Using Large Data Sets for Open-Ended Inquiry in Undergraduate Science Classrooms

O’Reilly, Catherine M.; Gougis, Rebekka Darner; Klug, Jennifer L.; Carey, Cayelan C.; Richardson, David C.; Bader, Nicholas E.; Soule, D. C.; Castendyk, Devin; Meixner, Thomas; Stomberg, Janet F.; Weathers, Kathleen C.; Hunter, William J.

doi:10.1093/biosci/bix118

Cited by 28 publications

(46 citation statements)

References 20 publications

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“…However, teaching computational literacy to intermediate and upper‐level undergraduates may be the ideal time to expose students to these skills to help them prepare for diverse future careers. Moreover, embedding prepackaged, flexible modules into undergraduate classrooms to teach inquiry‐based ecology, as is done through Project EDDIE (Environmental Data‐Driven Inquiry and Exploration, http://www.ProjectEDDIE.org; Carey & Gougis, ; Klug, Carey, Richardson, & Gougis, ; O'Reilly et al., ), Macrosystems EDDIE (http://www.MacrosystemsEDDIE.org), Data Carpentry (von Hardenberg et al., ), and other projects that integrate data science and ecology (Table ) may provide a viable solution for overcoming the pitfalls of programming instruction while stimulating undergraduates to think about ecology in a predictive way.…”

Section: Introductionmentioning

confidence: 99%

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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Environmental research requires understanding nonlinear ecological dynamics that interact across multiple spatial and temporal scales. The analysis of long‐term and high‐frequency sensor data combined with simulation modeling enables interpretation of complex ecological phenomena, and the computational skills needed to conduct these analyses are increasingly being integrated into graduate student training programs in ecology. Despite its importance, however, computational literacy—that is, the ability to harness the power of computer technologies to accomplish tasks—is rarely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experience developing undergraduate curricula in long‐term and high‐frequency data analysis and simulation modeling for two environmental science pedagogical initiatives, Project EDDIE (Environmental Data‐Driven Inquiry and Exploration) and Macrosystems EDDIE, we have found that students often feel intimidated by computational tasks, which is compounded by the lack of familiarity with software (e.g., R) and the steep learning curves associated with script‐based analytical tools. The use of prepackaged, flexible modules that introduce programming as a mechanism to explore environmental datasets and teach inquiry‐based ecology, such as those developed for Project EDDIE and Macrosystems EDDIE, can significantly increase students’ experience and comfort levels with advanced computational tools. These types of modules in turn provide great potential for empowering students with the computational literacy needed to ask ecological questions and test hypotheses on their own. As continental‐scale sensor observatory networks rapidly expand the availability of long‐term and high‐frequency data, students with the skills to manipulate, visualize, and interpret such data will be well‐prepared for diverse careers in data science, and will help advance the future of open, reproducible science in ecology.

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Section: Introductionmentioning

confidence: 99%

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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“…, O'Reilly et al. ), especially because the modules can be easily adapted for a wide range of classrooms. Ecology undergraduate and graduate students who become familiar with computing techniques early in their careers will not only be able to tackle new ecology research questions, but also be able to gain increasingly essential skills in data science, programming, and computing.…”

Section: Enabling Greater Collaboration Among Ecologists and Computermentioning

confidence: 99%

“…Furthermore, in our experience, integrating training modules that analyze big data into undergraduate ecology courses has successfully built students' computational literacy at a range of experience levels (www.MacrosystemsEDDIE. org; Carey et al 2015, Klug et al 2017, O'Reilly et al 2017, especially because the modules can be easily adapted for a wide range of classrooms. Ecology undergraduate and graduate students who become familiar with computing techniques early in their careers will not only be able to tackle new ecology research questions, but also be able to gain increasingly essential skills in data science, programming, and computing.…”

Section: Enabling Greater Collaboration Among Ecologists and Computermentioning

confidence: 99%

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

et al. 2019

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In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high‐frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long‐term and high‐frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long‐term.

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“…These data, made publicly available in real time, constitute a valuable resource that undergraduate educators can use to teach a range of oceanographic principles and make the scientific process tangible. Using real data in undergraduate science education offers significant benefits for developing students' analytical and problem-solving skills (e.g., Gougis et al, 2017;O'Reilly et al, 2017;McDonnell et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Engaging students in active learning by modeling the scientific process using real-world data is a high-impact educational practice (Rubin and Abrams, 2015;O'Reilly et al, 2017;Deslauriers et al, 2019). Data explorations allow students to engage in academically complex and challenging activities that require conceptual thought through the validation of physical models (Resnick et al, 2018) and the procedural knowledge needed to produce an analytical result (Kastens et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Using Authentic Data from NSF’s Ocean Observatories Initiative in Undergraduate Teaching: An Invitation

Greengrove¹,

Lichtenwalner²,

Palevsky³

et al. 2020

Oceanog

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Using Large Data Sets for Open-Ended Inquiry in Undergraduate Science Classrooms

Cited by 28 publications

References 20 publications

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

Using Authentic Data from NSF’s Ocean Observatories Initiative in Undergraduate Teaching: An Invitation

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