EcoMOBILE: Helping students understand data variability in ecosystems through collection and interpretation of messy, real-world data supported by mobile technologies

Background/Question/Methods

Learners may use data to understand connections between observations and ecological mechanisms, but can experience difficulties with variation in data, multiple mechanisms that contribute to an observed data set, and aggregation and representation of data. In light of these challenges, instructors may use data sets with reduced variability, or that are carefully pruned to illustrate a key concept, leaving students few opportunities to interpret authentic, highly variable real-world data. We argue that reasoning with messy, first-hand environmental data is critical in developing an understanding of ecological systems, and hypothesize that this can be facilitated by 1) using immersive technologies as a means of data collection and 2) using first-hand data in classroom discussions. EcoMOBILE accomplishes this strategy using mobile broadband devices with augmented reality software and probeware to collect geo-referenced environmental data during outdoor field trips. We implemented EcoMOBILE with four classes of seventh graders (n=75) using an embedded mixed methods research design, including collection of video recordings, worksheets, and pre-post surveys. To assess students’ ability to describe data, understand variability, and support claims with evidence, we compared learning outcomes on the surveys and investigated changes in students’ thinking using emergent coding of worksheets and transcripts from classroom discussions.

Results/Conclusions

Pre-post assessment items covered three themes: describing data, understanding variability, and using evidence to support a claim, with five items covering each. Scores for each theme were averaged, and changes pre to post were assessed using paired t-tests. Initial analyses indicate no significant gain in describing data (p = 0.292), but significant changes related to both understanding variability (p < 0.05) and using evidence to support a claim (p < 0.001). Classroom discussions revealed that students were able to make comparisons between datasets collected in the field; when discussing graphed data, students were able to describe data using such terms as mean, median, mode, outliers, and range. Two additional interpretative themes-- personal relationship to the data and knowledge of the physical context--emerged during analysis, offering insight into the benefits of using mobile technologies to support real-world data collection. Additional analyses of the data set are underway, and results will be shared in the final presentation. In summary, the EcoMobile experience shows promise in supporting a personal, physically contextualized learning experience that enables students to offer deeper interpretations of messy, real-world ecological data.