Although 1.5 billion people around the world are studying English, many will never get proficient enough to use English on the job. Instead, they get stuck at learning plateaus. They feel that they are failing to make progress and have a long way to go. As English-language-learning expert Fan Yi has noted, “They find they know a lot about the English language, but they can hardly say that they know English.” This can cause them to become frustrated and discouraged, and even stop learning altogether. Why do learners get stuck? And how can technology help them get unstuck?

A field only moves forward if it chooses the right problems: big goals that make a difference. Many–perhaps most–of the adaptive learning systems out there are based on the same technology from the 1990s. Much of the research published today is tiny optimizations on small problems. Incremental progress can be valuable. But it shouldn’t be the primary focus of a field.

Do your students need more opportunities to practice speaking English? Is lack of self-confidence holding them back? This webinar introduces Alelo’s Enskill simulation platform and explains how to use Alelo simulations to help your students develop self-confidence and increase fluency.

In Educational Technology communities, there is a debate about the best way to structure online learning programs. Should platforms focus on the individual student or the classroom? Should teachers act as a leader of the class, or supplement students’ independent practice?

By Neil Heffernan, Worcester Polytechnic University Shared scientific instruments help address big challenges in research. The Hubble Space Telescope has helped hundreds of astronomers do studies that none of them could have done if they had to also build the instrument they use. Physicists benefit from shared particle accelerators that are expensive to build and…

What does the field of CSCL (computer-supported collaborative learning) have to say to the problem that the World Economic Forum estimates that up to 5 million jobs may be lost to disruptive labor changes by 2020? We suggest an answer that is designed to enable embedding collaborative learning opportunities within project work.

For many millennia, adult learning has primarily consisted of interactions with human mentors, tutors, teachers, and peers. During the last 25 years, these communicative actions and interactions have been simulated by computer agents in learning environments that have often shown significant learning gains.

The rapid progress of Machine Learning AI that has been built in our human image brings into stark relief the need for us to re-conceptualise Human Intelligence (HI) for an AI augmented world. We have built smart machines in our own intelligent image and these machines can now complete some of the intelligent activities that have traditionally been valued in our education systems, such as passing exams through memorizing and applying knowledge.

One of the ultimate goals of education is to effectively prepare students for long-term career and workforce success. Most existing intelligent systems in education focus on tutoring for specific academic subjects and deliver personalized learning on a relatively short timescale. As we face an ever-changing 21st century job market due to increased automation and AI technologies, it is more important and challenging than ever to deliver learning and mentorship that leads to long-term success in the workforce. In my webinar, I discuss some of the unique challenges presented by this goal as well as promising approaches that can address the challenges and move us closer to building effective, workforce-driven intelligent mentoring systems.