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Introduction

The impact of computing can be seen all around us. This impact is perhaps most visible to people in moments when they engage directly and intentionally with computing, such as using a computer to access information, using GPS to navigate while driving, and using a smartphone to connect and engage through social media. Overall, the pervasive use of computing has dramatically transformed our personal, professional, and public lives.

Today’s learners will enter as adults into a world that is different from the world as it is today. Progress in computing has led to significant advances (e.g., artificial intelligence and automation), and these technologies will continue to evolve. It is even possible that programming may become less essential. A recent report by the National Academies of Sciences, Engineering, and Medicine ([NASEM], 2018a) defined computing as a “term used broadly to refer to all areas of computer science, all interdisciplinary areas computer scientists work in, and all fields using computer science or computational methods and principles to advance the field. This includes both academic and occupational fields, such a bioinformatics, medical informatics, library sciences, digital archives, computational sciences, and more” (p. 17).¹ Because nearly all jobs involve computing, it is important to consider the ways computing will likely continue to shape the future workforce and how learners today are educated. Realizing the

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¹ This report also defined computer science as “[T]he study of computers and algorithmic processes, including the principles, their hardware and software designs, their applications, and their impact on society” (NASEM, 2018a, p. 17).

profound impact of computing on the future workforce has led to debates centering on the importance of learners engaging with computing in the present. These debates have largely been positioned in two ways: workforce development and computing literacy for civic participation.

When considering workforce development, it is important to envision the potential workforce needs, given advancements in technology and computing (Guzdial, 2015). A number of federal strategy documents produced over the past couple of years acknowledge the trends and needs for growing sectors of the workforce (e.g., artificial intelligence, computer science, data science). For example, the development of the computing workforce is called out in the 2019 report National Artificial Intelligence R&D Strategic Plan: 2019 Update² and the 2018 report National Strategic Overview for Quantum Information Science.³ Throughout these reports, and others (see the 2019 report National Strategic Computing Initiative Update 2019⁴), there is an emphasis on developing a more diverse workforce, stemming from recognition that the science, technology, engineering, and mathematics (STEM) workforce, including computing, has lacked representation among women and individuals of color.

In addition to workforce development, debates about learner engagement in computing have also focused on the development of computing literacy for civic participation. The 2018 report Charting a Course for Success: America’s Strategy for STEM Education produced by the National Science & Technology Council⁵ stresses that computing is a necessary critical skill for understanding our changing technological and social landscape (Lee and Soep, 2016; Vee, 2013). Computing literacy goes beyond simply knowing how to use a computer or engaging with technology, it also includes being able to use computing to make and create new products (Ito et al., 2019; Kafai, Fields, and Searle, 2019; Rushkoff, 2010). As learners continue to have computing opportunities that leverage “making with,” they are able to learn and understand the complex issues and approaches that are necessary for true digital literacy (Buckingham, 2007, 2013; Guzdial et al., 2012; Marty et al., 2013).

These shifts in computing have also emphasized the need to develop skills that are essential to life in the 21st century, calling attention to the need to emphasize teaching computational thinking (Blikstein, 2018;

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² The full report is available at https://www.whitehouse.gov/wp-content/uploads/2019/06/National-AI-Research-and-Development-Strategic-Plan-2019-Update-June-2019.pdf.

³ The full report is available at https://www.whitehouse.gov/wp-content/uploads/2018/09/National-Strategic-Overview-for-Quantum-Information-Science.pdf.

⁴ The full report is available at https://www.whitehouse.gov/wp-content/uploads/2019/11/National-Strategic-Computing-Initiative-Update-2019.pdf.

⁵ The full report is available at https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf.

Buitrago Flórez et al., 2017; Shein, 2014; Vogel, Santo, and Ching, 2017; Wing, 2006). Wing (2006) described computational thinking as: (1) conceptualizing, not programming, (2) fundamental, not rote skill, (3) a way that humans, not computers, think, (4) complements and combines mathematical and engineering thinking, (5) ideas, not artifacts, and (6) for everyone, everywhere. Conversations continue to focus on how to define these important skills, whether as a set of practices emerging from computer science (Wing, 2006), a set of dispositions (Computer Science Teachers Association [CSTA] and International Society for Technology in Education [ISTE], 2011), or a way of thinking (National Research Council [NRC], 2011a). Although its boundaries and definitions are often contested (Tedre and Denning, 2016), there is some consensus that computational thinking is a valuable skill for learners to engage in an increasingly technological and computational world (Grover and Pea, 2013; NRC, 2011a).

Computational thinking can also be valuable as a method for developing disciplinary understanding (diSessa, 2000; Papert, 1980). Computational thinking includes engaging in logical thinking and problem-solving and is observed in national standards for mathematics (i.e., Common Core State Mathematics Standards)⁶ and science (i.e., Next Generation Science Standards [NGSS]; NRC, 2013).⁷ As such, there is a close resemblance between aspects of scientific inquiry and aspects of computational thinking, such as data collection and analysis. In engineering, for example, the computational thinking practices of defining problems through abstraction and approaching solutions systematically parallel typical applications of working with robots and testing solutions iteratively.

