The digitalization of science education: Déjà vu all over again?
The digitalization of science education: Déjà vu all over again?
复制标题
科学教育数字化：似曾相识？
DOI：
10.1002/tea.21668
复制
发表时间：
2020
影响因子：
4.6
通讯作者：
Noemi Waight
中科院分区：
文献类型：
--
作者：
K. Neumann;Noemi Waight
本期特刊旨在提供一个报道实证研究的平台，该研究考察了21世纪尖端数字技术和生态系统对科学教学、学习和评估的使用和影响。几十年来，技术，以及最近的数字技术，一直被认为是教育、STEM教育，更具体地说，是科学教育的革命。这项运动始于20世纪80年代，当时个人电脑开始在教室里使用。个人电脑和相应的软件为教与学开辟了丰富的新可能性。模拟提供了复杂内容的动态可视化，以便更好地支持学生掌握对这些内容的理解(Marks，1982)，基于视频的交互式学习程序使学生能够按照自己的节奏完成课程，从而获得更个性化的学习体验(Leonard，1985)。前者迅速发展为大量旨在支持学生科学学习的软件工具--从设计用于模拟真实现象的工具(Doerr，1996)到模拟实验室环境以吸引学生进行真实的探究(Niesink等人，1997)。后者在一定程度上受到互联网和超文本协议等相关技术的推动，发展成了各种基于计算机的学习环境--从为远程、自主学习提供的在线(E)学习环境(Ajadi，Salawu&Adeoye，2007)到旨在自动监控和支持学生学习的智能辅导系统(Graesser，Conley&Olney，2012)。最近的发展一方面是由计算能力的大幅增加推动的，另一方面是研究结果表明，(数字)技术，如模拟或建模工具，本身并不一定有助于学生的学习，而是需要嵌入到有意义的课程中(例如，张，2012；概述见Krajcik&Mun，2014)。该领域的许多新发展使学生通过模拟现实世界来体验真实的学习体验(Barab等人，2009)，并将多种单独的技术，如模拟、建模或数据分析工具集成到精心排序的课程活动中(Gerard，Spitulnik&Linn，2010)。最近的发展甚至包括通过学习环境本身或通过教师自动跟踪学生的学习和各自的支持(Gobert等人，2013年；Gobert&Sao Pedro，2017)。未来被展望得更加光明：增强现实设备预计将创造真实的学习体验，人工智能将允许更开放、更具探索性的(E)学习环境，根据学生的特定需求自动指导他们的学习。据说所有这些技术很快就会通过低成本的个人智能设备交付，比如每个人都能负担得起的手机或平板电脑。然而，这些发展带来的问题不仅仅是这些新技术是否会支持更好的科学学习，或者这些技术需要如何设计
This special issue set out to provide a platform for reporting on empirical research that examines the use and impact of 21st century cutting-edge digital technologies and ecologies on science teaching, learning, and assessment. For decades technologies, and more recently digital technologies, have been said to revolutionize education, STEM education and, more specifically, science education. This movement started in the 1980s, when personal computers began to become available in classrooms. Personal computers, together with the respective software opened up a wealth of new possibilities in teaching and learning. Simulations provided dynamic visualizations of complex content in order to better support students in mastering understanding of these contents (Marks, 1982), and interactive learning programs based on videos allowed students to work through the curriculum at their own pace enabling a more individualized learning experience (Leonard, 1985). The former quickly developed into a vast amount of software tools designed to support students' science learning—from tools designed to model authentic phenomena (Doerr, 1996) to simulations of laboratory environments to engage students in authentic inquiry (Niesink et al., 1997). The latter, partially fueled by the advent of the internet and related technologies such as the hypertext protocol, developed into a variety of computer-based learning environments—from online (e) learning environments provided for remote, self-determined learning (Ajadi, Salawu, & Adeoye, 2007) to intelligent tutoring systems designed to automatically monitor and support students' learning (Graesser, Conley & Olney, 2012). More recent developments are driven by substantial increases in computing power on the one hand and research findings suggesting that (digital) technologies, such as simulations or modeling tools, alone are not necessarily helping students' learning, but instead need to be embedded in meaningful curriculum (eg, Zhang, 2012; for an overview see Krajcik & Mun, 2014) on the other. Many new developments in the field engage students in authentic learning experiences through simulations of the real world (Barab et al., 2009), and integrate multiple individual technologies, such as simulations, modeling, or data analysis tools into a carefully sequenced curriculum activities (Gerard, Spitulnik & Linn, 2010). Most recent developments even integrate an automated tracking of students' learning and respective supports either through the learning environment itself or through the teacher (Gobert et al., 2013; Gobert & Sao Pedro, 2017). And the future is envisioned even brighter: Augmented Reality devices are expected to create authentic learning experiences, and Artificial Intelligence to allow for more open, exploratory (e) learning environments that automatically guide students in their learning based on their specific needs. All these technologies are said to be soon delivered through low cost personal smart devices, such as phones or tablets affordable to everyone. These developments, however, raise questions beyond the ones asking whether these new technologies will support better science learning or how these technologies need to be designed