Most digital products in education, awarding, and professional development are hooked on the exact same commercial metric: Attention. Daily Active Users (DAU), session length, streaks, push notifications, and time-in-app sit at the absolute center of traditional EdTech strategy. What if that entire metric is fundamentally broken? What if the true measure of a product's success is that individual usage falls over time?
Across school systems and education ministries, assessment is increasingly focused on complex, open-ended outcomes. But consistently evaluating skills, knowledge and understanding in this context can be difficult. Traditional rubric-based marking can lead to variation between evaluators, an emphasis on surface-level features rather than the quality of learning, and substantial moderation workload. This study examined whether that shared standard could help create reliable assessment scales with fewer judgements over time.
Whether you are leading assessment strategy across a school trust, maintaining standards for an awarding body, or driving digital transformation in an enterprise, the core operational challenge remains the same: how to capture subjective human judgment and turn it into a consistent, scalable asset.
As artificial intelligence and Large Language Models (LLMs) become deeply embedded in global education, a central question looms over the industry: Can we truly trust AI to act as an autonomous judge of quality?
Spring has sprung with a UI refresh and some other exciting announcements
In today's interconnected global society, the ability to understand and appreciate diverse cultural values is more crucial than ever, especially in fields that inherently involve creativity and innovation
We have had a bit of a tidy up to prevent users from tripping and falling. This is the BIGGEST refresh we have ever undertaken on the RM Compare User Interface
There is a renewed focus on holistic assessment recognising the need for a broader approach that can better appreciate the whole learner. The possibilities are exciting - they are also complex.
It's not quite spring yet but change is in the air. Read more about some imminent changes to the RM Compare user experience as well as other news.
In RM Compare sessions, parameter values are numerical representations of the relative quality or difficulty of the items being judged.
In the context of comparative judgement, the term "misfit" refers to a judge or an item that consistently deviates from the expectations of the model being used. Misfit can occur in two main contexts: judge misfit and item misfit.
In 2023 RM Compare was used in every continent (ok - with the exception of Antarctica!). In doing so it helped to validate one of our key hypotheses.
2024 is going to be an even more exciting year for RM Compare.