Design and Evaluation of a Tutor Platform for Personalized Vocabulary Learning
This work addresses personalized education for young learners, but it is incremental as it builds on existing intelligent tutoring concepts.
The paper tackled personalized vocabulary learning for kindergarten children by designing an intelligent tutor platform with adaptive content and assessment, piloting it with 180 learners to enable A/B testing and insights at individual and class levels.
This paper presents our experiences in designing, implementing, and piloting an intelligent vocabulary learning tutor. The design builds on several intelligent tutoring design concepts, including graph-based knowledge representation, learner modeling, and adaptive learning content and assessment exposition. Specifically, we design a novel phased learner model approach to enable systematic exposure to words during vocabulary instruction. We also built an example application over the tutor platform that uses a learning activity involving videos and an assessment activity involving word to picture/image association. More importantly, the tutor adapts to the significant variation in children's knowledge at the beginning of kindergarten, and evolves the application at the speed of each individual learner. A pilot study with 180 kindergarten learners allowed the tutor to collect various kinds of activity information suitable for insights and interventions both at an individual- and class-level. The effort also demonstrates that we can do A/B testing for a variety of hypotheses at scale with such a framework.