1 INTRODUCTION

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 (e.g., 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 and/or used to support better science learning. Much like understanding the reciprocal relationship between science and technology that has long been a major goal of STEM education, research on digital technologies in education, STEM education and, more specifically, science education, has been driven by the wish to understand how (digital) technologies can support better teaching, learning, and assessment and how a better understanding of science teaching, learning, and assessment can help improved learning technologies. However, as discussed in the introduction to this special issue, equally important to understanding the relationship between science, engineering, and technology is understanding the impact that science, engineering, and technology have on the world we live in. In fact, as we are living in a world driven by ever accelerating scientific and technological progress, understanding the impact that this progress has on the natural world as well as our society, may very well be the more important aim. After all, few people will understand the science and technology behind self‐driving cars, but many should be able to understand the technical challenges (e.g., modeling other road user behavior) and related ethical issues (e.g., responsibility in case of a crash) related to this technology. In terms of science education, this means that in addition to researching aspects of the use of digital technologies in science teaching, learning, and assessment, researchers should also focus on the broader impacts that the use of such technologies may have on science teaching, learning, and assessment as an ecology of its own right. Imagine, for example, teachers using automated essay scoring software, which will automatically grade essays written by students. Teachers may exhibit bias toward certain students or groups of students, but what about the algorithms underlying such software? They are widely considered neutral, but are they really? Or are they biased as well, and if so, how? Which students and language are represented in these algorithms? What would the teacher need to know about these algorithms in order to recognize and avoid bias? What implications does this have for teacher education? When digital technologies were still tools mastered by teachers it seemed reasonable to focus on the effectiveness of these tools and the conditions under which they were effective. But now that these technologies have become an integral part of our lives and, subsequently, science classrooms, it seems imperative to develop a deeper understanding of how these technologies impact science teaching, learning, and assessment, in particular in terms of the potential associated risks with respect, but not limited, to the goal of helping all students develop scientific literacy.

This is why we conceptualized this special issue to explicitly call for submissions examining not only the use of 21st century, cutting‐edge digital technologies but also the impact on science teaching, learning, and assessment. When technologies and more specifically digital technologies were first used in science teaching, learning, and assessment the focus was on showing how they better support student learning. The initial findings were not optimistic and for the most part the impact on learning has revealed numerous gaps (e.g., findings that the use of digital technologies does not lead to improvement in teaching and learning per se or that digital technologies mostly benefit students who already have a deeper understanding of the content). Hence, the holistic impact of these technologies is still unknown. Instead, research appeared to suggest that it depends on how these technologies were being used.

Accordingly, one theme we were looking for in this special issue's studies was research aiming to produce knowledge about the conditions under which specific technologies are effectively used in science classrooms and the implications for how these technologies are best used.

A second theme was the impact cutting‐edge technologies might have on science education. We used the term ecologies in conjunction with digital technologies to acknowledge that cutting‐edge technologies currently finding their way into science classrooms are often not just based on a single technology but rather represent a digital ecology—a learning space integrating different technologies and associated curricular and pedagogical practices that frame the context of learners and the learning environment. This applies to many e‐learning environments as well as to science classrooms themselves. Science teaching, learning, and assessment as it happens in science classrooms, often draws on multiple, different digital technologies. Teachers may use modeling tools in conjunction with data collection tools, smart phone apps supporting students in constructing explanations. They may also use grading software for grading, learning management systems to organize lesson content and allow access to students, and online word processors to organize the collaborative work.

A third theme we were interested in was the extent to which research still focuses on individual technologies (or technologies that may be perceived as individual) versus digital ecologies bringing together different technologies or even science education as a digital ecology itself.

As a result of our call, we received 45 submissions, covering a broad range of topics from students' learning in mixed‐reality environments to teachers’ integration of innovative technologies. Below, we briefly summarize and discuss the findings of the papers that were selected into this special issue, followed by a summative discussion in light of the three themes outlined above. We conclude by summarizing the state of research on the use and impact of 21st century digital technologies and ecologies on science teaching, learning, and assessment, and formulating suggestions for issues that researchers should attend to in the future in order for digital technologies to support a 21st century science education that can meet the vision outlined in the introduction to this special issue.

中文翻译：

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科学教育的数字化：Déjàvu又来了吗？

1引言

本期专刊旨在提供一个实证研究报告平台，以检验21世纪尖端数字技术和生态学在科学教学，学习和评估中的用途和影响。数十年来，人们一直认为技术以及最近的数字技术彻底改变了教育，STEM教育，尤其是科学教育。这项运动始于1980年代，当时教室开始使用个人计算机。个人计算机以及相应的软件为教学提供了许多新的可能性。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年）。），以及基于视频的交互式学习程序，使学生可以按照自己的进度完成课程设置，从而获得更加个性化的学习体验（Leonard，1985年）。前者迅速发展为旨在支持学生的科学学习的大量软件工具，从旨在模拟真实现象的工具（Doerr，1996年）到模拟实验室环境以使学生参与真实询问的工具（Niesink等，1997年）。 。后者在互联网和相关技术（例如超文本协议）的出现的推动下，从提供用于远程自主学习的在线（e）学习环境（Ajadi，Salawu）发展为各种基于计算机的学习环境。和Adeoye，2007年）到旨在自动监控和支持学生学习的智能补习系统（Graesser，Conley和Olney，2012年）。一方面，计算能力的显着提高推动了最近的发展；研究结果表明，仅数字技术（例如模拟或建模工具）并不一定能帮助学生学习，而是需要嵌入有意义的课程中（例如，Zhang，2012；有关概述，请参见Krajcik＆Mun，2014）。该领域的许多新发展通过模拟现实世界，使学生参与到真实的学习体验中（Barab等，2009），并将多种独立技术（例如仿真，建模或数据分析工具）集成到精心安排的课程活动中（Gerard，Spitulnik＆Linn，2010年）。最新的发展甚至整合了通过学习环境本身或通过老师对学生的学习和各自的支持进行自动跟踪的方法（Gobert等，2013 ; Gobert＆Sao Pedro，2017）。未来的前景更加光明：增强现实设备有望创造出真实的学习体验，而人工智能技术则将提供更加开放的探索性（e）学习环境，从而根据学生的特定需求自动指导他们的学习。据说所有这些技术都将很快通过低成本的个人智能设备提供，例如所有人都可以负担的电话或平板电脑。

