Kidaptive announced the close of a $19.1M Series C financing round led by Formation 8 and Woongjin ThinkBig, a leading Korea-based education company.
Kidaptive will use the funding to fuel the development and growth of its Adaptive Learning Platform (“ALP”) for the global education market. The team plans to extend ALP’s core functionality of adaptivity and personalization by developing new features such as enhanced parent and teacher insights, score prediction and adaptive assessment development – all leveraging machine-learning AI techniques.
“Big data for learning is finally beginning to take off thanks in part to our pioneering efforts in this area. And our partnerships are ramping up as companies realize they must give contextual meaning to the torrent of data coming in and create personalized experiences for their learners,” said P.J. Gunsagar, Kidaptive’s CEO and co-founder. Co-founder and Chief Learning Scientist Dylan Arena added, “This funding lets us build out several aspects of the Kidaptive platform that our scientists have been working on, combining the latest in machine learning with solid educational research findings to improve outcomes for learners worldwide.”
Kidaptive’s lead investor from Formation 8 and Board Member, Brian Koo further explained: “Woongjin ThinkBig’s support of Kidaptive validates both the efficacy of ALP and its power when combined with learning-relevant proprietary data. I am excited about Kidaptive’s potential to be a global leader in educational technology.”
Kidaptive’s ALP is a cloud-based assessment and reporting platform designed to combine data streams from a variety of learning-relevant contexts—formal and informal, online and offline, from the first years of life through continuing professional education. This creates a universal, longitudinal, high-dimensional psychometric profile of a learner, with which ALP can:
Personalize learning experiences: Because ALP draws data from many different contexts, its model of the learner will be more comprehensive than what any individual context could generate in isolation. This more comprehensive model can help each context to better achieve its learning and engagement goals. ALP shares relevant aspects of the learner profile to help a learning context personalize each learner’s experience by presenting him or her with the optimal next task. This might be to overcome a particular challenge, explore instructional material, or engage in a specific real-world activity.
Empower stakeholders: A key part of Kidaptive’s mission is to empower parents and teachers. ALP helps make this possible by turning arcane streams of heterogeneous data into meaningful, actionable insights that can be shared with relevant teachers and family so they can support their learners in the real world.
Evaluate efficacy: Each learning context that integrates with ALP registers the learning dimensions it aims to support (e.g., analogical reasoning, phoneme recognition, or expressing and managing emotions).
As ALP monitors learners’ progress through that context, it can provide objective measurements of the context’s success or failure at supporting those dimensions of learning, at the level of ecosystems, classrooms, curricular units, single applications, activities within applications, or even individual challenges within activities. Because ALP integrates multiple sources of evidence about a single learning dimension, it can triangulate its measurements of progress to make inferences about the transfer of skills from one context to another.
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