Overview of Discipline-Based
Education Research
As the previous chapters show, discipline-based education research (DBER) is a relatively new area of research composed of a set of loosely affiliated fields with common goals and methods. The fields share some common history, but follow unique trajectories that reflect the characteristics of their parent disciplines. In addition, DBER has close ties to related research on teaching and learning in education and psychology.
In this chapter, we provide an overview of the research foci of the fields of DBER and consider their similarities and differences. This overview sets the context for the more detailed synthesis of DBER presented in Chapters 4 through 7. In the following sections we discuss the substantive focus of research in each field of DBER, typical methods used across the fields of DBER, and the relationship of DBER to broader principles and theories of learning and instruction. The chapter concludes by identifying some key strengths and limitations of DBER as a whole.
Across the fields of DBER, broad-level learning goals drive instruction and the concomitant research on instruction. The different disciplines of science and engineering continue to clarify goals regarding core ideas, crosscutting concepts, and science and engineering practices. Participants in a 2008 workshop series on promising practices in undergraduate science, technology, engineering, and mathematics education identified the following general learning goals for students, which also are relevant for DBER (National Research Council, 2011):
• Master a few major concepts well and indepth
• Retain what is learned over the long term
• Build a mental framework that serves as a foundation for future learning
• Develop visualization competence, including the ability to critique, interpret, construct, and connect with physical systems
• Develop skills (analytic and critical judgment) needed to use scientific information to make informed decisions
• Understand the nature of science
• Find satisfaction in engaging in real-world issues that require knowledge of science
The committee acknowledged the difficulty of identifying a common set of learning goals for science education at the undergraduate level because the missions and goals of courses and programs vary widely. Thus, this list does not represent our consensus on learning goals for undergraduate science education. However, as the following discussions of scope reveal, these goals are reflected to some extent across the fields of DBER.
Physics Education Research
The extensive scope of contemporary physics education research has been reviewed by Docktor and Mestre (2011). Over time, the focus of inquiry has expanded from narrow investigations of students’ difficulties in learning specific concepts to reflect the realization that improving physics learning is a complex and multifaceted problem. As a result of this shift, current physics education research addresses the following topics:
• characterizing students with respect to conceptual knowledge, problem solving, use of representations, attitudes toward physics and toward learning more broadly, knowledge of scientific processes, and knowledge transfer;
• defining goals for physics instruction based on rates of student learning, needs for future learning, transfer, or population diversity;
• developing curricular materials and pedagogies to facilitate conceptual change, improve problem-solving skills and the use of representations, improve attitudes toward physics and general learning, or provide experiences with the practices of science;
• investigating how students and instructors use curricular materials and pedagogies such as textbooks, problems, group work, or electronic feedback;
• investigating the difficulties of changing instructional paradigms, including the role of instructor beliefs and values, institutional constraints, student expectations, and student backgrounds; and
• investigating the role of basic thought processes in learning physics.
Chemistry Education Research
In 1991, a groundbreaking article introduced what is now known as “Johnstone’s Triangle” (Johnstone, 1991), which portrays the three central components of chemistry knowledge: the macroscopic, particulate, and symbolic (letters, numbers, and other symbols used to succinctly communicate chemistry knowledge) domains. These three domains have since provided a structure for chemistry education research. Indeed, questions about what students of chemistry know, or how teachers of chemistry ought to teach, mirror the quest of chemists to connect the macroscopic properties (color, smell, taste, solubility, etc.) of matter to the structure and particulate nature of matter.
Current areas of interest in chemistry education include
• students’ conceptual understanding, especially of the particulate nature of matter (see Chapter 4);
• the use of technology to shape student reasoning;
• analysis of student argumentation patterns;
• the use of heuristics in student reasoning; and
• the development of assessment tools to measure thinking about chemistry (see Chapter 7).
