Community drive


Research theme

Children, young people and the education of the future (ICT as enabler of connected societies).


Community Drive is a technical and humanistic research and development project, focusing on education, learning and the co-production of knowledge.

Through technical and humanistic cooperation, the project seeks to create models for children and young people to participate academically in the resolution of future social challenges by becoming an active and contributing part of research and development efforts. 

In addition, the project aims to add to the knowledge about community-driven science and propose how design thinking can be used as an approach to learning ‘21st-century skills’. Further, the project contributes knowledge about community-driven game tools, user-driven big data and the Internet of Things and their connection with intelligent and socially responsible urban development. The project is conducted in cooperation with the city of Copenhagen, local schools and Aalborg University.

Community Drive involves students aged 10–14 attending schools in deprived neighbourhoods near Aalborg University Copenhagen in southern Copenhagen. This area is characterised by a high rate of unemployment, low income and little or no education, and it has been defined as an area of focus by various projects administrated by the city of Copenhagen. As a result, resources have been allocated for reconditioning the subsidised housing in this area.

Keywords: community-driven game tools, community-driven research, data-driven education, urban development.


  • +


    Schools and educational institutions do not adequately educate students to engage in independent knowledge collaboration and solve complex societal challenges (Bundsgaard & Hansen, 2016; Slot et al., 2017). As an alternative strategy to formal learning, community-driven research can break the boundaries between research institutions and surrounding communities through the involvement of new types of actors, forms of knowledge and institutions (OECD, 2011). Involvement of citizens and communities beyond universities and traditional research institutions as participants in research systems has been defined as a megatrend that will influence future research policy (Barreneche et al., 2016).

    There is an increasing focus on how laypeople and communities outside of traditional research institutions can be involved in all levels of research activities, including data collection and categorisation. In the field of learning games, and specifically in the development of science game formats, games’ ability to introduce new approaches to authentic science education has been the main topic of focus (Gee, 2007). The field of learning games was inspired by new types of games in which players are invited to participate in real-life professional research processes rather than simulations (Cooper, 2015; Magnussen, 2017).

    The development of so-called citizen science games, or scientific discovery games, within the past few years has introduced new elements into the issue of game-based participation in a knowledge domain. The main goal of this type of game is to create a platform that enables and motivates players to help solve scientific problems. This paper presents the project Community Drive, a three-year cross-disciplinary community-driven game and a data-based project in which students collaborated with urban planners to redesign their neighbourhood by applying game tools and sensor technology.

  • +


    Community Drive addresses extensive scientific and societal challenges regarding the integration of research and education in elementary schools, cooperation across institutions and openness and access concerning the way research is conducted. The project is intended to create a new field of interdisciplinary research based on community-driven research. Here, community-driven research is defined as research that is produced, communicated and applied in cooperation with non-researchers and is based on citizens’ involvement and openness, from the earliest phases of problem formulation to the last phases of implementation and evaluation of efforts.

    Community-driven research is inspired by experiments in open science, open data, open methods and open methods citizen science, but goes further and establishes future and shared engagement knowledge communities with external actors. Specifically, the project addresses three challenges regarding the movement towards greater openness, community and impact in the research world. We will briefly outline the three challenges the project addresses.

  • +


    The first challenge is the development of future education and student skills to solve complex problems. The need to identify which competences will be key in the future has been the subject of education policy debate for several years (Griffin et al., 2012; Dumont et al, 2010; Greenstein, 2012). One proposed type of competence involves the so-called ‘21st century skills’, which are derived from a number of fields and include skills related to learning, innovation, information, media and technology (Partnership for 21st Century Skills, 2004). Research has partly focused on defining and redefining competencies and curricula (Dede, 2010) and partly on developing methods to evaluate 21st century skills (Voogt & Roblin, 2012).

    In this project, a range of tools are developed based on cases involving 21st century skills. Citizen awareness and innovation and learning skills are highlighted as key conditions of collaboration between students and actors across institutions and disciplines. The project thus involves both theoretically based learning design as well as competency assessment tools, addressing the need to develop theories, definitions and tools for community-driven research in elementary school education.

    The strategic background for this research effort can be found in the Danish school reform ‘The Open School’, which requires schools to be more open to society and cooperate with actors outside the schools (Christiansen et al., 2015). Early studies, however, show that Danish elementary schools insufficiently educate children and young people to self-produce knowledge and solve authentic, complex problems (Bundsgaard & Hansen, 2016; Slot et al., 2017). A central part of opening schools to community-driven science is inclusion of game tools for in the development process. This will be discussed later in the findings session.

