that through computational and collaborative learning experiences, learners participate as members of STEM learning communities in ways that mirror how STEM professionals collaborate, applying interdisciplinary and transdisciplinary approaches to complex real-world problems. Computational participation consequently creates opportunities for learner-technology pairings to (re)shape STEM learning behaviors, empowering learners to engage collaboratively as members of a STEM community of practice.

collaborative learning, computational participation, computational thinking, community of practice, citizen science, game-based learning, learner-technology, STEM practices, STEM education, technology Advances in computing and information technologies over the past four decades have resulted in innovative applications of computational technologies across multiple disciplines (Swaid, 2015). This paper is grounded in a conceptualization of science, technology, engineering, and mathematics (STEM) that embraces the advent of modern technologies and calls for emergent technologies to continually redefine the T in STEM education. We examine how computational participation integrates emergent technologies as both tools and practices, and serves as an epistemological and pedagogical approach that promotes collaborative STEM practices.

In the context of STEM practices, the term emerging or emergent technologies has in recent decades included a variety of innovations related to information and communication technologies, biotechnology, and nanotechnology (Einsiedel, 2009). Emergent technologies, consequently, are defined to encompass a wide variety of devices, tools, artifacts, and representations that continually shape the landscape of scientific investigations, knowledge advancement, and practices, by enabling increased access to the world around us. In a literature review of emergent technologies in the field of science education, Oliveira et al. (2019) argued that emergent technologies (including computational thinking, simulations and virtual labs, pedagogic robotics, mobile devices, computational modeling, gaming and technology-mediated play, and artistic technologies) shape how K-12 learners indirectly relate to the physical world through technological representations and technology-mediated interactions. Oliveira and colleagues used the term emergent technologies broadly, including emergent technology tools and devices (simulations and virtual labs, pedagogic robotics, mobile devices, gaming and technology-mediated play, creative and artistic technologies) and emergent technology practices (computational modeling and computational thinking). In this paper, we examine how the use of computational participation as an epistemological and pedagogical approach in STEM education creates opportunities for K-12 STEM learners to utilize technological practices used by STEM professionals, allowing learners to engage in authentic STEM practices. We look at how technological approaches, such as technology-mediated game-based learning and citizen science projects, mediate STEM learner experiences and behaviors by empowering learners to develop scientific knowledge through collaborative participation and interactive relationships with technology. In addition, we examine how computational participation creates opportunities for learner-technology pairings that (re)shape STEM learning behaviors, and empowers learners to engage as members of a STEM community of practice.

Current policy documents call for K-12 science classrooms to employ integrated STEM strategies to provide a more authentic learning environment for learners. For example, the addition of engineering in the science education standards (NGSS Lead States, 2013) acknowledges not only that learners need to engage in learning about natural phenomena in science, but also that teaching and learning should address the human-constructed and technological world in which we live.

Unfortunately, there is a lack of consensus on a definition of integrated STEM (e.g., Breiner, Harkness, Johnson, & Koehler, 2012;Brown, Brown, Reardon, & Merrill, 2011;Bybee, 2010;English, 2016;Koehler, Binns, & Bloom, 2016;Ring, Dare, Crotty, & Roehrig, 2017;Sgro, Bobowski, & Oliveira, in press). However, there is some agreement on desired learner behaviors related to engagement with integrated STEM. Integrated STEM instruction should engage learners in solving real-world problems that require learners to apply scientific, mathematical, and engineering concepts to authentic problems (Breiner et al., 2012;Brown et al., 2011;Kelley & Knowles, 2016;Kennedy & Odell, 2014;Moore et al., 2014;Sanders, 2009). Learners are expected to participate in design thinking, applying the practices of science and engineering to propose explanations and design solutions (NRC, 2012). It is expected that learners should develop teamwork, communication, critical thinking, and other 21st century competencies by engaging in integrated STEM activities (Brown et al., 2011;Honey, Pearson, & Schweingruber, 2014;Kennedy & Odell, 2014;Moore et al., 2014). While some of the desired learner behaviors are agreed upon, the lack of clear and consistent conceptualizations of STEM makes it difficult to achieve these goals.

