Higher education for sustainability by means of transdisciplinary case studies: an innovative approach for solving complex, real-world problems

2006 ◽  
Vol 14 (9-11) ◽  
pp. 877-890 ◽  
Author(s):  
Gerald Steiner ◽  
Alfred Posch
Keyword(s):  
Higher Education ◽  
Case Studies ◽  
Real World ◽  
Innovative Approach ◽  
Education For Sustainability ◽  
Real World Problems

scholarly journals Handrails through the Swamp? A Pilot to Test the Integration and Implementation Science Framework in Complex Real-World Research

2021 ◽  
Vol 13 (10) ◽  
pp. 5491
Author(s):  
Melissa Robson-Williams ◽  
Bruce Small ◽  
Roger Robson-Williams ◽  
Nick Kirk
Keyword(s):  
Pilot Study ◽  
Case Studies ◽  
Implementation Science ◽  
Real World ◽  
Environmental Problems ◽  
Environmental Research ◽  
Integrative Framework ◽  
The World ◽  
Science Framework ◽  
Real World Problems

The socio-environmental challenges the world faces are ‘swamps’: situations that are messy, complex, and uncertain. The aim of this paper is to help disciplinary scientists navigate these swamps. To achieve this, the paper evaluates an integrative framework designed for researching complex real-world problems, the Integration and Implementation Science (i2S) framework. As a pilot study, we examine seven inter and transdisciplinary agri-environmental case studies against the concepts presented in the i2S framework, and we hypothesise that considering concepts in the i2S framework during the planning and delivery of agri-environmental research will increase the usefulness of the research for next users. We found that for the types of complex, real-world research done in the case studies, increasing attention to the i2S dimensions correlated with increased usefulness for the end users. We conclude that using the i2S framework could provide handrails for researchers, to help them navigate the swamps when engaging with the complexity of socio-environmental problems.

Human Factors in Architecture: A View from the Field

1982 ◽  
Vol 26 (2) ◽  
pp. 203-203
Author(s):  
James A. Wise
Keyword(s):  
Built Environment ◽  
Human Factors ◽  
Case Studies ◽  
Real World ◽  
Architectural Design ◽  
Recent Case ◽  
Panel Session ◽  
Real World Problems

This is a panel session focused on the applications of Human Factors to real world problems in architectural design. Five representatives from various design & research professions will present recent case studies of theirs, and examine the contribution that Human Factors made to these projects. The diversity of their examples shows the usefulness and importance on integrating concerns for the human user into plans for the built environment.

Networking Industry and Academia

2009 ◽  
Vol 23 (6) ◽  
pp. 437-443 ◽  
Author(s):  
Simon Stephens ◽  
George Onofrei
Keyword(s):  
Higher Education ◽  
Case Studies ◽  
Real World ◽  
Success Factors ◽  
Higher Education Institutions ◽  
Practical Application ◽  
Policy Makers ◽  
Key Success Factors ◽  
Primary Means ◽  
Education Business

Graduate development programmes such as FUSION continue to be seen by policy makers, higher education institutions and small and medium-sized enterprises (SMEs) as primary means of strengthening higher education–business links and in turn improving the match between graduate output and the needs of industry. This paper provides evidence from case studies. The findings indicate that the practical application of academic principles in real-world settings provides a useful learning vehicle for academics, graduates and SMEs. Key success factors and strategies for overcoming obstacles emerged from the case studies. In light of these findings, the authors make tentative recommendations to aid the future delivery of similar programmes.

scholarly journals Collaborative learning experiences of fourth-year students in a social work module

2021 ◽  
Vol 5 (2) ◽  
pp. 52-68
Author(s):  
Erica Pretorius ◽  
Hanna Nel
Keyword(s):  
Higher Education ◽  
Social Work ◽  
Collaborative Learning ◽  
Real World ◽  
Industrial Revolution ◽  
Learning Task ◽  
Problem Solving Skills ◽  
Innovative Thinking ◽  
Insight Into ◽  
Real World Problems

This article provides insight into a fourth-year social work module, integrating an authentic learning task. This task focused on the development of a funding proposal for a social service organization. It attempted to integrate collaborative learning by scaffolding students’ participation in the world of work, rather than just receiving a qualification. In view of the prevalent conversation around the Fourth Industrial Revolution and the Covid-19 pandemic, it is essential that lecturers at higher education institutions embrace collaborative and problem-solving skills for student tasks. Recent evidence suggests that higher education graduates’ learning and their readiness for work in a professional environment require a greater focus on creative and innovative thinking to solve real-world problems. The results from this qualitative investigation revealed that students found working in teams and collaborating with their peers both challenging and rewarding. This process contributed to the holistic development of social workers ready to work in the real-world.

