Multimodal Interactional Analysis in Analyzing Youth's Engagement

• In Event: 64.060 - Participation in Interaction: Multimodal Understandings of Interaction in a Range of Education Settings

Abstract

Objective: This paper presents our methodological approach to data collection and analysis in analyzing resettled Burmese refugee youth’s engagement in an afterschool science program. We show how we collected data to examine youth’s learning and engagement. Highlighting the multimodal analysis techniques we employed, we share three short episodes and demonstrate our analysis.

Theoretical perspectives:
Learning is inherently multimodal in that it involves multiple representation modalities (e.g., language, image, music) and various embodied interactions (e.g., talk, gesture, gaze; Jewitt, 2017). Multimodality as a pedagogical approach enables learners to engage in varied forms of meaning-making, moving away from a heavy emphasis on talk- or writing-focused approaches (Anderson, et.al., 2017). This perspective may empower learners who are otherwise marginalized and minoritized (Hull & Katz, 2006). As a research methodology, multimodality encourages us to examine learning as situated in social, material, and semiotic practices. Thus, this analytic approach sheds light on learning aspects that attention to language alone may fail to capture and offer insights into youth’s agency through their use of multiple modes in science learning practices.
In our research, we promote multimodal engagement by offering various social, material, and semiotic learning activities (e.g., lab experiments, multimodal composition, poster presentations; Kress, et.al., 2014). In our data analysis, we draw on multimodality to examine how learners coordinate their own and their peers’ participation, thereby constantly negotiating participation positions in an afterschool science learning setting.

Methods:
Data collection: We utilized ethnographic techniques in collecting our corpus of data that include: video- and audio-recordings, participant-generated artifacts, interviews and field notes.
Data analysis: In selecting and analyzing episodes from session video-recordings, we employ methods widely adopted in video analysis (Derry, et al., 2010): individual members watch unedited video-recordings and choose potential episodes for close analysis; collaboratively view selected episodes, analyze, and generate analytic notes and video logs. In collaborative data analysis, we adopted several multimodal analysis techniques (Norris, 2004): viewing data iteratively with and without sounds; focusing on utterances, bodily engagement, and interaction with artifacts to aid analysis; synchronizing session video-recordings and computer screencasts; and generating transcripts to capture multiplicity and coordination of communicative modes.

Findings: We show three accounts of our analysis of how youth participants utilize various communicational modes for their participation, meaning-making, and learning. In the first episode, we show how Thiri, through coordination of space, reconfigured the learning task to allow for collaborative work. In the second episode, we show how learners used gestures and phonetic resources to construct a definition of a word. In the third episode, we show how Da Hnin adjusted her body positioning and used gestures to make space for herself and others to be involved in STEM learning.

Significance: Multimodal interactional analysis may offer richer and/or different insights than utterance-focused analysis. By foregrounding multimodality, we have made obvious agentive moves learners make that would have otherwise been overlooked in the learning setting. In doing so, we may redefine engagement as beyond talk-focused, thereby offering a perspective which values student agency as an integral part of learning.

Authors

• Mavreen Rose Sta Ana Tuvilla, Purdue University

• Casey Wright, Purdue University

• Minjung Ryu, University of Illinois at Chicago

• Shannon Daniel, Vanderbilt University