Submitted (author version) of an article accepted for publication in BioScience
Designing and Studying Student-Centered Undergraduate Field Experiences: The UFERN
Kari O’Connella, Kelly L. Hokeb, Michael Giamellaroc, Alan R. Berkowitzd, Janet Branchawe
aSenior Researcher at STEM Research Center, 254 Gilbert Hall, Oregon State University,
Corvallis, OR, 97331, USA. email@example.com.
bResearcher at STEM Research Center, 254 Gilbert Hall, Oregon State University, Corvallis,
OR, 97331, USA. firstname.lastname@example.org.
cAssociate professor of Science Education, Oregon State University, College of Education,
Cascades Campus. OSU-Cascades Graduate & Research Center, 1500 SW Simpson Ave., Bend,
OR, 97702, USA. email@example.com.
dHead of Education, Cary Institute of Ecosystem Studies, PO Box AB, Millbrook, New York
12545, USA. firstname.lastname@example.org.
eAssociate Professor of Kinesiology, School of Education, and Director of Wisconsin Institute for
Science Education and Community Engagement, University of Wisconsin - Madison, 1215
Linden Drive, Rm. 351A Bardeen Medical Laboratories, Madison, WI 53706.
Undergraduate field experiences (UFEs), where students learn and sometimes live
together in nature, are critical for the field-based science disciplines. The Undergraduate Field
Experiences Research Network (UFERN) brings together UFE educators and researchers to
improve and broaden participation in field education. Integrating research on UFEs and general
STEM education, and the expertise of the UFERN community, we present a model and evidence
that describes the impact of intended student outcomes, student context factors and program
design factors on UFE student outcomes. The UFERN Model is relevant for a diversity of UFE
formats and the diverse students potentially engaged in them, and thus it supports the field
science community to consider a range of ways students can engage with “the field.” The
UFERN Model can be applied to guide the design, implementation, and evaluation of inclusive
UFEs and to guide research on the mechanisms underlying outcomes across UFE formats and
Key Words: undergraduate, field education, student-centered, student outcomes,
Undergraduate field experiences (UFEs), where students learn and sometimes live
together in nature, are viewed as critical to graduate and career opportunities in field-based
sciences (Petcovic et al. 2014, Fleischner et al. 2017, Klemow et al. 2019, Giles et al. 2020).
Student outcomes from UFEs of various formats include cognitive gains in disciplinary content
(Easton and Gilburn 2012), improved understanding of the process of science (Patrick 2010), the
development of discipline specific technical skills (Peasland et al. 2019), general skills, such as
teamwork, decision-making and autonomy (Boyle 2007), and affective gains such as
development of sense of place (Van De Hoeven Kraft et al. 2011, Semken et al. 2017, Jolley et
al. 2018), and increased self-efficacy (Kortz et al. 2020, Beltran et al. 2020). Recent research has
shown that participating in field courses decreased the achievement gap for students historically
excluded from STEM (Beltran 2020). However, many barriers exist for participation in UFEs
(Morales et al. 2020). More research is needed to understand the distinctive nature of UFEs and
how they can be designed to improve access and inclusion, and UFE educators (e.g., faculty,
instructors, program directors and coordinators) need guidance for designing UFEs that is
grounded in research and that considers how program elements will impact students from all
backgrounds. The research-based Undergraduate Field Experiences Research Network (UFERN)
Model presented in this paper is meant as a resource to support UFE educators in designing and
evaluating inclusive, student-centered UFEs and education researchers in studying the impact
that UFE program elements, herein called design factors, have on student outcomes.
UFERN and the UFERN Model
The NSF-funded UFERN (https://ufern.net/) is an interdisciplinary community of social
and behavioral scientists and UFE educators focused on improving and broadening participation
in undergraduate field education. UFERN emerged from the biological field stations and marine
labs (FSML) community and as proposed first only focused on the summer-long, residential
undergraduate field experience that has a long history at FSMLs (McNulty et al. 2017).
However, during the first UFERN meeting (Kellogg Biological Station on April 30 – May 2,
2018), the conversations about broadening participation in undergraduate field education
centered around broadening the idea of what undergraduate field education could look like – to
go beyond the summer-long, residential UFE – and think about the most distinctive and valuable
elements of an UFE offered in more formats to be accessible and inclusive for a broader diversity
of students. To help guide this thinking and following the approach taken by CUREnet
(Auchincloss et al. 2014), we formed a small working group of people with expertise in
undergraduate field education and STEM learning research to design what became the UFERN
Model. This small working group started with a proposed list of UFE factors generated in the
first UFERN meeting, a landscape study about the nature of UFEs (O’Connell et al. 2020) and
the literature (Falk & Storksdieck 2005; Stokes & Boyle 2009; Mogk & Goodwin 2012;
Auchincloss et al. 2014; Giamellaro 2017; NASEM 2018) to inform the initial development of
the model. Further design came from an iterative process of gathering feedback from the
UFERN community (specifically UFERN webinar on April 10, 2019 and the 2nd annual UFERN
meeting at H.J. Andrews Experimental Forest on October 15 - 18, 2019) to help identify
additional factors, areas for clarification or reorganization, and additional relevant literature.
