research education of new scientists: implications for science teacher education

JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 46, NO. 4, PP. 442–459 (2009)

Research Education of New Scientists: Implications for Science Teacher Education

Allan Feldman, Kent Divoll, Allyson Rogan-Klyve

School of Education, University of Massachusetts, 813 N Pleasant Street, Amherst,

Massachusetts 010003

Received 23 November 2007; Accepted 22 September 2008

Abstract: This study examined an interdisciplinary scientific research project to understand how graduate and

undergraduate honors students learn to do science. It was found that the education of the students occurs as part of an

apprenticeship. The apprenticeship takes place in research groups. In general, research groups are structured in two ways:

loosely organized and tightly organized, and have characteristics of both communities of practice and epistemic

communities. Students have different roles in the research groups: novice researcher, proficient technician, or knowledge

producer. Their role depends on their knowledge and skills, and their degree programs. It is possible for students to

develop expertise along a continuum from novice researcher to knowledge producer. The members of the research group,

including the professor and other students, facilitate the development of the students along the continuum of roles.

Implications for the education of science teachers are discussed. � 2009 Wiley Periodicals, Inc. J Res Sci Teach 46:

442–459, 2009

Keywords: research experiences; science teacher education; graduate education

Introduction

There is an international call for students to understand the nature of science as inquiry, as can be seen in

the framework developed for the PISA international assessment. The PISA 2003 Assessment Framework

(OECD, 2003) calls for students to have the opportunity to ‘‘experience and understand how scientific

understanding is built up, and ideally, the nature of scientific enquiry and scientific knowledge (p. 137).’’

The PISA Framework relates scientific inquiry to three process skills: (1) the describing, explaining and

predicting of scientific phenomena; (2) the understanding of scientific investigation; and (3) the interpretation

of scientific evidence and conclusions (OECD, 2003). Similarly, in the United States, the National Science

Education Standards (NSES) (National Research Council, 1996) state that students should understand the

nature of science as inquiry.

Anderson, in his review of research on inquiry (Anderson, 2002), found three ways in which the NSES

refers to inquiry. First, there is inquiry teaching in which teachers engage student in activities centered on

‘‘authentic questions generated from student experiences (National Research Council, 1996, p. 31).’’ The NSES

also ‘‘requires that students combine processes and scientific knowledge as they use scientific reasoning and

critical thinking to develop their understanding of science’’ (p. 105), which is what Anderson refers to as inquiry

learning. Finally, the NSES uses the term scientific inquiry to refer to ‘‘the diverseways in which scientists study

the natural world and propose explanations based on the evidence from their work (29).’’ In addition, as we

noted above, the NSES calls for students to understand the nature of science as inquiry.

To Crawford (2007) these different ways of thinking about inquiry make up what she calls the NSES

vision ‘‘that students in K-12 science classrooms develop abilities to do scientific inquiry, gain

understandings about scientific inquiry, and that teachers facilitate students in acquiring deep understanding

of science concepts through inquiry approaches (2007, 614).’’ Crawford details this in the following list of

student outcomes:

Correspondence to: A. Feldman; E-mail: [email protected]

DOI 10.1002/tea.20285

Published online 9 March 2009 in Wiley InterScience (www.interscience.wiley.com).

� 2009 Wiley Periodicals, Inc.

appreciating the diverse ways in which scientists conduct their work; understanding the power of

observations; knowledge of and ability to ask testable questions, make hypotheses; use various forms

of data to search for patterns, confirm or reject hypotheses; construct and defend a model or argument;

consider alternate explanations, and gain an understanding of the tentativeness of science, including

the human aspects of science, such as subjectivity and societal influences. (Crawford, 2007, 614)

Her list of outcomes, as well as her list of aspects of scientific inquiry (p. 618), is similar to those

developed by other science education researchers (e.g., Brown, Abell, Demir, & Schmidt, 2006; Center for

Inquiry-Based Learning, 2007; Lotter, Harwood, & Bonner, 2007; Westerlund, Garcia, & Koke, 2002).

Clearly, if science teachers are to help their students learn the nature of science as inquiry, know how to

engage in scientific inquiry, and learn science through inquiry, then they must have the knowledge and skills

to make this happen. Crawford puts it this way:

To enact teaching science as inquiry requires that teachers develop approaches that situate learning in

authentic problems, model actions of scientists to guide and facilitate students in making sense of data,

and support students in developing their personal understandings of science concepts. (Crawford,

2007, 614)

The NSES is more straightforward about this: for teachers to be able to teach in this way, they should

be ‘‘familiar enough with a science discipline to take part in research activities in that discipline’’ (p. 60).

Unfortunately, few teachers have this knowledge (Roth, McGinn, & Bowen, 1998) or have had the

opportunity to participate in scientific research activities (Russell, 2005). Accordingly, their students learn

science as pre-packaged and delivered knowledge (Brickhouse, 1990; Flick, Lederman, & Enochs, 1996;

Lederman, 1992; Minstrell & Van Zee, 2000).

Science teacher educators have been promoting the aspects of teaching science as inquiry for at least

50 years beginning with the various curriculum reforms efforts such as those funded by the National Science

Foundation and the Nuffield Foundation. Although many teachers have participated in professional

development activities promoting inquiry, there have been only modest gains in the adoption of these

practices in schools (Lotter et al., 2007; Stake & Easley, 1979; Supovitz & Turner, 2000). More recently there

has been a move to do what the NSES recommends: providing pre- and inservice teachers with the

opportunity to engage in research activities along with scientists. However, while these efforts have resulted

in teachers learning more science and learning science research practices, there has been little carryover to

their classrooms (Brown & Melear, 2007; Lotter et al., 2007; Lunsford, Melear, Roth, Perkins, & Hickok,

2007; Westerlund et al., 2002). Most of this research has attributed the lack of carryover to teachers’ core

beliefs about teaching and the complexities involved in implementing science teaching as inquiry into real

classrooms. This is supported by the findings of Brown et al. (2006) that beliefs about teaching and inquiry

constrain the implementation of inquiry teaching practices in the classes of research scientists.

