This proposed full paper will present the results of empirical research. This study employs a multiple-case study approach that examines first-year engineering students’ conceptual understanding and associated gestures for central tendency, variance, and linear regression, which are critical concepts in statistics and engineering education. Prior research has demonstrated that statistics education is fundamental to both STEM and Non-STEM careers and is relevant to peoples’ everyday lives including personal choices and workplace success across professions. However, people tend to struggle with interpreting, communicating, and applying statistics in professional settings and their daily lives. One way to address this is to design opportunities where students’ learning of statistics is meaningful and connected to their personal experiences. The embodied learning literature shows increasing evidence that gestures are particularly useful in accomplishing this and promoting learning, transfer, and retention. Importantly, a handful of studies have documented the benefits from both instructors’ and students’ gestures in learning statistics. However, these studies do not focus on the fundamental statistical concepts of central tendency, variance, and linear regression or specifically on how engineering students conceptualize these concepts. Additionally, education in the post-pandemic age is no longer limited to in-person learning, but rather capitalizes on the advantages of accessibility, reach, and flexibility that online and asynchronous methods offer in higher education. The challenge lies in bringing the benefits of effective embodied design practices used in in-person educational settings to remote and asynchronous learning. There is sparse research on how students leverage their body movement and actions when reasoning about statistical concepts and even less on how to apply these into online and asynchronous settings. This study uses video recordings of semi-structured interviews that focus on conceptual understanding of the target concepts. Thematic analysis will be used to identify key themes that emerge across and within topics from the qualitative data. Students’ spontaneous gestures will be coded using gesture categories in combination with their speech to connect gestures to themes and identify students’ representations of their understanding of central tendency, variation, and linear regression. These results will seek to inform future studies on the development of gesture-based Digital Video Learning Environments to support engineering students' learning of statistics. This future research aims to identify how students respond to cueing and changes in their conceptual understanding.
The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025