Tamer E. Abuelsaad

Supporting Multiple Learning Experiences on a Childhood Vocabulary Tutoring Platform

We present a unified learner modeling approach in a childhood vocabulary tutoring platform that enables learning continuum across multiple learning experiences. By decoupling experiences from learner modeling, multiple learning experiences can be developed independently making learning approaches scalable in an inherently diverse setting: each child, his/her family, environment, geography, cultural differences can all make each learner really unique. By understanding the information-theoretic equivalence of different assessment types, and mapping different play and learning activities to one or more of these types, we can enable a rapid convergence of the learner model to better represent a young learner’s knowledge. More interestingly, when normalized, different assessment types converge at different levels of the normalized score.

Adaptive Visual Dialog for Intelligent Tutoring Systems.

Conversational dialog systems are well known to be an effective tool for learning. Modern approaches to natural language processing and machine learning have enabled various enhancements to conversational systems but they mostly rely on text- or speech-only interactions, which puts limits on how learners can express and explore their knowledge. We introduce a novel method that addresses such limitations by adopting a visualization that is coordinated with a text-based conversational interface. This allows learners to seamlessly perceive and express knowledge through language and visual representations.