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Implications of the Research for Designing Integrated STEM Experiences

The recent surge of interest in designing programs that successfully engage students in integrated STEM learning experiences has created a demand for guidance about what constitutes “effective” integrated STEM education. Yet, as evidenced in the previous chapter, research on integrated STEM is at the preliminary stages and there are few large-scale studies that systematically compare different approaches to integration. However, the smaller-scale research efforts in the field can be supplemented with relevant findings from research on cognition, learning, and teaching to formulate hypotheses about how to design effective integrated STEM learning experiences and the limitations that need to be considered.

In this chapter we identify implications for design based on the research reviewed in the previous chapter, as well as evidence related to cognition and learning more generally. In the first section, we explore research on how people learn in order to determine how integrated experiences in STEM might support learning, thinking, interest, and identity development, and, conversely, why they might do little to change students’ attitudes, thinking, and behaviors.

Drawing on these discussions together with the research findings and limitations reviewed in Chapter 3, we identify issues related to designing integrated STEM experiences so that they more effectively support learning within and across the STEM disciplines. We also lay out important areas for future research and development.



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4 Implications of the Research for Designing Integrated STEM Experiences T he recent surge of interest in designing programs that successfully engage students in integrated STEM learning experiences has created a demand for guidance about what constitutes “effective” integrated STEM education. Yet, as evidenced in the previous chapter, research on integrated STEM is at the preliminary stages and there are few large-scale studies that systematically compare different approaches to integration. However, the smaller-scale research efforts in the field can be supplemented with relevant findings from research on cognition, learning, and teaching to formulate hypotheses about how to design effective integrated STEM learn- ing experiences and the limitations that need to be considered. In this chapter we identify implications for design based on the research reviewed in the previous chapter, as well as evidence related to cognition and learning more generally. In the first section, we explore research on how people learn in order to determine how integrated experiences in STEM might support learning, thinking, interest, and identity development, and, conversely, why they might do little to change students’ attitudes, thinking, and behaviors. Drawing on these discussions together with the research findings and limitations reviewed in Chapter 3, we identify issues related to designing integrated STEM experiences so that they more effectively support learning within and across the STEM disciplines. We also lay out important areas for future research and development. 77

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78 STEM INTEGRATION IN K–12 EDUCATION INTEGRATED EXPERIENCES AND HOW PEOPLE LEARN1 In this section we draw on a substantial body of research on cognition and learning to explore the mechanisms by which integration might support, or be an obstacle to, learning within and across the STEM disciplines. Several decades of research in cognitive psychology, the learning sciences, educational psychology, curriculum and instruction, and other fields have shed light on how the mind works and how best to support learning. This research provides a foundation for understanding how and why integrated STEM experiences can support improvement in learning and thinking, where they might pose difficulties for learners, and how they can be designed to be more effective. The committee considered findings from studies on learning and teaching across a range of research traditions including those informed by situative, sociocultural, cognitive, pragmatist, and constructivist theoretical perspectives. Findings obtained using diverse research methods, applied across several fields and perspectives, converge to create a picture of learning as an active process that is deeply social, embedded in a particular cultural context, and enhanced by intentional support provided by more knowledge- able individuals, be they peers, mentors, or teachers. Based on what is known about cognition and learning, it is possible to hypothesize both advantages and disadvantages for learning from inte- grated experiences. But such experiences have only recently become a focus of research in STEM educational contexts, so important research questions remain. These are discussed in the final chapter of this report. We begin with a discussion of key basic processes of cognition and learn- ing and their implications for integrated instruction—how it supports learn- ing and where it might introduce challenges. It appears that integration can be effective because basic qualities of cognition favor connected concepts over unconnected concepts; the former are better organized for future retrieval and meaning making than the latter. But it can also impede learning because it (1) places excessive demands on resource-limited cognitive processes such as attention and working memory, or (2) attempts to make bridges between ideas that were not well learned, or (3) obscures important differences in STEM disciplines about how knowledge is constructed and revised. 1 This section is based on the review of the cognitive sciences literature conducted by Eli M. Silk and Christian D. Schunn, University of Pittsburgh, and on commissioned papers by Mary Gauvain, University of California, Riverside, Angela Calabrese Barton, Michigan State University, and Steven Marc Weisberg, Temple University.

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 79 Given the focus of this report and the breadth of the research on cogni- tion and learning, it is impossible to provide a detailed review of all the cur- rent research (for a summary see NRC 2000). Instead, we focus on the aspects of cognition and learning that, in the committee’s judgment, are relevant to an understanding of integrated STEM. Building Connected Knowledge Structures A major insight from research on cognition and learning is that the organiza- tion of knowledge—that is, the ability to make connections between concepts and representations2—is key to the development of expertise in a domain. Multiple studies have shown that experts do not just know more about a domain, they understand how ideas are related to each other and their rela- tive importance and usefulness in the domain. They also notice features and meaningful patterns of information in the context of their field of expertise that are often not noticed by novices (see NRC 2000, Chapter 2, for a sum- mary of research on expert knowledge). This organized knowledge gives experts multiple advantages for thinking and learning. For example, when they approach a new problem they are able to attend to its deep, structural aspects rather than surface features (Chi et al. 1981) and thus connect new tasks or concepts to prior experiences more readily and more meaningfully. The importance of organized knowledge relates directly to some of the aims of STEM integration described in Chapter 3, such as helping students connect ideas learned at different stages of project-based learning or devel- oping students’ representational fluency. Thus one way to frame the goals of learning is to think of it as helping novices build and reorganize their knowl- edge to develop more expertlike competence in a domain. For integrated STEM it is important to determine how to help students both build knowl- edge in individual disciplines and learn to make connections among them. The foundation of knowledge building and rebuilding is the learner’s experience. All new knowledge builds on existing knowledge and involves making connections from previous experiences to the current context (NRC 2000, 2007). But learners often do not spontaneously relate the knowledge they possess, however relevant, to new tasks, a phenomenon referred to as a problem of transfer (see the discussion of transfer below as well as NRC 2  A representation expresses or symbolizes an idea or relationship. Examples of represen- tations include drawings, schematics, graphs, and mathematical equations.