  • +


    The second challenge the project addresses is the development of a more open and engaging research community. In recent years, research policy institutions such as the EU Commission, the Organisation for Economic Co-operation and Development (OECD) and a number of private research funds focused on new practices for open research and innovation have encouraged non-academics to participate in the research and development process (Barreneche et al., 2016). These open ‘quadruple helix’ collaborators involve representatives from research, businesses, authorities and civil society and are considered in a number of publications to be the key for greater and more responsible use of research knowledge (OECD, 2011). Open research is, first and foremost, a research agenda that can cause research to have a greater impact on society. Although at first it was primarily related to scientific publishing (open access), increasingly more open research has aimed to encourage more knowledge sharing and involvement among users (OECD, 2011; Geoghegan-Quinn, 2014; European Commission, 2014; Budtz Pedersen & Martiny, 2016).

    The research policy agenda is based on the finding that scientific knowledge has the greatest possible impact in a society in which citizens, businesses and stakeholders are invited to participate in research as early as possible. This finding is emphasised by the strong increase in diversity and the amount of open data that is available from public authorities. Previously, specialised expertise was required to analyse big data, but now there are more examples of tools that allow citizens and other laymen to independently perform big data analysis (Marr, 2016).

    A number of challenges must be taken into account when open data is used in community-driven research, such as challenges related to data quality, bias in data and transparency in tools (Allan & Redden, 2017; Martiny, Budtz Pedersen, & Birkegaard, 2016). One example can be found in research using game elements and involving participants who contribute to the development of knowledge and solutions, also known as ‘scientific discovery games’ (Good & Su, 2011; Cooper, 2015). These studies show that the involvement of children and young people in teaching has much educational potential and that there is strong motivation for students to participate in authentic research and development processes in collaboration with professional actors (Magnussen et al., 2014).

    At the same time, reviews show that this type of community-driven research is largely defined by research and not participatory needs and that laymen are often included in complicated research processes without development of their competencies (Magnussen, 2017). Community Drive investigates how collaboration can be based on both citizens’ expertise and professionals’ skills.

  • +


    The third challenge addressed by the project is the city’s big open data and citizens’ involvement in urban development. Sensor technology and data have been given a central role in the development of cities in recent years. The smart city (and smart society) are well-known concepts related to information and communication technology (ICT) and collection of information about the city’s status, which, in collaboration with citizens, can be used to optimise resources and offer citizens new and better services (Ojo et al., 2015). In a smart city, it can be difficult to ensure citizens’ privacy, when information is collected (Gidari, 2017). Smart citizens are defined as citizens who use open sources or their own ICT to investigate other citizens’ experiences regarding one or more parameters. Citizen measurements may be based on climate-related measurements of citizen mobility and use of urban space.

    Community Drive focuses on the many types of data obtained in a city and the way in which one can enable citizens to collect relevant data about the city, its use and its inhabitants. In particular, it examines studies on how existing data can be represented and applied by both students and other actors. This contributes to the development of not only smart cities but also smart citizens, which is important because the potential of the smart city is best realised by citizens cooperating with urban developers and planners. The technical research contributions include: 1) representation and synthesis of existing big data collected by the city of Copenhagen, 2) collection and representation of live data and 3) possible collection of data as needed in a user-friendly and cost-effective way, including identification of the most suitable communication protocols.

    In summary, the goal of Community Drive is to develop a model for establishing comprehensive game- and data-driven research and development cooperation, focusing on education of children and young people in community-driven research. The project thus aims to create a new research platform and approach based on research co-produced with children, young people, professionals and a municipality. It aims to answer the following research question: Through game- and data-driven methods, how can children and young people develop the competences needed to participate in the development of technical and humanistic scientific solutions for a city’s complex problems in cooperation with professional actors? In the following sections, we describe the hypotheses and approaches on which Community Drive is based and present and discuss the results of previous pilot projects.

