DEVELOPING FUTURE VISION LANDSCAPE AND MODELS OF TECHNOLOGY ENHANCED LEARNING

D. Woodgate, G.M. Isabwe

University of Agder (NORWAY)
The use of digital media technology in teaching and learning is increasingly getting diverse both in terms of the available technology solutions and the pedagogical approaches for various teaching & learning contexts. On one hand, this diversity of technology and contexts of use could open more possibilities to enhance the human performance, through the support of a wider variety of learning activities, human-human (H2H) & human-machine (H2M) interactions. On the other hand, however, the diversity calls for more research to devise effective pedagogical models and new concepts of digital technology mediated interactions for learning. 

We are entering a period of potentially dramatic transformation in the overall ecosystem, sometimes referred to as the 3rd Revolution in Education. The essential arguments underpinning the critical need for such transformation at this juncture are frequently based upon the assumption that current structures are failing to deliver the knowledge, competencies and performance required to successfully negotiate the emerging challenges of the multiple dimensions of change (social, cultural, human and technological), as we enter the third decade of the 21st century. This work discusses the need for future teaching and learning models and concepts that would be relevant a decade from now.

The article presents a novel approach to developing a future vision landscape that is sufficiently robust, actionable and preferable, through the application of the science of foresight with and its expansive methodologies, alternative thinking techniques and rigorous research and evaluation processes. The approach allows to develop transformative teaching and learning models that will have relevance with a 10-year horizon, using a six stages process:
(1) futureframing,
(2) futurepulsing,
(3) futuremapping,
(4) futurescaping,
(5) futuretuning
(6) futurefabbing.

The process is applied to the problem of integrating and connecting the potential future drivers and disruptors which could influence and reframe the essential concepts of education and learning by 2028. The results of this work contribute towards addressing the challenge of increasing motivation and engagement in learning for the future connected learner. Some of the potential influences we consider include: intelligent learning environments, transmedia and mixed media, personalization and learner modeling, future workforce needs, competencies and curricula transformation, supportive learning technologies, future eduenterprises, decentralized and distributed learning, learner well-being and engagement, future teaching approaches, socio-cultural change, assessment, certification and new learning pathways.