Even as the number of COVID-19 cases is slowing in some areas of the world, the pandemic’s scars on the world’s education system will remain for years, driving urgency to innovate and constantly improve the way both children and adults learn.
For elementary and secondary students, learning loss will be substantial. In the United States, cumulative learning loss is projected to be up to nine months of learning by the end of the 2020-2021 school year among white students, and up to a full year of learning among students of color. While U.S. schools are resuming in-person instruction, other countries have a longer road before the fight against COVID-19 turns the corner.
Efforts to accelerate learning are also critical for adults. COVID-19 has had a disproportionate impact on the unemployment of individuals with a secondary diploma or less. The pandemic accelerated industries’ reliance on technology, creating a type of “double” shock for low-income workers. More is needed to ensure higher education pathways are accessible and supportive to all students. For those already in the workforce, additional efforts are necessary to provide retraining, so all workers can obtain the jobs of today and tomorrow’s economy.
New tools are needed to address these issues, while also encouraging continuous improvement and other approaches to maximize our understanding of what works for student learning.
The Tools Competition aims to spur the development and deployment of technologies that address pressing education issues in elementary and secondary education and adult learning while advancing the field of learning engineering.
Rather than designing silver bullet solutions, these tools will be designed for continuous improvement to maximize their effectiveness over time.
Learning engineering is an emerging discipline at the intersection of learning science and computer science that seeks to design learning systems with the instrumentation, data, and partnerships with the research community to drive tight feedback loops and continuous improvements in how that learning is delivered in online and blended settings.
What is Learning Engineering?
Learning engineering is the use of computer science to pursue rapid experimentation and continuous improvement with the goal of improving student outcomes.
Learn more about the emerging discipline with these resources:
The Learning Engineering Tools Competition invites technologists, digital learning platforms, researchers, students and teachers from around the globe to propose innovative tools or technologies that address one of the pressing challenges in education.
The multi-phased selection process provides competitors time for ideation, team-building, and project refinement. The organizers will award $4 million in prizes.
Proposals will be considered relative to submissions within the same track.
To encourage both new entrants, as well as developing and established platforms, competitors can request an award in one of two prize bands and name a specific amount within their selected range. Proposals requesting larger amounts will be subject to a higher bar for the evaluation criteria.
Competition organizers will take the requested reward into account, but the final prize is at the discretion of the judges and the competition organizers. In some instances, competition organizers may increase the maximum prize.
In addition to the prize funds, winners will have the opportunity to connect with prominent education researchers, edtech leaders, and representatives of large philanthropic organizations to scale their work.
For more information on the eligibility requirements for prize bands see here.
The Tools Competition has a phased selection process, in order to give competitors time and feedback to strengthen their tool and build a team.
To participate, first submit a brief concept with the designated form that identifies the track, award amount, and describes the tool and team. The description of the tool should describe how users will interact with the tool, how it is architectured for rapid experimentation, how it will improve learning – especially for historically marginalised populations – and how it has the potential to scale.
For more information see the full timeline here.