In the past decade, computer science (CS) education has been, and continues to be, supported broadly through public policy and a growing number of public K–12 institutions. For example, the increased interest and emphasis in computing and CS education is observed nationally through broader educational initiatives such as the CSforALL movement as well as through private-sector support (see Box 1-1).⁸ It is also observed in the creation of a framework for K–12 CS education and subsequent national standards.⁹ These national endeavors have called for the development of interdisciplinary approaches to the integration of computing within STEM teaching and learning; building capacity within the educational system to support CS education; and examining ways to broaden access and partici-

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⁶ For specific information regarding the mathematics standards, see http://www.corestandards.org/Math.

⁷ For specific information regarding the science standards, see https://www.nextgenscience.org.

⁸ For more information, see https://www.csforall.org.

⁹ For more information on the framework, see https://k12cs.org/ and for more information on the standards, see https://www.csteachers.org/page/about-csta-s-k-12-nbsp-standards.

pation of learners who have been historically underrepresented based on gender, race, ethnicity, or perceived ability.

Overall, the push has been for learners to develop the skills and competencies that are reflective of the discipline (i.e., professional authenticity). As programs in and out of school continue to develop and provide learners with opportunities to engage in computing experiences, it is essential to consider how the design of these experiences develop interest and competencies for computing. Learners’ technology experiences have been dominated by playing and tinkering with commercial games, software, and social media platforms since they first began gaining access to computers in the 1980s (Ito, 2009). Authentic, open-ended learning activities—through project- or problem-based learning and makerspaces—have been offered as an approach to support broader access to STEM learning and can catalyze interests and learning in computing (Calabrese Barton, Tan, and Greenberg, 2017; Capraro and Slough, 2013; Dischino et al., 2011; LaForce, Noble, and Blackwell, 2017; Resnick, 2017). These open-ended experiences are “authentic” in the sense that they are designed to reflect the practices of the discipline; that is, they are close approximations to the work that a STEM professional would engage in. In addition to approximating the work of the professional, there has been increasing attention to designing authentic STEM experiences so that they are connected to real-world problems learners’ care about and the challenges they face.

In the past decade, making and makerspaces have emerged as a movement within and outside of learning spaces (see Box 1-2). Whether in formal or informal educational settings, many maker efforts are oriented toward STEM skills and workforce development (Blikstein, 2013b; Martin, 2015; National Science Foundation, 2017; Vossoughi and Bevan, 2014), where making is the vehicle to STEM + Computing (STEM+C) careers or to accessing new opportunities (Fancsali et al., 2019). Within learning spaces, making has been embraced by both in-school and out-of-school communities, in early childhood all the way to higher education (Fields and Lee, 2016; Peppler, Halverson, and Kafai, 2016). Proponents and some empirical research suggest that collaboration (Clapp et al., 2017), interest, content, and practices oriented toward STEM (Kafai, Fields, and Searle, 2014; Sheridan et al., 2014), failure, persistence, and iteration (Maltese, Simpson, and Anderson, 2018; Ryoo and Kekelis, 2018; Ryoo et al., 2015), and a number of other skills are fostered through maker learning approaches and environments (Fancsali et al., 2019).

Research suggests that authentic STEM experiences may foster the development of deep conceptual understanding and skills for STEM disciplines (NRC, 2009; 2014; 2015). That is, through these types of experiences, which may be more intrinsically motivating, individuals learn how to identify a problem or need as well as how to plan, model, test, and iterate

solutions, all of which makes their higher-order thinking skills tangible and visible (Bennett and Monahan, 2013). Moreover, emerging research has begun to examine the ways in which these types of activities, rooted in authenticity, have the potential to invite in learners from underrepresented communities (based on gender, race, ethnicity, or perceived ability) in STEM fields, particularly computing (Lim and Calabrese Barton, 2006; Migus, 2014). As such, it is important to understand the ways in which authentic STEM experiences can develop interests and competencies for computing.

CHARGE TO THE COMMITTEE

Sponsored by Google and the Grable Foundation, the Board on Science Education (BOSE) of the National Academies of Sciences, Engineering, and Medicine, in collaboration with the Computer Science and Telecommunication Board (CSTB) convened an expert committee to examine the evidence on the ways in which authentic STEM experiences develop interest and competencies for computing (see Box 1-3). The 16-member expert Committee on the Role of Authentic STEM Learning Experiences for Developing Interest and Competencies for Computing included individuals with expertise in the design and construction of learning spaces in formal and informal educational settings that are aimed at providing opportunities to engage in STEM and computing. The expertise spans the K–12 range in a number of important areas, including disciplinary knowledge in science,

mathematics, and computer science; the development of curriculum; teacher professional learning and development; as well as perspectives on issues around diversity, equity, and inclusion.

STUDY APPROACH

The committee met six times over a 15-month period in 2019 and 2020 to gather information and explore what is known about the role of authentic experiences for the development of interest and competences for computing. During this time, the committee reviewed the published literature pertaining to its charge and had opportunities to engage with many experts. Evidence was gathered from presentations and a review of the existing literature (including peer-reviewed materials, book chapters, reports, working papers, government documents, white papers and evaluations, and editorials) and previous reports by the National Academies (see Box 1-4).