We believe that there is at least another factor at play. Much of what the PISA Framework and the NSES

standards say about inquiry teaching in schools suggests that teachers need to be able to teach their students

how to do science, which, in turn, suggests that the teachers need to know how to do science. By doing science,

we mean engaging in the activities that are usually referred to as scientific research in ways that are similar to

those of experts, that is, scientists (e.g., see the list of skills developed by Kardash (2000)). As it turns out,

while there have been numerous studies done on how scientists do science (Dunbar, 1999; Knorr Cetina,

1999; Latour, 1988; Nersessian, 2005; Pickering, 1995; Stucky & Bond-Robinson, 2004), little research has

been done on how scientists learn to do science. Therefore, professional development activities that attempt to

teach teachers how to do science have little knowledge of how scientists learn to do science to draw upon.

Given this, we ask, ‘‘How are scientists taught to do science?’’ and ‘‘What implication does this have for the

research education of teachers?’’

In this article, we present some answers to these questions based on interviews with science and

engineering faculty in a Research I university. In doing so we sought to uncover the beliefs that science and

engineering professors have about the research education of their undergraduate and graduate students. This

is of importance to science teacher education because if we want to know how to teach teachers how to engage

in scientific research, we ought to know how scientists are taught to be researchers.

RESEARCH EDUCATION OF NEW SCIENTISTS 443

Journal of Research in Science Teaching

Graduate study in science in the United States has two main components. The first consists of the

accumulation of subject matter knowledge and the development of a deep conceptual understanding that

occurs through coursework. This component occurs in the formal structure of an academic program in which

the students are enrolled in courses. While the level of the content of the course, the number of students in the

course, and the relationship between students and instructor are very different from undergraduate courses,

the methods of instruction and the assessment of student learning continue to have a strong resemblance to

what the students typically experience as undergraduates. The second component is participation in research

activities that leads to extensive knowledge of a subset of the subject domain, the learning of research skills,

and the ability to frame and answer researchable questions. Except for those undergraduates who may have

had the opportunity to do research, for example as part of an honors project, the research project is a new

opportunity for graduate students. This study focuses on the second component—how graduate students and

undergraduate honors students are taught to do science while engaged in empirical research.

Literature Review

In our review of the literature we examine three areas of research: (a) research on research experiences

for students and teachers, (b) apprenticeships, and (c) communities of practice.

Research Experiences for Students

Most of the literature that examines students’ authentic research experiences focuses on precollege

students (Barab & Hay, 2001; Bleicher, 1996; Charney et al., 2007; Etkina, Matilsky, & Lawrence, 2003;

Richmond & Kurth, 1999; Ritchie & Rigano, 1996), preservice and inservice teachers (Brown, Bolton,

Chadwell, & Melear, 2002; Schwartz, Lederman, & Crawford, 2004; Varelas, House, & Wenzel, 2005) and

undergraduates (Hunter, Laursen, & Seymour, 2007; Kardash, 2000; Lopatto, 2004; Rauckhorst, Czaja, &

Baxter Magolda, 2001). While we refer to this literature when appropriate, it is important to note that

the research experiences of graduate students are very different than those of precollege students,

undergraduates, and teachers.

One difference is the amount of time actually engaged in research activities. In most of the studies that

we cited above the research experiences last from several weeks to 2 months during one summer. Others have

academic year components, but the participants are engaged in research activities for 3–5 hours/week. Even

the longer experiences for teachers, such as the 15-week, 9-hour/week experience for preservice teachers

studied by Brown and Melear (2007), or the 8-week summer experience studied by Westerlund et al. (2002)

are far short of the 20 hours/week or more over the course of 2–5 years experienced by the graduate students

in our study.

A second difference is that participants in most of the research experiences for precollege students,

undergraduates, and teachers have little time or opportunity to learn background information about the

research experience that they are joining. As a result, they are ‘‘drop-in’’ experiences in which they can only

contribute to the research in the most mundane ways. One exception is the situation described by Westerlund

et al. (2002) in which teachers began their experiences by doing reading in the research area and preparing

literature reviews.

A third difference, which is related to the first two, is the relationship between the participant and the

faculty member. Faculty members see many benefits for themselves and for students in undergraduate

research programs, including the possibility of the recruitment of students (Hunter et al., 2007; Kardash,

2000). Faculty have a different type of relationship with their graduate students that has at its core a mutual

responsibility that arises in the longer term graduate experiences in part because of the graduate students’

commitment to completing their degrees and the faculty members’ need for well-trained assistants for their

research. Finally, it is important to note that for the graduate students that we studied their research was their

paid work—it provided them with income to live on and with tuition and fees benefits.

While there is extensive research on research experiences for precollege students, undergraduates, and

teachers, there is little research that has focused on the graduate education of scientists and engineers.

Exceptions include Bucher and Stelling’s (1977) study of biochemistry graduate students and the recent

studies by Bond-Robinson and Stucky (2005) and Stucky and Bond-Robinson (2004), as well as the work by

444 FELDMAN, DIVOLL, AND ROGAN-KLYVE


Fernandez-Esquinas (2003) and LaPidus (1997). Richmond (1998) gives a compelling description of how

scientists are educated through apprenticeship experiences in her editorial in Journal of Research in Science

Teaching, however she does not cite any research to support it.

Apprenticeships

In order to understand how people learn to be scientists while engaged in research activities, we

developed a theoretical framework that draws upon studies of apprenticeships and communities of practice.

We use the concept of apprenticeship as legitimate peripheral participation in a community of practice that

results in situated learning of the skills and knowledge needed to be a working scientist (Lave & Wenger,

1991). Apprenticeship models of learning can be found in a wide variety of contexts and to a certain extent

have characteristics unique to the context in which they occur. However, apprenticeships also share some key

commonalities. An important characteristic of apprenticeships is the indistinguishable nature of learning

and the practice of work (Lave & Wenger, 1991). This is quite different from how students are taught in

formal instructional settings in which they learn skills in isolation from their use in practice. Additional key

elements of an apprenticeship can be understood in terms of the role the teacher and learner play in the

learning process in an apprenticeship. For an apprentice, learning is demonstrated by performing tasks in a

way that is analogous to the expert. For the instructor in an apprenticeship model, successful teaching is the

ability to partition tasks into appropriate sizes that are useful for the developmental trajectory of the

apprentice (Lave & Wenger, 1991).

