knowledge maturing services described in D4.2 which we have put to use in various settings. The extended collection consists of 27 Maturing Services which together form the Knowledge Maturing Services Prototype V2. These services have been implemented in the project by various partners to support knowledge maturing, mostly in the demonstrators presented in MATURE Deliverable D2.3/D3.3.
Moreover, we have refined our categorisation of Maturing Services, by distinguishing representation services, modelling services, and reseeding services. Representation services include all services that enable the storage and analysis of data within the MATURE framework with respect to three aspects: content, structure and usage. Model services exploit information from the representation services (in order to build models for the following three entities: tasks, users, and (digital) resources. Reseeding services are services that support the maturing of knowledge about content, semantic, processes and people.
After providing a rationale for our refined categorisation, we provide a complete collection of all existing maturing services clustered in the above categories of maturing services. For each maturing service, we give some theoretical rationale, implementation details, such as details about the algorithms employed, the relationship with other services, and their status of development. We also discuss future plans for each service. In a technical sense, the services have been developed in a lightweight service infrastructure 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. An overview of all Maturing Services is given in Figure 1-1.
Apart from this technical development, we have also continued to work on the theoretical basis and have conducted a number of empirical studies to strengthen and extend the foundations of our work. This work is presented together with each Maturing Service.
The set of services are now ready to be employed in the summative evaluation. There, they should be used in realistic work settings to model resource and user characteristics over a more extensive duration of time and to provide reseeding services to spark knowledge maturing. The two main areas of research will be to investigate effectiveness of these reseeding services both for guiding evolutionary growth as well as supporting explicit guidance in the process of gardening of knowledge structures. This will be mainly based on contextual information for persons and artefacts, derived from the users‘ interaction histories.