Despite the acknowledged importance of emotions and feelings in science education, systematic research concerning affect remains sparse. In this regard, a promising research field is the potential of immersive and/or interactive visualizations to enhance learning by promoting affective responses. In building upon our previous research with an interactive visualization, this study focuses on affective dimensions. The aim of the study is to investigate whether interacting with DeepTree, a dynamic visualization of phylogenetic relationships represented as a tree of life, induces the affective responses of curiosity, surprise, confusion and awe. And if so, to ascertain which features and experiences of interacting with the visualization promotes these affections.

Ten students were each interviewed during a clinical, semi-structured interview while answering questions and performing tasks during interaction with the DeepTree application. Data was collected using a video camera mounted above the tabletop capturing auditory, and visual information. In a synthesis of the literature, we formulated a predetermined affective framework to apply in the analysis of the data set to identify affective responses.

Piloting of the framework during a detailed analysis of three students’ transcripts and corresponding video material, preliminary findings reveal that the most commonly expressed affective response was awe followed by confusion and curiosity, with few utterances or experiences indicating surprise. Most conceptual aspects associated with elicited affective responses included time and the unity of life, and to a lesser extent, relationships between organisms and the diversity of life. A strong elicitor of affective responses was the “zooming” feature of DeepTree. Findings of this pilot study confirm previous research that immersive visualizations can induce and enhance affective responses. Encouragingly, the findings also validate the application of the framework as a systematic tool for identifying affective responses.

The work provides further insight into the potential influence of affect in learning science with visualizations.