Collins and Brown and their colleagues (Brown, Collins, & Duguid, 1989; Collins, Brown, & Newman,

1989) distinguish cognitive apprenticeships from traditional apprenticeships like that of the tailors studied

by Lave and Wenger (1991). While cognitive apprenticeships engage students in activities and social

interactions similar to those in traditional apprenticeships, the former have as their goal to both enculturate

students into authentic practices and to develop deep conceptual understanding in the domain (Brown et al.,

1989). In addition to this emphasis on decontexutalizing knowledge, Collins et al. (1989) give two other

ways in which cognitive apprenticeships differ from traditional apprenticeship. One is that traditional

apprenticeships are set in the workplace while cognitive apprenticeships occur in settings that have as their

primary purpose education. This leads to another difference: traditional apprenticeships are constrained by

the demands of the workplace, which are, of course, tied to the market. Cognitive apprenticeships, on the

other hand, must satisfy the learning demands, including state and national standards.

These distinctions between cognitive and traditional apprenticeships can help us to understand how

research experiences for precollege students, undergraduates, and teachers differ from the experiences that

graduate students have doing research. Cognitive apprenticeships are designed to help students learn about

science, how it is done, and to gain deep conceptual understanding. The goal of traditional apprenticeships is

to develop expert practitioners in the field. While the latter requires legitimate peripheral participation in the

actual doing of science, the former requires participation in activities that have characteristics that make them

appear to be authentic. These characteristics are what are found in the lists developed by Crawford (2007) and

others. This suggests that there is a difference between participation in activities that have the characteristics

of authentic science and activities that are legitimate participation in science.

The effect of these different ways of participating in research is evident in the work of Baxter

Magolda (1992). In her longitudinal study of undergraduates (Baxter Magolda, 1992) she developed an

epistemological reflection model of the procession of complex thinking that has been used to understand what

students learn in scientific apprenticeships (Hunter et al., 2007; Rauckhorst et al., 2001). Baxter Magolda

postulates four categories of ways of knowing. Category 1 is ‘‘Absolute Knowing’’ in which students see

knowledge as certain and that their role is to obtain it from experts. In ‘‘Transitional Knowing’’ (Category 2)

students see knowledge as less absolute and begin to use processes that allow them to search for the truth.

Students begin to think for themselves, begin to give credence to their own beliefs, and question the

absoluteness of experts’ knowledge when they are in Category 3, ‘‘Independent Knowing.’’ Category 4 is

‘‘Contextual Knowing,’’ in which ‘‘students believe that theories are constructed in a context based on

judgment of evidence; their role is to exchange and compare perspectives, think through problems, and

integrate and test theories’’ (Rauckhorst et al., 2001, p. 5).



Both Hunter et al. (2007) and Rauckhorst et al. (2001) used Baxter Magolda’s categories to understand

the ways that undergraduates develop intellectually while engaged in research apprenticeships. Rauckhorst

and his colleagues found that few of the undergraduates that they studied were in Category 1 at the beginning

of the summer experience, and that there was a much larger movement from Categories 2–3 as compared to a

control group of students. However, none of the students reached Category 4 by the end of the summer

experience. While Hunter and her colleagues did not use Baxter Magolda’s model to measure students’

development, they did find that few of the students or faculty in their study ‘‘mentioned increases in higher

order thinking skills, particularly the development of a complex epistemological understanding of science or

the ability to define a research question and develop experimental design’’ (Hunter et al., 2007, p. 66).

Kardash (2000) had similar findings. Faculty and undergraduate students agreed that the students had

increased their ability to observe and collect data, understand the importance of controls, interpret data, orally

communicate their results, to think independently, all of which could be interpreted as being in Categories 2

and 3 of Baxter Magolda’s model. However, there were much smaller gains in skills such as ‘‘identifying a

specific question for investigation, formulating a hypothesis, and designing a test of the hypothesis’’

(Kardash, 2000, pp. 196–197). ‘‘ The lowest gains were for the skills needed to make use of research

literature, relating one’s research results of the big picture, and writing a research paper (Kardash, 2000, 197),

all of which would most likely be found in someone in Category 4. What these studies suggest is that while

short-term research experiences for undergraduates are highly successful in helping them to develop to

Category 3, they are less likely to promote in students the ability to engage in the higher-order intellectual

skills used by expert scientists.

Communities of Practice

Apprenticeships occur in practice situations that have the characteristics of communities of practice.1

A community of practice defines itself along three dimensions: mutual engagement; a joint enterprise; and a

shared repertoire. A community of practice involves more than the technical knowledge or skill associated

with undertaking some task (Wenger, 1998)—its members are involved in a set of interpersonal and

professional relationships (Lave & Wenger, 1991; Wenger, 1998), which results in the community developing

around things that matter to its members (Wenger, 1998). Members of communities of practice gain a sense of

joint enterprise and identity because they are organized around a particular area of knowledge and activity.

Because they are communities of practice, their members need to generate and appropriate a shared

repertoire of ideas, commitments and memories; and various resources such as tools, documents, routines,

vocabulary and symbols that in some way carry the knowledge of the community. In other words, it involves

practice: ways of doing and approaching things that are shared to some significant extent among members.

The practice situation in which the graduate or undergraduate honors student participates is typically a

research group. A research group consists of at least one professor, a group of students and possibly one or

more post-doctoral fellows who engage in a joint research project or different, but related ones. Members of

the research group meet regularly and report on and critique one another’s research (Clark, 1997). Research

groups can be as small as one professor working with one or two students, or as large as those in high-energy

physics, which can number in the hundreds (Knorr Cetina, 1999).

Research groups are often associated with laboratories. A laboratory can be thought of as a place in

which the natural world is manipulated and transformed through experimental work (Knorr Cetina, 1999).

