Abstract: The Problem-Solution Chain (PSC) models proposed in this exploratory paper are conceived as describing chains of problem-solution links, thereby modelling specific multi-link ‘problem-solving’ paths, typically (but not exclusively) from a high-level business problem to lower-level functional solution components. The main elements are ‘Problems’ and ‘Solutions’. These may be selected from purpose-made, domain-specific collections of elements. Single ‘Problem-Solution links’ are comparable to compact, high-level descriptions of design patterns and can be directly related to design problem templates as used in Design Science. Coherent collections of such links would resemble boiled-down representations of pattern languages. Instantiations of PSCs for specific situations aim to help conceptualise and discuss pre-architectural, high-level overviews, for example, of (options for) functionalities or applications representing ‘solutions’ for ‘solving’ some business need or capability ‘problem’. A useful metaphor is that PSCs help describe and discuss basic ingredients (related problems and solutions) for some specific situation, which can later (out of scope here) be developed into a recipe (e.g. an enterprise or process architecture and roadmap) and eventually into an actual dish (realisation of the architecture/solution). Thus, PSCs can, for example, be conceptualisations and conversation aids in the early stages of business-IT alignment efforts and system design. This explorative, practice-oriented paper presents our initial conceptualisation of PSCs. We also present a syntax and notation for problem-solution chains as specified for the Simplified Modelling Platform (SMP), and we briefly discuss the possibility of supporting PSC modelling with guided conversations for PSC modelling. We demonstrate and evaluate our proposed concepts by applying them in a single real case. Much work lies ahead.
Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
MULTIFILE
Learning objects are bits of learning content. They may be reused 'as is' (simple reuse) or first be adapted to a learner's particular needs (flexible reuse). Reuse matters because it lowers the development costs of learning objects, flexible reuse matters because it allows one to address learners' needs in an affordable way. Flexible reuse is particularly important in the knowledge economy, where learners not only have very spefic demands but often also need to pay for their own further education. The technical problems to simple and flexible are rapidly being resolved in various learning technology standardisation bodies. This may suggest that a learning object economy, in which learning objects are freely exchanged, updated and adapted, is about to emerge. Such a belief, however, ignores the significant psychological, social and organizational barriers to reuse that still abound. An inventory of these problems is made and possible ways to overcome them are discussed.
The SPRONG-collaboration “Collective process development for an innovative chemical industry” (CONNECT) aims to accelerate the chemical industry’s climate/sustainability transition by process development of innovative chemical processes. The CONNECT SPRONG-group integrates the expertise of the research groups “Material Sciences” (Zuyd Hogeschool), “Making Industry Sustainable” (Hogeschool Rotterdam), “Innovative Testing in Life Sciences & Chemistry” and “Circular Water” (both Hogeschool Utrecht) and affiliated knowledge centres (Centres of Expertise CHILL [affiliated to Zuyd] and HRTech, and Utrecht Science Park InnovationLab). The combined CONNECT-expertise generates critical mass to facilitate process development of necessary energy-/material-efficient processes for the 2050 goals of the Knowledge and Innovation Agenda (KIA) Climate and Energy (mission C) using Chemical Key Technologies. CONNECT focuses on process development/chemical engineering. We will collaborate with SPRONG-groups centred on chemistry and other non-SPRONG initiatives. The CONNECT-consortium will generate a Learning Community of the core group (universities of applied science and knowledge centres), companies (high-tech equipment, engineering and chemical end-users), secondary vocational training, universities, sustainability institutes and regional network organizations that will facilitate research, demand articulation and professionalization of students and professionals. In the CONNECT-trajectory, four field labs will be integrated and strengthened with necessary coordination, organisation, expertise and equipment to facilitate chemical innovations to bridge the innovation valley-of-death between feasibility studies and high technology-readiness-level pilot plant infrastructure. The CONNECT-field labs will combine experimental and theoretical approaches to generate high-quality data that can be used for modelling and predict the impact of flow chemical technologies. The CONNECT-trajectory will optimize research quality systems (e.g. PDCA, data management, impact). At the end of the CONNECT-trajectory, the SPRONG-group will have become the process development/chemical engineering SPRONG-group in the Netherlands. We can then meaningfully contribute to further integrate the (inter)national research ecosystem to valorise innovative chemical processes for the KIA Climate and Energy.
