Embodiment and Framing: Key Pieces of Deep Learning in One Student Interview

• In Event: 52.027 - Fostering Deep Learning in Problem-Solving Contexts Through Effective Design of Learning Environments With Technology Support

Abstract

This case study will explore interview framing and embodiment as important aspects of how students are able to represent “deep learning” that has occurred throughout a problem-based human-centered robotics (HCR) curriculum. This HCR approach considers human needs, desires, and delights in everyday spaces in the design of robotic technologies (Oviatt, 2006; Norman, 2013). Our work introducing HCR took place in a Midwestern U.S. middle school science classroom. We asked students to engage in imagining, building, and operating a telepresence robot used to serve a local need in their middle school and to communicate with a distant group of students in Alaska. This intervention was designed to mirror the design process that engineers navigate daily—brainstorming, identifying problems, applying relevant information, finding solutions, and adjusting iteratively (Resnick, 2007).

We examined one 7th grade student’s understanding through a clinical post-interview—focusing on the interview setting as a problem context where deep learning can be negotiated through interview framing and the use of gesture. Student orientation to the interview scenario explored includes three frames: inquiry (seeing the interview as an opportunity to construct explanations), oral examination (seeing the interview as an opportunity to produce correct responses), and expert interview (seeing the interview as an opportunity to share one’s own thinking) (Russ et al., 2012). Principles of interaction analysis and conversation analysis pertaining to verbal and nonverbal behaviors are used to reveal interview orientation (Heritage, 2013; Jordan & Henderson, 1995). Alibali & Nathan’s (2012) embodiment principles will be used to analyze the role gesture plays in revealing student understanding in this post-interview.

Results indicate that expert and inquiry interviewer framing and gesture add value to the interview setting by providing the learner with additional agency to represent knowledge. In this case study, gesture demonstrated understanding of the use of degree units in programming and designing for a universal audience (i.e. a robot that works for every teacher). The featured student (Evan ) used gesture to support his description of the iterative process his group went through. He also used gesture to direct interviewer attention—pointing out elements of the interview environment in order to highlight the features of the classroom environment his group used to problem solve (i.e. floor tiles). See Figure for an example of Evan’s use of gesture to explain his robot’s movement. Using gesture to support and construct his responses in real time, the student took up expert and inquiry frames. These frames should be designed for in future interview scenarios and attended to in analysis (Russ, Lee, & Sherin, 2012).

This analysis reveals how embodied action can support expert and inquiry orientations to the interview and provide another lens on deep learning. Future work will examine additional cases to look at the generalizability of this perspective on interview framing to elicit student understanding. Additional research is needed to determine how interviews can be structured to encourage embodied action that adds value to the interpretation of students’ understanding.

Authors

• Andrea Sarah Gomoll, Indiana University - Bloomington

• Cindy E. Hmelo-Silver, Indiana University

• Selma Sabanovic, Indiana University

• Matt Francisco, Indiana University