Saturday, February 18, 2017
Exhibit Hall (Hynes Convention Center)
Joseph Harsh, James Madison University, Harrisonburg, VA
Given the centrality of graphs to the communication of scientific information, increasing emphasis has been placed on the development of students’ graph literacy – one’s ability to generate and interpret data representations – to foster understanding of domain-specific knowledge and the successful navigation of everyday life. Despite the merit of prior research in identifying student difficulties and methods to improve graphing competencies, there is little understanding to how learners develop these skills. To gain a better resolution to the cognitive basis by which individuals “see” graph, this ongoing study uses eye tracking (ET) and cognitive interviewing (CI) to compare how students and scientists make sense of and use visual data. Participant eye movements were recorded while they completed an online instrument, consisting of 29 graph-based tasks selected to provide a range of complexity, using a head-free ET system. Upon completion of the ET sessions, CI was conducted to gain a deeper insight to participants’ general graphing experience and decision-making processes after reviewing video playback of how they directed their attention during task-completion. ET data were analyzed for the parameters of focal attention and visual search, which are based on the participants’ fixation at an area of interest within the scene (e.g., graph data, axes), while, the CI “think aloud” data were coded and examined for patterns and trends in participant perceptions as to how they progress through graphical information. Data (to date) were collected from 36 participants of varying educational backgrounds, including nonscience majors (n=15), early science majors (n=8), advanced science majors (n=4), science graduate students (n=5), and science faculty (n=6). Early results of this study highlight variation in how individuals direct their attention in completing a graph-based assessment as a function of science expertise. Experts were more likely to focus on information relevant to data interpretation (i.e. variables, title, graph data) using directed search patterns towards graph information and data. In comparison, novices were more likely to (a) try to complete the graphing tasks by drawing on cues (e.g., answers) than the data and relevant information, and (b) demonstrated a more sporadic search pattern with a lack of alignment between their perceived and actual actions. The early findings reported here highlight a level of variation in how individuals, across five levels of scientific expertise, direct their attention when completing graph-based tasks. Although such outcomes are to be expected, research on the transition from novice to expert is crucially important in designing curricula that help novices move toward more expert-like performance. Thus, we feel this study has implications for the advancement of new strategies to aid the teaching and learning of data analysis skills.