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PurposeThis study aims to explore the role of planned, sudden shifts in lived experiences, in influencing learner capabilities towards improved problem-solving for sustainable development outcomes. The authors responded to employers of engineering and built environment graduates observing limited "real-life" problem-solving skills, beyond using established formulae and methods, in spite of attempts over more than two decades, to train engineers and other built environment disciplines in areas such as whole system design and sustainable design.Design/methodology/approachA grounded theory approach was used to guide the analysis of data collected through ethnographic methods. The process involved reflecting on authors' efforts to develop context appreciation within a course called "International Engineering Practice", using two years of collected data (archived course information, including course profile; completed assessment; lecture and field visit evaluations; and focus groups). The study is built on the authors' working knowledge of Bloom's Taxonomy and Threshold Learning Theory, and the well-established role of "context appreciation" in complex problem-solving. After the first iteration of the course, the authors looked for additional theoretical support to help explain findings. The Cynefin framework was subsequently used to augment the authors' appreciation of "context" – beyond physical context to include relational context, and to evaluate students' competency development across the four domains of "clear", "complicated", "complex" and "chaotic".FindingsThis study helped the authors to understand that there was increased capacity of the students to distinguish between three important contexts for problem-solving, including an increased awareness about the importance of factual and relevant information, increased acknowledgement of the varying roles of professional practitioners in problem-solving depending on the type of problem and increased appreciation of the importance of interdisciplinary teams in tackling complex and complicated problems. There were several opportunities for such courses to be more effective in preparing students for dealing with "chaotic" situations that are prevalent in addressing the United Nations' 17 sustainable development goals (UNSDGs). Drawing on the course-based learnings, the authors present a "context integration model" for developing problem-solving knowledge and skills.Research limitations/implicationsThe research findings are important because context appreciation – including both physical context and relational context – is critical to problem-solving for the UNSDGs, including its 169 targets and 232 indicators. The research findings highlight the opportunity for the Cynefin framework to inform holistic curriculum renewal processes, enhancing an educator's ability to design, implement and evaluate coursework that develops physical and relational context appreciation.Practical implicationsThe study's findings and context integration model can help educators develop the full range of necessary problem-solving graduate competencies, including for chaotic situations involving high degrees of uncertainty. Looking ahead, acknowledging the significant carbon footprint of global travel, the authors are interested in applying the model to a domestic and/or online format of the same course, to attempt similar learning outcomes.Originality/valueConnecting Bloom's taxonomy deep learning and threshold learning theory critical path learning insights with the Cynefin framework context domains, provides a novel model to evaluate competency development for problem-solving towards improved holistic physical and relational "context appreciation" outcomes.

The United Nations Sustainable Development Goal 9 on building resilient infrastructure highlights the urgent need for enabling evidence-based decision making for Infrastructure Asset Management supported by targeted platforms such as digital engineering and digital earth. In this paper the authors argue that an Asset Information Requirement matrix is an essential decision support tool for authorities and practitioners to evaluate right time, right place use of infrastructure data for resilient outcomes. The authors present an exploratory study that synthesizes the experiences of senior asset management decision-makers from road research institutes, state and local government bodies based in South East Queensland, Australia. The findings are discussed in relation to: digital engineering for managing complex data; current practice and outlook; key asset information requirements; and data structures, interactions and interdependencies. The authors present an 'Asset Information Requirement Matrix' that categorises 66 data requirements across four key infrastructure data types (including 13 information categories), and asserts the relevance of these data requirements for the six key phases of planning, design, construction, acquisition, operations and end-of-life treatment. The authors also present an 'Asset Interaction Matrix' which depicts the temporal, spatial and logical relationships between the 13 information categories. The authors conclude the importance of these asset matrices to leverage digital engineering for resilience infrastructure outcomes. The two matrices create a common language platform for engaging in digital engineering conversations, wherein authorities and practitioners can establish clear arrangements for measuring and monitoring road infrastructure through its life cycle.