Efficient inference for agent-based models of real-world phenomena

2021 ◽  
Author(s):  
Andreas Christ Sølvsten Jørgensen ◽  
Atiyo Ghosh ◽  
Marc Sturrock ◽  
Vahid Shahrezaei
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Parameter Space ◽  
Real World ◽  
Autonomous Agents ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Learning Approaches ◽  
Real World Applications ◽  
Real World Problems

AbstractThe modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners.Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

scholarly journals Competences for Solving Complex Problems: A Cross-Sectional Survey on Higher Education for Sustainability Learning and Transdisciplinarity

2020 ◽  
Vol 12 (15) ◽  
pp. 6016 ◽  
Author(s):  
Filippina Risopoulos-Pichler ◽  
Fedor Daghofer ◽  
Gerald Steiner
Keyword(s):  
Higher Education ◽  
Sustainable Development ◽  
Continuing Education ◽  
Real World ◽  
Academic Disciplines ◽  
Education For Sustainability ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Human Environment ◽  
Complex Problems

Successfully coping with complex, real-world challenges, such as those related to sustainable development and the resilience of coupled human–environment systems, calls increasingly for adapted forms of education and extended competences. Hence, we argue that, beyond knowledge and expertise in professional domains, additionally, personal, systemic, creative, and sociocultural competences are required to meet such challenges. Herefor, institutions of higher and continuing education play a crucial role. In this paper, universities as institutions of higher education are critically considered in relation to delivering education for sustainable development by raising awareness and providing the necessary competences to cope with complex problems such as sustainable development through effective forms of higher and continuing education as well as training. Research on attitudes and perceptions regarding sustainable development and the perceived need for comprehensive competences required to deal with such complex problems is still lacking. Our study provides a first attempt to elucidate core aspects of these attitudes, perceptions, and competences aiming to contribute to future, more tailored education approaches. We discuss the outcomes of a survey on sustainability in teaching and learning conducted at four Austrian universities. The analyzed sample comprised 3200 students as the recipients of, and 498 lecturers as the providers of, sustainability education in various academic disciplines at four distinct Austrian universities. Applying a questionnaire-based investigation of self-reported sustainability-related perceptions, attitudes, and competences and conducting factor analysis and cluster analysis, five sustainability types were identified that revealed a type of specific core awareness of sustainability and the perception of required competences related to sustainable development. The results presented are positioned to build a basis for further investigation that goes beyond the self-reported assessments to enable a comparison with sustainability-related, real-world problem-solving performance.

scholarly journals Towards Mathematical Literacy in the 21st Century: Perspectives from Indonesia

2011 ◽  
Vol 1 (1) ◽  
pp. 75-84
Author(s):  
Wanty Widjaja
Keyword(s):  
Teacher Education ◽  
Mathematical Modelling ◽  
Case Studies ◽  
21St Century ◽  
Real World ◽  
Oecd Countries ◽  
Education Level ◽  
Partner Country ◽  
Mathematical Literacy ◽  
Real World Problems

The notion of mathematical literacy advocated by PISA (OECD, 2006) offers a broader conception for assessing mathematical competences and processes with the main focus on the relevant use of mathematics in life. This notion of mathematical literacy is closely connected to the notion of mathematical modelling whereby mathematics is put to solving real world problems. Indonesia has participated as a partner country in PISA since 2000. The PISA trends in mathematics from 2003 to 2009 revealed unsatisfactory mathematical literacy among 15-year-old students from Indonesia who lagged behind the average of OECD countries. In this paper, exemplary cases will be discussed to examine and to promote mathematical literacy at teacher education level. Lesson ideas and instruments were adapted from PISA released items 2006. The potential of such tasks will be discussed based on case studies of implementing these instruments with samples of pre-service teachers in Yogyakarta.

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