但是，这些发展提出的问题超出了以下问题：这些新技术是否将支持更好的科学学习，或者如何设计和/或使用这些技术来支持更好的科学学习。就像了解长期以来一直是STEM教育的主要目标的科学与技术之间的相互关系一样，对数字技术的研究，STEM教育，更具体地说是科学教育也受到了了解（数字）方式的推动。技术可以支持更好的教学，学习和评估，以及对科学教学，学习和评估的更好理解可以如何帮助改进学习技术。但是，正如本期特刊简介中所述，这对于理解科学，工程学，和技术正在理解科学，工程和技术对我们所生活的世界的影响。实际上，当我们生活在一个由不断加速的科学技术进步驱动的世界中时，请了解这种进步对自然产生的影响世界以及我们的社会，很可能是更重要的目标。毕竟，很少有人会理解自动驾驶汽车背后的科学和技术，但很多人应该能够理解技术挑战（例如，对其他道路使用者行为进行建模）和相关的道德问题（例如，发生撞车时的责任）与这项技术有关。在科学教育方面，这意味着除了研究在科学教学，学习和评估中使用数字技术的方面外，研究人员还应关注于更广泛这种技术的使用可能会对科学教学，学习和评估产生影响，这本身就是一种生态。例如，想象一下，教师使用自动作文评分软件，该软件将自动对学生撰写的论文进行评分。教师可能会对某些学生或一组学生表现出偏见，但是这种软件所基于的算法又如何呢？他们被普遍认为是中立的，但确实如此吗？还是他们也有偏见？如果是，怎么样？这些算法代表哪些学生和语言？为了识别并避免偏见，老师需要对这些算法了解什么？这对教师教育有什么影响？当数字技术仍然是教师掌握的工具时，将注意力集中在这些工具的有效性及其有效条件上似乎是合理的。但是，既然这些技术已经成为我们生活以及随后的科学教室中不可或缺的一部分，那么就必须更深入地了解这些技术如何影响科学教学，学习和评估，尤其是在潜在的相关风险方面，尊重但不限于帮助所有学生发展科学素养的目标。

这就是为什么我们将这个特殊问题概念化，以明确要求提交的材料不仅要审查21世纪，尖端数字技术的使用，还要审查对科学教学，学习和评估的影响。当技术（尤其是数字技术）首次用于科学教学，学习和评估时，重点是展示它们如何更好地支持学生的学习。最初的发现并不乐观，并且在很大程度上对学习的影响揭示了许多差距（例如，发现使用数字技术并不会导致教与学本身的改善，或者发现数字技术主要使已经拥有学习能力的学生受益）对内容有更深入的了解）。因此，这些技术的整体影响仍然未知。代替，

因此，我们希望在本期特刊的研究中寻找的主题是旨在产生有关在科学教室中有效使用特定技术的条件以及如何最好地使用这些技术的知识的研究。

第二个主题是尖端技术可能对科学教育产生的影响。我们将“生态学”一词与数字技术结合使用，以承认当前进入科学课堂的尖端技术通常不仅基于单一技术，而且代表了一种数字生态学-一种整合了不同技术以及相关课程和技术的学习空间构成学习者背景和学习环境的教学实践。这适用于许多电子学习环境以及科学教室本身。在科学教室中进行的科学教学，学习和评估通常采用多种不同的数字技术。教师可以结合使用建模工具和数据收集工具，支持学生构建解释的智能手机应用程序。他们还可以使用评分软件进行评分，学习管理系统来组织课程内容并允许学生访问，还可以使用在线文字处理器来组织协作工作。

我们感兴趣的第三个主题是，研究在多大程度上仍侧重于单个技术（或可能被视为个体的技术）与将不同技术甚至科学教育整合为数字生态本身的数字生态。

接到电话后，我们收到了45份论文，涵盖了从混合现实环境中的学生学习到教师的创新技术整合等广泛主题。下面，我们简要总结和讨论入选本期特刊的论文的发现，然后根据上述三个主题进行总结性讨论。最后，我们总结了21世纪数字技术和生态学在科学教学，学习和评估中的使用和影响的研究现状，并提出了研究人员今后应注意的问题的建议，以使数字技术能够支持科学技术的发展。可以满足此特刊简介中概述的愿景的21世纪科学教育。