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80 STEM INTEGRATION IN K–12 EDUCATION 2000, Chapter 3); they often need cues or explicit supports to help them make connections. One emerging view (e.g., Koedinger et al. 2012; Rau et al. 2012) is that integrated approaches benefit individuals who already have knowledge per- tinent to the integrating elements, whereas individuals with limited knowl- edge are less adept at building connections among conceptual structures. This situation can produce so-called aptitude-treatment interactions; that is, an intervention produces different results depending on an individual’s initial level of knowledge or skill (e.g., Cronbach and Snow 1977; Serlin and Levin 1980). Integrated STEM experiences vary depending on whether they are designed to target discipline-specific knowledge and skills or to support integration of knowledge across disciplines. In some cases a context or activity incorporates knowledge, and requires use of practices from more than one discipline, but students are expected to demonstrate learning gains in only one discipline. In other cases, experiences are designed to help stu- dents advance in more than one discipline, but students are not expected to ­demonstrate an ability to make connections across disciplines. And a smaller number of integrated experiences are designed to help students make and demonstrate connections between ideas across disciplines. Depending on the outcomes of interest, an integrated learning experi- ence should take account of students’ knowledge within individual disci- plines as well as help them make connections between disciplines, drawing on the disciplinary knowledge they already possess. Transfer Transfer is one of the principal goals of learning in school: students should be able to take the knowledge and skills learned in one context and apply them in another. Typically, teaching for transfer aims to increase transfer within a discipline. Integrated STEM educational experiences, by design, ask students to engage in the transfer of disciplinary knowledge and, ideally, enable the students to reliably transfer their knowledge to other areas and activities in the future. Transfer can be explored at a variety of levels—from one context to another, one set of concepts to another, one school subject to another, one year of school to another, across school, and to everyday nonschool activi- ties. A recent NRC report on transfer in the context of learning 21st century

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 81 skills (NRC 2012) found that there is little research on how to help learners transfer competencies learned in one discipline or topic area to another. The report identifies features of instruction that may support transfer (NRC 2012, p. 9): • Using multiple and varied representations of concepts and tasks • Encouraging elaboration, questioning, and explanation • Engaging learners in challenging tasks • Teaching with examples and cases • Priming student motivation • Using formative assessment Many of these features are present in integrated STEM programs, but research is needed to assess whether and how they support development of both disciplinary competence and the ability to make connections across disciplines. Integrating Across Multiple Representations Representations that express or symbolize an idea or relationship are an important element of disciplinary knowledge and can facilitate learning. In STEM disciplines, each form of representation highlights or amplifies an aspect of a natural or designed system while simultaneously reducing or summarizing its essence (Latour 1999). Within a discipline, the development of connections among different representations is an important way in which disciplinary knowledge grows (Latour 1999, p. 24). Kozma and colleagues (2000) reported on integrative thinking among chemists who made explicit and implicit connections between a structural drawing, an experimental design, and data and used language to support these connections. Conse- quently, the chemists were able to reason with one representation (a draw- ing), while making inferences about another (e.g., a spectrum). In integrated STEM learning experiences, students often need to make connections across different kinds of representations from a single discipline and learn to recognize how representations from different disciplines are related. For example, high school geometry students who use interactive software such as the Geometer’s Sketchpad® may construct multiple cases for exploring invariant relations that exist when a triangle is inscribed in a circle. The initial representations will start out as visual-spatial, but the stu-

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82 STEM INTEGRATION IN K–12 EDUCATION dents may be called on to present verbal proof in support of their conjectures (Nathan et al. 2013), thus demonstrating connections between visual and verbal representations in a single discipline. Similarly, students participating in an engineering project on ballistic behavior may use geometry modeling software, such as AutoDesk, to formalize a sketch into the design of a device such as a catapult. They will make connections across disciplinary bound- aries when they relate the specifications of the device created in the CAD/ CAM system (technology) to trigonometric relations (geometry) used in the quadratic equations (algebra) that model the kinematic laws (physics) that specify the ideal trajectory of the ballistic flight. Psychological research has shown important benefits for learning and performance in people who make connections between multiple representa- tions of a particular concept or relationship. Evidence from both behavioral (e.g., Griffin et al. 1994; Stenning and Oberlander 1995) and neuroscience research (e.g., Dehaene et al. 1999) points to a dual system of linguistic and spatial representations that supports mathematical reasoning. Tabachneck (1992; Tabachneck et al. 1994) showed that an expert in economics success- fully conveyed an economic situation that was thought to be out of reach for novices by combining graphical and verbal representations. Schwartz (1995) found that the availability of multiple representations played a key role in students’ generation of abstract representations. And Case and Okamoto (1996) demonstrated that when children form an integrated conceptual understanding, they exhibit new capabilities. For example, they can under- stand a concept presented in one modality using their understanding of another system that shares deep conceptual structure but has vastly different surface features and operations. Each of these cases represents discipline- specific integrative thinking. Learning from Real-World Situations One hallmark of integrated approaches, though not unique to them, is the use of real-world situations or problems. They can bring STEM fields alive for students and deepen their learning, but they may also pose particular challenges for them. There is evidence that use of detailed concrete situations with rich perceptual information can prevent students from identifying the abstract structural characteristics needed to transfer their experiences to other settings. Goldstone and Sakamoto (2003) and Sloutsky and colleagues (Kaminski et al.

