Brown Bag Lecture Series: Alina von Davier

Wednesday, February 27, 2019 11:45am - 1:00pm

GSE Lecture Hall (room 124)

Brown Bag Lecture Series

An Illustration of AI-based Education Assistants 

Alina von Davier

ACTNext by ACT

Intelligent technology is quickly becoming commonplace in schools. As AI-assistants learn from reliable data about students and engage with the cognitive theory of learning that governs these data, they can explicate the dependencies across knowledge domains in a relational way, so enabling better assessment and advice to students through a myriad of situations. This trend is supported by the growing need for learning and assessment systems (LAS) that capture a broad range of learner behavior necessary for the evaluation of complex skills such as problem solving, communication and collaboration in addition to the academic skills. A key feature of such an AI-assistant/LAS is the use of interfaces that enable rich, immersive interactions and can capture multimodal process data. However, the analysis of such data poses a significant challenge: how do we extract meaningful evidence of construct competency from complex performances as captured in varied and unstructured multimodal data? In addition, analyzing each of the multiple data modalities in isolation may result in incongruities and without appropriate use of context it may be difficult to interpret student activity as they show significant behavioral variations over time. To address these challenges, we present a methodology that utilizes advances in computational psychometrics and artificial intelligence. This approach exploits concept hierarchies that reflect the nature of the data and goals of the assessment. Most importantly, capturing data in realistic tasks and settings with multiple modalities makes this approach more ecologically valid for assessment of complicated competencies. We illustrate the proposed framework in a variety of settings including a prototype developed at ACT called the Companion App.  


ACT Senior Vice President, Alina von Davier, Ph.D., leads ACTNext, a multidisciplinary innovation unit founded in 2016 by ACT CEO Marten Roorda. Her team is comprised of experts in fields ranging from psychometrics and learning sciences to software development, and artificial intelligence (AI) & machine learning (ML). von Davier and her team operate at the forefront of Computational Psychometrics, an emerging interdisciplinary field concerned with the application of theoretical and data-driven computational methods and statistical modeling of multimodal, large scale/high dimensional learning and assessment data. This unique approach allows von Davier and her colleagues at ACTNext to not only develop innovative solutions to challenging problems but to change the very ways in which assessment is traditionally thought of. Her current research interests involve developing and adapting methodologies in support of learning in virtual environments that allow for collaboration using techniques incorporating machine learning, data mining, Bayesian inference methods, and stochastic processes. Two publications, a co-edited volume on Computerized Multistage Testing (2014) and an edited volume on test equating, Statistical Models for Test Equating, Scaling, and Linking (2011), were selected as the winners of the Division D Significant Contribution to Educational Measurement and Research Methodology award at American Educational Research Association (AERA).  Additionally, she has written and/or co-edited five other books and volumes on statistic and psychometric topics.

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Who to contact:

Colleen McDermott