Engineering Education Research
Guided by the ABET accreditation criteria (ABET, 2009) and their implementation, the principal areas of inquiry for engineering education research include the following:
• the extent to which engineering education reflects engineering approaches by integrating and aligning content, assessment, and pedagogy for learning module, course, and program design (the equivalent of developing requirements or specifications, assigning relevant metrics, and preparing prototypes that meet the requirements) and by engaging in a cycle of improvement that closes the loop between research and practice;
• the extent to which engineering faculty adopt evidence-based practices;
• the extent to which faculty take a scholarly approach to teaching and learning or envision a developmental process for learning and inquiry;
• the extent of collaboration with higher education researchers, learning scientists, and other scholars of teaching and learning;
• the implicit and explicit values that departmental, college, and university cultures place on teaching and learning compared with traditional disciplinary research;
• the balance that Ph.D. programs strike between disciplinary research and the development of teaching and learning knowledge and skills;
• how engineers understand the nature of engineering work, especially early in their careers, but also across the career span; and
• strategies for helping students develop an understanding of what it means to be, and to become, an engineer.
As discussed in Chapter 2, these areas of inquiry and the ABET-defined areas of knowledge and skill development for engineering students have provided a framework for engineering education research since the late 1990s. One particular area of emphasis has been students’ understanding of engineering concepts (Svinicki, 2011), with a concomitant focus on methods to promote greater conceptual understanding. Engineering education research also investigates methods for improving students’ problem-solving and design skills.
Engineers pursue solutions to problems or improvements in the current state of the art, and engineering education researchers do the same. In aeronautical engineering courses, for example, prototypes such as sailplanes are used to demonstrate conceptual understanding, higher order thinking skills, and other dimensions of learning (Hansen, Long, and Dellert, 2002). However, these outcomes are not the focus of the research per se. Instead, engineering education research in this instance attends to how well the curriculum and instruction prepares students to understand the complexities of aeronautical engineering. The goal of preparing students for the future also highlights the importance of translating skills learned in the classroom to the workplace, which is another concern of engineering education research.
Some skills that are emphasized in ABET—teamwork, communication, and ethics/professionalism—are important in the engineering workplace, but have received relatively little attention from the engineering education research community. The awareness skills identified by ABET (appreciation for the impact of engineering on society locally and globally, commitment to lifelong learning, knowledge of contemporary issues) have received similarly little research attention.
Biology Education Research
Since the mid-1990s, biology education research has followed the lead of physics education research by identifying students’ conceptual understanding, building concept inventories, and assessing the effects of instructional interventions such as increased classroom engagement and group problem solving on students’ learning (Dirks, 2011). Biology is a quantitative science, yet many students with math phobia enroll in biology, rather than other science courses, either to fulfill general education distributions or as a major. Thus, a current challenge for biology education researchers is to identify instructional approaches that can help overcome the math phobia of many biology students and introduce more quantitative skills into the introductory curriculum, as computational biology and other mathematical approaches become more central to the field of biology (National Research Council, 2003).
Geoscience Education Research
Defining the scope of geoscience education research presents a challenge because there is no central “canon” of knowledge that is encompassed by the disciplines that study the earth (geology, oceanography, geophysics, geochemistry, atmospheric science, meteorology, climatology, planetary science, and physical geography). Geoscience content may be taught in a variety of courses, in different departments.
In the balance between implementing research findings to improve educational practice and accruing more such findings, geoscience education research has, to date, heavily emphasized the former. However, following other fields of DBER, geoscience education research built its first body of research around students’ understanding of basic topics. These topics include the seasons, land forms, geological time, and natural hazards (Dahl, Anderson, and Libarkin, 2005; DeLaughter, Stein, and Bain, 1998; Kusnick, 2002; Libarkin, Kurdziel, and Anderson, 2007; Shepardon et al., 2007). Current areas of active inquiry include spatial thinking, temporal thinking, systems thinking, and field-based teaching and learning (Kastens, Agrawal, and Liben, 2009). In spatial thinking (Liben and Titus, 2012), geoscience education research finds common ground with geography education research (National Research Council, 2006), and in systems thinking (Stillings, 2012) with biology education research. Temporal thinking (Cervato and Frodeman, 2012; Dodick and Orion, 2006) and field-based learning (Maskall and Stokes, 2008; Mogk and Goodwin, 2012) appear at present to be distinctive to geoscience education research, with some parallel work in biology education research. Research on climate change education is an emerging interdisciplinary field (Gautier, Deutsch, and Rebich, 2006;
Marx et al., 2007; Mohan, Chen, and Anderson, 2009; Rebich and Gautier, 2005; Sterman and Sweeney, 2007; Weber, 2006), and an interesting example of the interplay between DBER and societal challenges.