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 83 2005, 2006a, 2006b; Sloutsky et al. 2005) found disadvantages to increasing levels of perceptual richness, especially when the added features were irrel- evant to the structural features the students were meant to learn. Goldstone and Sakamoto (2003) found that the effects of the perceptual richness differed depending on students’ initial capabilities: students who were already able to attend to the abstract features of a situation were unlikely to be distracted by perceptual richness, whereas those who had difficulty grasping the abstract information were more likely to be distracted by superficial features. It may be that real-world situations can be designed to encourage students to attend to the critical (as opposed to irrelevant) features of the situation. Kaminski and colleagues (2009) tested this idea by having stu- dents learn a mathematical rule either with entirely generic materials, so the rule’s connection to the symbols was entirely arbitrary, or with materials in a familiar context that follows the rule (in this case pictures of beakers of liquid combined with some left over). They found again that the relevant concreteness had advantages for learning of the particular rule, but that the generic materials resulted in better transfer to another context that followed the same rule but in which the objects didn’t compel the rule as they had in the relevant concrete learning materials. Although Kaminski and colleagues do not test claims about rich contexts directly (their “concrete” condition is an abstract image meant to resemble real objects), this work does reveal some of the trade-offs for perceptually rich and lean curriculum materials when measuring learning and transfer. One implication of this research is that classroom instruction designed to promote the use of knowledge across different contexts should include instruction in the abstract or generic representations of the concept being taught. Teachers should not expect students to be able to infer the under­lying symbolic or abstract representation of a problem by solving the problem using a single concrete instantiation (e.g., Goldstone and Sakamoto 2003; Goldstone and Son 2005; Kaminski et al. 2006a, 2006b; Sloutsky et al. 2005). Cognitive Limitations: Attention and Memory Research in cognitive psychology demonstrates that the amount of informa- tion a learner can simultaneously attend to and process deeply is very limited (Anderson 1996, 2004; Miller 1956). One’s intellectual abilities can appear to be outsized, however, when one effortlessly perceives information as connected and meaningful. Strings of random numbers will quickly exceed

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84 STEM INTEGRATION IN K–12 EDUCATION an individual’s information processing capabilities, unless s/he can readily group them in familiar “chunks,” such as important dates or the time (in minutes and seconds) needed to run a race of a certain distance (Ericsson et al. 1980). When presented with multiple sources of information, learners must direct their attention to each individual source, encode separate pieces of information, manage the stored information, and discern the relevant con- nections. Split attention—simultaneously dividing one’s attention between competing sources of information—is cognitively demanding and can be a major obstacle to understanding and learning. The split-attention effect is evidenced by difficulties in storing and processing information that is physi- cally separated (Mayer 2001; Mayer and Moreno 1998; Sweller et al. 1998). But it can be remedied: student learning improves when individual sources of information are visually integrated so they can be processed together in a single image (Bobis et al. 1993; Chandler and Sweller 1992, 1996; Mayer and Anderson 1991, 1992; Moreno and Mayer 1999; Mwangi and Sweller 1998; Sweller et al. 1990). These aspects of cognition point to a potential drawback of integra- tion: without effective guidance, the effort to make connections among multiple disciplines in the context of a complex problem or situation could overwhelm students and inhibit learning. Design of integrated experiences must balance the richness of integration and real-world contexts against the constraints of the cognitive demands of processing information that is separated in time, in space, or across disciplines and types of representation. Learning by Doing and Embodied Cognition Integrated STEM experiences typically call on students to engage in activities that involve the use of tools or manipulation of objects, and claims have been made that this use enhances learning. Although such instructional strategies are widely used in mathematics education (e.g., Fuson et al. 2000; Clements 2000) and mathematics education research (Chao et al. 2000; Martin and Schwartz 2005; Uttal et al. 1997), there is little research in other STEM fields on the relationship between physical manipulation of objects and learn- ing, although some studies of physics learning do demonstrate benefits. One study reported that students who actually felt the angular momentum change when a rotating bicycle wheel was held performed better on written

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 85 tests about angular momentum than students who merely watched other students hold the bicycle wheel (Kontra et al. 2012). A related approach to understanding learning involves embodied cogni- tion, the perspective that cognition occurs in a physical organism interacting with its environment; to understand the structures that mediate learning, one must consider the brain, body, and environment as an interactive unit.  This approach considers forms of “embodied learning” such as gesture, sketching, and arranging objects, which can help mitigate the brain’s limited processing ability (Kirsh and Maglio 1994). Embodied experiences may provide pathways for coordinating math- FIGURE 4-1 Three complementary representations of rotaryelementary grades (upper left), ematical and scientific concepts. For example, children in motion: embodied (upper right), and mechanical/linkage (lower). SOURCE: Bolger et al., 2010. FIGURE 4-1  Three complementary representations of rotary motion: embodied (upper left), geometrical (upper right), and mechanical/linkage (lower). SOURCE: Bolger et al. (2010). Reprinted with permission.

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86 STEM INTEGRATION IN K–12 EDUCATION are often expected to understand how simple machines work. But developing mechanistic reasoning is challenging, and many elementary students fail to anticipate or explain how interactions among components of the devices account for how they work (Bolger et al. 2012; Metz 1985). Simple forms of embodiment and mathematical representation appear to substantially support the development of mechanistic reasoning. For exam- ple, in one study, children participated in rope walks, in which one student, “the holder,” acted as a fixed pivot (the fulcrum) and the other, “the walker” at the other end of the rope, as the end of a lever arm (Figure 4-1). When the child at the end of the rope attempted to walk in a direction perpendicular to the line joining the two children, the path was constrained to be circular. Challenged to represent the essential difference between ends of lever arms near and far from the fulcrum, by walking toward and away from the holder students came to see the usefulness of circles and their properties for describ- ing how linkages (connected levers) function (Bolger et al. 2010, 2011). Social Aspects of Learning and Cognition Social and cultural factors are fundamental to all learning experiences and particularly important in integrated experiences, which typically require students to work with each other and actively engage in discussion, joint decision making, and collaborative problem solving. Integrated STEM edu- cation often involves extensive collaboration among teachers and students, and therefore its success depends on the design and effectiveness of the social aspects of the approach. Social supports for learning are ubiquitous, occur in a variety of settings, and are present in all cultures (Lancy et al. 2009). Key ingredients for effective learning are the availability of appropriate support to help learners engage in an activity in a meaningful way, the gradual withdrawal of these supports as the learner’s competence increases, and instruction and guidance in the use of tools that support learning (NRC 2000). Social processes of learning are inherent to three major components of integrated STEM education: the participation of the learners, the assistance provided by the teacher(s), and the nature and meaning of the learning activ- ity itself. Because research has shown that not all forms of social experience are conducive to learning (Slavin 1983), careful attention to the design of social processes in integrated STEM education is essential.