  • +

    3. Methods of the previous pilot study

    Previous pilot projects—the so-called City at Play project—were developed in close collaboration with the Copenhagen City Council Social Services Department and Aalborg University Copenhagen. The project aimed to involve young people in deprived areas as experts on their own living environments and to educate them on the influence of structural factors on their welfare and well-being and on how to use game tools to apply their knowledge and ideas to recreate and strengthen their neighbourhoods (Magnussen & Elming, 2017). From the start, the project was intended to define problems and introduce game-based methodological solutions to implement structural changes in neighbourhoods in deprived areas of Copenhagen, addressing both social and educational objectives. The project aimed to provide real-world contributions to the City Council’s urban development and planning and, ultimately, help to realise of some of the presented ideas.

    The methodology used to develop the components of Cities at Play followed a design-based research process involving various design cycles, interventions, analyses and redesigns (Brown, 1992). Design-based research was applied as a methodological framework, and various methods were employed to develop and study the game-based community-driven urban planning environment. The project involved two iterations of a design-based research process (Brown, 1992) that involved an increasing number of school classes and departments of the Copenhagen City Council. The first iteration is described in another paper (Magnussen & Elming, 2017).

  • +

    3.1 Study design, methods and data analysis

    Cities at Play included four teachers, two seventh grade classes and two ninth grade classes (in total, 90 students aged 13–15) from a school in a deprived area of southern Copenhagen. This area was chosen due to its high rate of unemployment and its residents’ limited or lack of education. The school is located in an area with older public housing that suffers from problems involving gangs and drugs. A library, nursing home and kindergartens are near the school. The project was conducted in the local library over a three-week period. The classes worked separately on their models for one week and then worked in parallel during the third week to finish their models for presentation to urban planners from the technical department of the city of Copenhagen. A mixed methods approach was used. Video observations were used to document the three weeks of student design sessions, particularly students’ dialogue in the design process, to understand how various models were developed and the types of local technical knowledge that were used to do so (Brown & Wyatt, 2010). Specifically, the video observations focused on elements that strengthened students’ competences.

    Pre- and post-surveys were conducted to measure 1) students’ motivation to participate in the project, 2) local knowledge about the area and urban planning, 3) how well the project supported learning of 21st-century skills such as real-world problem solving and collaboration compared to what students defined as ‘everyday school’, 4) how much the project differed from ‘everyday school’ according to students and 5) students’ understanding of their ability to structurally change their living conditions. The digital surveys provided opportunities for quantitative answers, which created an overview of the students’ knowledge and experiences, as well as for qualitative answers, which clarify the background for the quantitative answers. The teachers administered the surveys to their classes the day before the course started and on the day the course ended. Semi-structured qualitative interviews with teachers and students were conducted to reveal the possible outcomes and challenges of the project (Brinkmann & Kvale, 1996). Qualitative data were analysed, applying grounded theory as a method of data categorisation, and themes were defined based on participant-defined concepts related to perceived knowledge generation and learning practices (Strauss & Corbin, 1998).

  • +

    4. Findings of pilot studies and discussion of the Community Drive approach

    As described in previous sections, the three-year research and development project Community Drive builds on previous pilot studies such as City at Play. In this section, the central findings and potentials and challenges of the pilot studies will be discussed in relation to the research approach of Community Drive.

  • +

    4.1 Structure of courses and educational approach – design thinking

    The design of Cities at Play: Community Drive included five phases and was based on the results of previous studies on game-based innovation education and community-driven science games (Magnussen et al., 2014). As described in Table 1, the participating students progressed through the following phases: 1) inspiration, 2) identification of the opportunities and problems in their area, 3) development of ideas and building of models in the game Minecraft and with other materials and 4) presentation to and feedback from professional architects and urban planners from the Copenhagen City Council departments.




    Phase 1 (Week 1) Inspiration Field trips to newly developed areas in the city and introduction to core concepts of urban planning by architects and urban planners
    Phase 2 (Week 1) Identification of the opportunities and problems Identification of core strengths and challenges in the area
    Phase 3 (Week 1) Development of ideas Development of ideas to solve local problems and strengthen the potential of the area
    Phase 4 (Weeks 1 & 2) Modelling Building of models with Minecraft, LEGO and other digital and physical tools
    Phase 5 (Week 2) Presentation Presentation of models to the head of the Department of Transport, Technology and Environment and urban planners from the Copenhagen City Council

    Table 1: Phases of the students’ development process in City at Play 2 (modification of IDEO, 2009)

    Overall, community-driven education involving design and innovation was welcomed by schools as a valuable way to build the competence of both students and teachers. Combined with local development, the design innovation approach was viewed as a new didactic approach for schools in deprived areas that allowed for other approaches, as expressed by the leader of the central school involved in the project:

    School leader: The way in which student and teachers work in the project makes it possible for all students, regardless of the academic level, to bid in, giving everyone their best.