Astronomy Education Research
To date, astronomy education research has predominantly identified students’ conceptual understanding. Another prominent focus of early research in astronomy education has been to address questions of overall teaching effectiveness (Bailey, 2011).
The methods DBER scholars use are as diverse as the research questions they investigate. Depending on the focus of the research, these methods range from qualitative interview studies or classroom observations of a few or dozens of students, to quasi-experimental comparisons of the learning of hundreds of students in similar courses across multiple institutions, to experimental manipulations in a research setting.
In some cases, the methods used by DBER scholars reflect the influence of the parent discipline. For example, astronomy is a quantitative science conducted by scholars with formal training in quantitative scientific methods, and the early history of astronomy education research was similarly dominated by quantitative research. Only recently has astronomy education begun to address questions similar to those pursued in the behavioral and social sciences, including questions that are best answered with qualitative methods (Bailey, Slater, and Slater, 2010). This trajectory of methodological approaches is similar to physics education research, and the trend to include a more robust combination of quantitative and qualitative studies is evidence that astronomy education research is maturing. Biology education research is another DBER field that is newly emerging from a quantitative discipline. As a result, the preponderance of biology education research is quantitative, and includes a relatively strong emphasis on quasi-experimental studies. In contrast, while experimental design is the norm in chemistry, chemistry education research has a long history of incorporating a wider range of qualitative and quantitative methods than are typically used in the parent discipline.
Research Settings and Study Populations
Across the disciplines in this study, DBER scholars have studied similar types of courses. Despite the overall similarity of courses studied, however, not all institutions or student populations are equivalent in terms of class
size, social background, and institutional priorities. These variations can have profound effects on outcomes and are important to consider when assessing the inferences that can be made from DBER findings.
Research Settings
Large introductory courses are the primary setting for research in all DBER fields because these courses reach the most students. Research on student learning in these courses is often spurred by and related to the traditional overemphasis on memorization of factual information in a discipline, with an accompanying lack of student interest, shallow conceptual understanding, and poor retention (Sundberg, Dini, and Li, 1994).
Despite the prevalence of laboratory courses in the sciences and engineering and despite the importance of fieldwork in biology and the geosciences, very little DBER has been conducted in those settings. Moreover, relatively little research has been conducted in graduate or advanced-level undergraduate courses. Most of the latter comes from physics (e.g., Baily and Finkelstein, 2011; Pollock et al., 2011; Smith, Thompson, and Mountcastle, 2010) and chemistry (Bhattacharyya and Bodner, 2005; Orgill and Bodner, 2006; Sandi-Urena et al., 2011).
Some DBER has been conducted in the K-12 setting. Early research on learning and teaching chemistry, for example, investigated K-12 students because it was conducted by faculty who supervised preservice teacher training. Over time, chemistry education research came to include postsecondary students as faculty who taught introductory courses in chemistry departments began conducting research on those courses.
Conducting and interpreting research in introductory courses poses a number of challenges. A particular challenge in introductory biology courses is the breadth of the various divergent biology subfields, which further encourages broad, shallow introductory surveys of the discipline and hampers development of conceptual assessments that measure general biological knowledge across subfields of biology. In addition, the different subfields rely to some extent on different methodologies, for example the observational field work in ecology and the experimental laboratory research of molecular biology.
In contrast, astronomy education research has been motivated largely by a desire to improve teaching and learning in a single undergraduate course: the general education, introductory, nonmathematically oriented astronomy survey course known colloquially as ASTRO 101. The challenges of conducting research on ASTRO 101 and introductory geoscience courses are similar. In both disciplines, introductory courses typically include students who have little or no background in the subject and who usually are not considering careers in the discipline; undergraduates in
ASTRO 101 are most often future teachers or nonscience majors. Thus, faculty members are compelled to make these courses attractive, accessible and relevant to recruit and retain majors to the discipline, which means that the goals for these courses are often diffuse and broad. Moreover, ASTRO 101 is “terminal” in nature, rarely serving as a prerequisite for upper level courses. Because of these factors, introductory courses in the geosciences and astronomy can vary widely within and across institutions, posing a challenge for developing a coherent body of research on learning in these courses.
