Intergenerational Learning - a Topic of Discussion or a Reality? Taking a Closer Look at the Academics

In the current sharing economy, intergenerational learning is seen as a solution to the aging society phenomenon. Nevertheless, this field is still in an embryonic stage of development and most studies are either conceptual or based on a qualitative approach. This research concentrates on the academics who analyze the concept of “intergenerational learning” to determine whether they are treating this issue as a research topic or they are actively supporting the process in their daily activity. To achieve this goal, the qualitative and quantitative approaches are combined and a multi-stage research strategy is employed. The latter is dominated by an inductive character which is reflected by the fact that the focus is on analyzing previously researched phenomena from a different perspective. Thus, a documentary study that focuses on the articles published on SCOPUS and Web of Science, during 2008 – 2019, is combined with social network analysis, and the relationships established among the academics are emphasized. The results bring forward that: (i) most academics come from Europe and North America, and they share their knowledge with those who work on the same continent; (ii) most studies regarding intergenerational learning represent the result of the cooperation established between the members of Generation X and Generation Y; and (iii) through intergenerational cooperation, the academics share knowledge regarding education sciences, knowledge management, and human resource management. The results have both theoretical and practical implications. On the one hand, they extend the literature on intergenerational learning by providing an empirical analysis of the intergenerational knowledge flows that are shared among the academics. On the other hand, they ensure the policy-makers that the concept of intergenerational learning is approached from a multi-criteria perspective and it proves that mixed-aged teams are a viable solution for encouraging intergenerational learning

Analysis of knowledge flows in university-industry collaboration: a materials innovation case

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência e Engenharia de Materiais, Florianópolis, 2021.Materiais avançados são fundamentais para a inovação, pois desempenham um papel importante no desenvolvimento de todos os tipos de novos produtos e processos. Materiais avançados, entretanto, apresentam vários desafios devido à sua posição a montante na cadeia de valor. O desenvolvimento de materiais requer longos períodos de desenvolvimento e altos investimentos antes de obter os primeiros feedbacks dos clientes, aumentando assim os riscos de investimento. Portanto, a cooperação universidade-indústria (CUI), apoiada por políticas científicas nacionais e programas de fomento, desempenha um papel importante no apoio ao desenvolvimento de materiais avançados e na construção de vantagem competitiva no mercado. Na CUI, o compartilhamento de conhecimento é um dos principais objetivos, portanto, o gerenciamento dos fluxos de conhecimento (FC) entre a universidade e a indústria com práticas de gestão do conhecimento (GC) é uma questão importante para a eficácia da colaboração. A maioria dos estudos anteriores avalia a CUI e o fluxo de conhecimento no nível organizacional, deixando o nível de equipe (nível micro) desses construtos obscuros. O objetivo deste trabalho qualitativo foi analisar, usando uma abordagem de método misto, como o conhecimento flui em uma colaboração universidade-indústria para a inovação de materiais, a fim de propor uma estrutura de análise e um conjunto de práticas para melhorar a colaboração. A caracterização do fluxo de conhecimento mostra redes distintas de conhecimento técnico, gerencial e de mercado, nós principais espalhados pelas redes e uma série de práticas de gestão do conhecimento. Os resultados evidenciaram a relação entre o fluxo de conhecimento, CUI e fatores de influência, mas a relação quantitativa entre o desempenho da CUI e as práticas de GC não pôde ser identificada com os instrumentos empregados. A estrutura de análise sugere investigar FC pela rede, densidade, atividade do intermediador, capacidade absortiva e práticas; resultados da CUI, por seus principais produtos como tecnologias, componentes, publicações, patentes, pessoas treinadas, ganhos técnicos e econômicos e continuidade de parcerias; e fatores de influência, por setor, área de conhecimento, nível de maturidade tecnológica, posição da cadeia de valor, sobreposição de conhecimento e velocidade das mudanças. Os resultados podem ser gerados mapeando a rede usando a técnica da bola de neve e entrevistando os principais participantes da colaboração. A avaliação de nível micro forneceu informações da gestão da colaboração de nível operacional que permitiu uma visão mais profunda da colaboração e, portanto, a proposição da estrutura de análise e práticas para ajudar no sucesso da CUI. A partir dos fatores influenciadores encontrados neste trabalho, duas práticas foram concebidas e ainda não testadas: (i) implementar uma estrutura de encontros periódicos para conectar pesquisadores de todas as áreas; e (ii) dividir a colaboração em projetos de curto e longo prazo.Abstract: Advanced materials are fundamental for innovation as they play a major role in all sorts of new products and processes development. Advanced materials however present several challenges due to its upstream position in the value chain. Materials development requires long periods of development and high investments before getting the first customers? feedbacks, thus increasing investment risks. Therefore, university-industry cooperation (UIC) supported by national science policies and granting programs plays an important role supporting the development of advanced materials and building market competitive advantage. In UIC, knowledge sharing is one of the main objectives, thus managing knowledge flows (KF) between university and industry with knowledge management (KM) practices is an important issue for collaboration effectiveness. Most of previous studies assess UIC and knowledge flow at organizational-level, leaving team-level (micro-level) of these constructs unclear. The objective of this qualitative work was to analyze, using a mixed method approach, how knowledge flows in a university-industry collaboration for materials innovation, in order to propose a framework of analysis and a set of practices to improve collaboration. Knowledge flow characterization show distinct networks of technical, managerial and market knowledge, key nodes scattered across the networks and a series of knowledge management practices. Results evidenced the relationship between knowledge flow, UIC and influencing factors, but the quantitative relationship between UIC performance and KM practices couldn?t be identified with the instruments employed. The framework of analysis suggests investigating KF by its network, density, broker activity, absorptive capacity and practices; UIC outcomes, by its main outputs such as technologies, components, publications, patents, people trained, technical and economic gains and partnership continuity; and influencing factors, by industry, knowledge field, technology readiness level, value chain position, knowledge overlap and speed of changes. Results can be generated by mapping the network using the snowball technique and interviewing key participants of the collaboration. Micro-level assessment provided information from bottom-level collaboration management that allowed a deeper view of the collaboration and thus the proposition of the framework of analysis and practices to help UIC success. Based on influencing factors found in this work, two practices were conceived and not tested: (i) implement a structure of periodic meetings to connect researchers across areas; and (ii) split collaboration in short-term and long-term projects