Feedback was collected from 33 people (see list in acknowledgements) with a range of expertise
including biology and geosciences field course instructors, directors of field-based REU
programs, experts in broadening participation in STEM, discipline-based education researchers
in biology and the geosciences, researchers in educational psychology, human development, and
STEM learning, and professionals in these areas from groups historically excluded in STEM. In
response to this feedback, we added, deleted or re-defined factors, and reviewed additional
literature as suggested and through forward and reverse citation mining. Thus, the model we
developed and present here represents a synthesis of a large body of literature and expert input
for understanding student outcomes from UFEs, simplified as much as possible in light of the
complexity and overwhelming number of factors involved in the learning process. We present
the UFERN Model, which is based on a very simple backward design, as a launch pad for
subsequent testing and application to various contexts and also as an overview that provides a
broad summary about undergraduate learning in field settings.
The UFERN Model (Figure 1) illustrates that actual student outcomes of an UFE are a
function of the experience each student has, and that this experience results from an interaction
between the contexts that frame who the student is (student context factors, Table 1) and how the
experience was designed and implemented (UFE design factors, Table 2). Intended student
outcomes, in turn, influence both the student context and design factors. The recruitment and
selection of students is an opportunity to know and consider those student context factors while
program design and instructional choices are opportunities to optimize those students’
experiences. While evidence exists that each kind of factor can impact STEM learning broadly,
few factors have been studied in-depth or in more than one field learning experience, and thus
the UFERN Model defines an agenda for future research on UFEs. For more comprehensive
information about the model components and arrows and an example of how the model is being
used in a field-based Research Experience for Undergraduates program, see supplementary
materials. While this paper presents the foundation of the UFERN Model, a second paper
describing application of the UFERN Model, based on pilot testing underway with a range of
UFE educators, to design, implement, and improve UFEs is forthcoming.
The UFERN Model assumes that UFEs are embedded in authentic physical and social
contexts where students engage with the natural world in a structured way that is grounded in
academic learning and student development goals. Therefore, field crew jobs, internships, or
individual undergraduate research experiences without explicit learning goals are not addressed
in the model. UFEs can take many forms, from short field “labs” as part of traditional on-campus
university courses, immersive weeks or months-long field courses at field stations and marine
labs, traveling geology or ecology courses, or weeks-long field research experiences (Lonergan
and Andresen 1988, Hodder 2009, Whitmeyer et al. 2009, Fleischner et al. 2017, O’Connell et al.
2020). It is our intent that the UFERN Model can be used to consider and research a range of
UFE formats as well as the diversity of undergraduate students potentially engaged in them.
UFEs are an important part of many disciplines (e.g., archaeology, forestry, agriculture,
atmospheric science). For what we present here, our focus is on biology field experiences (from
which UFERN emerged) and also geosciences field experiences, where much of the research
about UFEs is located. However, we think the model is possibly applicable to other disciplines,
and we invite others to test, use, build on and refine this model.
UFERN Model: Factors for Learning and Personal Growth
A summary of the empirical evidence for the influence of each factor on student learning
and personal growth and how each factor varies among programs is presented here.
Students’ experiences in UFEs and ultimately the outcomes they achieve are influenced
by the goals and intended student outcomes set by the instructor or program. Intended student
outcomes may occur in three domains of learning (Anderson et al. 2001): 1) cognitive (e.g.,
increased science knowledge or proficiency in research practices,) 2) psychomotor (e.g.,
improved discipline-specific skills), and 3) affective (e.g., increased interest in a field-based
science career or stronger connections to place). Among the broad diversity of program types
represented in the UFERN Model, the prominence and nature of the field-specific intended
outcomes varies, both among participants within programs and between programs. Readers are
referred to O’Connell et al. 2020 and Shortlidge et al. (in press) for a more comprehensive
discussion of the variability of UFE student outcomes. The processes, program design and
instructional choices, and student recruitment & selection should be guided by the intended
student outcomes and by factors that are fixed at an institutional or other level.