Although there is consensus on certain aspects of STEM integration, the role of technology in STEM education remains particularly unclear and needs stronger operational definition (Honey et al., 2014;Sivaraj, Ellis, & Roehrig, 2019). Ellis et al. (2020) reviewed several common perspectives on the T in STEM education, which included technology as (a) the product of engineering, (b) instructional technologies, (c) computational thinking, and (d) tools and practices used by science, mathematics, and engineering professionals. In this paper, we seek to expand the theoretical perspective of the T in STEM, with a focus on learner behaviors and the potential of technology-mediated experiences with computational participation in shaping STEM learning. In particular, we wish to de-emphasize the what about technology (i.e., a focus on specific products, tools, and software) and bring renewed focus to the how associated with these technologies (i.e., the behaviors, practices, and outcomes that impact student learning).

We define STEM practices based on the science and engineering practices identified by the Framework for K-12 Science Education (NRC, 2012), a national policy document that seeks to address challenges related to how scientific concepts are often taught disconnected from scientific practices. These practices include: asking questions (for science) and defining problems (for engineering); developing and using models; planning and carrying out investigations; analyzing and interpreting data; using mathematics and computational thinking; constructing explanations (for science) and designing solutions (for engineering); engaging in argument from evidence; and obtaining, evaluating, and communicating information. Because these STEM practices are consistent with the type of work done by science, engineering, and mathematics professionals, students' use of STEM practices engages them in more authentic learning experiences. Below, we discuss computational thinking, a relatively recent approach to using technology in K-12 classrooms that engages students in these kinds of learning experiences.

From the introduction of Logo as a programming language in the 1980s to programmable LEGO Bricks in the mid-1990s and more recent innovations in programmable interfaces, there continues to be a surge in the incorporation of computational activities in STEM learning experiences. The remarkable growth in computational opportunities offered across K-12 STEM learning in particular has led to the creation of tangible products as well as computer programs that control these products (e.g., Blikstein & Wilensky, 2009;Kynigos, 2007). Papert (1980) used the term computational thinking (CT) to describe how computational representations and programming enabled learners to make their thinking more apparent by constructing, deconstructing, and reconstructing artifacts. More recently, Wing (2006) defined CT to include diverse aspects of designing systems, solving problems, and understanding human behavior. Similarly, Barr and Stephenson (2011) presented CT as a problem-solving process, which includes decomposing a problem in order to make it solvable using computational tools, analyzing and representing data systematically, utilizing algorithms, and applying a wide range of solutions.

Researchers have noted the overlap of these CT practices (such as problem representation, abstraction, decomposition, simulation, verification, and prediction) with scientific and mathematical practices, making a case for integrating CT into K-12 STEM curricula (Sengupta, Kinnebrew, Basu, Biswas, & Clark, 2013). Several scholars support the view that CT is highly compatible with STEM instruction and can be an effective practice in STEM classrooms, which are also focused on problem solving (e.g., Ellis et al., 2020;Kynigos, 2007;Weintrop et al., 2016;Wilensky & Reisman, 2006). Thus, it is unsurprising that the Framework for K-12 Science Education (NRC, 2012) included CT as one of the essential science and engineering practices that K-12 students should engage in. Built from the Framework, the Next Generation Science Standards (NGSS) specifically describes CT as "strategies for organizing and searching data, creating sequences of steps called algorithms, and using and developing new simulations of natural and designed systems" (NGSS Lead States, 2013, Appendix F, p. 10).

Learners are, therefore, expected to deepen their understanding of core concepts using CT and engaging with scientific and engineering practices as outlined in the NGSS.

In science classrooms, technology tools and devices are primarily focused on practices related to data collection. For instance, virtual laboratories or simulations are used as a replacement for collecting data in a physical space. Similarly, sensors on mobile devices are increasingly utilized to gather data efficiently. In essence, these are examples of technology as a tool to help learners engage in the same scientific practices used by scientists as a replacement (virtual vs. physical laboratory), as a way to improve efficiency (sensors vs. traditional tools), or as affordances to collect data that would otherwise be unavailable (slow motion cameras, simulations, computer models). While emergent technological tools can and should be used to reduce burdens on materials, time, and cognitive load, we seek to advance the argument that the use of technologies in STEM education must move beyond tool-based approaches focused on data collection. Kafai and Burke (2014) extended the discussion of CT in educational contexts, calling for CT to be reframed as computational participation (CP), emphasizing "the ability to solve problems with others, design systems for and with others, and draw on computer science concepts, practices, and perspectives (p. 6)" so that learners are able to meaningfully participate as critical thinkers, as well as producers, consumers, and distributors of technology. They argue that CP enables insight into sociological and cultural dimensions as learners move between the digital and physical world. CP, therefore, includes not just CT skills, but also utilizes CT skills toward specifically purposed, collaborative projects, such as using code to generate websites, programming to create interactive art projects and digital stories, and interacting through computational activities to build and sustain relationships.