Scholarly Collaboration In Engineering Education: From Big-Data Scientometrics To User-Centered Software Design

Engineering education research has grown into a flourishing community with an-ever increasing number of publications and scholars. However, recent studies show that a significant amount of engineering education knowledge retains a clear disciplinary orientation. If the gaps in scholarly collaboration continue to be prevalent within the entire community, it will become increasingly difficult to sustain community memory. This will eventually inhibit the propagation of innovations and slow the movement of research findings into practice. This dissertation studies scholarly collaboration in the engineering education research community. It provides a clear characterization of collaboration problems and proposes potential solutions. The dissertation is composed of four studies. First, the dissertation recognizes gaps in scholarly collaboration in the engineering education research community. To achieve this goal, a bibliometric analysis based on 24,172 academic articles was performed to describe the anatomy of collaboration patterns. Second, the dissertation reviewed existing technologies that enhance communication and collaboration in engineering and science. This review elaborated and compared features in 12 popular social research network sites to examine how these features support scholarly communication and collaboration. Third, this dissertation attempted to understand engineering education scholars‟ behaviors and needs related to scholarly collaboration. A grounded theory study was conducted to investigate engineering education scholars‟ behaviors in developing collaboration and their technology usage. Finally, a user-centered software design was proposed as a technological solution that addressed community collaboration needs. Results show that the engineering education research community is at its early stage of forming a small world network relying primarily on a small number of key scholars in the community. Scholars‟ disciplinary background, research areas, and geographical locations are factors that affect scholarly collaboration. To facilitate scholarly communication and collaboration, social research network sites started to be adopted by scholars in various disciplines. However, engineering education scholars still prefer face-to-face interactions, emails, and phone calls for connecting and collaborating with other scholars. Instead of connecting to other scholars online, the present study shows that scholars develop new connections and maintain existing connections mainly by attending academic conferences. Some of these connections may eventually develop into collaborative relationships. Therefore, one way to increase scholarly collaboration in engineering education is to help scholars better network with others during conferences. A new mobile/web application is designed in this dissertation to meet this user need. The diffusion of innovation theory and the small world network model suggest that a well-connected community has real advantages in disseminating information quickly and broadly among its members. It allows research innovations to produce greater impacts and to reach a broader range of audiences. It can also close the gap between scholars with different disciplinary backgrounds. This dissertation contributes to enhancing community awareness of the overall collaboration status in engineering education research. It informs policy making on how to improve collaboration and helps individual scientists recognize potential collaboration opportunities. It also guides the future development of communication and collaboration tools used in engineering education research