    (to the question on how it is different from normal teaching) The students work with different approaches to learning, which means that all students are engaged (…) The teachers have subsequently been able to use the experience to dare to use more learning pathways in the classroom (interview with school leader, 13th of Sep. 2016)

    According to the pilot studies, involving innovation and design thinking approaches in education showed potential in the context of community-driven research education. However, these studies also showed that there is a strong need for didactical and methodological development when implementing design and innovation methods in community-driven research (Magnussen & Elming, 2017).

    This project aims to develop educational models for developing design thinking as an educational approach in community-driven science to teach 21st-century skills. ‘Design thinking’ is defined as the use of design methods and tools to solve complex problems (Brown & Wyatt, 2010). Design is an independent knowledge paradigm (Buchanan, 1992) with its own intellectual culture (Cross, 2007).

    Community Drive is methodologically rooted in a design-based research approach (Brown, 1990). Design-based research places equal focus on development of research-based practice and theory and therefore involves iterative processes of problem definition, domain-specific mapping, education design, design intervention, qualitative data collection, analysis and redesign (Cobb et al., 2003). The intervention phase of the project is structured around two design-based iterations, and the teaching design is based on the results of the project mapping phase, developed, involved in interventions, analysed, redesigned and tested based on the results of the analysis.

  • +

    4.2 Competences: Real-world problem solving and community-driven urban development

    Pre- and post-surveys of students’ developed knowledge was conducted in the pilot project City at Play. These surveys investigated how students perceive the tasks in City at Play and how they differ from other project-based teaching tasks as well as what type of knowledge students think they develop during the course. The surveys indicated that a majority of students (78%) believed that the overall focus of real-world problem solving in City as Play was different from that of everyday schoolwork (Magnussen & Elming, 2017). When asked what was different, students provided various responses, which can be categorised into several themes (Table 2).


    Examples of student responses

    Changing things ‘Yes, because we normally don’t work with changing things’, ‘Yes because we were working with changing something in our city, which is something we don’t do in class’
    Something in the real world ‘Yes, a lot, because it concerns the real world and it involved problems we could solve for the entire neighbourhood’, ‘Yes, because in a way it did not involve problems related to school subjects but something in the real world’
    Helping people, not just working for your own benefit ‘In school we work more for our own benefit. In City at Play we made something that everybody could benefit from’, ‘In school you need to improve your grades, here we needed to help other people … #Thatwasnew’, ‘Yes, because we had to consider whether it would work because here it’s all about people’
    More freedom to make decisions/not predetermined ‘What we had to make was not predetermined’, ‘It’s kind of good because we had to decide on what we needed to build and so on. It’s not like that in daily teaching, where teachers have the right to decide’, ‘We were allowed to determine/decide most things’
    Using one’s imagination and inventing ‘We had to use our imaginations’, ‘We don’t usually talk to architects and invent things’
    Being active ‘We didn’t sit down all the time’, ‘You were free to choose what to do’, ‘We got to move around and independently decide things’, ‘We were active in City at Play’
    Other tools ‘We used other tools’, ‘We had to play a game to do our assignment’, ‘We were building with LEGO blocks and made models with them’, ‘No books, a lot of collaboration’

    Table 2: Themes of responses to the two post-survey questions regarding City at Play: ‘Were the problems you worked with in City at Play different from the problems you normally work with at school?’ and ‘What was different in City at Play compared to everyday teaching?’

    The central focus of Community Drive is assessment of the competences students develop in a community-driven research environment with a design thinking approach. It is supplemented by assessment of the competences students develop through participation in complex, authentic problem solving. Through collaboration with the project GBL21 (, the project develops a unique quantitative competency measurement tool to test which 21st century skills students develop during complex, authentic problem solving. The competence test is developed as a series of scenario-based modules that are standardised through Rasch analyses. It is based on previously developed tools (Bundsgaard & Hansen, 2016).

  • +

    4.3 Documenting and representing local knowledge

    A central aspect of Community Drive and previous pilot projects has been visualisation and representation of students’ knowledge and developed ideas so they have an impact on formal decisions made by the city of Copenhagen regarding particular areas.

