Ecosystems as a game-changer
The emergence of multi-institution education ecosystems must be seen as a radical game-changer that has developed as a response to the exhaustion and shift in the purpose of the existing educational paradigm in the context of twenty-first-century connectivity and the increasing erosion of resources for public education. Higher Education Institutions (HEIs), as skills creators (providing learners around the globe with both the technical business skills and the soft skills of management), “credentialers” and idea laboratories, are challenging and transforming the traditional, conservative, top-down educational hierarchies.
Through a holistic approach suggested by ecosystems, business schools can leverage their strengths, spread risk and investment by establishing new relationships across the business, academia, the public policy sphere, and broader society. In the process, they are able to advance their transformation with better reach to ecosystem partners at the local, national and global level.
Such creative disruption results in new frameworks and organisational paradigms for inclusive education and lifelong learning. It will encourage the emergence of learner-driven experiences, flexible delivery methods, economies of scale thanks to the use of technology, and innovative credentialing systems that can replace or supplement conventional assessment practices.
Three perspectives on ecosystems
Sharing resources and control of the different dimensions of their mission – teaching, research and outreach – can result in three types of ecosystem:
- Knowledge-sharing ecosystems comprising complex, evolving networks of organisations including think tanks, foundations, governmental and global agencies connecting to build a global shared knowledge base (about education and learning, innovation, funding opportunities, scaling innovation) to develop opportunities not only within but between networks (see also Wu and Lei p34 in this volume).
- Innovation ecosystems to accelerate radical innovation through the combination of multiple players, policies and platforms. These innovation ecosystems tend to contain traditional and new education providers, formal and informal learning opportunities, the involvement of business, Edtech developers and providers and HEIs, often supported by digital technology (see also Soda and Troilo p40 in this volume).
- Learning ecosystems comprising diverse combinations of providers (schools, businesses, community organisations as well as government agencies) to create new learning opportunities and pathways. They are usually supported by an innovative credentialing systems or technology platforms.
This shift across the different types of ecosystems has initiated significant changes in the ways HEIs operate, providing exciting times for quality assurance (QA) professionals, practices and providers. We aim to explore the inherent challenges in redesigning QA practices in these cross-function, cross-institution or cross-sector operations.
Strategy, risk and QA leadership
A higher education ecosystem is a self-regulating and fluid network of players with a common focus on value-co-creation. Its establishment can imply enormous complexities in terms of coordinating people, capital and technology. When thinking of an ecosystem as a collaborative model, there is a need for a common understanding of its strategy, vision, framework, values, culture, and each member’s contribution to align and achieve QA objectives in a seamless manner. Trust and credibility of partners in this key journey of change are paramount to how quality assurance can be delivered.
The design of a new quality assurance model, including appropriate metrics and KPIs, is an integral part of the strategy and leadership, and should be established right from the start. Like all transformation initiatives, organisations will need to absorb the change and management and staff must clearly understand their roles and responsibilities in ensuring continual quality delivery whilst managing the cultural shift.
A fundamental strategic question is that of the governance and leadership of QA within the ecosystem identifying where the overall responsibility and processes reside. Two approaches exist: a centralised model (already in existence for networks or consortium degrees which have a leading institution) or a shared responsibility model across partners federating their QA processes and teams.
The collaborative model requires balancing the tension of two types of risk: the risk of poor-quality provision vs. the risk of stifling innovation. It requires a dynamic systems approach to risk assessment and strategy – to identify and mitigate strategy risks, external risks, and even novel risks – to understand the dynamics over time, and provide advanced risk assessment tools to support multi-criteria decision-making. Due diligence mechanisms are even more critical to make sure the motives, goals, and integrity are aligned, rather than rushing after a “quick fix” or immediate revenue or endowment stream. Processes to manage and, if necessary, separate from dysfunctional members are of key importance as well.
QA and technology platforms and tools
Technology platforms and cloud-based systems serve to choreograph and automate the mutualised learning resources of ecosystem players. Architectural design and distributed governance of learning infrastructure must ensure proper deployment and sharing to support the diverse learning journeys of students within the ecosystem as well as other forms of shared value creation. QA professionals will need to take a front seat role and be engaged earlier to build quality-enhancing features into educational design and delivery practices. They will also need to chaperone a shift away from a quality assurance ideology based on the philosophy of inspection to one that focuses on set-up and system integrity checks.