Utilizing this perspective, we make a case for using CP to unpack the potential of the T in STEM, which can create authentic and collaborative opportunities for STEM learners to engage in STEM practices through interactions with technologies.

We propose that CP has the potential to integrate continually evolving technological tools, providing opportunities for STEM learners to move between digital and physical worlds, constructing knowledge of concepts and skills through collaborative STEM-related practices and collaborative interactions with emergent technologies. We share examples of specific approaches (such as technology-mediated gaming and citizen science) in STEM learning that reveal the power of CP as an epistemological and pedagogical approach to integrating a wide range of technologies that promote STEM practices and learner behaviors. We argue that, through computational and collaborative components in learning experiences, learners participate as members of STEM learning communities in ways that mirror how STEM professionals collaborate, applying interdisciplinary and transdisciplinary approaches to complex real-world problems. Within STEM education, CP requires the collaborative application of CT and STEM practices, as learners interact with technologies and with each other as members of a learning community. Learners' interactions with technologies and others allow them to engage simultaneously in social and discipline-specific practices as members of a STEM community of practice, taking on critical roles as makers, designers, and innovators.

The theoretical framework for this paper is grounded in communities of practice (Wenger, 1998), where learning is necessarily relational, situated, and dependent on interactions with the learning environment. Specifically, a community of practice (CoP) is a group of people with common goals, who come together by engaging in shared ways of doing things (practices) and committing to a shared domain of interest (Lave & Wenger, 1991;Wenger, 1998). Wenger (1998) describes three defining characteristics for a CoP as (a) shared domain of interest, (b) shared community built through interactions, and (c) shared repertoire of resources and practices. Members of a CoP develop a shared system of values and practices, which new members gradually learn and take on as they engage in interactions.

In our work, the learning environment includes individuals (both peers and mentors), technology, information, and artifacts. Learning, constructing meaning, and developing transferable knowledge of concepts and practices are viewed as relational processes, shaped by interactions embedded in cultural, historical, and social contexts, where knowledge and behavior are constructed through shared repertoires of discourse, interactions, and practices (Wenger, 1998); these include learners' interactions with each other and with various material, social, and cultural components in their learning environment (John-Steiner & Mahn, 1996). The learner is significant not as a receiver of knowledge, but in terms of what they bring to the digital and physical learning environment as an active (and interactive) problem-solver. Behavior and learning, therefore, significantly and dynamically evolve through social, collaborative interactions.

With a focus on CP in STEM education and learners as members of STEM CoPs, this theoretical framework allows us to explore how the learner-technology pairing shapes learner behaviors and their use of STEM practices. As STEM learners interact with emergent technologies, using a CoP theoretical lens guides us toward examining the social and collaborative aspects of these interactions and the learning opportunities that such interactions afford. In this paper, we examine how learners access authentic STEM learning experiences as members of a STEM CoP and how CP creates opportunities for STEM learnertechnology pairings to shape learner behaviors through collaborative interactions with emergent technologies.

