The digitalization of science education

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. The papers in this special issue cover a wide range of different topics—from engaging elementary school students in scientific inquiry to promoting science education reforms. The digital technologies involved range from simulations, to game-based technologies, to social media. In terms of research focus, the majority of papers zero in on improving science teaching and learning or understanding how to improve science teaching and learning. The paper “A quasi-experimental study comparing learning gains associated with serious educational gameplay and hands-on science in elementary classrooms” by Hodges et al., for example, presents findings from two rigorous studies investigating the efficacy of a serious educational game, engaging students in inquiry learning in comparison to a regular inquiry learning unit specifically designed to meet the same learning goals as the game. The paper goes a step further, exploring additional student characteristics, such as students' reading level to examine the extent to which efficacy may depend on individual student characteristics; that is, whether games may be particularly effective for students who are proficient readers, whereas the comparison unit may be more effective for those who are less proficient in reading (as a game will generally involve more reading). Exploring such an aptitude-treatment interaction (for details on aptitude-treatment interactions, see Koran & Koran, 1984) is an important, yet rarely attended to, aspect of science teaching in learning. Many studies work from the hypothesis that a certain feature will be generally effective, although it is much more realistic to assume that different types of interventions will resonate with different types of student preferences or characteristics (e.g., some students may prefer game-based learning, where others will not). Similarly, it is important to explore how specific features of an intervention contribute to students' learning. The paper by Saleh et al., titled “synergistic scaffolds for collaborative inquiry in a game-based learning environment” is one example of such research. Focusing on supporting students' collaborative inquiry learning, the paper investigates the complex interplay of hard (i.e., predefined) scaffolds and soft (just-in-time) scaffolds provided by a facilitator. Based on a synergistic scaffolding framework (i.e., a framework delineating the role of multiple co-occurring scaffolds), the paper presents findings on how hard and soft scaffolds work together to successfully support students' learning. As such, the paper provides important insights into how to effectively design an important feature of digital learning environments such as games: the scaffolding of students’ learning. The paper by Mikeska, Howell, and Croft, “Simulations as practice-based spaces to support elementary teachers in learning how to facilitate argumentation-focused science discussions” focuses on simulations and, importantly, aligns nicely with the previous two papers, in that it is centered around the learning of the critical inquiry practice of argumentation: the construction and use of arguments. More specifically, the paper is about engaging preservice elementary teachers (PSETs) in supporting elementary students in argumentation. In order to do so, the authors use what they call a “simulation.” This simulation, however, is different from simulations commonly used in science teaching and learning such as simulations of phenomena (e.g., Moore, Chamberlain, Parson, & Perkins, 2014) or scientific laboratory investigations (e.g., Quellmalz, Timms, Silberglitt, & Buckley, 2012). Instead this tool simulates a classroom situation in which the PSETs engage with a group of avatars representing elementary school students. The paper presents findings from a study of how the PSETs support students in argumentation (i.e., the construction and critique of arguments) and the teaching moves the PSETs are using. The study also presents findings from interviews with the PSETs about their perception of the usefulness of the simulation for their learning process. This paper highlights the potential of digital technologies to create authentic learning experiences for PSETs that are otherwise hard to create (like micro-teaching situations). Collectively, these papers indicate that this is where the field has moved in terms of using digital technologies for science teaching and learning (i.e., the first theme discussed above): The creation of unique learning settings that present students with an authentic learning experience to support the learning of specific knowledge, skills or abilities that are otherwise hard(er) to develop. To that end, this movement reflects the increased focus on engaging students in learning of such knowledge, skills or abilities (e.g., engaging teachers in developing specific teaching strategies instead of just teaching them about the strategies). Two of the papers in this special issue focus on the assessment of student science learning. Both papers highlight recent advancements in the field of assessment and how they can be utilized for a more reliable and valid assessment of the knowledge, skills, and abilities learners are envisioned to develop in the 21st century. The paper titled “Identifying patterns of students' performance on simulated inquiry tasks using PISA 2015 log-file data” by Teig et al. addresses the use of simulations as a means for a more valid assessment of students' inquiry skills. In particular, the paper focuses on a specific feature that assessments using computer-based simulations offer: log-file data. Log-file data allow for analyzing how students approach the task (i.e., the and not just the task (i.e., the on data from the PISA 2015 which inquiry the authors examine log-file data to patterns of