While the manipulation and transformation usually occurs to physical objects, it can also occur through the

quantification of data and its subsequent numerical or statistical manipulation. However, research groups are

not necessarily associated with laboratories. For example, some geologists collect all their data in the field and

do their data analysis in offices or computer centers. Of course, there are also research groups such as those in

theoretical physics that neither collect nor analyze data.

The research group can be thought of as a community of practice in which new members acquire the

skills and knowledge needed to maintain the laboratory and to do experimental work, such as the standard

methods published for each field (e.g., Clesceri, Greenberg, & Eaton, 1999). Even if there is no laboratory, the

research group can be a community in which new practices are developed and shared with a larger community

of practicing scientists (Creplet, Dupouet, & Vaast, 2003).



However, being a scientist is more than being a skilled practitioner in the laboratory. Scientists also have

as their goal to create and warrant new knowledge. As a result, the research group not only has characteristics

of a community of practice, it also has those of an epistemic community (Knorr Cetina, 1999). Epistemic

communities are those in which participants have the knowledge and skills needed to create and warrant new

knowledge. Like a community of practice, an epistemic community is a group of people with a shared

repertoire, mutually engaged in a shared activity. However, while the community of practice has as its

primary goal the improvement of practice, the epistemic community has as its primary goal the creation

and warranting of knowledge. Because epistemic communities have as their goal the creation of knowledge

for the use by people who are not necessarily members of the local research group, there is the need to

convince the others that the members of the group are correct; that is, the knowledge must be warranted in

some way. As a result, epistemic communities must rely on some type of implicit or explicit procedural

authority that plays a role in how the knowledge is warranted. Therefore, graduate education in the sciences

has as its goal to teach new researchers how to warrant their knowledge by responding to the procedural

authority that explicitly resides in guidelines for research and publication, and more implicitly in the

review process for journal articles, conference papers and funding proposals (Creplet et al., 2003; Knorr

Cetina, 1999).

In summary, our review of the literature suggests that the research education of new scientists consists of

novices engaged in legitimate peripheral participation as members of research groups engaged in scientific

research, and that this is different from participation in authentic science experiences for the purpose of

learning science. It is possible to understand research groups as communities of practice, but because

scientific research is a knowledge production endeavor, the research groups must also have the characteristics

of epistemic communities. In this study we examine the beliefs of university scientists and engineers who are

research group leaders and who work with novice scientists and research engineers, who are their students.

We seek to understand how their beliefs relate to this framework of research education as apprenticeship in

research groups, and their beliefs about how their students’ research experience develops, and their role in

facilitating that growth of expertise.

Methods

The setting for this study is an interdisciplinary collaboration among geologists, microbiologists,

environmental engineers, and science educators to study the natural remediation of acid mine drainage

(AMD) at an abandoned pyrite mine. The project has five principal investigators, all of whom are professors

in a large, public Research I university. Four of the professors are scientists or engineers. Each oversees a

research group that can include undergraduate, masters’, and doctoral students, as well as practicing middle

or high school science teachers. The fifth professor does research in science education and is one of

the authors of this paper. He, too, has a research group, which includes the two graduate students who are

co-authors of this article. Our study uses a phenomenological design.

Phenomenology is a. . . theoretical point of view that advocates the study of direct experience taken at

face value; and one which sees behavior as determined by the phenomena of experience rather than by

external, objective, and physically described reality. (Cohen & Manion, 1994, p. 29)

Following on the work of Schutz (1967), we focus on the experienced world that our participants—the science

and engineering professors—take for granted. We also seek to understand the ways in which the professors

use what Schutz calls ‘‘ideal types’’ to make sense of their experience. For example, in the context of this

study, a category of ideal type used by the participants was level of student, that is, undergraduate, master’s

degree, or doctoral. Because we are concerned with their experience and how they make meaning of it, rather

than how they behave, our primary form of data collection for this study was interviews with the professors

(Seidman, 2006). The structure of phenomenological interviews is relatively simple—they use broad, open-

ended questions that build upon and explore the participants’ responses to the questions. The goal is for the

participant to reconstruct his or her experience and to relate it to the interviewer. The interviewer asks

questions that follow-up on the participants’ responses to help fill them out, and to check the interviewer’s

understanding of the participant (Seidman, 2006).



Using Seidman’s guidelines for phenomenological interviewing we developed an interview protocol

that provided consistency across the interviews and the opportunity to engage in in-depth conversations about

the professors’ views of graduate education. Each of the interviews lasted for 60–90 minutes and was audio

taped and transcribed. We were also participant observers in the research project. As such we kept field notes

and recorded selected meetings. This data was primarily used to design the interview protocol, tailor it to the

situations of the different professors, and to triangulate the interview data.

Pre-conceived categories for coding were derived from the research literature on graduate education and

apprenticeships, while emergent categories were derived inductively from the data, following the methods of

the development of grounded theory (Strauss & Corbin, 1990). That is, we began with open coding, which we

did individually, to identify themes and patterns. We then used axial coding to group and label the themes and

patterns into categories. At this point we conferred with one another to compare and contrast the categories

that had emerged from the data. We then returned to the data and collapsed the categories into the five

dimensions of our findings. We used the qualitative analysis software HyperResearch to help us with our

analysis. We also used Inspiration software to graphically represent the relationships among the research

group members.

Findings

We organized our results along five dimensions: (1) the configuration of the research groups;

(2) conceptualizations of students’ roles; (3) conceptualizations of the growth of expertise; (4) the

apprenticeship; and (5) type of community.