    To do so, the study has identified how students perceive their knowledge and how it can contribute to development of the area. In the project’s pre-and post-surveys, students were asked if they had knowledge about their area that the urban planners participating in City at Play did not possess. In the pre-survey, 9% answered either ‘Yes, I know a lot that they don’t know’ or ‘Yes, I know a bit more’. This percentage changed to 45% in the post-survey (Figure 3).

    Figure 3: The bar chart on the left shows the pre-survey results, and the bar chart on the right shows the post-survey results. The pre-survey question was ‘Do you have knowledge about Folehaven that the architects redeveloping Folehaven do not have?’ In the pre-survey, 7% (green) answered ‘Yes, I know a lot that they don’t know’, 2% (light blue) answered ‘Yes, I know a bit more’, 26% (orange) answered ‘Yes, some’, 38% (dark grey) answered ‘Maybe a little’, 17% (dark blue) answered ‘No, not very much’ and 10% (light grey) answered ‘No, not at all’. Students answered a similar question in the post-survey: ‘Think about the City at Play course. Did you possess knowledge about Folehaven that the architects redeveloping Folehaven did not have?’ Ten per cent of the students answered ‘Yes, I knew a lot that they didn’t know’, 35% answered ‘Yes, I knew quite a bit more’, 23% answered ‘Yes, some’, 16% answered ‘Maybe a little’, 6% answered ‘No, not a lot’ and 10% answered ‘No, none at all’.

    These results indicate that students’ perceptions of their knowledge about their neighbourhood and how it compares to that of professional urban planners change after participating in the pilot project. The study closely examined this change in their perception, asking students to qualitatively specify the knowledge that they felt urban planners did not have. The following were mentioned:

    1. Physical buildings or facilities in the area
      - ‘I know a little about the area and the buildings’, ‘supermarkets and lighting’.
    2. Experiences or feelings
      - ‘What the atmosphere is like, what’s good and what’s bad, what it’s like in general to be here’, ‘I can find my way around Folehaven with my eyes closed, I’m part of it’.
    3. Experiences or feelings concerning locations or facilities in the neighbourhood
      - ‘That it’s boring to be here/live here. They couldn’t know that there isn’t much light in the evening, which makes it scary’, ‘Where it’s safe and unsafe’, ‘Safe and unsafe places. What needs to be changed’.
    4. Social aspects of the community
      - ‘I know more about the things that some people need’, ‘I know, for instance, what it’s like to live here and what most people want /don’t want’.

    In phase 4 of the project, students built models of their proposed neighbourhoods using Minecraft, LEGOs and other digital tools. The pilot studies, however, showed that students’ models had very little impact on the departments involved in developing the area (Magnussen & Elming, 2017). To understand this finding, Community Drive further investigated which forms of knowledge and knowledge processes can impact students’ knowledge. Part of this is integrating new types of documentation of students’ access to the Internet of Things, using sensors and trackers to document challenges and opportunities in their neighbourhoods and access to technical, social and socio-economic big data from the city of Copenhagen. In addition, the project’s activities focus on providing children and young people access to the city’s big open data and live data, which were measured, documented and represented by young citizens. As a starting point, access to the city’s data allows students to access a wide range of information, such as data about traffic, pollution, light and use of different areas, that is essential to the situation and development of their city and neighbourhood.

  • +


    This work was supported by cross-disciplinary funding by Aalborg University.

  • +


    Allan, S., & Redden, J. (2017). Making citizen science newsworthy in the era of “big data.” Journal of Science Communication, 16(02), 1–12.

    Barreneche, A., Keenan, M., Saritas, O. et al. (2016). An OECD horizon scan of megatrends and technology trends in the context of future research policy. A report prepared by the OECD Directorate for Science, Technology and Innovation, commissioned by Danish Agency for Science, Technology and Innovation (DASTI), Copenhagen.

    Brinkmann, S., & Kvale, S. (2005). Confronting the ethics of qualitative research. Journal of Constructivist Psychology, 18, 157–181.

    Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141–178.

    Brown, T., & Wyatt, J. (2010). Design thinking for social innovation IDEO. Development Outreach, 12(1), 29–31.

    Buchanan, R. 1992. Wicked problems in design thinking. Design Issues, 8(2), 5–21.

    Budtz Pedersen, D., & Martiny, K. M. (2016). Open human science: Transdisciplinary and transmedial research. In C. Emmeche et al. (Eds.), Mapping frontier research in the humanities (pp. 137–156). London: Bloomsbury Publishers.