The high compositionality of services is fundamental for both structuring and controlling quality assurance processes. Growing and changing expectations of students around the ease of access and engagement call for an integrative view of quality management that combines the consumer view and the technical view of the service delivered. Support tools are no longer limited to commercial off-the-shelf tools and increasingly include open-source alternatives that can all be aligned like Lego blocks on a platform. Technological innovation must be increasingly matched by schools with sizable resource commitments, but this is not the only dimension where investments is required.
QA and the learning experience
Heightened customer expectations regarding the broader learning experience and the career-specific value-added, call for the seamless delivery of well-connected learning interventions as students travel on their individual learning trajectories through the ecosystem. At the start, this necessitates the amalgamation of user experience design and instructional design, driven by a design thinking logic. Maintaining consistency of standards is another challenge when rolling out the distributed delivery model with diverse sets of providers and learners.
In order to respond to the constant pressures ever more quickly and adopt emerging technology faster, QA teams must apply an “end-to-end” approach for the learning journey as a whole rather than the traditional piecemeal approach. They must focus on student-centric learning outcomes that cover both formal and informal learning scenarios co-created by host institutions in the ecosystem with the support of third-party ed-tech providers.
Quality indicators, traditionally expressed as one-dimensional proxy measures such as contact hours or credit hours, will need to evolve as a single, centrally designed set of rules will no longer describe the full breadth of the learning experience. Stringent criteria capturing the accumulation of experiences across a range of institutions and modalities for transferring credits for a degree award must form the basis of ensuring quality and enforcing achievement standards as they replace or augment the traditional linear system of examinations and graduation in the same institution.
QA and learning analytics
As learning pathways become more flexible and personalised, the curricular footprint of a degree programme is more difficult to grasp ex-ante. In this context, with the help of advanced data analytics capabilities, faculty advisors and mentors must leverage actionable dashboard data to provide students with targeted guidance on how to build their learning pathways incrementally while keeping their overarching career ambitions in mind.
In traditional systems, it is typically the needs of the organisation, not the needs of the learner, that inform the design and the requirements of a learning system. Learners’ intellectual progression is treated as an implied outcome of, on the one hand, quality-assured module content, pedagogy and assessment and, on the other hand, pre-approved module sequencing.
Within ecosystems, curricula are more flexible and fluid requiring QA to become more learner-centred. Better learner-produced data, beyond end-of-course evaluations and student experience survey data, will assist HEIs to develop insights on students’ attainment of skills, their learning outcomes, and graduate satisfaction levels. Gaining information on the pace of student learning, the content they choose to engage with, and the reasons for students not completing a specific learning intervention, will become foundational components of QA.
Data collection and monitoring the quality of data generation processes will represent formidable challenges within an ecosystem environment. Getting the metrics right from the start is vital, as is ongoing validation by QA teams on performance requirements and changes over time. However, given the stronger data privacy and protection laws, the QA data collection processes put in place must safeguard data and include a robust and secure system for the integration and sharing of data across and amongst the ecosystem partners.
Agile QA executives needed
These are exciting times. The eco-systemic way of working across sectors and stakeholder groups will create deep and lasting changes to the organisation of learning and its alignment with the needs of business and society as a whole. It paves the way to a greater diversity of growth opportunities (programme development, revenue generation, and knowledge creation), agile governance structures and, ideally, greater resilience to unanticipated shifts or shocks.
This new paradigm of adaptability and scalability requires agile leadership of QA departments as well. QA work has to move away from the enforcement of bureaucratic rules that often emphasise process over outcomes and, instead, must assume the role of driver of innovation and ecosystem success within the HEI. This identity shift will put QA executives at the forefront of balancing control and arm’s-length management of shared value chains.
QA leaders need to be consistent and stability-enhancing in their actions and, at the same time, be ready to disrupt in order to take advantage of the dynamically evolving business opportunities within the ecosystem. New skill sets will be required to match this identity shift, implying a fundamental rethink of how the expanding competencies required for QA work can most effectively be developed going forward.
- Ambidexterity strengthens quality management during COVID-19 - February 15, 2021
- More than just compliance - October 31, 2019
- EFMD GN launches the Career Professionals Development Institute - October 30, 2019