To better understand how the learner-technology pairing shapes student behaviors in STEM educational settings, we utilize Ihde's philosophical work as a conceptual framework. Ihde (1991) argued that technology is inherent in the pursuit of scientific knowledge because the human-technology pairing shapes how scientists construct meaning. This pairing is experiential, influencing how both scientists and learners relate to the world, as well as collaborative and embedded within a social context in which the human-technology pairing continually shapes how learning and society evolve (Ihde, 1993). In this perspective, technology is not merely an aid for learning; rather, technology is an essential mediator that shapes scientific investigation and discovery through the human-technology pairing. In particular, irrespective of whether technology is understood as devices, tools, representations, or artifacts, technology and science are symbiotically interconnected because technology-mediated relations shape the epistemic evolution of scientific knowledge and practices (Ihde, 1991). Ihde (1991) described four types of technology-mediated relations: embodiment, hermeneutic, alterity, and background relations. Table 1 sum In alterity relations, learners engage with technology as an independent, anthropomorphic entity, and generally as a focal point that causes the physical world to become secondary (Ihde, 1991). Navigational tools like Google Maps and SatNav, for example, are often viewed as intelligent machines, performing actions by responding to symbols, text, or voice commands, and even capable of eliciting emotional responses, trust, and attachment from humans during interactions (Brey, 2000;Hogan & Hornecker, 2011). Interactions with intelligent robots, simulations, and digital games are examples of alterity relations in classrooms, where the technology is often viewed as quasi-autonomous and mediates how learners reflect on the physical world as they interact with and respond to technologies.

T A B L E 1 Technology-mediated relations for the STEM learner (based on Ihde, 1991) T-mediated relations Learner relating to world STEM example Embodiment Observes the world, T becomes transparent Telescopes, electron microscopes Hermeneutic Interprets the world, reading T Thermometers, pH sensors Alterity Focus on technology, world becomes secondary Robots, citizen science apps Background Implicitly accepts T as part of the context/ physical world Air conditioners, projectors In the sections that follow, we reflect on how CP leverages technology-mediated hermeneutic and alterity relations, creating meaningful opportunities for collaborative interactions between learners and technologies through STEM practices. We also examine technological intentionality within the context of CP, and how the learner-technology pairing shapes learner behavior with respect to STEM practices by drawing on empirical examples to make a case for the potential of CP within STEM learning contexts. For example, when learners use simulations to examine specific phenomena, the STEM context creates the intentional circumstances for interactions with technologies in use to facilitate learning. STEM learners, consequently, have opportunities to leverage technology-mediated relations and engage in authentic STEM practices. In particular, we focus on technology-mediated game-based learning and citizen science, which are relatively new approaches in STEM contexts (Bonney et al., 2014; Oliveira et al., 2019), and examine how CP as an epistemological and pedagogical approach can create opportunities for STEM learners to leverage learning with technology in innovative ways.

In the following sections, we share two learning experiences from game-based learning and citizen science, and examine these as examples of CP providing computational and collaborative opportunities to STEM learners. We explore technology-mediated relations during these learning experiences for STEM learners. Reflecting on these examples, we draw attention to computational and collaborative components of each learning experience. To identify computational components, we use categories proposed by Weintrop et al. (2016), which include (a) data practices, (b) modeling and simulation practices, (c) problem-solving practices, and (d) systems thinking practices. The presence (or absence) of activities that match these categories demonstrates the extent to which CP integrates both CT skills and STEM practices through collaborative learning experiences. We also examine how CP enables learners to leverage social, collaborative aspects of learning.

Although simulation-based games have a long history, it was not until the 1970s that simulation techniques and gaming were used for teaching science and technology (Ellington, Gordon, & Fowlie, 1998).

Educational simulations are a basic form of digital games that are generally understood as interactive, instructional, 2-dimensional environments that create a conceivable reality, where learners can manipulate variables in order to explore different real-life scenarios and systems (Aldrich, 2004). Digital game-based learning has evolved in the last two decades, with electronic and digital games becoming increasingly more accessible to K-12 learners (Echeverría et al., 2011;Shaffer, Squire, Halverson, & Gee, 2005). Digital games include a wide variety of games on a console, handheld device, smartphone, or computer, and also include an array of genres and platforms, such as simulations, serious educational games, and augmented reality games. (Scarlatos, Tomkiewicz, & Courtney, 2013). Scholars also emphasize how gaming helps learners with conceptual understanding, along with domain-specific knowledge and practices, especially when learners contribute to game design or creation of new games (Mitchell & Savill-Smith, 2004).

However, there is continued debate, resistance, and skepticism related to the value, use, implementation, and appropriateness of incorporating gaming as instructional technology (Rice, 2007).