Configuration of Research Groups

The configuration of the research groups, their relationship to the laboratory, and the relationships of the

individuals in the groups varied among the professors according to their research areas. The differences

among the type of group that the professors foster can be broken down into two categories: (a) a tightly

organized research group or (b) a loosely organized research group. Two of the professors, Karl, a

microbiologist, and Sarah, an environmental engineer, established tightly organized research groups by

maintaining traditional laboratories in which all their graduate and undergraduate students associated with all

of their funded projects work together. They each connected lab experiences to the members’ outside lives by

holding community building events such as cookouts, dinner parties, baby showers, and birthday parties, to

name a few. Both Karl and Sarah work in the same type of physical setting, have organized their students into

research groups that meet on a weekly basis, require their students to give regular presentations on their work

to the group, and hold group discussions. In his interview, Karl stated:

I introduce them [new research group members] to everybody who is working in the lab. They know

all the projects that are going on. They are socially integrated by. . . we have a birthday list. The person

who has a birthday gets a cake from the person who just had a birthday, so you do not have to bring

your own cake. We have outings: we have a summer picnic, we have a winter trip, and we have a

Christmas party. The newest lab member brings the turkey for the entire Christmas party; it is at my

house. In the lab, people are encouraged to talk to everybody and when a new person comes I talk to

the senior grad students to talk to the new person, to stimulate that, to get that culture going with the

new person also. (Interview 6/23/05)

In addition to holding social events, Sarah fosters her tightly organized research group in the way that she

tries ‘‘to team students up together . . . one student with a more experienced student so that they’ll learn some

basic skills in the lab’’ (Interview 7/5/05). For example, she always has new students meet with Elaine,

because she has the best organized lab notebooks. Sarah regularly meets with her graduate and undergraduate

students ‘‘in teams with students working on common projects’’ (Interview 7/5/05).

The other two professors have loosely organized research groups. Robert, a geologist, works with

students on an individual basis rather than as a group. His students do field work, bring samples back to the

laboratory where they are analyzed on communal instruments to quantify the data, and then they work

individually on their analysis. In his case, while there is a lab, the lab is not used as a place where the group



congregates to do work. Douglas, who is a hydrologist, also works individually with students. However, since

most of the work that they do is computer modeling, they infrequently make use of a traditional laboratory and

therefore do not often come together as a group. Both Robert and Douglas meet with their students by

appointment. Because of the way in which their groups are structured, Robert and Douglas build relationships

with their students on an individual basis. For example, Douglas told us the following about how he works

with students:

Well, normally, it’s you have to go out on a certain date and drill these holes or take these

measurements or whatever. So it’s actually more relationship building, in other words, conversations

about sports or whatever takes us to the work at hand. (Interview 7/6/05)

One of the important differences between the two types of research groups is the center for action.

In the tightly organized research groups in our study, the laboratory served as a center of action. Although the

professors were important in developing the groups and facilitating interactions among the students including

social activities, it was the student–student interactions that took place on a continuing basis because they

shared a common workspace that resulted in the tight organization. In the loosely organized research groups,

the professors were the centers of action. There were few student-student interactions because of the small

amount of that students spent together and the fact that students’ connections to the group were through the

professor.

The differences between the tightly connected and loosely connected research groups are illustrated in

the web of connections shown in Figure 1.The four science and engineering professors form a square in the

center of the figure. They are identified by the letter that begins their names. The lines emanating from the

professors go to their advisees. The lines connecting the students (numbered ovals) signify connections that

we observed and which were described to us by the students. The figure clearly shows that there are many

more connections among students in the two tightly connected research groups (Sarah and Karl) than in the

loosely connected groups (Robert and Douglas).

Figure 1. Web of connections among participants in research groups.



Figure 1 also indicates the sizes of the four research groups. As can be seen, Sarah’s group has

eight students while Robert’s has 10 students, and Karl’s and Douglas’ have six and four students

respectively. Clearly the size of the research group is not in itself a determining factor in whether it is tightly

or loosely organized. We also do not believe that the type of organization is due to physical proximity of

the members. That is, we do not think that if Robert’s or Douglas’ groups were to have communal space like

Sarah’s and Karl’s that they would have the features of tightly-organized groups. Rather, we believe that

the structure of the disciplines (Schwab, 1978)—geology, microbiology, hydrology, and environmental

engineering—affect the nature of work in the research groups and therefore play a major role. And of course,

the research and advising styles of the professors could also have an effect on the way in which the group is

organized.

Conceptualizations of Students’ Roles

While the there were physical differences among the settings of the research groups, and differences in

the way the professors and the students interacted with one another, we found large similarities in how the

professors conceptualized their students’ roles in the research groups. Although they used different language

to label the roles of their students, the interview data suggested that the professors had a shared vision of

their students’ roles. They spoke about their students as Novice Researchers, Proficient Technicians, and

Knowledge Producers, and in fact at times used these terms to describe the students’ roles. Novice

Researchers have little or no experience with scientific research, such as many if not most beginning graduate

and undergraduate honors students. As novice researchers they are generally seen as temporary members

who can, for example, develop the skills to help maintain the laboratory and collect data, but are not

expected to contribute much if anything to the analysis of data or the creation of new knowledge. In the

interviews, the professors talked about the inability of students at this level to formulate research questions,

their lack of laboratory or research skills, and the difficulty the students have in drawing defensible

conclusions from data.

Proficient Technicians have developed the skills needed to collect and analyze data, and to report results

to other researchers. In the interviews the professors noted that they did not expect students taking this role to

be adept at developing research questions. As Sarah told us,

When they [master’s degree students] come to work with me I pretty much hand them a project. It’s a

proposal that has been written and that’s funded and that has particular objectives and tasks that need

to be accomplished. (Interview 7/05/05)

The professors do expect them to have the research skills that allow them to do what is necessary for the

research project. The students can also apply the methods that they have learned to new situations. From our

analyses of the interviews, it became clear that the professors had the expectation that at the end of their

studies, master’s students would be at least Proficient Technicians. That is, they would have attained the

knowledge and skills necessary to become skilled practitioners in their field.

All of the professors supervise doctoral students. Karl noted that a PhD indicates that a researcher

has ‘‘intellectual proficiency’’ as well as technical proficiency. To Sarah this means being able to make

‘‘a significant contribution to the science and to the engineering’’ (Interview 7/05/05). Douglas and Robert

shared this expectation that doctoral students should be able to formulate their own research questions, to

develop new research methods, and to add to the literature. In short, all the professors expected their doctoral

students to become Knowledge Producers.

Conceptualizations of the Growth of Expertise

The science and engineering professors perceived the typology that we described above as

developmental: with appropriate experience and guidance individuals can move along a continuum from

Novice Researcher to Proficient Technician to Knowledge Producer. An important aspect of the professor’s

awareness of the developmental nature of participation in the research group is that they keep a watch out for

likely prospects whom they nurture along this developmental path.



