Fostering data literacy with citizen science experiments
Urs Braendle, ETH Zurich Department of Environmental Systems Science, Switzerland
Abstract: With the increasing importance of data in many areas of daily life, schools and universities face the challenge of imparting not only information technology, but also data literacy as one of the key future skills. This also includes the basic competence to deal with data in a planned manner, to consciously use it in the respective context and, above all, to deal with data quality.
We have developed a practical approach that encourages students to explore much of the aspects of data literacy through real experiments. In a citizen science approach, they use their GPS-enabled smartphones to collaboratively collect large data sets on spatially relevant issues. First interpretations, still on-site, are followed by deeper analysis and evaluation of the collected data in the classroom. Subsequent courses, focusing on specific aspects of data literacy, can build on the conceptual framework that students have acquired with these introductory experiments.
Our workshop guides the participants through all phases of such a so-called GIS supported mobile experiment from planning to data acquisition and interpretation. In the first part, the participants actively experience the whole process from a student's point of view, using commercial GIS software and Excel. In the second part, they switch to the instructor role and design their own data model and sampling scheme, and perform data visualisation and analysis using Google tools.
This workshop enables participants to familiarize their students with some of the key aspects of data literacy using GIS supported data collection and analysis as a model case. The workshop activities are designed to spark a wide variety of discussions about more general approaches to data literacy education.
In particular, the participants completing the workshop, will be able to:
• Develop a suitable scenario for spatial data collection in their field of expertise and for the intended group of students.
• Assess the necessary technological basics and tools for data collection and analysis with the chosen scenario and identify the needed experts in their home institution.
• Setup simple data collection for mobile devices in Google MyMaps.
• Describe different possibilities for direct data interpretation in the field, including their advantages and disadvantages.
• Conduct exploratory data visualisation with students using standard spreadsheet software.
• Stimulate discussions among students about the quality of the data they have collected and potential adjustments to the experimental design.
• Identify which phases of the experiment provide important basics for the deepening of particular components of data literacy later in the curriculum.
We start with an introduction to future skills and data literacy, based on our own examples.
After an introduction to the citizien science experiment, the participants are divided into groups. A 30-minute collection phase follows, in which site-related data on a specific topic will be collected near the congress building. Afterwards, the group meets to exchange first impressions and to take an explorative look at the results that are visible on the app.
Back in the classroom, the participants will use their collaboratively collected data to answer given and newly developed questions, using tools offered by the GIS software and Excel. We will also provide examples for different levels of questions and analysis, based on our experience in different courses.
In a next step, the participants develop their own data collection and analysis scenario, and get feedback from the group.
Then follows a step-by-step implementation of the data collection application using Google MyMaps. Depending on the number of participants and available time, the applications may be tested right a way or later during the conference. In any case, the participants create an artificial test data in order to simulate the intended data analysis.
The workshop concludes with a discussion on how the proposed methodology contributes to increasing data competence.
This workshop is aimed at all persons in Higher and Secondary Education who e.g.
• are interested in implementing “data literacy" as a general future skill in their teaching
• use or will use spatial data in their teaching
• have a general interest in novel mobile learning approaches
• would like to gain practical experience and know-how on how to raise students' data-awareness with a mobile experiment approach.
Participants need some basic knowledge of spreadsheet software with the operating system of their choice. They must bring their own laptop (if possible with Microsoft Excel) and a GPS enabled smartphone or tablet with some free memory to install the ESRI Collector for ArcGIS app. The workshop organisers will provide the necessary accounts for the software.
Please note: The workshop includes a 45-minute data collection phase where participants walk around in the proximity of the conference centre; however, we will find alternatives for people who are not able to walk such distances.
Monika Niederhuber (1969) studied geography with a focus on physical geography at the Catholic University of Eichstätt. After graduating, she worked as a research assistant in the field of remote sensing and geoinformation. Since 2002, she has been a research assistant / IT specialist at the Chair of Forest Engineering at ETH Zurich, where she is responsible for GIS teaching at the Department of Environmental Systems Sciences. The integration of new learning methods such as mobile learning and podcasts is a focus of her activities.
Urs Brändle (1965) received his PhD in molecular population genetics from ETH Zurich, then worked as a software trainer in medical technology, taught chemistry and trained computer science apprentices. Since 2008, he has been an educational developer at the Department of Environmental Systems at ETH, where he is responsible for course development and teaching innovation with special focus on groupwork, GIS supported field experiments and learning analytics.
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