Study Populations
Given the focus of DBER on introductory courses, most studies include a mix of majors and nonmajors. Even in studies that investigate the conceptual understanding of individual students rather than the effectiveness of instruction as a whole, study participants typically are drawn from the enrollment in an introductory course. Majors and nonmajors in an introductory course can differ along many dimensions, including their motivations for taking the course, the extent to which they consider the course to be relevant to their studies and their futures, and their goals for learning and achievement. DBER studies do not always measure or explain these factors, which could play a role in learning. Further, as the following chapters show, very little DBER analyzes issues of teaching and learning as they relate to any different subpopulations of students. Although these limitations to the applicability of findings are not always explicitly acknowledged in DBER studies, they should be considered when drawing inferences from the research.
THE ROLE OF LEARNING THEORIES AND PRINCIPLES
The extent to which DBER is grounded in broader theories and principles of learning and teaching varies widely. Many DBER studies either do not situate themselves in a broader theoretical frame, or do not explicitly define that frame. However, whether stated implicitly or explicitly, across the disciplines DBER is heavily influenced by constructivist ideas of learning, which propose that students generate understanding and meaning through experience (Ausubel, 2000; Dewey, 1916). Some DBER studies on collaborative learning are also influenced to varying degrees by socio-cultural learning perspectives, which argue that students generate meaning and understanding by interacting in groups that share a common interest and learn together (Lave and Wenger, 1991), or through cognitive apprenticeships, where experts make tacit processes more explicit for novices
(Brown, Collins, and Duguid, 1989). The extent to which DBER studies use these perspectives to explain or extend their findings typically is limited.
The different fields of DBER approach the role of theory differently. Physics education research has strong ties to cognitive science research (Docktor and Mestre, 2011). Indeed, many cognitive science studies have investigated problem solving and the use of representations in physics, typically examining students’ cognitive processing principles and internal mental processes (Bassok and Novick, 2012).
As with chemistry more broadly, the symbiosis of theory and measurement shape chemistry education research. The role of theory in experiment design is central to chemistry—data either support or refute theory—and theory plays a similarly important role in chemistry education research. Several resources have been published detailing how learning theory (Bretz and Nakhleh, 2001), methodologies (Orgill and Bodner, 2007), and experimental design in chemistry education research (Sanger, 2008; Towns, 2008) are grounded in the intersection of chemistry with several other disciplines.
In engineering, the Foundation Coalition, with funding from the National Science Foundation, undertook one of the few efforts to tie the ABET accreditation criteria to cognitive theories of learning. These efforts were designed to make the ABET criteria actionable and ground them in broader research. The coalition used Bloom’s taxonomy of learning domains to develop a conceptual map linking ABET student learning criteria with learning objectives in the cognitive, affective, and psychomotor domains; assessments of those objectives; theories of cognition; and instructional approaches (see McGourty, Scoles, and Thorpe, 2002).
As discussed in Chapter 1, and as is evident from the synthesis in Chapters 4 through 7, DBER overlaps conceptually and theoretically with science education, educational psychology, cognitive science, and educational evaluation. More explicitly situating DBER in learning theories and principles from these fields would help to advance the conversations about teaching and learning in a given discipline, and in science and engineering more broadly. These principles and theories could explain some DBER findings, extend others, and form the foundations for deeper study.
As with all research, DBER has strengths and limitations. DBER’s greatest strength is its contribution of deep disciplinary knowledge to questions of teaching and learning. This knowledge has the potential to guide research that is focused on the most important concepts in a discipline, and offers a framework for interpreting findings about students’ learning and understanding in a discipline. In these ways, even as an emerging field of
inquiry, DBER has deepened the collective understanding of undergraduate learning in the sciences and engineering. When explicitly leveraged, the overlap of DBER with research from K-12 science education, educational psychology, and cognitive science can highlight findings that appear to be robust across different disciplines and learning contexts, and can help to identify differences that merit further exploration.
As described in Chapter 1, two of the long-term goals of DBER are to understand how people learn the concepts, practices, and ways of thinking of science and engineering and to help identify approaches to make science and engineering education broad and inclusive. Meeting these goals begins with an understanding of similarities and differences among different groups of students, yet very little DBER focuses on different sub-populations of students. At a time when the undergraduate population is becoming increasingly socially, economically, and ethnically diverse, a rich opportunity exists to enhance the understanding of the learning experiences of different groups. In a related vein, DBER could paint a more complete picture of undergraduate learning by taking into account differences among majors and nonmajors in introductory courses and structural differences among introductory courses, service courses for majors in other disciplines, and courses for majors.