The first move along this continuum is from Novice Researcher to Proficient Technician. Although each

of the four professors saw the first transformation differently, they all recognized the transformation. Karl saw

this transition as a growth in confidence and the ability to do more independent thinking:

The confidence level. . . Up front they’re not confident. They’re very conservative and sometimes

reactionary where they try to throw everything away and sometimes don’t trust their own data,

question everything. (Interview 6/23/05)

Sarah described the shift from Novice Researcher to Proficient Technician in her master’s students’

ability to design their own experiments. Douglas suggested that this transition could take place for some

undergraduate students:

Paul is a good example of an undergraduate who became almost like a Master’s student in the sense of

problem solving, and we developed a relationship in which I trusted him, where I could say, here’s the

problem go survey the site, you figure out the details, you solve the little problems, here’s the big issue

that needs to be resolved and he went and did it. (Interview 7/6/05)

The second move along this continuum is from Proficient Technician to Knowledge Producer. Typically,

only the doctoral candidates made the transformation. As was the case with the first transition, each of

the professors recognized the second transformation, but in different terms. Three of the professors

(Douglas, Sarah, and Robert) presented this transformation in terms of product. Douglas expected his

doctoral students, ‘‘to demonstrate an ability to add something to the literature, that’s sort of the test.

If it’s added to the literature, it’s got to be new and have some element of uniqueness’’ (Interview 7/6/05).

Sarah described this transformation in one of her students, ‘‘I could see in the journal articles that

she’s written that there was much more discussion, that there’s much more analysis of her data’’ (Interview

7/05/05). Although Robert suggested that a Knowledge Producer should publish, he elevated this notion:

For the PhD project, I expect that it will have global implications in their subject. That whatever the

finished product is can be put into a context that can be referred to in another part of the world, another

part of the country, lots of references to parallel problems, parallel sites, here’s what I’ve found and

this is what it means in terms of the context of geology or geochemistry. (Interview 6/20/05)

Sarah and Karl also spoke about the intellectual relationship that they expected to have with their

students as they became Knowledge Producers. For example, Sarah told us that ‘‘Sanghe used to ask me

questions all the time and now I ask her for advice’’ (Interview 5/5/05) and ‘‘Ajay has a much better grasp of

the literature than I do’’ (Interview 5/5/05). Karl expects his students to engage him like a colleague, ‘‘In the

end, they should criticize me; they should correct me in what I’m saying because they then become the experts

in their niche in their field’’ (Interview 6/23/05). All four professors acknowledged that they have lofty

expectations of Knowledge Producers and that in most cases it is only achieved at the end of a doctoral

program.

The Apprenticeship

It is important to note that none of the doctoral programs with which the professors are associated—

microbiology, environmental engineering, or geosciences—have courses that explicitly teach students how

to conduct research in that field. As a result, while students did take courses like statistics or laboratory

methods, the remainder of their education as researchers was informal and ‘‘on the job.’’ Given the informal

nature of this education, it is not surprising that none of the professors had given it much thought before we

interviewed them. However, as they responded to our questions, it became clear that they all went about it the

same way, that is, by using an apprenticeship model, which was how their mentors trained them. The methods

that they use are similar to what you would expect in any apprenticeship—in the early stages the students were

heavily supervised and given specific tasks to accomplish, including review and critique of the literature. As

students progressed in the programs, the professors became less directive and turned more to questioning



students. In the final stages of dissertation research, the professors expected to engage in collegial

conversations with the students about the students’ research. Throughout the process the professors tailored

their methods to the backgrounds and needs of individual students.

All four of the professors believed that students in the early stages of an apprenticeship need close

support and direction. For example, Karl told us how doctoral candidates need close support early in their

programs:

In the first year [doctoral students] need to have their hand held, even if they come with a master’s from

someplace else, from a different lab. It should be at least the first half-year but preferably the first full

year that they should be intensively advised and mentored. (Interview 6/23/05)

Because Sarah, Robert, and Douglas work with undergraduate honors students and master’s students in

addition to doctoral students, they also talked about the close support that those students need as they begin

their research education.

As the students gain expertise as researchers, the professors become less directive and more focused on

the students taking on a more active role. For example, Douglas described his work with master’s students and

beginning doctoral candidates in this way:

I think that both masters’ and PhD students shouldn’t just take up what I might teach them in terms of

skills but to develop on their own skills to search the literature about how to do something, and to come

up with a new idea on how to perform some analysis. (Interview 7/06/05)

Toward the end of the apprenticeship period the professors expected their students to become able

to develop and guide their own research. Sarah spoke about how she expected to see her students show

independence as they begin to work on their dissertations:

When [doctoral students] see the project in relation to their dissertation they will take the next step of

actually going on to develop their knowledge by starting to collect literature and to organize it and

make sense out of it. (Interview 7/05/05)

Douglas told us as students advanced in their research education he, ‘‘would expect them to be able to do

analysis and interpretation or learn how to do it or figure out how to do it or devise a new way to do it’’

(Interview 7/06/05). Robert suggested that students at this level have collegial conversations that are ‘‘about

the bigger picture’’ and about ‘‘the meaning of the results that you’ve gotten’’ (Interview 6/20/05). Karl

described his expectations of advanced students in this way:

They should be independent. If new grad students come in to the lab they shouldn’t wait for me to tell

them to help them, they should approach them and say, ‘‘Here, look this is how it’s done,’’ they should

want to help others. If there is a meeting in Boston or at Yale or another university they should suggest

to me that they would like to go there and present. When we go to a meeting together they should stay

with me and I’ll introduce them to other people because they know by that time how important

networking is to all these things. (Interview 6/23/05)

Our interview data shows that while the professors had not explicitly thought about how they educated

their students to be researchers, they were in fact behaving in ways that Lave and Wenger (1991) would call

successful apprenticeship teaching. They also engaged in many of the practices of cognitive apprenticeships

(Collins et al., 1989). We return to this is in our discussion section below.

Communities of Practice and Epistemic Communities

The research groups that we studied were more than a community of practice because of their objective

to create and warrant new knowledge. While all group members participated legitimately in the research

group, some did so only in the community of practice, while others participated in that community and the

epistemic community. We illustrate this in Figure 2.



