datAR: A Situated Learning Approach for Data Literacy Through Everyday Objects

Abstract

Data literacy, the ability to work with data, is essential for the younger generation. However, high school instructors often struggle to engage students from diverse backgrounds with abstract concepts that may not seem immediately tangible to them. This paper introduces datAR, an augmented reality application grounded in situated learning theory that integrates data into everyday objects. The tablet-based application uses tangible analysis blocks to help students explore and interact with data. We present a case study for the use of datAR in a local high school, where 15 students with little to no prior data science experience were introduced to data literacy through the analysis of nutrition information in familiar snacks. Our findings show that, despite students struggling to recognize the relevance of the learned concepts for their daily lives, datAR helped them develop data science skills through hands-on interaction with familiar objects.

Zeyu Xiong

PhD Student

My research interests include Human-Computer Interaction, Human-AI Collaboration, Assistive Technology, Educational Technology, and Multimedia.