    Bundsgaard, J., & Hansen, T. I. (2016). Blik på undervisning: Rapport om observationsstudier af undervisning gennemført i demonstrationsskoleforsøgene. Læ

    Christiansen, R. B., Gynther, K., Hestbech, A. M., Vergmann Jørnø, R. L., & Rosenlund, L. T. (2015). Den Åbne Skole: Didaktiske, synkrone, online koblinger mellem skole og omverden. Retrieved from


    Cobb, P., Confre, J., diSessa, A., Lehrer, R., & Schauble, L. (2003) Design experiments in education research. The Educational Researcher, 32(1), 9–13.

    Cooper, S. (2015) Massively multiplayer research: Gamifying and (citizen) science. In S. P. Walz & S. Deterding (Eds.), The gameful world: Approaches, issues, applications (pp. 487–500).

    Cross, N. (2007). From a design science to a design discipline: Understanding designerly ways of knowing and thinking in design. In: R. Michel (Ed.), Design research now (pp. 4–54). Basel: Birkhäuser.

    Dede, C. (2010). Comparing frameworks for 21st century skills. In J. Bellance, & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 51-76). Bloomington, IN: Solution Tree Press.

    Dumont, H., Istance, D., & Benavides, F. (Eds.). (2010). The nature of learning: Using research to inspire practice. Paris: OECD.

    European Commission. (2014). Public consultation on science 2.0: Science in transition. Brussels.

    Geoghegan-Quinn, M. (2014). Science 2.0: Europe can lead the next scientific transformation. In EuroScience Open Forum, Copenhagen 24 June 2014. Brussels: European Commission.

    Gidari, A. (2017). “Smart cities” are too smart for your privacy, Retrieved from

    Gee, J. P. (2007). Are video games good for learning? Digital kompetense. Nordic Journal of Digital Literacy. Oslo: Universitetsforlaget.

    Good, B. M., & Su, A. I. (2011). Games with a scientific purpose. Genome Biology, 12, 135.

    Greenstein, L. M. (2012). Assessing 21st century skills: A guide to evaluating mastery and authentic learning. Corwin Press.

    Griffin, P., Care, E., & McGaw, B. (2012). The changing role of education and schools. In P. Griffin, B. McGaw, & E. Care (Eds.), Assessment and teaching of 21st century skills. Dordrecht:

    Springer Netherlands.

    Magnussen, R. (2017) Involving lay people in research and professional development through gaming: A systematic mapping review. In M. Pivec & J. Grüdler (Eds.), Proceedings of the 11th European Conference on Game-Based Learning (pp. 394–404). Graz, Austria.

    Magnussen, R., & Elming, A. L. (2017). Student re-design of deprived neighborhoods in Minecraft: Game-assisted community-driven urban development. In 12th International Conference on Computer Supported Collaborative Learning, CSCL 2017. Pennsylvania: Drexel University and the University of Pennsylvania.

    Magnussen, R., Hansen, S. D., Planke, T., & Sherson J. F. (2014). Games as a platform for student participation in authentic scientific research. Electronic Journal of E-learning, 12(3), 258–269.

    Marr, B. (2016). Big data in practice - How 45 successful companies used big data analytics to deliver extraordinary results. Chichester: Wiley.

    Martiny, K. M., Budtz Pedersen, D., & Birkegaard, A. (2016). Open media science. Journal of Science Communication, 15(06), 1–20.

    OECD (2011). Open science: policy challenges and opportunities. Internal working document. Paris: OECD.

    Ojo, A., Dzhusupova Z., & Curry, E. (2015). Exploring the nature of the smart cities research landscape. In J. R. Gil-Garcia, T. A. Pardo, T. NamPublic (Eds.), Smarter as the new urban agenda: A comprehensive view of the 21st century city. Springer.

    Partnership for 21st Century Skills. (2004). Learning for the 21st century: A report and MILE guide for 21st century skills.

    Slot, M. F., Hansen, R., & Bremholm, J. (2017). Elevopgaver og elevproduktion i det 21. århundrede – en kvantitativ og kvalitativ analyse af elevproduktion i matematik, dansk og naturfag.

    Strauss, A. L., & Corbin, J. (1998). Basics of qualitative research: Procedures and techniques for developing grounded theory. Thousand Oaks, CA: Sage.

    Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299–321.