students' with computer-based assessment and to whether unique characteristics of these as of inquiry This paper highlights the potential of digital technologies for student science in particular, the assessment of complex patterns of knowledge, and which a more complex assessment & the use of digital technologies for students' science learning not only the for authentic settings to complex patterns of knowledge, skills, and abilities (e.g., & and analysis of respective student of the of student a wealth of data are (i.e., available for this up the for automated scoring has been a in science education research for a More however, research has exploring automated scoring that on the developments in the most being learning is a widely used technology where the from data to and just as from Based on these the can student knowledge, skills, and abilities based on new data et al., this The paper to A framework of science by et al. presents findings from a of the use of learning in science assessment to the of the extent to which has science assessment. 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In the century, digital technologies all of or dynamic under is, what has been simulated has been science with the to help students this The digital technologies in the papers of this special issue in that they authentic settings to support or students' more learning of complex patterns of knowledge, skills, and abilities (as by This reflects the from on single knowledge, skills or abilities in science teaching and learning and assessment of science teaching and learning. In terms of science teaching and learning, the field also to into the role of the individual and their environment in for the that is single learning environment that supports effective learning for all, but that one of learning environment (e.g., an educational will resonate with one group of students (e.g., but not with (e.g., the papers in this special issue highlight the potential of simulations of authentic settings to support student learning, suggesting that more research is in order to understand what of simulation may be for which group of students, and how simulations need to be designed to best support students' learning. This is not about the specific or design of a game, but more generally about simulation feature types more such as how to learners is and how learning is (i.e., In terms of the assessment of science teaching and learning, the papers the benefit of simulation for authentic settings to complex patterns of students' The papers in this special issue also the potential of to use digital technologies in science teaching and learning This is the by et al. that the use of learning is still mostly in the to improve the features (i.e., and of or to through automated scoring instead of student The potential of learning, however, in that it can allow for student learning as it to the call for less assessments of students' learning (e.g., & learning can help not only with automatically scoring students but also to a wide range of data as students with digital technologies. for example, help student in in a such as that by Hodges et al., students' of hard and soft scaffolds as a in the game-based learning environment by Saleh et al., or by students' and them with the that they as in the study by et al. can even help between different digital technologies used in the As by et al., learning can on data by different digital technologies to monitor learning and even learning The use of or devices to access different technologies a digital in which students This for their science learning when they use a designed to help them in of science at as well as when they up on why the is on in the This vision a range of questions how such use of digital technologies will science education (i.e., our third and what the implications such use will have for our (i.e., our second this vision may quickly become we these questions should be rather The paper by et al. provides an example of what such research may like and how such research will need to also explore new to its Focusing on a digital technology (i.e., of the digital ecology that we live in we can to the paper the that digital technologies and ecologies can have on science education (or science education in this it would not have been to such who are not only in their role (e.g., teacher but also in terms of and to create such experiences in fact, all However, in order to explore the of or more specifically the authors other that are not to science education research. What this approach are new questions that also new other that and new of that the practices of science teaching and learning with digital technologies. In of the papers in this special issue provides a of what through the use of digital technologies and ecologies in science education. In particular, if the papers highlight what would be if different technologies be to a digital ecology that is not used for science teaching, learning, and assessment, but in which science teaching, learning, and assessment and which provides students, and with the means to organize more and more science education. This new questions that of these questions may be or even to using research design and However, the of understanding the digital science education is and the impact it has on our the that with these recent technological developments provide digital ecologies for and create respective ecologies of science education that can help improve science teaching, learning, and in terms of an increased but in terms of new and, more importantly, to allow students experiences that would otherwise be (or at to to science education more and to science education to a new research should explore these to help understand how these technologies impact science teaching, learning, and assessment and how they can meet the

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