Academic journal article Journal of Education for Library and Information Science

Preparing for Complexity and Wicked Problems through Transformational Learning Approaches

Academic journal article Journal of Education for Library and Information Science

Preparing for Complexity and Wicked Problems through Transformational Learning Approaches

Article excerpt

Introduction

As society becomes increasingly complex and reshaped by technological innovation, Library and Information Science (LIS) professionals are called upon to develop themselves and to assist those they serve to meet new challenges emerging in the digital age. At the 2014 ALA Summit on imagining the future of libraries, thought leaders from libraries, educational organizations, federal agencies, and foundations identified key themes for the future: the changing role of libraries and the implications for LIS education, reenvisioning library service, libraries as community hubs, and rebranding libraries (Bolt, 2014, p. 1-3). Recently, the Association of College and Research Libraries (ACRL) Research Planning and Review Committee (2013) undertook an environmental scan that also identified key trends and changes needed for the future, notably entrepreneurial thinking, radical collaboration, and LIS education that fosters innovation and creative leadership skills.

These are complex challenges with disruptive and transformational potential. As we move from ideas to action, a critical issue that arises is the gap between the world's complexity and our abilities to manage such complexity (Kegan & Lahey, 2009). Managing complexity demands more than technical knowledge; it requires the ability to make adaptive changes in our thinking, beliefs, and behavior (Heifetz & Linsky, 2002a). Recent developments in learning theories and teaching practice are often responses to some form of this critical question: How can we help our students prepare for the future in a complex, rapidly changing environment?

Behind that question is a belief that new forms will emerge that are unimaginable now, and that increasing our knowledge and skills within existing frameworks will not suffice in dealing with novel situations. What we come to know through informational learning will still be fundamental, but changes in how we know through transformational learning will also be critical (Kegan, 2000). That is the premise for this examination of three transformational learning approaches that have the potential to help students move toward higher levels of mental complexity: (1) overcoming im munity to change, (2) threshold concepts and variation theories, and (3) transformative learning theory.

We begin the examination with a look at some difficult challenges LIS professionals are likely to face in the future. Against that backdrop, the approaches will be introduced in turn, covering brief history, significance, and key tenets. Each approach illuminates a different kind of transformation, rests on a solid research foundation, and has had significant impact on education or practice. The final section compares the approaches and discusses the implications for LIS education.

A Glimpse of the Future: In the Shadow of Complexity

That social systems, technological systems, and the information environment are becoming increasingly complex is undeniable, but our understanding of what this means is more nuanced due to work on complexity theory and wicked problems. Understanding degrees of complexity allows us to make distinctions to guide decision-making (Miller & Page, 2007; Snowden & Boone, 2007). Simple contexts are stable and governed by causal relationships that are obvious to all; problems are solved through best practices. In complicated systems, elements are numerous and varied, and the causal relationships among them are less obvious. Because the elements are relatively independent of each other, changes to or removal of one element will not affect the others. Solving problems in complicated contexts requires data gathering and analysis by experts to find a good solution from among possible solutions. Finally, complex adaptive systems consist of interdependent, interacting elements that respond as an integrated whole to internal or environmental changes. This type of system is irreducible to its parts and solutions are emergent, discovered through experimentation and observation. …

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