Data Science + Learning Science

Providing you with the most meaningful insights about your learning context requires more than just data science. Our Learning Team understands which signals to measure and how to interpret them, allowing ALP to provide optimal results with lower requirements for data and computational resources.

ALP combines cutting-edge machine-learning technology with the training and expert vision of world-class learning scientists to produce richer, more varied, and more accurate insights.

ALP's learning-relevant capabilities include:

Skill Measurement

Understanding which skills each activity supports is critical for evaluating learner progress. Drawing upon our extensive experience developing and analyzing learning contexts that range from games to test preparation, we designed ALP to handle complex skill mapping and fine-grained measurement.

Behavior Identification

Insight into specific learner behaviors offers a richer picture than just scores alone. We have developed models for identifying behaviors such as guessing, re-checking work, working too quickly or too slowly, and more. ALP uses these models to provide insights about trends, changes in behavior, and comparisons with other learners.

Performance Prediction

We have developed highly accurate predictive models based on learner and peer performance on individual items to provide information such as the learner's next score, the expected difficulty of certain items for the learner, and estimates of working speed.

Recommendations

ALP's content recommendation models go beyond typical demographic and ratings-based information to include learning-relevant factors such as the learner's and population's skills, interests, and curricular focus.

Meet Our Learning Scientists

"We built ALP because we believe that personalized learning should start with a holistic picture of the learner. ALP's most basic goal is to help parents, teachers, tutors, and creators of educational products understand each learner deeply enough to effectively support that learner's progress."
Dylan Arena, Ph.D.
Co-Founder & Chief Learning Scientist
Learn More About Dylan
"As education moves online and large quantities of educational data become available, we work on the frontier of knowledge in the fields of psychometrics and educational data science to develop models that go beyond prediction of outcomes and give meaningful insights to support learners in their online educational journey."
Josine Verhagen, Ph.D.
Senior Director of Psychometrics & Data Science
Learn More About Josine
"Helping partners make the most meaningful use of their learning data and see new opportunities to support learners and those who care about their development is exciting, challenging, and deeply rewarding."
David Hatfield, Ph.D.
Senior Director of Assessment & Product
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"In today's educational landscape, we can collect large amounts of data as learners interact with educational products. With intelligent models we can adapt to each learner and reach the whole spectrum of learners. We can help level the playing field and bring learning excellence within reach of all learners."
Uma Vijh, Ph.D.
Data Scientist
Learn More About Uma
"Access to engaging technologies and the ability to interpret learner behaviors using machine intelligence sets the bar for educational experiences higher than ever. I am thrilled to work in this quickly advancing space, where learning can be not only adaptive to all kinds of learners, but engaging as well, encouraging learners of all ages and backgrounds to push themselves and allowing them to have a rewarding and fun experience while doing so!"
Chelsea Gordon, Ph.D.
Learning Scientist
Learn More About Chelsea
"Educational measurement was initiated to provide fair and equitable information for making decisions and providing learning opportunities. It’s about gathering the best evidence to support claims about learning. As the world becomes increasingly digital, there is no better time than now to use various new types of data as evidence to gain holistic insights about learners and to provide them with personalized learning paths."
Yuning Xu, Ph.D.
Data Scientist
Learn More About Yuning
Dylan Arena, Ph.D.
Co-Founder & Chief Learning Scientist
Dylan is a learning scientist with a background in cognitive science, philosophy, and statistics. He has studied, presented, and written extensively about game-based learning and next-generation assessment. Dylan started out as a software developer at Oracle, but after a few years he returned to graduate school at Stanford, where he spent several years as a MacArthur Emerging Scholar in Digital Media and Learning. Dylan has also been a Gordon Commission Science and Technology Fellow, a Stanford Graduate Fellow in Science and Engineering, a Gerald J. Lieberman Fellow, a FrameWorks Fellow, and a United States Presidential Scholar. Dylan has earned a bachelor's degree in Symbolic Systems, a master's in Philosophy and a master's in Statistics, and a Ph.D. in Learning Sciences and Technology Design (in the program for Developmental and Psychological Sciences), all from Stanford University. Dylan has two sons, ages and .
Josine Verhagen, Ph.D.
Senior Director of Psychometrics & Data Science
Josine is a psychometrician with a background in Bayesian statistics, educational testing, and psychology. She has studied the application of Bayesian statistics to cross-national and longitudinal comparisons of tests and questionnaires, hypothesis testing and adaptive educational tests. Josine graduated from Leiden University in the Netherlands with MSc.'s in both Social Psychology and Methodology and Statistics. She started working on non-response in surveys at the Netherlands Institute for Social Research (SCP). After two years, she moved on to obtain a Ph.D. in psychometrics at the University of Twente, conducting research on Bayesian psychometrics with Jean Paul Fox and Cees Glas. Josine did postdoctoral research on Bayesian hypothesis testing and adaptive educational tests at the University of Amsterdam, where she was subsequently hired as an Assistant Professor of Psychological Methods before joining Kidaptive.
David Hatfield, Ph.D.
Senior Director of Assessment & Product
David is a learning scientist with a background in science education, video game design, and assessment. His Ph.D. research focused on developing and testing innovative performance-based assessment models for measuring complex thinking and designing simulated professional practicum experiences for young people. Prior to joining Kidaptive, David was a research scientist with the Epistemic Games research lab and the Games, Learning and Society center, both at the University of Wisconsin. David also has a bachelor's degree in Biology from Virginia Tech, and studied English Literature at North Carolina State University. David has a son and daughter, ages and .
Uma Vijh, Ph.D.
Data Scientist
Uma is a data scientist with a background in astrophysics. She has led several NSF/NASA projects and published extensively about the nature of interstellar dust. She has a bachelor's in Physics from St. Xavier's College in Bombay and a master's in Physics from the Indian Institute of Technology in Bombay (IITB). Uma worked briefly at Infosys Technologies before earning another master's and a Ph.D. in Physics from the University of Toledo in Ohio. Uma's doctoral work explored photoluminescence from interstellar dust. After a post-doctoral stint at the Space Telescope Science Institute in Baltimore, she returned to the University of Toledo as a Research Professor. Before joining Kidaptive, Uma worked as a Data Scientist for California's Department of Justice, analyzing juvenile data to help guide juvenile justice reform. Uma has a daughter and a son, ages and .
Chelsea Gordon, Ph.D.
Learning Scientist
Chelsea is a cognitive scientist with a background in cognitive neuroscience, statistics, and gamified learning in virtual reality. After earning a bachelor's degree in Psychology at Michigan State University, she worked as a STEM outreach instructor at the nonprofit Drake Planetarium and Science Center near Cincinnati, Ohio. Chelsea then went on to earn a Ph.D. in Cognitive Science at the University of California, Merced, where she used neuroscience methods that alter the activity of particular regions of the brain to explore how particular brain areas contribute to processes such as perception and learning.
Yuning, Ph.D.
Data Scientist
Yuning is a psychometrician with a background in educational measurement and statistics. She has studied methodologies and applications of psychometric modeling such as structural equation modeling, item response theory, classification models, and Bayesian approaches to standardized tests, as well as innovative forms of assessments. Before joining Kidaptive, Yuning was an Education Researcher at SRI International, where she led psychometric analysis for assessment development and validation on computational thinking for K12 students. At SRI, she also worked in education program evaluation, with a focus on designing and analyzing experimental and quasi-experimental studies. Yuning received her undergraduate degree in Psychology from Beijing Normal University and her M.A. and Ph.D. degrees in Measurement and Statistics in Educational Psychology from Arizona State University.