Drivers, affordances and challenges of digital badges

Digital badges point to a significant and innovative disruption to higher education in how learning achievements will be recognised, made more visible and reach beyond institutions. Digital badges provide a means to display transparent and information-rich links directly via metadata to standards achieved, the badge issuer, the criteria for earning the badge, as well as evidence of the skill or competency the badge represents (Bowen, Open badges anatomy (post on blog Class Hack). Retrieved from http://classhack.com/post/45364649211/open-badge-anatomy-updated, 2013). There are many factors that drive institutions to consider digital badges, including credentialing of lifelong learning and the need for personalised learning approaches. They can play an important part in the credentialing of flexible and more cost effective pathways for learners, and provide meaningful and relevant ways to identify progress and achievements in a more granular way (Finkelstein et al., The potential and value of using digital badges for adult learners. Washington, DC: American Institutes for Research, 2013). Digital badges open opportunities for personalised learning pathways for students (Grant, What counts as learning: open digital badges for new opportunities. Irvine, CA: Digital Media and Learning Research Hub, 2014) and for employers to gain clarity around skillsets. Badges enable an alternate credentialing system that supports pathways for, recognition of prior learning, and portability outside the institution they were achieved, linking the worlds of education, work and community in meaningful ways. However, like any new systems, digital badges are not without their challenges. Predictably, digital badges are not universally embraced and differences in strategy and enabling structures range as widely institutions within the sector. This chapter explores drivers, affordances and challenges for the use of digital badges. Drawing on historical roots and influences such as lifelong learning, opportunities and challenges are discussed in light of specific use cases and emerging examples.