Researchers, therefore, call for the inclusion of thoughtful pedagogical approaches along with appropriate instructional facilitation to maximize the potential of well-designed games in STEM learning (Barab & Dede, 2007;Echeverría et al., 2011). Effective game design and productive pedagogical approaches that take into consideration theoretical perspectives related to the role of technology in STEM learning, for instance, contribute toward the integration of inquiry-driven multiplayer games in STEM contexts, which foster collaborative learning, along with positive STEM learning behaviors.

In the following sections, we explore CP as a thoughtful pedagogical approach to maximizing STEM learning within a gaming experience.

6.2 | Whypox: Experiencing a virtual pandemic

Whyville (www.whyville.net) was created as an educational virtual world that could serve as an engaging science environment (Kafai, Feldon, Fields, Giang, & Quintero, 2007;Kafai & Fields, 2013;Kafai, Quintero, & Feldon, 2010;Neulight, Kafai, Kao, Foley, & Galas, 2007).

The virtual world was designed with a rich, reality-based context, including an economy (where each player received a daily salary), a newspaper, and multiple opportunities for interactive STEM engagement through various science and mathematics tasks/activities. This multi-user virtual environment was integrated into a 10-week science curriculum about infectious disease across two sixth grade classes. Learners accessed the Whyville website in science class and outside school, creating avatars, or online representations. Through these avatars, learners experienced the outbreak of a virtual epidemic called Whypox. Whypox was a flu-like virus with symptoms that included alterations to avatar appearance (red spots) and communication (sneezes in chats). As avatars interacted with other community members in the virtual world, proximity to symptomatic members resulted in the spread of the virus. As a virtual community of epidemiologists, learners investigated the spread of Whypox, engaging in inquirydriven STEM learning using infection and epidemic simulators in the Whyville Center for Disease Control (CDC), where they developed understandings related to the spread of infectious disease. In later iterations of the game, learners were also able to visit a virtual Bio-Plex center in Whyville to create virtual vaccines and variations of the WhyFlu virus. Throughout the gaming experience, STEM learners participated in various classroom activities in the physical world, such as using microscopes to examine cells, completing worksheets related to cells, bacteria, and viruses, building and updating concept maps related to infectious disease, and engaging in teacher-facilitated whole class discussions related to experiences in Whyville.

As learners entered the virtual world of Whyville, CP provided multiple opportunities for them to engage in STEM practices based on their interest and familiarity with social and discipline-specific components.

These opportunities included exploring science and mathematics tasks as teams, conducting collaborative research and writing related to the history of Whypox, and role-playing to take on different STEMrelated roles, such as epidemiologists and research scientists. Learners investigated how infectious disease spreads through a population by tracking the movements of avatars before and after they were infected. The documentation of the spread of Whypox took place both online, using asynchronous community bulletin boards, as well as offline in whole class discussions, using wall-sized charts, concept maps, and other organizers in the classroom. Learners also examined the spread of Whypox by visiting Whyville's CDC as teams of scientists, using simulation and graphing tools to analyze data, make predictions, and manipulate variables, using mathematical models and computational thinking to generate different outcomes. The multiuser design and multiple modes of engagement created an authentic, collaborative context. Learners worked as a team using tools, such as simulators, not just as data practices, but to situate their data analysis with systems thinking practices. Web-based tools embedded in this virtual world facilitated efficient data practices, such as data collection and representation, thereby enabling learners to focus collaborative efforts toward data analysis and the creation of explanations and models. Thus, the learner-technology pairing facilitated STEM learners' collaborative development of scientific knowledge, concepts, and practices. Table 3 shows examples of specific STEM practices, along with computational components as learners engaged in the Whypox investigation.

Learners also worked collaboratively toward explanations of the spread of the epidemic as a virtual community of scientists, mirroring practices used by STEM professionals, interacting with technology and each other in digital and physical spaces. For example, learners had opportunities to "visit" the BioPlex center and explore vaccines and virus variants through coding. They were able to determine when and how to distribute these vaccines, considering scientific and ethical issues during collaborative decision making. Thus, the learnertechnology pairing supported collaborative STEM practices, such as constructing explanations and developing and using models, while approaching the inquiry from different perspectives (e.g., as a research scientist, data analyst, and vaccine developer). As STEM learners engaged in inquiry, they had authentic opportunities to collaboratively solve complex problems as a virtual community of scientists. Their use of emergent technologies, computational thinking, and collaboration as members of a STEM CoP in both the digital and physical world provide a useful example of CP in practice.

