Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses.
Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses.
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DOI：
10.1002/ece3.4363
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发表时间：
2018-08
影响因子：
2.6
通讯作者：
Carey CC
中科院分区：
文献类型：
--
作者：
Farrell KJ;Carey CC
环境研究需要了解跨多个空间和时间尺度相互作用的非线性生态动力学。对长期和高频传感器数据的分析与模拟建模相结合，可以解释复杂的生态现象，进行这些分析所需的计算技能越来越多地被整合到生态学研究生培训计划中。尽管它的重要性，但是，计算能力，即利用计算机技术的力量来完成任务的能力，很少在本科生态学课堂上教授，这代表了培养学生应对复杂环境挑战的主要差距。通过我们为两个环境科学教学计划项目EDDIE（环境数据驱动的调查和探索）和宏观系统EDDIE开发长期和高频数据分析和模拟建模本科课程的经验，我们发现学生经常对计算任务感到害怕，这是由于缺乏对软件的熟悉而加剧的（例如，R）和与基于脚本的分析工具相关的陡峭的学习曲线。使用预先打包的灵活模块，引入编程作为探索环境数据集和教授基于探究的生态学的机制，例如为Project EDDIE和Macrosystems EDDIE开发的模块，可以显着提高学生使用高级计算工具的体验和舒适度。这些类型的模块反过来又为学生提供了巨大的潜力，使他们能够自己提出生态问题和测试假设所需的计算素养。随着大陆规模的传感器观测网络迅速扩大长期和高频数据的可用性，具有操作，可视化和解释这些数据的技能的学生将为数据科学的各种职业做好充分准备，并将有助于推进生态学开放，可复制科学的未来。
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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影响因子：
10.3
作者：
Hampton, Stephanie E.;Strasser, Carly A.;Porter, John H.
通讯作者：
Porter, John H.
影响因子：
10.1
作者：
O'Reilly, Catherine M.;Gougis, Rebekka D.;Hunter, William
通讯作者：
Hunter, William
影响因子：
4.4
作者：
Carey, Cayelan C.;Gougis, Rebekka Darner
通讯作者：
Gougis, Rebekka Darner
DOI：
10.1187/cbe.16-12-0344
发表时间：
2017
期刊：
CBE life sciences education
影响因子：
--
作者：
Ballen CJ;Wieman C;Salehi S;Searle JB;Zamudio KR
通讯作者：
Zamudio KR
影响因子：
3.3
作者：
Louca, Loucas T.;Zacharia, Zacharias C.
通讯作者：
Zacharia, Zacharias C.