Each year, the Learning Engineering Tools Competition identifies tracks and priority areas addressing the most pressing challenges in education. Competitors will compete in one of the four tracks below.
This year, the Learning Engineering Tools Competition is open to solutions for Pre-K to secondary learners and teachers.
Tools that a) capture traditionally unmeasured elements of learning and development; b) generally improve the quality of assessment to better meet the needs of educators, children, and families; or c) reduce the time and cost to develop, administer, or interpret assessments.
We are interested in tools for all forms of assessment – diagnostic, formative, summative, direct-to-family, etc.
Please note: The Tools Competition has different competitive priorities from year to year. As a result, the examples of winning tools will not necessarily reflect the 2022 competitive priorities.
Explore all winning proposals from the 2020 and 2021 competitions.
Based on the most pressing needs in assessment technology, you may want to target your proposal to one or more of the following areas:
There are competitive priorities and the Pre-K and K-12 level for tools that evaluate non-academic measures and approaches to learning.
At the Pre-K level, this may include tools that measure elements like code switching, agency, initiative, creativity, etc. A subset of the overall prize money will be reserved for proposals for Pre-K or pre-literate children, specifically.
At the K-12 level, this may include tools that measure elements like social emotional learning, relationships with adults and peers, sense of identity, grit, etc.
Many of these measures are ‘unconstrained’ or developed gradually and without a ‘ceiling.’ This will influence the way the tool evaluates and helps users interpret progress. Consider a tool that detects emotions through facial recognition as an example.
There is a competitive priority for tools that capture performance related to math across all grade levels - from number sense to advanced arithmetic expressions to data science.
Tools that leverage gamification or other methods where the user is not aware that assessment is taking place. Stealth assessments can effectively evaluate many academic and non-academic measures.
Consider a tool that evaluates motivation and growth mindset by monitoring response time and error rate as an example.