Second-order learning in developmental evaluation for community-based sustainability

• Main Author: Mitchell, Andrew
• Published: De Montfort University 2017
• Subjects: 363.7
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720982

It is increasingly common for complex social, economic and environmental policy concerns to be delivered via funded community-based projects. A project’s contribution is typically monitored and evaluated relative to pre-defined outcomes, supported by a set of indicators. Available research suggests that when judged against such criteria, the performance of many funded international developmental and community-based sustainability (CBS) projects are variable, with evidence suggesting that changes elicited are negligible in duration, type, and scale. However, evaluating project performance relative to pre-defined outcomes may overlook the practical learning accumulated by actors in realising key objectives under conditions afforded by the operational context. To address this gap, developmental evaluation (DE) foregrounds and supports project practitioner learning and innovation under dynamic, complex, and uncertain operating conditions. Applying the DE focus on project actor learning and innovation, the present research thematically analyses how practitioners in a funded CBS case study project make sense of their practice. Despite its explicit focus on learning however, DE has not articulated a coherent cognitive paradigm, and a contribution of the present study is to equip DE with a conceptual architecture drawn from the enactive cognitive science paradigm, rooted in an explicit accounting of complexity. Using this base, a prototype DE framework was designed and provisionally field-tested in the form of a set of prompts to be used with CBS practitioners to augment traditional monitoring and evaluation activities. This framework is intended to support practitioners in surfacing and capturing second-order learning about their practices and to explore opportunities for innovative responses to dynamic complex operational conditions. Recommendations are offered for further research and how these findings might be incorporated into future CBS design and funding considerations.