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 87 The social contexts that support learning include the physical settings themselves and the social psychological processes that occur in these set- tings. Learning is promoted by many social processes—observation, imita- tion, regulation of joint attention, demonstration, instruction, and shaping. Research has shown, for example, that children learn how to solve problems, including how to attend to important features and the knowledge and strat- egies needed to solve problems, by observing more experienced partners solve similar problems. Research has also demonstrated that learning can result when social support is carefully arranged, learning is monitored, and adjustments are made if learning strays too far from the goal (Gauvain 2001). What children learn from observation and collaborative activity depends on their developmental status. Whereas preschool children benefit from assistance in understanding problems, following rules, and manipulating materials, school-age children gain more from help with strategies. Research suggests that the social psychological processes available in the learning environment, including the composition and activities of learning groups and the involvement of the teacher, are important design features for inte- grated STEM education. Social guidance and support for learning also exist in cultural tools that aid thinking and problem solving and in the type and structure of the learning activities in which children engage. Certain social processes that support learning involve deliberate efforts to convey knowledge and strategies. Among these are instruction in the zone of proximal development (Vygotsky 1978), scaffolding (Wood and M ­ iddleton 1978), and peer collaboration. The zone of proximal development (ZPD) is defined as “the distance between the actual developmental level as determined by independent prob- lem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers” (Vygotsky 1978, p. 86). The ZPD is the region of sensitivity for learn- ing in a particular domain. One of the primary means by which teachers and other more experi- enced partners (such as more advanced peers, older students or parents) support children’s learning in the ZPD is with a learner-focused instructional technique known as scaffolding, which involves verbal and nonverbal efforts tailored to the learner’s needs to help him or her engage with a challenging activity (Renninger and Granott 2005; Wood et al. 1976, 1978; Wood and Middleton 1975). For instance, an activity, such as planning errands, may be broken into a series of actions (Gauvain and Rogoff 1989) and strategies for solving the problem (how to do the errands in an efficient manner) modeled ­

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96 STEM INTEGRATION IN K–12 EDUCATION more narrowly defined views of science practice, thus limiting the opportu- nity for all students in the class to engage actively. In contrast, an ethnographic study of an urban magnet high school (Buxton 2005) shows how students resisted and transformed the identity of the “preferred student” and in doing so impacted the cultural, institutional, and structural features of their school. The students’ interactions with each other and with their teachers led to the development of new tasks and relationships that helped to change perceptions of what counted as a “good science student.” Teachers became more open to redefining what counted as successful student work, and course scheduling patterns were changed to address students’ needs and interests. These two studies suggest that teachers play a dynamic role in the devel- opment of science identity. In the comparative study by Carlone and col- leagues (2011), the teacher who privileged the sharing and vetting of ideas and tools over securing the right answer created more spaces for students to try out being scientific. The implication is that, even when teachers commit to enacting reform-based science, without explicit attention to the ways they support different possible identities it may be difficult to foster the kinds of identities that support meaningful learning. Looking across studies of science learning, there is evidence that class- room interventions and design experiments grounded in reform-based c ­ urriculum/pedagogy and intended to explicitly incorporate students’ iden- tities in instruction can positively impact learning and identity. Calabrese Barton and Tan (2009) report that one teacher’s approach to a reform-based inquiry unit on dynamic equilibrium and the human body led students not only to learn the relevant concepts but also to exhibit strong science identities. Using a design experiment approach, the authors collaborated with the teacher and a small group of students to design lesson extensions that incorporated students’ existing knowledge. Use of the students’ own knowledge positioned them as experts and positively impacted how they viewed themselves and ultimately participated in class. Thus the design of the learning environment, including how resources are made accessible and legitimized, norms, routines, and expectations, all play crucial roles in how identities are formed. Youth who engage in project-based investigations with local significance, codeveloping research questions and identifying connections to the work of practicing scientists and engineers, develop positive science identities (Calabrese Barton 1998; Furman and Calabrese Barton 2006; Rahm and Ash 2008). A number of studies show that when youth engage in science

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 97 p ­ rojects, they activate a combination of traditionally scientific and non­ scientific resources, and this engagement supports them in being recognized as experts, as successful in school/science, while they maintain cultural allegiance. But the research has focused on programs in which participation is voluntary and long-term. Given the time scale of identity development (see next section) long-term engagement could be a critical component of these programs. Summary Looking across the literatures on instruction that supports development of interest and identity, a few key features emerge. It is important to provide learning opportunities that make students feel competent and give them opportunities to express that competence. Learning experiences that allow flexibility and choice for students and that make connections to the real world are also important. Project- and problem-based experiences seem to be especially effective in supporting the development of interest and identity, suggesting that integrated STEM experiences can be powerful tools for build- ing students’ interest and identity in STEM fields. In sum, integrated STEM can provide opportunities for students to productively engage in STEM in ways that spark their interest and transform their identity. But the research base is sparse, particularly on the subject of designing integrated STEM experiences to intentionally support interest and identity. CONCLUSIONS Disciplinary integration can support learning because basic qualities of cog- nition favor connected concepts and representations, so they are associated with other knowledge and grounded in familiar experiences. In some cases, however, the presentation of concepts in the context of activities that inte- grate multiple disciplines can impede comprehension and learning because of the cognitive processing demands associated with split attention. More- over, there are substantial differences in how different disciplines generate and validate knowledge, and it is not clear when these differences matter for learning and when they do not.