The creation of public interest groups and engagement of volunteers from the general public to form coalitions with goals related to advancing scientific knowledge and environmental stewardship has a long tradition, with science has been integrally connected with spheres of civics, economics, and politics (Dickinson & Bonney, 2012).

For example, journals and other artifacts from the 17th century indicate that amateur naturalists collected specimens, recorded observations, and advanced knowledge related to habitats (Miller-Rushing, Primack, & Bonney, 2012). Similarly, volunteer associations of amateur field naturalists have led organized efforts to integrate ecological education and conservation since the late nineteenth century, shaping the formation of societies like Nature London and Nature Canada. Citizen science was a phrase initially introduced by Kerson (1989), who described the Audubon Society's acid rain campaign, where "volunteers collect[ed] rain samples, test[ed] their acidity levels, and report

[ed] the results to Audubon headquarters" (p. 11). Subsequently, the reports generated using these results were used to lobby Congress, thus mobilizing participation of citizens from all 50 states in an effort to influence national policies. In the last two decades, public engagement in interdisciplinary and transdisciplinary STEM fields, such as environmental and agricultural sciences, has resulted in citizen science evolving as a distinct research model, with a "growing, global citizen science community devoted to working together to bridge the science-society-policy interface" (Hecker et al., 2018, p. 4).

In recent years, with the evolution of user-friendly websites, easily accessible databases, and a wide range of convenient mobile apps, citizen science projects have found their way into formal and informal STEM learning environments, where learners are involved directly in real-time research as members of a larger learning community (e.g., Higgins et al., 2016;Raddick et al., 2010). Emergent technologies like mobile apps provide platforms for learners to contribute directly to data practices, resulting in large data sets that advance understandings related to the physical world in real time. Table 4 lists five different types of citizen science projects identified by Wiggins and Crowston (2010), with examples of each in a STEM context.

Researchers have made a case for technology-mediated citizen science to be incorporated into K-12 STEM learning in order to foster an action-oriented approach toward STEM learning, use of authentic STEM practices, and an appreciation of civic engagement through STEM (e.g., Meyer et al., 2014;Mueller, Tippins, & Bryan, 2012). For example, Harris and Ballard (2018) discussed how third-graders participated in citizen science through The Lost Ladybug Project, utilizing a web-based platform to document local ladybug species through art T A B L E 3 Examples of Whypox investigation activities with STEM practices Whyville STEM activity STEM practices Computational components Identifying source of infection by tracking avatars' movements; discussing trends and hypotheses related to symptoms Planning and carrying out investigations; analyzing and interpreting data Problem-solving practices; systems thinking practices Utilizing infection and epidemic simulators to analyze data and develop alternate models of epidemic and exponential growth Analyzing and interpreting data; developing and using models; using mathematical and computational thinking Modeling and simulation practices; systems thinking practices Designing a vaccine and creating a virus variant in the Bioplex center Constructing explanations and designing solutions Problem-solving practices; systems thinking practices

and photography. The authors described how learners developed agency when they had opportunities to take ownership of data quality, to share their findings with outside audiences, and to engage with complex, socio-ecological systems, creating a year-round ladybug habitat in collaboration with experts, for instance.