The Interdependence of Scientists in the Era of Team Science: An Exploratory Study Using Temporal Network Analysis

How is the rise in team science and the emergence of the research group as the fundamental unit of organization of science affecting scientists’ opportunities to collaborate? Are the majority of scientists becoming dependent on a select subset of their peers to organize the intergroup collaborations that are becoming the norm in science? This dissertation set out to explore the evolving nature of scientists’ interdependence in team-based research environments. The research was motivated by the desire to reconcile emerging views on the organization of scientific collaboration with the theoretical and methodological tendencies to think about and study scientists as autonomous actors who negotiate collaboration in a dyadic manner. Complex Adaptive Social Systems served as the framework for understanding the dynamics involved in the formation of collaborative relationships. Temporal network analysis at the mesoscopic level was used to study the collaboration dynamics of a specific research community, in this case the genomic research community emerging around GenBank, the international nucleotide sequence databank. The investigation into the dynamics of the mesoscopic layer of a scientific collaboration networked revealed the following—(1) there is a prominent half-life to collaborative relationships; (2) the half-life can be used to construct weighted decay networks for extracting the group structure influencing collaboration; (3) scientists across all levels of status are becoming increasingly interdependent, with the qualification that interdependence is highly asymmetrical, and (4) the group structure is increasingly influential on the collaborative interactions of scientists. The results from this study advance theoretical and empirical understanding of scientific collaboration in team-based research environments and methodological approaches to studying temporal networks at the mesoscopic level. The findings also have implications for policy researchers interested in the career cycles of scientists and the maintenance and building of scientific capacity in research areas of national interest

Application of Software Engineering Principles to Synthetic Biology and Emerging Regulatory Concerns

As the science of synthetic biology matures, engineers have begun to deliver real-world applications which are the beginning of what could radically transform our lives. Recent progress indicates synthetic biology will produce transformative breakthroughs. Examples include: 1) synthesizing chemicals for medicines which are expensive and difficult to produce; 2) producing protein alternatives; 3) altering genomes to combat deadly diseases; 4) killing antibiotic-resistant pathogens; and 5) speeding up vaccine production. Although synthetic biology promises great benefits, many stakeholders have expressed concerns over safety and security risks from creating biological behavior never seen before in nature. As with any emerging technology, there is the risk of malicious use known as the dual-use problem. The technology is becoming democratized and de-skilled, and people in do-it-yourself communities can tinker with genetic code, similar to how programming has become prevalent through the ease of using macros in spreadsheets. While easy to program, it may be non-trivial to validate novel biological behavior. Nevertheless, we must be able to certify synthetically engineered organisms behave as expected, and be confident they will not harm natural life or the environment. Synthetic biology is an interdisciplinary engineering domain, and interdisciplinary problems require interdisciplinary solutions. Using an interdisciplinary approach, this dissertation lays foundations for verifying, validating, and certifying safety and security of synthetic biology applications through traditional software engineering concepts about safety, security, and reliability of systems. These techniques can help stakeholders navigate what is currently a confusing regulatory process. The contributions of this dissertation are: 1) creation of domain-specific patterns to help synthetic biologists develop assurance cases using evidence and arguments to validate safety and security of designs; 2) application of software product lines and feature models to the modular DNA parts of synthetic biology commonly known as BioBricks, making it easier to find safety features during design; 3) a technique for analyzing DNA sequence motifs to help characterize proteins as toxins or non-toxins; 4) a legal investigation regarding what makes regulating synthetic biology challenging; and 5) a repeatable workflow for leveraging safety and security artifacts to develop assurance cases for synthetic biology systems. Advisers: Myra B. Cohen and Brittany A. Dunca