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98 STEM INTEGRATION IN K–12 EDUCATION Work on complex, real-world problems, which almost always call on multiple disciplines, can support both short-term learning and longer-term application or transfer to new contexts. However, these desired outcomes are not a given and depend on factors related to the design and implementation of the learning experience as well as the teacher’s ability to effectively support student problem-solving efforts. Integrated STEM experiences should be designed so that they support students’ development of knowledge and practices in individual disciplines and their ability to recognize and make connections across disciplines. STEM curricula should also attend to discipline-specific learning progressions; if the learning goals of one discipline are primary, the knowledge and skills of other disciplines should be integrated into the curriculum with the learning progressions of that discipline in mind. STEM connections that may appear obvious to teachers, curriculum developers, and disciplinary experts often are not obvious to novice learners and cannot be assumed to occur simply because certain concepts and prac- tices are introduced at the same time or place. Integrated STEM instruction needs to make connections explicit to students through scaffolding, sufficient opportunities to engage in activities that address connected ideas, and other approaches described in this chapter. References Ainsworth, S.E., P.A. Bibby, and D.J. Wood. 2002. Examining the effects of different mul- tiple representational systems in learning primary mathematics. Journal of the Learn- ing Sciences 11(1): 25–62. Anderson, J.R. 1996. ACT: A simple theory of complex cognition. American Psychologist 51(4): 355–365. Anderson, J.R., D. Bothell, M.D. Byrne, S. Douglass, C. Lebiere, and Y. Qin. 2004. An inte- grated theory of the mind. Psychological Review 111(4):1036–1060. Azevedo, F.S. 2006. Personal excursions: Investigating the dynamics of student engagement. International Journal of Computers for Mathematical Learning 11:57–98. Barron, B., C. Kennedy-Martin, L. Takeuchi, and R. Fithian. 2009. Parents as learning part- ners in the development of technological fluency. International Journal of Learning and Media 1(2):55–77. Bobis, J., J. Sweller, and M. Cooper, 1993. Cognitive load effects in a primary school geom- etry task. Learning and Instruction 3:1–21. Bolger, M.S., M. Kobiela, P.J. Weinberg, and R. Lehrer. 2012. Children’s mechanistic reason- ing. Cognition and Instruction 30(2):170–206.

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RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 99 Bolger, M.S., M.A. Kobiela, P.J. Weinberg, and R. Lehrer. 2010. Embodied experiences within an engineering curriculum. Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010), Vol. 1, 706–713. International Society of the Learning Sciences: Chicago, IL. Bolger, M.S., P.J. Weinberg, M.A. Kobiela, R.J. Rouse, and R. Lehrer. 2011, April. Embodied experiences as a resource for children’s mechanistic and mathematical reasoning in an engineering curriculum. Paper presented at the 2011 Meeting of the National As- sociation for Research in Science Teaching, Orlando, FL. Bottge, B.A., E. Rueda, P.T. LaRoque, R.C. Serlin, and J. Kwon. 2007. Integrating reform- oriented math instruction in special education settings. Learning Disabilities Research and Practice 22:96–109. Bricker, L.A., and P. Bell. 2012. “GodMode is his video game name”: Situating learning and identity in structures of social practice. Cultural Studies of Science Education 28:1–20. doi:10.1007/s11422–012–9410–6. Brickhouse, N.W., and J.T. Potter. 2001. Young women’s scientific identity formation in an urban context. Journal of Research in Science Teaching 38:965–980. doi: 10.1002/ tea.1041. Brickhouse, N.W. 2001. Embodying science: A feminist perspective on learning. Journal of Research in Science Teaching 38(3):282–295. Brickhouse, N.W., P. Lowery, and K. Schultz. 2000. What kind of a girl does science? The construction of school science identities. Journal of Research in Science Teaching 37:441–458. doi: 10.1002/(SICI)1098–2736200005)37:53.0.CO;2–3. Buxton, C. 2005. Creating a culture of academic success in an urban science and math magnet high school. Science Education 89(3):392–417. Calabrese Barton, A. 1998. Teaching science with homeless children: Pedagogy, representa- tion, and identity. Journal of Research in Science Teaching 35:379–394. Calabrese Barton, A., and E. Tan. 2009. Funds of knowledge, discourses and hybrid space. Journal of Research in Science Teaching 46(1):50–73. Calabrese Barton, A., and E. Tan. 2010a. We be burnin’: Agency, identity and learning. Journal of the Learning Sciences 19:187–229. Calabrese Barton, A., and E. Tan. 2010b. The new green roof: Activism, science and green- ing the community. Journal of Canadian Journal of Science, Mathematics and Tech- nology Education 10(3):207–222. Carlone, H.B., J. Haun-Frank, and A. Webb. 2011. Assessing equity beyond knowledge- and skills based outcomes: A comparative ethnography of two fourth-grade reform-based science classrooms. Journal of Research in Science Teaching 48(5):459–485. Case, R., and Y. Okamoto. 1996. The role of central conceptual structures in the develop- ment of children’s thought. Monographs of the Society for Research in Child Develop- ment 61(1–2, Serial No. 246). Chao, S.J., J. W. Stigler, and J.A. Woodward. 2000. The effects of physical materials on kin- dergartners’ learning of number concepts. Cognition and Instruction 18(3):285–316. Chandler, P., and J. Sweller. 1992. The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology 62:233–246. Chandler, P., and J. Sweller. 1996. Cognitive load while learning to use a computer program. Applied Cognitive Psychology 10:151–170.

