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Prof. Shakil Akhtar

Clayton State University, Canada

Dr. Shakil Akhtar is currently Professor of IT and Computer Science at Clayton State University. Before this he was the IT Department head from July 2007 to December 2008. He was a Professor in the College of Information Technology at UAE University from 2002 to 2007 (Interim Dean 2002-03). During 2000 to 2002, he was a Performance and Simulation Engineer at Lucent Technologies in Naperville, Illinois, where he was responsible for performance analysis and simulation of telecommunications equipment including third generation mobile systems. His prior work experience includes Computer Science/Engineering Departments at Central Michigan University, University of Toledo, and King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Title: Effective E-Learning in STEM

Abstract: According to the US Bureau of Labor Statistics (BLS), the growth in Science, Technology, Engineering and Mathematics (STEM) related job positions from 2021 to 2031 is expected to be around 10.8% compared to the growth of 4.9% in non-STEM related positions. Furthermore, a recent survey conducted by the U.S. Department of Education revealed that only 16% of high school students are interested in STEM careers and perform well in mathematics. Additionally, the study revealed that only 28% of high school freshman are interested in the STEM discipline and want to go on to earn a college degree related to the field after graduation.
Unlike traditional classroom training, STEM education integrates various topics via enhancing problem-solving skills. It teaches students how to frame problems as puzzles, analyze information and form their own conclusions, and fosters their creativity and innovation as they work through STEM-related lessons.
Most of the commercially available learning platforms such as Moodle, Brightspace/D2L, or Canvas provide the E-Learning requirements. However, the task of content development to tailor classroom teaching remains with the educator. Younger learners tend to learn faster with visual lessons. The demand lies on the educators to develop and use the contents. Although there are efforts to develop contents, the demand exceeds far more than the available contents. This keynote address will address various development efforts in the STEM related areas and will highlight the needs for development in many other areas.

 

Prof. Jon Dron

Athabasca University, Canada

Dr. Jon Dron is a full professor in the School of Computing and Information Systems and member of the Technology Enhanced Knowledge Research Institute (TEKRI) at Athabasca University, Canada. Until 2007 he was a principal lecturer at the University of Brighton, UK, where he remains an Honorary Faculty Fellow working with the Centre for Learning and Teaching.
Straddling the technology/education divide, his research interests broadly centre around social and structural aspects of learning technologies, with a particular emphasis on discovering, designing and employing methods and technologies to enable learners to help one another to learn.
He is the author of the book Control & Constraint in E-Learning: Choosing When to Choose . He has been a keynote speaker at many international workshops and conferences, is author of scores of articles in journals, books and conference proceedings, several of which have received top paper awards at international conferences. His most recently published book, co-written with Terry Anderson, is Teaching Crowds: Learning & Social Media. His next book, How Education Works: Teaching, Technology, and Technique is under review.

Title: Artificial humanity and human artificiality

Abstract: We are participants in, not just users of technologies. Sometimes we participate as orchestrators (for instance, when choosing words that we write) and sometimes as part of the orchestration (for instance, when spelling those words correctly). Usually, we play both roles. When we automate aspects of technologies in which we are just parts of the orchestration, it frees us up to be able to orchestrate more, to do creative and problem-solving tasks, while our tools perform the hard, mechanical tasks better, more consistently, and faster than we could ourselves. Collectively and individually, we therefore become smarter. Generative AIs are the first of our technologies to successfully automate those soft, open-ended, creative cognitive tasks. If we lack sufficient time and/or knowledge to do what they do ourselves, they are like tireless, endlessly flexible personal assistants, expanding what we can do alone. If we cannot draw, or draw up a rental agreement, say, an AI will do it for us, so we may get on with other things. Teachers are therefore scrambling to use AIs to assist in their teaching as fast as students use AIs to assist with their assessments.

For achieving measurable learning outcomes, AIs are or will be effective teachers, opening up greater learning opportunities that are more personalized, at lower cost, in ways that are superior to average human teachers. But human teachers, be they professionals, other students, or authors of websites, do more than help learners to achieve measurable outcomes. They model ways of thinking, ways of being, tacit knowledge, and values: things that make us human. Education is a preparation to participate in human cultures, not just a means of imparting economically valuable skills. What will happen as we increasingly learn those ways of being from a machine? If machines can replicate skills like drawing, reasoning, writing, and planning, will humans need to learn them at all? Are there aspects of those skills that must not atrophy, and what will happen to us at a global scale if we lose them? What parts of our cognition should we allow AIs to replace? What kinds of credentials, if any, will be needed? In this talk I will use the theory presented in my latest book, How Education Works: Teaching, Technology, and Technique to provide a framework for exploring why, how, and for what purpose our educational institutions exist, and what the future may hold for them.

Pre-conference background reading, including the book, articles, and blog posts on generative AI and education may be found linked from https://howeducationworks.ca