Participation in citizen science projects has been connected to enjoyment, motivation, literacy, cognitive processes, competence, self-efficacy, social community, and experiences in authentic scientific research in real time, including opportunities for direct communication with STEM professionals (e.g., Green & Medina-Jerez, 2012;Phillips, Ferguson, Minarchek, Porticella, & Bonney, 2014;Raddick et al., 2010). However, citizen science projects are often designed primarily as contributory projects with a focus on the collection of highquality data, limiting interactions with emergent technologies to data practices (Bonney et al., 2009). Several scholars call for co-created projects to be designed to enable learners to participate with more science in K-12 STEM curricula, designing projects with ethical considerations of informed consent and participation, and thoughtful consideration of design elements that foster authentic experiences and collaborative learning (Hiller & Kitsantas, 2014;Martin, 2017;Reiheld & Gay, 2019;Sturm et al., 2018). Scholars also point out overlaps between inquiry-driven STEM practices and civic engagement, making a case for citizen science to be incorporated into K-12 STEM curricula and instruction with thoughtful support for teachers through professional development so that technological tools can be used by learners in ways that promote STEM and civic engagement, along with motivation to take on real-world ecological challenges (Condon & Wichowsky, 2018). Below, we examine CP as a pedagogical approach within a technology-mediated citizen science experience incorporated into STEM curricula. Type of project Primary characteristics STEM example Action Encourages participant intervention in local concerns; co-created or citizen-created; civic engagement Monitor my watershed: Watershed monitoring utilizing mobile apps to collect and analyze water quality Conservation Place-based; contributory or collaborative, with emphasis on stewardship; affiliated with larger state or federal agencies iNaturalist app: Identifying wildlife or specific species utilizing mobile apps contributing toward creation of extensive field guides Investigation Focused on scientific research and data collection; regional to international; contributory, with emphasis on data collection IveGot1 Bugwood app: Helping scientists track spread of invasive species utilizing apps that document photos or GPS locations Virtual Technology-mediated, with no physical world component; contributory, with emphasis on data collection Planet four, Zooniverse: Contributing to exploration of the surface of Mars by examining images from NASA and sharing observations as input Education Explicitly education-oriented; offer learning resources and cumulative learning experiences; contributory and collaborative; affiliated with multiple partner organizations Journey north: Collecting, sharing, and analyzing evidence about seasonal change by utilizing websites apps to track and share observable changes

These data were then uploaded to the WeatherBlur platform and further analyzed using web-based mathematics learning tools, with learners working in teams to create graphs and charts to answer specific questions based on their data. Utilizing WeatherBlur as a data repository, learners were also able to compare data from their water samples to data from samples from different locations. Students used a combination of online and offline resources to further examine microplastic pollution in marine food webs and effects on the environment. Based on their learning experiences, learners initiated the idea of a culminating presentation that they could share with the community and decided to make a collaborative documentary. Experts from the larger community were virtually interviewed for the documentary using Skype. In addition to presenting their documentary to a large community audience, learners also presented posters at a state-wide summit hosted by WeatherBlur, where they interacted with peers from other participating schools.

In another investigation, learners were interested in examining which organisms got caught with lobsters in traps. They utilized WeatherBlur as a platform to document details, such as the species, number, sex, and sizes of creatures caught in lobster traps, and through partnerships with local fishermen, they found a large number of invasive European green crabs as the primary bycatch. They used mobile data tools to upload, share, and comment on data in multimedia formats (e.g., numerical, photo, and video). Learners also used various web-based graphical analysis tools to help interpret and connect participant-posted data to scientific data related to climate and the oceans, such as temperature and depth of the water. This project subsequently "evolved into a green crab information exchange between students, fishermen, scientists, and community members sharing the number of green crabs caught per trap" (Kermish-Allen, Peterman, & Bevc, 2019, p. 629). Learners' contributions of data and findings supported the Governor's creation of a state-wide task force to further investigate the impact of the invasive green crab species on fisheries.

Examining the WeatherBlur investigations as examples of CP enables us to further explore the social and collaborative contexts embedded in the learning experience. For example, the web-based platform integrated citizen science with participant-driven inquiry, empowering learners by providing opportunities for them to pose questions based on their observations and communities. Through CP, learners were able to engage in multiple ways, posing questions on the iWonder space, for example, where they received immediate feedback from the larger community, which helped refine their questions toward inquiry of specific, investigable topics. Interactions with this larger community of peers, teachers, researchers, community members, and professionals further empowered learners to pose questions driven by curiosity and to engage with peers and experts as they collaboratively designed various aspects of their investigation. Learners were, for example, invited to engage in online discourse as members of a larger community, and were mentored by experts throughout their investigations, all of which shaped how learners participated as members of a STEM CoP. Table 5 demonstrates how various activities in WeatherBlur provided rich opportunities for CP through integration of STEM practices and computational thinking. as they participated as members of STEM CoPs. We now discuss implications of CP in STEM education, making a case for CP as a powerful epistemological and pedagogical approach that promotes STEM practices within a technologically-enhanced learning environment.

We argue that focusing on the T in STEM through CP enables an explicit emphasis on computational and collaborative aspects of interactions with technologies in use, where the learner-technology pairing promotes scientific investigation and knowledge-building as well as positive learning behaviors and STEM practices.

