Emotion in Analyzing and Interpreting Data: Implications for Children's Considerations of Distribution and Variation in Elementary Science

• In Event: Affect and Emotions as Productive Sense-Making Resources in STEM Education

Abstract

Objectives & Significance. Recent research in science education has detailed how emotion is integral to teaching and learning, inseparable from how disciplinary practices emerge and stabilize (Jaber & Hammer, 2016a, 2016b). Yet despite the centrality of collecting and representing data in science disciplinary practices, there have been limited studies examining how emotion shapes (and is shaped by) practices of data analysis and interpretation, a core practice in the Next Generation Science Standards (NRC, 2012). In this study, I examine how emotion both supported and constrained late elementary students in creating, analyzing and interpreting collaborative data displays within a broader multi-week ecology curriculum.

Theoretical Framework. Emotion is understood as emerging within science disciplinary practices, inseparable from the social, conceptual and epistemic threads of practice (Jaber & Hammer, 2016a). Feeling, sensemaking and practice co-emerge in emotional configurations, shaping and giving meaning to emotion in social disciplinary pursuits (Vea, 2020). Building from data feminist scholars (D'ignazio & Klein, 2020: Lee et al, 2022), emotion - whether stifled or embraced - is integral to how data is visualized and interpreted in professional and youth data sensemaking practices.

Data Sources & Methods. Data presented are drawn from a larger design‐based research project (Cobb et al, 2003) that aimed to support 4th and 5th grade students understanding socio-ecological systems through science and data science practices (Author, 2016, 2022). I focus on year 3 were I worked with a 5th grade class and their science teacher, co-teaching a 12-week curriculum focusing on the soil ecology in the children’s schoolyard and engaging students in cycles of data collection, visualization and discussion to learn what underground organisms needed to thrive. Data include whole class video where data visualizations were collaboratively constructed and semi-structured interviews where focal students interpreted and reflected on data visualizations. Drawing on multi-modal video analysis methodologies (Sakr et al, 2016; Jaber & Hammer, 2016b), I coded instances of emotional expression (see Table 1, Author, 2022) in as data were analyzed and interpreted.

Results. Findings focus on two focal pairs of students across three class lessons where data visualizations were collaboratively assembled and analyzed (e.g., histogram of earthworm data, two-way table of all invertebrates, digital data map of total aggregated data) and subsequent semi-structured interviews where these data representations were discussed. I chart how emotion such as competition and excitement were emergent in analyzing and interpreting these varying data visualizations, in turn opening up or foreclosing attention to distributions and patterns in the data (Lehrer & Schauble, 2004). For example, for Lena and Marcus, emotion such as competition emerged as they constructed a histogram of earthworm counts, leading the pair to falsifying their data to ensure they had the classes’ highest counts and talk about feelings of competition in their interviews (See Figure 1). In contrast, Amos and Mara expressed excitement during the construction of a two way table showing all invertebrates (Fig. 2), insisting that all animal data be included in the total tallies despite their particular species being hard to identify.

Author

• Kathryn Lanouette, College of William & Mary
  Presenting Author