The objective of DIGIREAL-XL is to build a Research, Development & Innovation (RD&I) Center (SPRONG GROUP, level 4) on Digital Realities (DR) for Societal-Economic Impact. DR are intelligent, interactive, and immersive digital environments that seamlessly integrate Data, Artificial Intelligence/Machine Learning, Modelling-Simulation, and Visualization by using Game and Media Technologies (Game platforms/VR/AR/MR). Examples of these DR disruptive innovations can be seen in many domains, such as in the entertainment and service industries (Digital Humans); in the entertainment, leisure, learning, and culture domain (Virtual Museums and Music festivals) and within the decision making and spatial planning domain (Digital Twins). There are many well-recognized innovations in each of the enabling technologies (Data, AI,V/AR). However, DIGIREAL-XL goes beyond these disconnected state-of-the-art developments and technologies in its focus on DR as an integrated socio-technical concept. This requires pre-commercial, interdisciplinary RD&I, in cross-sectoral and inter-organizational networks. There is a need for integrating theories, methodologies, smart tools, and cross-disciplinary field labs for the effective and efficient design and production of DR. In doing so, DIGIREAL-XL addresses the challenges formulated under the KIA-Enabling Technologies / Key Methodologies for sectoral and societal transformation. BUas (lead partner) and FONTYS built a SPRONG group level 4 based on four pillars: RD&I-Program, Field Labs, Lab-Infrastructure, and Organizational Excellence Program. This provides a solid foundation to initiate and execute challenging, externally funded RD&I projects with partners in SPRONG stage one ('21-'25) and beyond (until' 29). DIGIREAL-XL is organized in a coherent set of Work Packages with clear objectives, tasks, deliverables, and milestones. The SPRONG group is well-positioned within the emerging MINDLABS Interactive Technologies eco-system and strengthens the regional (North-Brabant) digitalization agenda. Field labs on DR work with support and co-funding by many network organizations such as Digishape and Chronosphere and public, private, and societal organizations.
The objective of DIGIREAL-XL is to build a Research, Development & Innovation (RD&I) Center (SPRONG GROUP, level 4) onDigital Realities (DR) for Societal-Economic Impact. DR are intelligent, interactive, and immersive digital environments thatseamlessly integrate Data, Artificial Intelligence/Machine Learning, Modelling-Simulation, and Visualization by using Gameand Media Technologies (Game platforms/VR/AR/MR). Examples of these DR disruptive innovations can be seen in manydomains, such as in the entertainment and service industries (Digital Humans); in the entertainment, leisure, learning, andculture domain (Virtual Museums and Music festivals) and within the decision making and spatial planning domain (DigitalTwins). There are many well-recognized innovations in each of the enabling technologies (Data, AI,V/AR). However, DIGIREAL-XL goes beyond these disconnected state-of-the-art developments and technologies in its focus on DR as an integrated socio-technical concept. This requires pre-commercial, interdisciplinary RD&I, in cross-sectoral andinter-organizational networks. There is a need for integrating theories, methodologies, smart tools, and cross-disciplinaryfield labs for the effective and efficient design and production of DR. In doing so, DIGIREAL-XL addresses the challengesformulated under the KIA-Enabling Technologies / Key Methodologies for sectoral and societal transformation. BUas (lead partner) and FONTYS built a SPRONG group level 4 based on four pillars: RD&I-Program, Field Labs, Lab-Infrastructure, and Organizational Excellence Program. This provides a solid foundation to initiate and execute challenging, externally funded RD&I projects with partners in SPRONG stage one ('21-'25) and beyond (until' 29). DIGIREAL-XL is organized in a coherent set of Work Packages with clear objectives, tasks, deliverables, and milestones. The SPRONG group is well-positioned within the emerging MINDLABS Interactive Technologies eco-system and strengthens the regional (North-Brabant) digitalization agenda. Field labs on DR work with support and co-funding by many network organizations such as Digishape and Chronosphere and public, private, and societal organizations