OCR for page 77
100 STEM INTEGRATION IN K–12 EDUCATION Chi, M.T.H., P.J. Feltovich, and R. Glaser. 1981. Categorization and representation of phys- ics problems by experts and novices. Cognitive Science 5(2):121–152. Clements, D.H. 2000. Concrete manipulatives, concrete ideas. Contemporary Issues in Early Childhood 1(1):5–60. Crismond, D., and R. Adams. 2012. The informed design teaching and learning matrix. Journal of Engineering Education 101(4):738–797. Cronbach, L., and R. Snow. 1977. Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington. Danish, J.A., and N. Enyedy. 2007. Negotiated representational mediators: How young children decide what to include in their science representations. Science Education 91:1–35. Dehaene, S., E. Spelke, P. Pinel, R. Stanescu, and S. Tsivkin. 1999. Sources of mathematical thinking: Behavioral and brain-imaging evidence. Science 284:970–974. diSessa, A.A. 2004. Meta-representation: Native competence and targets for instruction. Cognition and Instruction 22(3):293–331. Durik, A., and J.M. Harackiewicz. 2007. Different strokes for different folks: How individual interest moderates the effects of situational factors on task interest. Journal of Educa- tional Psychology 99(3):597–610. Ellis, S.A., and M. Gauvain. 1992. Social and cultural influences on children’s collaborative interactions. In L. T. Winegar and J. Valsiner (Eds.), Children’s development within so- cial context: Research and methodology (Vol. 2, pp. 155–180). Hillsdale, NJ: Erlbaum. Ericsson, K.A., W.G. Chase, and S. Faloon. 1980. Acquisition of a memory skill. Science 208:1181–1182. Flum, H., and A. Kaplan. 2006. Exploratory orientation as an educational goal. Educational Psychologist 41(2):9–110. Ford, M.J. 2010. Critique in academic disciplines and active learning of academic content. Cambridge Journal of Education 40(3):265–280. Ford, M.J., and E.A. Forman. 2006. Refining disciplinary learning in classroom contexts. Review of Research in Education 30:1–32. Fortus, D., R.C. Dershimer, J. Krajcik, R.W. Marx, and R. Mamlok-Naaman. 2004. De- sign based science and student learning. Journal of Research in Science Teaching 41(10):1081. Furman, M., and A. Calabrese Barton. 2006. Capturing urban student voices in the creation of a science minidocumentary. Journal of Research on Science Teaching 43:667–694. doi: 10.1002/tea.20164. Fuson, K.C., W.M. Carroll, and J.V. Drueck. 2000. Achievement results for second and third graders using the Standards-based curriculum Everyday Mathematics. Journal for Research in Mathematics Education 31(3):277-295. Gauvain, M., and B. Rogoff. 1989. Collaborative problem solving and children’s planning skills. Developmental Psychology 25:139–151. Gauvain, M. 2001. The social context of cognitive development. New York: Guilford. Gauvain, M. 1992. Social influences on the development of planning in advance and during action. International Journal of Behavioral Development 15:139–151. Goldstone, R.L., and Y. Sakamoto. 2003. The transfer of abstract principles governing complex adaptive systems. Cognitive Psychology 46(4):414–466.

OCR for page 77
RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 101 Goldstone, R.L., and J. . Son. 2005. The transfer of scientific principles using concrete and idealized simulations. Journal of the Learning Sciences 14(1):69–110. Graesser, A.C., D.F. Halpern, and M. Hakel. 2008. 25 principles of learning. Washing- ton: Task Force on Lifelong Learning at Work and at Home. Available at www.psyc. memphis.edu/learning/whatweknow/index.shtml (retrieved July 29, 2013). Greeno, J.G., and R.P. Hall. 1997. Practicing representation. Phi Delta Kappan 78(5):361–367. Griffin, S., R. Case, and R. Siegler. 1994. Rightstart: Providing the central conceptual pre- requisites for first formal learning of arithmetic to students at risk for school failure. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 24–49). Cambridge, MA: MIT Press. Kaminski, J.A., V.M. Sloutsky, and A.F. Heckler. 2006a. Effects of concreteness on repre- sentation: An explanation for differential transfer. Proceedings of the XXVIII Annual Conference of the Cognitive Science Society, pp. 1581–1586. Mahwah, NJ: Erlbaum. Kaminski, J.A., V.M. Sloutsky, and A.F. Heckler. 2006b. Do children need concrete instantia- tions to learn an abstract concept? Proceedings of the XXVIII Annual Conference of the Cognitive Science Society, pp. 411–416. Mahwah, NJ: Erlbaum. Kaminski, J.A., V.M. Sloutsky, and A. Heckler. 2009. Transfer of mathematical knowl- edge: The portability of generic instantiations. Child Development Perspectives 3(3):151–155. Kanter, D.E. 2010. Doing the project and learning the content: Designing project-based science curricula for meaningful understanding. Science Education 94(3):525–551. Karabenick, S.A., and R.S. Newman. 2006. Help seeking in academic settings: Goals, groups, and contexts. Mahwah, NJ: Erlbaum. Kirsh, D., and P. Maglio. 1994. On distinguishing epistemic from pragmatic action. Cogni- tive Science 18(4):513–549. Koedinger, K.R., A.C. Corbett, and C. Perfetti. 2012. The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science 36(5):757–798. Kolodner, J.L., P.J. Camp, D. Crismond, B. Fasse, J. Gray, J. Holbrook, S. Puntambekar, and M. Ryan. 2003. Problem-based learning meets case-based reasoning in the middle- school science classroom: Putting Learning by Design™ into practice. Journal of the Learning Sciences 12(4):495–547. doi: 10.1207/S15327809JLS1204_2. Kontra, C.E., S. Goldin-Meadow, and S.L. Beilock. 2012. Embodied learning across the life span. Topics in Cognitive Science 4:731–739. Kozma, R. 2003. The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction 13:205–226. Kozma, R., E. Chin, J. Russell, and N. Marx. 2000. The role of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences 9(3):105–144. Lancy, D.F., S. Gaskins, and J. Bock. (Eds.). 2010. The anthropology of learning in child- hood. Lanham, MD: Alta-Mira Press. Latour, B. 1999. Pandora’s hope. Essays on the reality of science studies. Cambridge, MA: Harvard University Press. Lehrer, R., and R. Lesh. 2013. Mathematical learning. In I.B. Weiner (Ed.), Handbook of psychology, 2nd ed. (pp. 283–320). New York: Wiley.

