Mobile Technologies Support Student Understanding of Variability in Messy Real-World Data in Ecosystem Science

• In Event: 75.043 - Research on Students' Motivation and Learning in Augmented Realities

Abstract

Objectives and Theoretical Framework: Scientific reasoning involves reconciling conceptual understanding of scientific phenomena with empirical observations, personal experience, and data; data serve as a link between the real-world and generalizable concepts behind the phenomenon (Passmore, Stewart, & Cartier 2009). Yet, using data to support connections between phenomena and conceptual understanding can be difficult due to cognitive and procedural hurdles (Krajcik et al., 1998; Kuhn, 2007; Sadler 2004). These challenges are difficult to address because students have few opportunities to apply conceptual understanding to interpretation of highly variable real-world data.

Augmented reality offers potentially powerful affordances to support student collection and interpretation of messy real-world data through localized, just-in-time information, along with varying levels of structure and freedom to support personally-meaningful data collection (Dunleavy & Dede, 2014; Kamarainen et al., 2013). We describe a curriculum that integrates these affordances using an augmented reality (AR) application run on mobile broadband devices (i.e., smartphones) and handheld digital environmental probeware. Students use these technologies to explore their local environment and collect variable real world data sets during a field trip to a pond near their school.

Methods and Data Sources. The mobile technology-enabled curriculum was used in a design-based research context with one teacher and four classes of students (n = 71) during the fall of 2013. Students used a multi-user virtual environment (MUVE) during a two-week period, followed by a one-week intervention consisting of a day of training with mobile devices and environmental probeware, a 1.5 hour field trip to a local pond, and post-field trip class discussion. The field trip experience included use of an AR application to collect water quality measurements at multiple locations in the pond, and the focus of activity was on recognizing variability and collecting observational data to help explain the variability in the measurements. A pre-post survey to assess student understanding of variability and data interpretation skills was administered to students after completion of the two-week MUVE curriculum (“pre”) and after the post-field trip class discussion (“post). Items used on the survey were modified from previously validated measures (NY Regents Board Examination; Mooney, 2002). Further, video data were collected during the field trip and post-field trip class discussions.

Results and significance. Based on analysis of a subset of survey responses, a marginally significant number of students (McNemar’s test, p=0.062) were able to distinguish between measurement differences that were “not big enough to matter” vs. samples from “different areas of the pond”. Further, a marginally significant number of students (McNemar’s test, p = 0.086) shifted to recognizing values that were outside the expected range. These initial results suggest that students developed a more contextualized knowledge of environmental data. In the final paper, we will offer more detailed analysis of the full survey results, descriptive accounts of data collection processes, and understanding of data variability in the field and classroom. Findings will lend insight into whether and how mobile technologies contribute to students’ abilities to reconcile conceptual understanding and analytical skills during scientific reasoning with variable real-world data.

Authors

• Amy M. Kamarainen, Harvard Graduate School of Education

• Denise Bressler, Stevens Institute of Technology

• Shari J. Metcalf, Harvard University

• Tina A. Grotzer, Harvard University

• Christopher J. Dede, Harvard University