There were many examples described by the four professors of their students’ participation in a

community of practice. This included the collection of samples, the analysis of samples using ion

chromatography, and the analysis of data using statistical and graphical methods. There was also the

acknowledgement among the professors that they, as scientists and engineers, are involved in epistemic

communities and that they expected their doctoral students to become part of those communities by learning

how to produce and warrant new knowledge; by showing how it relates to the field; and demonstrating that it

has implications that go beyond the research at hand. To some of the professors, this was indicated by a

transition in which the doctoral student becomes the producer of knowledge, as can be seen in this comment

from Douglas:

I would expect (doctoral students) to demonstrate an ability to add something to the literature, that’s

sort of the test. And if it’s added to the literature it’s got to be new and have some element of

uniqueness. (Interview 7/6/05)

Karl described his department’s expectations for doctoral students’ participation in the epistemic

community in this way:

In our program we have the requirement that you have to have at least one research paper in a good

journal in press before you finish. Most people get two or three, some get eight, it varies. But you have

to have a complete and convincing analysis of all your data to have it published and have it go through

professional reviewers so by the time that PhD students are finished they have the ability to analyze the

data and take anything out of their data that they can. When they write the first paper it’s a lot of work

for me because it goes back and forth sometimes 30 times editing and reediting sometimes 30 times.

But when they write the next paper or the third paper it’s a joy and I would like to keep them because

they’re so good then and they’ve started producing. (Interview 6/23/05)

Robert also alluded to the knowledge generation capabilities of doctoral students when he told us of his

disappointment of not having a doctoral student to work closely with, because he found that with his master’s

students, ‘‘the knowledge always flowed from him to them and never vice versa’’ (Interview 6/20/05).

Discussion

As we noted in our review of the literature, most studies of students engaged in scientific research focus

on undergraduates and high school students. Our typology of student roles in research groups suggests that the

undergraduates and high school students enter their research experiences as novice researchers. During their

relatively short-term participation in research groups, the novice researchers gain skills such as the ability to

collect and work with data, and to communicate their results (Hunter et al., 2007; Kardash, 2000; Lopatto,

Figure 2. Members of communities of practice and epistemic communities.



2004; Rauckhorst et al., 2001). These are also the skills that teachers developed in similar, short-term

authentic experiences (e.g., Brown & Melear, 2007; Lotter et al., 2007; Lunsford et al., 2007; Westerlund

et al., 2002). These are some of the skills that the four professors in our study expected to be developed as their

students go through the transformation from novice researcher to proficient technician.

The professors in our study expect more from their masters level students who spend at least 1 or 2 years

in their research groups. They expect that when their students complete their master’s degrees they have the

expertise to work as professionals in the field. What this means is that while they may not have the knowledge

and skills to be knowledge producers, they can, for example, work in industry as engineers or scientists

applying the methods that they learned to collect and analyze appropriate data, and to prepare reports for their

employers or clients. While the undergraduates and teachers can develop the same types of skills in their

research experiences, they lack the ability to work as professionals in the field. This difference may be one of

degree—the master’s degree students just are better at doing the same things as the undergraduates and

teachers, or the difference may be attributed to a category of behaviors and skills that Hunter et al. (2007) label

‘‘becoming a scientist.’’ These include:

� demonstrating attitudes and behaviors needed to practice science;

� understanding the nature of research work;

� understanding how scientists practice their profession; and

� beginning to see themselves as scientists (2007, p. 49).

The professors in our study also referred to these types of behaviors and beliefs and saw them as

important indicators of the shift from novice researcher to proficient technician.

The studies of undergraduates that we reviewed indicated that the students had not developed the skills

that would place them in Category 4 of Baxter Magolda’s cognitive reflection model (Rauckhorst et al.,

2001). Our study suggests that the skills and beliefs of Category 4 are developed as students transform from

Proficient Technicians to Knowledge Producers. This transformation can also be seen as the students learn to

participate in the epistemic community. Participation in the epistemic community requires the ability to

produce and warrant new knowledge, and to demonstrate that it has implications that go beyond the research

at hand. To do this requires the set of cognitive skills that are Baxter Magolda’ Category 4, such as being

able ‘‘to exchange and compare perspectives, think through problems, and integrate and test theories’’

(Rauckhorst et al., 2001, p. 5). What this suggests is that the development of skills that corresponds to a

movement from Categories 2–3 can occur through participation in the community of practice, but that the

move from Categories 3–4 may require students to participate in an epistemic community as well.

Our data suggest that the research education of scientists occurs primarily as an apprenticeship. The

graduate students learn to do research by performing tasks in ways that are analogous to how their professors

perform them. That is, they are legitimate participants in the research process. In addition, the professors

structure the ways that the students participate by assigning them tasks appropriate to their development

as researchers (Lave & Wenger, 1991). Clearly it also has the characteristics of a cognitive apprenticeship.

The professors model and coach their students, and provide the scaffolding they need to collect and analyze

data, and to prepare reports. In research groups and in one-on-one meetings with the professors the students

are expected to articulate their knowledge, reasoning, and problem solving. As part of the research group they

have the opportunity to reflect on and compare their problem solving processes with their peers, more

advanced students, the professors, and, with the research literature. And of course, in order to fulfill the

requirements for their degree programs, they need to learn to problem solve on their own. Needless to say, all

of this is situated in authentic scientific research.

It is important to note that none of the professors had ever heard of cognitive apprenticeships, and as we

noted above, none had given much thought to the way they teach their students how to do research. One way to

explain the similarity between the professors’ methods and those of the cognitive apprenticeship is to

recognize that the research education of new scientists is some sort of natural cognitive apprenticeship. That

is, because an important part of the work of scientists goes beyond the practice of craft skills, the education of

new scientists requires an apprenticeship model that develops higher order cognitive skills, which is the goal

of the cognitive apprenticeship.