OCR for page 77
102 STEM INTEGRATION IN K–12 EDUCATION Lehrer, R., and L. Schauble. 2012. Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education 96(4):701–724. Lehrer, R., M. Kobiela, and P. Weinberg. 2013. Cultivating inquiry about space in a middle school mathematics classroom. International Journal on Mathematics Education (ZDM) 45(3):365–376. Light, P., and K. Littleton. 1999. Social processes in children’s learning. Cambridge, Eng- land: Cambridge University Press. Manz, E. 2012. Understanding the codevelopment of modeling practice and ecological knowledge. Science Education 96(6):1071–1105. Martin, T., and D.L. Schwartz. 2005. Physically distributed learning: Adapting and rein- terpreting physical environments in the development of fraction concepts. Cognitive Science 29(4):587–625. Mayer, R.E. 2001. Multimedia Learning. New York: Cambridge University Press. Mayer, R.E., and R.B. Anderson. 1991. Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology 83:484–490. Mayer, R.E., and R.B. Anderson. 1992. The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educa- tional Psychology 84:444–452. Mayer, R.E., and R. Moreno. 1998. A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology 90:312–320. Metz, K.E. 1985. The development of children’s problem solving in a gears task: A problem space perspective. Cognitive Science 9(4):431–471. Miller, G.A. 1956. The magical number seven, plus or minus two: Some limits on our ca- pacity for processing information. Psychological Review 63(2):81–97. Mitchell, M. 1993. Situational interest: Its multifaceted structure in the secondary school mathematics classroom. Journal of Educational Psychology 85:424–436. Moreno, R., and R.E. Mayer. 1999. Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology 91:358–368. Mwangi, W., and J. Sweller. 1998. Learning to solve compare word problems: The ef- fect of example format and generating self-explanations. Cognition and Instruction 16:173–199. NAE (National Academy of Engineering) and NRC (National Research Council). 2009. Engineering in K-12 Education: Understanding the status and improving the pros- pects. Available at www.nap.edu/catalog.php?record_id=12635 (retrieved September 6, 2013). Nathan, M.J., and K.R. Koedinger. 2000. Teachers’ and researchers’ beliefs about the de- velopment of algebraic reasoning. Journal for Research in Mathematics Education 31:168–190. Nathan, M.J., and A.J. Petrosino. 2003. expert blind spot among preservice teachers. Ameri- can Educational Research Journal 40(4):905–928. Nathan, M.J., R. Srisurichan, C. Walkington, M. Wolfgram, C. Williams, and M.W. Alibali. 2013. Cohesion as a mechanism of STEM integration. Journal of Engineering Educa- tion 102(1):1–216. (Special issue on representation in engineering education.)

OCR for page 77
RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 103 Nathan, M.J., M. Wolfgram, R. Srisurichan, and M.W. Alibali. 2011. Model engagements in precollege engineering: Tracking mathematics and science across symbols, sketches, software, silicon, and wood. Proceedings of the American Society of Engineering Education (ASEE) 2011 (Paper no. AC2011–315 pp. 1–31). Vancouver, BC: ASEE Publications. Nordine, J., J. Krajcik, and D. Fortus. 2010. Transforming energy instruction in middle school to support integrated understanding and future learning. Science Education 95(4):670–699. NRC (National Research Council). 2000. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington: The National Academies Press. NRC. 2001. Knowing What Students Know: The Science and Design of Educational Assess- ment. Committee on the Foundations of Assessment. J. Pelligrino, N. Chudowsky, and R. Glaser (Eds). Board on Testing and Assessment, Center for Education. Division of Behavioral and Social Sciences and Education. Washington: The National Academies Press. NRC. 2007. Taking Science to School: Learning and Teaching Science in Grades K-8. Com- mittee on Science Learning, Kindergarten Through Eighth Grade. R. A. Duschl, H. A. Schweingruber, and A. W. Shouse (Eds.). Board on Science Education, Center for Education. Division of Behavioral and Social Sciences and Education. Washington: The National Academies Press. NRC. 2012. Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Committee on Defining Deeper Learning and 21st Century Skills. J.W. Pelligrino and M.L. Hilton (Eds.). Board on Testing and Assessment and Board on Science Education, Division of Behavioral and Social Sciences and Education. Washington: The National Academies Press. Palmer, D.H. 2009. Student interest generated during an inquiry skills lesson. Journal of Research in Science Teaching 46(2):147–165. Palmer, D.H. 2004. Situational interest and the attitudes towards science of primary teacher education students. International Journal of Science Education 26(7):895–908. Piaget, J. 1952. The origins of intelligence in children. New York: Norton. Pressick-Killborn, K., and R. Walker. 2002. The social construction of interest in a learning community. In D.M. McInerney and S. Van Etten (Eds.), Research on sociocultural influences on motivation and learning (Vol. 2, pp. 153–182). Greenwich, CT: Infor- mation Age. Prevost, A., M.J. Nathan, B. Stein, N. Tran, and L.A. Phelps. 2009. Integration of mathemat- ics in pre-college engineering: the search for explicit connections. Proceedings of the American Society of Engineering Education (ASEE) 2009 (Paper no. AC 2009–1790, pp. 1–27). Austin, TX: ASEE Publications. Prevost, A., M.J. Nathan, B. Stein, and L.A. Phelps. 2010. The enacted curriculum: A video based analysis of instruction and learning in high school engineering classrooms. Proceedings of the American Society of Engineering Education (ASEE) 2010 (Paper no. AC 2010–2011). Louisville, KY: ASEE Publications. Puntambekar, S., and J.L. Kolodner. 2005. Toward implementing distributed scaffolding: Helping students learn science from design. Journal of Research in Science Teaching 42(2):185–217.

