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From April 25-27, 2018, at ASU’s Skysong Innovation Campus in Scottsdale, Arizona,129 higher education changemakers convened to dream, do, and drive the future of learning in the digital age. An unconference format enabled the free flow of conversation, with common themes emerging and 10 constructive actions coalescing. Each of the 10 actions represents a neighborhood in a greater higher education community that collectively drives innovations to improve student success, collaboration, leadership, next-generation learning environments, and digital fluency. Our ShapingEDU LIVE online event series provides a deeper dive into the landscape of each of the Actions. The goal is for dreamers, doers, and drivers from across the world to connect, collaborate, and find opportunities and build projects/strategies that continuously build upon and advance these 10 Actions.
The promise of educational technology as a force for access and equity has become a double-edged sword. Over the past 25 years, edtech has lowered the cost and opened new avenues for learning while exacerbating the divide between those with and without access. Institutional success is often measured by who is excluded from enrolling in the journey to advance learning and opportunity; selectivity excludes tens of millions of people, impacting minorities, regions, and workforce needs due to unequal access. Institutions have a social responsibility to blaze a path for learners of all backgrounds to attain their goals. Although progress is being made, it is also important to acknowledge a lack of diversity in higher education leadership -- and act now to foster inclusivity and equity.
How can technological capacity be applied towards increasing access, equity, and diversity in shaping a more inclusive higher education?
Higher education and the needs of the economy are intrinsically and inextricably linked. Predictions of the emergent machine learning economy’s impact on the future of work oscillate between utopian and dystopian scenarios. There are too few strategic conversations and associated planning about the role of higher education in this picture. Those charged with integrating technology into learning experiences today must help ascribe meaning to the products and services for tomorrow’s economic and workforce needs. Meanwhile, current and emergent industry leaders hiring graduates must convey the skills needed for career success to inform learning design. Both education and industry leaders must champion extraordinary and ongoing professional development.
How can higher education and industry collaborate to articulate the broad set of skills and capacities needed to meet the demands of the workforce of the future and enable meaningful lifelong learning experiences?
Too often our human networks are parochial and insular; participating organizations are similar in composition and mission, causing innovation to become stagnant in an echo chamber without diversity of perspective and thought. The concept of constellations expands the group dynamic to include a wider variety of individuals and organizations from different sectors or with different focuses, spurring participants to learn from each other and push the envelope past comfortable modes of thinking.
How can we evolve existing networks and build new ones to resemble constellations of innovation?
To remain relevant, higher education must champion lifelong learning, which entails informal learning experiences and professional development. A growing emphasis on competency mastery over seat time means there is more to the journey than the traditional degree. Developments in micro-credentialing and digital badging are forging new pathways for learners to demonstrate and gain formal recognition for upskilling in flexible, shorter courses, with industry supporting continuous learning through professional development programming.
How can we integrate a wider variety of learning experiences into institutions in ways that lead to learner success and relevant workforce development?
The promise of personalized learning is now more than 25 years in the making, aiming to meet individual learner needs, wherever they are, to help them create and achieve their goals while attaining established learning outcomes. Convergences in learning analytics, blockchain, machine learning, and adaptive learning are making it possible for institutions to understand each learner’s needs and respond with timely resources, interventions, and experiences. The same technological capabilities can enable learners to take even more ownership of their educational pathways.
With impetuses to grow higher education enrollment across the board, how can we scale personalized learning?
With institutions scaling their hybrid and fully online programs, digital evaluation techniques tend to be generic communications and postings that feel mechanical, impersonal, and often times alienating. Humanizing learning refers to faculty, instructors, and even next generation bots integrating intentional empathetic and authentic personal touches in course communications and materials that make learners feel welcome, supported, and understood.
How can we deepen human and humanizing interactions in online and particularly asynchronous learning experiences?
Learners are the ultimate stakeholders in higher education, but too often vision and strategy activities exclude them. Many of the leaders shaping the future of learning may not be present to feel the effects of their decisions. Creating opportunities for voices of all ages and multiple generations to come together towards addressing complex needs and challenges is key. Further, bridges must be built to find and empower the next generation of higher education technology leaders.
How can we continuously illuminate and develop younger voices in higher education?
Extended Reality (XR) learning, the vision of encompassing virtual reality, augmented reality, and mixed reality to transport learners to any setting to interact authentically with their surroundings is a powerful framing for shaping the future of education. At present, the technology itself can be cost-prohibitive for enterprise adoption and individual student purchase, but has vast potential for increasing engagement and access to high-quality learning experiences. There are also vital policy considerations, such as ethics and safety.
How can immersive experiences be created, scaled, and evaluated for their efficacy in improving learning and learning outcomes?
Artificial intelligence (AI) encompasses machines, systems, and applications that increasingly resemble humans in their ability to learn and make decisions. For higher education, AI holds potential to more intuitively respond to learner needs through advising, tutoring, and delivering content -- while illuminating engagement insights to instructors and institutions. Emerging learning approaches stand to benefit from AI for more efficient and sophisticated assessment.
How can AI be leveraged, in tandem with human capacity, to deepen learning and advising in the near- and long-term?
Data warehouses in education are mostly used for unit and institutional reporting needs. Institutions collect a bevy of student data to inform teaching and learning strategies; however, the risk of poor evaluation benchmarks and misinterpretation can lead to bad decisions. For example, a visit to a course page is not an adequate measure of learning engagement. Further, data often resides in institutional silos, not painting a holistic picture of student success. In the next phase of embedding data-driven approaches for student success, higher education must pinpoint the right questions before seeking answers in data.
What are genuine measures of student success and how can institutions establish processes to capture, analyze, and securely share such data?