The committee searched for information on a number of different outcomes for computing as well as on the different features of design and the institutional/organizational contexts that can facilitate or hinder learners’ participation in authentic experiences. When looking at particular outcomes, the committee focused on a number of cognitive, behavioral, and affective outcomes that included interest, identity/belonging, motivation, self-efficacy, knowledge and skills, engagement, and persistence and retention.

In reviewing the evidence, the committee sought to assemble a set of studies that represented the extent of available evidence. A search was conducted through Scopus requesting studies from the past two decades (2000–2020) and limited to English. The review focused on literature and programs centered around three categories of outcomes: (1) affective (such as interest, identity/belonging, motivation, and self-efficacy); (2) cognitive (such as knowledge and skills); and (3) behavioral (such as engagement, persistence, and retention). Details regarding this search can be found in Appendix A.

Many different types of studies were included in this review: meta-analyses and reviews, qualitative case studies, ethnographic and field studies, interview studies, and large-scale studies. The committee recognized that the literature consisted predominantly of studies that were largely descriptive in nature with few studies that could demonstrate causal effects. As appropriate, throughout the report, the evidence is qualified to articulate the type of research being reviewed and its strength.

Throughout the study, members of the committee benefited from discussion and presentations by a number of individuals who participated in the fact-finding meetings. At the first meeting, the committee had an opportunity to speak with the sponsors to ask questions and get clarity on the

statement of task. In particular, the committee wanted to better understand the sponsor’s stance on authenticity and what could be included in the range of authentic experiences. The committee also had the opportunity to hear more about the framing and state of evidence with respect to equitable access to authentic STEM opportunities.

During the second meeting, the committee considered the ways in which robotics competitions and engineering programs were reaching their participants either through the design of the experiences or through outreach efforts to ensure that girls and learners from minoritized groups had

access to these programs. Additional presentations described what is known about hobbies and their relationship to different STEM outcomes and the implications of this research for computing.

During the third meeting, there was a large public workshop that focused on a number of key issues. In particular, the presenters were asked to unpack the state of evidence on (1) the role of STEM learning opportunities, (2) promising approaches and strategies in the development of interest and competencies, and (3) what these mean for the goals, design, and implementation of such experiences for computing. A recurring theme throughout the workshop was the importance of evidence that emphasized the implications for increasing access for learners from minoritized communities.

At the fourth meeting, the committee discussed the draft of the report and reached consensus on a number of key issues (described in the previous section). In between meetings, the committee had in-depth conversations with several youth-serving STEM programs to understand their design and their evidence with respect to impacts on the desired learner outcomes. Additionally, members of the committee conducted structured interviews with several young adults who have pursued or are immediately pursuing computing and technology-intensive postsecondary education.¹⁰ These illustrative cases were intended to provide some longitudinal, retrospective data that could highlight aspects of the individual experiences that led the learners to persist in computing. These cases do not reflect the experiences of individuals who have opted not to persist (for a variety of reasons; see Chapter 2).

At the fifth meeting, the committee reviewed the draft report to ensure that there was sufficient evidence for the claims being made. As stated above, throughout the report, the type of research reviewed, and the strength of research evidence, are clearly articulated. The majority of the committee’s final meeting was devoted to discussing the conclusions, recommendations, and research agenda to reach consensus. During these discussions, the committee was careful to qualify and temper the conclusions and subsequent recommendations, based on the type and strength of the evidence presented.

REPORT ORGANIZATION

This report examines the research on authentic experiences in computing for learners in grades K–12 across formal and informal settings. Chapter 2 examines the structural (e.g., racism and sexism) and cultural barriers

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¹⁰ The committee sought IRB approval from the Institutional Review Board, and it was determined that the protocol qualified for exemption from IRB review, under category 2(i, ii) on March 5, 2020. Pseudonyms are used to protect confidentiality.

(e.g., stereotypes and implicit bias) to participation in computing that exist at multiple levels. These barriers to participation impact the nature of the learner’s experiences and their development of a computing identity. Chapter 3 articulates the committee’s theoretical framing that describes the varied factors that influence whether and how learning is positioned to develop interest and competencies for computing whereas, Chapter 4 presents the evidence on how individual programs or individual curricula speak to the intended outcomes of interest and competencies. Chapter 3 also describes the need for adopting an ecosystems approach to understanding learner’s contexts and motivating factors and how these may lead to continued pursuits with computing.

Chapters 5 and 6 describe the institutional and/or organizational contexts that provide the necessary infrastructure for learners to engage in educator-designed authentic experiences for computing. Chapter 5 focuses on authentic experiences that occur outside of school time and emphasizes the strengths and challenges with respect to ensuring equitable access to these programs. Chapter 6 describes the factors (e.g., school funding, teacher preparation, standards and certifications) that influence whether and how authentic experiences for computing are offered in formal educational contexts.

Chapter 7 then provides guidance on how to design authentic experiences for computing given the evidence and organizational constraints. Chapter 8 presents the consensus conclusions and recommendations that are derived from the evidence provided in earlier chapters and articulates an agenda for future research.

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