Another way to explain the similarity is to assume that all apprenticeships, or at least all that produce

master practitioners, use the methods described by Brown et al. (1989). Trades people like tailors,

electricians, carpenters and so on, as well as professionals who learn in apprenticeship settings (doctors,

lawyers, nurses, etc.) also need to learn how to problem solve, in addition to the skills needed to reproduce

what others have done before them. This suggests that the only significant difference between traditional

and cognitive apprenticeships is that the latter is the application of apprenticeship teaching methods in a

non-authentic setting, that is, schools.

Implications

In this study we examined the ways that four professors prepare their students to become researchers.

We grouped our findings into five areas: the configuration of their research groups; conceptualizations

of students’ expertise; conceptualizations of the growth of expertise; apprenticeship teaching; and type of

community. Returning to the introduction to this article, we believe that these findings have implications for

the type of teacher learning activities that are called for by the National Science Education Standards.

First, if science teachers engage in scientific inquiry along with scientists, the type of research group that

they join can affect their learning. As we saw in the tightly organized research groups, students have several

mentors beside their professor. The research groups were made up of students who were at different levels of

expertise and doing different types of tasks. Novice researchers are in regular contact with Proficient

Technicians, and Proficient Technicians were mentored by the growing expertise of the advanced doctoral

students as they became Knowledge Producers. This was not at all the case for the students in the loosely

organized research groups. They infrequently had contact with more advanced students and, therefore, for the

most part they relied solely on their professors as their mentors.

Teachers who work in these groups would most likely have experiences similar to that of graduate

students. In the tightly organized groups they would interact with research group members on a daily basis,

and be mentored by Proficient Technicians and Knowledge Producers, and in turn, mentor other members of

the group. Teachers in the loosely organized groups would not have close mentorship and therefore depend

more on supervision from the professors. Given the time constraints on professors, they would most likely

interact infrequently with the teachers. Therefore, teachers in the loosely organized groups would have less

support than those in the tightly organized research groups.

A second implication comes from the typology of roles that people play in research groups. When

science educators and policy makers say that they want teachers to have knowledge of doing science so that

they can teach their students how to do science, are they expecting them to have the expertise of a Novice

Researcher, Proficient Technician, or Knowledge Producer? That is, if the typology is developmental, what

level is the best fit for a K12 teacher and what level do they need to be at to adequately teach their students how

to do scientific research?

The level of expertise of the teacher may also determine the level that their students would be able to

reach. While it seems clear from the apprenticeship model that a Knowledge Producer can have apprentices

who are Novice Researchers, Proficient Technicians, and new Knowledge Producers, a Proficient Technician

would only be able to train Novice Researchers and new Proficient Technicians, and Novice Researchers may

not have the expertise to even produce others at their own level. There also appears to be a relationship

between the roles that one has in a research group and ones’ intellectual development. As we discussed earlier,

we see a connection between the transformation from novice researcher to proficient technician and the move

from Categories 2–3 in Baxter Magolda’s model, and a similar relationship between the transformation from

Proficient Technician to Knowledge Producer and the move from Categories 3–4. Does this mean that

teachers ought to be knowledge producers if they are to help their students gain the skills associated with

Category 4?

A third implication relates to the time and resources needed to educate Knowledge Producers. Scientists

and engineers are taught to be researchers through apprenticeship programs that can last for five or more

years. It would therefore require a tremendous investment in time, money, and other resources to train all

science teachers to be producers of scientific knowledge. It seems unlikely that society would be willing to

make such an investment. In addition, at some level it seems wasteful to invest so much to prepare teachers to

do work that is outside their practice. If teachers are to be Knowledge Producers it seems more reasonable for



them to become members of epistemic communities that generate knowledge about teaching and learning

rather than scientific knowledge.

We also believe that our research has important implications for the education of new researchers in the

sciences. One is that, as we noted above, the scientists and engineers who we interviewed had given little

thought to how they educate new researchers. Instead they go about doing what is familiar to them, which is

based on the experiences they had as new researchers. This suggests that even in the informal learning

situations of apprenticeships, teachers teach the way that they were taught. On the surface this appears to

work: new researchers who can contribute to the knowledge base of the field are produced. However, we have

little data about those who do not come through the process as ‘‘Knowledge Producers.’’ They not only

include the students who do not pass their comprehensive examinations, but also those who for one reason or

another choose to stop at the master’s degree level or choose to leave doctoral programs without completing

their dissertations. Therefore, we believe that it is important to continue this research line to gather data about

students’ experiences as well as the perceptions of their professors.

Another implication is that in those fields where there are tightly organized research groups, students are

teaching other students how to be researchers. It may even be the case that advanced students do the majority

of instruction in research methods. While interviews indicate that Karl provides his students with some

guidance on how to do this, there is the possibility that in most situations the more advanced students have

little or no guidance on how to be mentors. If we are going to continue to use apprenticeships as the primary

model for teaching people to be researchers, it may behoove us to provide the mentors, both faculty and

advanced students, with instruction in how to be successful mentors. One way to do this would be to use

cognitive apprenticeship to develop a model of apprenticeship learning in the sciences and to train graduate

students and professors in its implementation.

Conclusion

What does it mean that science teachers ought to know how to do science? Does it mean that they should

be Novice Researchers who have been exposed to a community of practice, but have developed little of the

skills needed to develop and carry out a research project? Or does it mean that a teacher should be a Proficient

Technician, who is a skilled member of the community of practice, but does not participate in the creation or

warranting of new knowledge? Or does it mean that for a teacher to adequately teach children how to do

science, he or she must be a Knowledge Producer? The answers to these questions would determine the time,

money and other resources needed to sufficiently educate science teachers so that they can serve as research

mentors to their students. As our research suggests, there is much more to becoming a scientist than can

be accomplished in the professional development models currently used, including the National Science

Foundation’s Research Experiences for Teachers, if the traditional apprenticeship model is used. This

suggests that either we must change the goal that we have that K12 students ought to learn how to do science

so that it does not require teachers who are at the level of Knowledge Producers or we need to find new and

more efficient ways to help teachers learn how to do science.

Notes

1 We believe that it is important to distinguish between the practice situation and the community of practice. The

practice situation consists of the workplace and people of the apprenticeship. The community of practice is a theoretical

construct that is used to understand the practice situation.

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