At this point, DBER faces some challenges to the goal of independent reproducibility of research findings. Many DBER findings have been generated by the faculty members who are implementing the innovations and who developed the instruments to assess those innovations. The potential for investigator bias exists in these cases because these scholars naturally have a vested interest in the research results. One approach to counter this bias is to study other instructors who are implementing the innovation in question. However, it can be difficult to recruit others to teach specific course content in specific ways, independently of the research team.
Similar to other education research, the scale of most DBER studies poses a challenge to generalizing results, and to translating research findings into practice. A considerable proportion of DBER has been conducted at the scale of a single course, using instruments developed to assess learning in that course. As described elsewhere in this chapter, the variation in introductory courses across a discipline poses challenges to studying learning across those courses. Moreover, to the extent that the studies rely on instruments designed to measure student learning in the context of a single course, they might reflect standard examinations for that course. Such instruments generate little insight into broader issues of student learning, and limit the extent to which findings are applicable to other settings.
DBER has made some progress in addressing these challenges. For example, in the more established fields of DBER, such as physics and chemistry, scholars are developing instruments that can be widely used to generate deeper insights into students’ understanding and learning experiences. And although multi-institutional studies are not the norm in DBER, they do exist. Part II of this report highlights these developments by describing the nature and quality of the existing evidence from discipline-based education research in physics, chemistry, engineering, biology, the geosciences, and astronomy, and synthesizing those literatures.
Across the next three chapters, we examine the literature on undergraduate students’ conceptual understanding (Chapter 4), problem solving and use of representations (Chapter 5), and instructional strategies to improve science and engineering learning (Chapter 6). We devote a subsequent chapter (Chapter 7) to several emerging topics for DBER: science and engineering practices, applying knowledge in different settings (transfer), metacognition, and students’ dispositions and motivations to study science and engineering (the affective domain).
Many of the topics in these chapters have been extensively studied in cognitive science, psychology, and science education. Our synthesis draws on relevant theoretical frameworks and findings from those disciplines to explain, extend, and contextualize DBER, while highlighting DBER’s unique contribution of deep disciplinary knowledge to the understanding of these topics.
In reading Chapters 4 through 7, it is important to keep in mind that the nature of engineering and engineering education, combined with the strong influence of the ABET accreditation criteria on engineering education research, distinguish engineering education research from the other disciplines in this study. As a result, the body of engineering education research does not fit neatly into the categories around which we have organized the synthesis of the literature. As one example, because engineering education research emphasizes the integration and alignment of content (or curriculum), assessment, and pedagogy, it is difficult to identify studies in engineering that examine the efficacy of specific instructional strategies—the main focus of Chapter 6. We have parsed the engineering education research to fit the organization of this report, and Table 3-1 maps the ABET criteria onto the major sections of Chapters 4 through 7. Because the research base did not support a discussion of all ABET criteria, the report only discusses the criteria for which there are relevant, peer-reviewed studies.
TABLE 3-1 Mapping ABET Student Learning Criteria onto Major Sections of the DBER Synthesis
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ABET Criteria (ABET, 2009, p. 3) |
Applicable Sections of the Report |
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A: Ability to apply knowledge of mathematics, science, and engineering |
Conceptual Understanding and Conceptual Change (Ch. 4) |
B: Ability to design and conduct experiments, as well as to analyze and interpret data |
The Role of Visualization and Representation in Promoting Conceptual Understanding and Problem Solving (Ch. 5) Metacognition (Ch. 7) Transfer (Ch. 7) |
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C: Ability to design a system, component, or process to meet desired needs |
Problem Solving (Ch. 7) |
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D: Ability to function on multidisciplinary teams |
Science and Engineering Practices (Ch. 7) Metacognition (Ch. 7) |
E: Ability to identify, formulate, and solve engineering problems |
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I: Recognition of the need for, and an ability to engage in lifelong learning |
Dispositions and Motivation to Study Science and Engineering (Ch. 7) |
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F: Understanding of professional and ethical responsibility |
Science and Engineering Practices (Ch. 7) Transfer (Ch. 7) |
G: Ability to communicate effectively |
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H: Understanding of the impact of engineering solutions in a global and societal context |
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J: Knowledge of contemporary issues |
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K: Ability to use the techniques, skills, and modern engineering tools necessary for engineering practice |
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