In the Whypox and WeatherBlur investigations, interactions with technology as tools, practices, and pedagogical approaches (such as web-based tools, mobile apps, and serious educational gaming) were significant in shaping the perceptions of STEM learners in digital and physical worlds. Creating an awareness of how technologies are inherently connected to practices driving scientific investigations, CP offered learners opportunities to develop knowledge of STEM concepts and practices as they collaboratively investigated phenomena in digital and physical spaces. CP as an epistemological and pedagogical approach leveraged different layers of technology-mediated relations as STEM learners collaboratively developed skills and knowledge of concepts and practices across digital and physical worlds. Several scholars have called for the thoughtful integration of technologies in STEM education, embedding technological tools and artifacts into curricula with intentional pedagogical approaches to maximize learning outcomes (e.g., Annetta et al., 2007; Hiller & Kitsantas, 2014; Sturm et al., 2018; Weintrop et al., 2016). CP as an epistemological and pedagogical approach integrates a wide range of technologies in STEM education as both tools and practices, with thoughtful consideration of how technologies mediate perceptions and knowledge construction. While CP provides opportunities to utilize various technologies to create learning environments that are motivating and engaging for STEM learners, the primary pedagogical strength of CP is that it requires learners to collaboratively interact with technologies, where the learner-technology pairing shapes learning behaviors through authentic STEM practices. CP addresses the need for integrated STEM instruction to engage learners in solving real-world problems by applying STEM practices toward finding innovative solutions to these problems (Brown et al., 2011;Kelley & Knowles, 2016;NRC, 2012) and creating multiple opportunities for learners to engage in STEM practices through discipline-specific and social, collaborative interactions. Kafai and Burke (2014) envisioned CP empowering learners by creating pathways for learners to participate as producers, consumers, and distributors of digital technologies. We extend this vision of CP to STEM education, where CP empowers learners to engage with technological tools, applying them through authentic STEM practices that mirror practices of STEM professionals, such as real-time virtual participation in ongoing STEM projects and inquiry-driven investigations into ongoing research and development efforts at the STEM frontline. 7.3 | CP empowers learning in a STEM CoP Whyville and WeatherBlur provide robust examples of web-based platforms offering immersive STEM learning experiences designed as shared community experiences. Both Whyville and WeatherBlur offered learners a larger virtual community to interact with, which included peers, teachers, mentors, members of local and global communities, and STEM professionals. As learners engaged in Whypox and WeatherBlur investigations, learners collaborated with a subset of their virtual and real-world communities. Through these interactions, STEM communities of practice were formed, fulfilling all of Wenger's (1998) defining characteristics of CoPs. First, these CoPs centered on a shared domain of interest defined by the investigation. Students continually built community through both virtual and inperson collaborative interactions. Online discourse in both examples supported learners' participation as members of a STEM CoPs, providing opportunities for direct communication with experts and peers.

In this paper, we explored the nature of the T in STEM, with a focus on learner behaviors and the potential of technology-mediated experiences with CP in shaping STEM learning. In particular, by de-

emphasizing the what about technology and bringing renewed focus to the how associated with these technologies, we make a case for CP as an epistemological and pedagogical approach that integrates technologies as tools and practices, with a focus on how they are used in STEM practices among students and STEM professionals alike.

Emphasizing both computational and collaborative components of the learning experience, CP provides STEM learners authentic opportunities to interact with emergent technologies and with each other as members of a STEM CoP. On the one hand, technology forms an integral part of the everyday lives of learners as they increasingly engage with technological tools, devices, and practices in social contexts. On the other hand, technology-mediated relations continually shape behaviors that are central to the practices of STEM professionals using interdisciplinary and transdisciplinary approaches to real-world issues in STEM contexts. Considering the T in STEM through a CP lens provides a natural link between technology-mediated hermeneutic and alterity relations and authentic STEM practices. Therefore, as we reflect on the T in STEM, and as STEM continues to evolve as an interdisciplinary and transdisciplinary entity in education, we propose that CP be leveraged in STEM contexts toward a perspective of technology as mediating innovative, novel approaches to complex problems in the real world, with learners collaboratively engaged as members of a STEM CoP.