OCR for page 77
104 STEM INTEGRATION IN K–12 EDUCATION Rahm, J. 2008. Urban youths’ hybrid identity projects in science practices at the margin: A look inside a school-museum-scientist partnership project and an afterschool science program. Cultural Studies of Science Education, 3(1):97–121. Rahm, J., and D. Ash. 2008 Learning environments at the margin: Case studies of disen- franchised youth doing science in an aquarium and an after-school program. Learning Environments Research 11(1):49–62. doi: 10.1007/s10984–007–9037–9. Rau, M.A., R. Scheines, V. Aleven, and N. Rummel. 2013. Does conceptual understanding enhance acquisition of fluency—or vice versa? Searching for models to investigate mediators. In S.K. D’Mello, R.A. Calvo, and A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 161–169). Redish, E.F., and K.A. Smith. 2008. Looking beyond content: Skill development for engi- neers. Journal of Engineering Education 97:295–307. Renninger, K.A. 2010. Working with and cultivating interest, self-efficacy, and self-regu- lation. In D. Preiss and R. Sternberg (Eds.), Innovations in educational psychology: Perspectives on learning, teaching and human development (pp. 107–138). New York: Springer. Renninger, K.A., and N. Granott. 2005. The process of scaffolding in learning and develop- ment. New Ideas in Psychology 23:111–114. Renninger, K.A., and S. Hidi. 2002. Student interest and achievement: Developmental issues raised by a case study. In A. Wigfield and J. S. Eccles (Eds.), Development of achieve- ment motivation (pp. 173–195). New York: Academic Press. Renninger, K.A., and R. Lipstein. 2006. Come si sviluppa l’interesse per la scrittura; cosa vogliono gli studenti e di cosa hanno bisogno? [Developing interest for writing: What do students want and what do students need?] Età Evolutiva 44(84):65–83. Renninger, K.A., and K. Riley. 2013. Interest, cognition, and the case of L– and science. In S. Kreitler (Ed.). Cognition and motivation: Forging an interdisciplinary perspective (pp. 352–382). New York: Cambridge University Press. Renninger, K.A., and S. Su. 2012. Interest and its development. In R. Ryan (Ed.), Oxford Handbook of Motivation (pp. 167-187). New York: Oxford University Press. Richland, L.E., O. Zur, and K.J. Holyoak. 2007. Cognitive supports for analogy in the math- ematics classroom. Science 316:1128–1129. Schnittka, C., and R. Bell. 2011. Engineering design and conceptual change in science: Ad- dressing thermal energy and heat transfer in eighth grade. International Journal of Science Education 33(13):1861–1887. Schwartz, D. 1995. The emergence of abstract representations in dyad problem solving. Journal of the Learning Sciences 4(3):321–354. Serlin, R.C., and J.R. Levin. 1980. Identifying regions of significance in aptitude by treat- ment research. American Educational Research Journal 17:389–399. Slavin, R. 1983. When does cooperative learning increase student achievement? Psychologi- cal Bulletin 94:429–445. Sloutsky, V.M., J.A. Kaminski, and A.F. Heckler. 2005. The advantage of simple symbols for learning and transfer. Psychonomic Bulletin and Review 12(3):508–513.

OCR for page 77
RESEARCH FOR DESIGNING INTEGRATED STEM EXPERIENCES 105 Stenning, K., and J. Oberlander. 1995. A cognitive theory of graphical and linguistic reason- ing: Logic and implementation. Cognitive Science 19:97–140. Stevens, R., S. Wineburg, L.R. Herrenkohl, and P. Bell. 2005. Comparative understand- ing of school subjects: Past, present, and future. Review of Educational Research 75(2):125–157. Sweller, J., P. Chandler, P. Tierney, and M. Cooper. 1990. Cognitive load and selective at- tention as factors in the structuring of technical material. Journal of Experimental Psychology: General 119:176–192. Sweller, J., J.J.G. van Merrienboer, and F.G.W.C. Paas. 1998. Cognitive architecture and instructional design. Educational Psychology Review 10:251–296. Tabachneck, H. 1992. Computational differences in mental representations: Effects of mode of data presentation on reasoning and understanding. Doctoral Dissertation. Carnegie Mellon University. Tabachneck, H.J.M., A.M. Leonardo, and H.A. Simon. 1994. How does an expert use a graph? A model of visual and verbal inferencing in economics. Proceedings of the 16th Annual Conference of the Cognitive Science Society, pp. 842–847. Uttal, D.H., K.V. Scudder, and J.S. DeLoache. 1997. Manipulatives as symbols: A new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology 18(1):37–54. Vygotsky, L.S. 1978. Mind in Society: The Development of Higher Psychological Processes. M. Cole, V. John-Steiner, S. Scribner, and E. Souberman (Eds.). Cambridge, MA: Harvard University Press. Walkington, C.A., M.J. Nathan, M. Wolfgram, M.W. Alibali, and R. Srisurichan. In press. Bridges and barriers to constructing conceptual cohesion across modalities and tem- poralities: Challenges of STEM integration in the precollege engineering classroom. In J. Strobel, S. Purzer and M. Cardella (Eds.), Engineering in PreCollege Settings: Research into Practice. Rotterdam, Netherlands: Sense Publishers. Welty, K., L. Katehi, G. Pearson, and M. Feder. 2008. Analysis of K–12 engineering edu- cation curricula in the United States: A preliminary report. American Society for Engineeering Education, Proceedings of the 2008 Annual Conference and Exposition. Wood, D.J., and D. Middleton. 1975. A study of assisted problem solving. British Journal of Psychology 66:181–191. Wood, D.J., J.S. Bruner, and G. Ross. 1976. The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry 17(2):89–100. Wood, D.J., H. Wood, and D. Middleton. 1978. An experimental evaluation of four face-to- face teaching strategies. International Journal of Behavioral Development 2:131–147.

OCR for page 77