Student Context Factors
Students’ experiences in UFEs, and ultimately, the outcomes they attain, are shaped by
who they are, why they participate, what they have done before, what they know, what they feel
and are able to do, and their individual needs and constraints. The list of student attributes
presented here - worldview, interests, identity, prior knowledge and skills, motivation and
expectations, prior experiences, and personal needs - is supported by research about how people
learn (e.g., NASEM 2018), research about undergraduate STEM education (e.g., NASEM 2017),
and research specifically about UFEs (Hughes 2016). These student context factors do not exist
in isolation, but instead overlap and interact with each other. Student-centered, culturally-
relevant pedagogy (Ladson-Billings 1995), and inclusive program design require understanding
students in all these dimensions. They should be considered in student recruitment and selection,
program design and instructional choices, as well as in the day-to-day implementation of
programs and the interpretation of and response to student outcomes data.
Storksdieck et al. (2005) define worldview as, “...a person's core values, beliefs, and
ways of understanding and creating meaning” (p. 356). UFE educators should expect that
students with differing worldviews may each perceive and interpret the field experience
differently. This has implications for students’ learning. Culture, defined as, “a system of
inherited values, goals, and language that provides members with a shared sense of who they are
and a common purpose for action (Posselt 2020; p. 3),” is especially relevant to worldview. In an
ethnographic case study of a graduate-level geoscience field course, Posselt (2020) remarks that,
“field culture has been and continues to align with traditional visions of masculinity, construing
only those who align with those visions as real insiders (p. 35).” Students may have cognitive
barriers to learning if they have worldviews that do not match with this or other conceptions of
field culture. For example, these barriers could be based on previous outdoor experiences as an
individual facing physical disabilities (Gilley et al. 2015), concerns over safety (Nelson et al.
2017), lack of family support for career interests (Balcarczyk et al. 2015), anxiety around
religious needs (Barnes et al. 2017), expectations for long days in the field (Posselt 2020) or a
view of how scientific knowledge is developed that is different than Western science (Bang et al.
Interest can be described as, “the psychological state of a person while engaging with
some type of content (e.g. mathematics, bass fishing, music) and also the cognitive and affective
motivational predisposition to re-engage with that content over time” (Renninger and Hidi 2019).
Interest has a powerful influence on learning (e.g., attention, goals, levels of learning) (Hidi and
Renninger 2006, Bang and Marin 2015), academic outcomes (Harackiewicz et al. 2002), how
individuals make choices about learning experiences (Lent et al. 1994, Renninger 2000), and
academic majors (Harackiewicz et al. 2002). The four-phase model of interest development
describes that “triggered situational interest,”(attention and emotion triggered in the moment)
with sufficient support, becomes “maintained situational interest,” developing over time into
“emerging individual interest” and ultimately “well-developed individual interest” (Hidi and
Renninger 2006). In common language, interest refers to “a long-term pattern of choices and
pursuits'' (Bell et al. 2019). Moving into later phases of individual interest provides a pathway to
developing identity (Bell et al. 2019).
The task of UFE educators is to “keep the door open” as long as possible for interest to
develop or deepen by creating learning experiences that support positive attitudes about these
topics so students will want to re-engage with them (Bell et al. 2019). Lent et al. (1994) present a
model of how career interests develop that suggests a student’s gender, ethnicity, and family are
contextual factors that might affect a students’ exposure to nature and interest in field-based
careers. LaDue and Pacheco (2013) highlight the importance of informal learning experiences
such as the role of family, engagement in outdoor recreation, and personal experiences with local
geology for interest development. These influences can affect the student’s self-efficacy and
outcome expectations that then may encourage or discourage the student to seek field learning
experiences (Haynes et al. 2015) or their level of interest in them.
Identity is a complex topic (Gee 2000, Bell et al. 2018), but can be defined as, “the way
that people answer questions such as: “Who do I think I am, or who can I be, where do I belong,
and how do I think other people see me?” (Bell et al. 2018). Identity is seen as both the way an
individual sees themselves, and the way an individual is recognized by others. Science identity
refers to the development of a professional identity within the scientific culture and relies on
individuals recognizing themselves as a potential scientist and others’ recognition of them as a
potential scientist (Carlone and Johnson 2007, Seymour et al. 2010, Williams and George-
Jackson 2014). Science identity impacts motivation and engagement in learning (Bell et al.
2018), and identity is a predictor of the persistence and educational success of students from
groups historically excluded from science (Hernandez et al. 2013, Estrada et al. 2016, Stets et al.
2017). Individuals make choices about learning opportunities based on their developing interests
and identities, provided the opportunities for these experiences are accessible to them (Barron
Streule and Craig (2016) discuss that “field trips are a powerful tool in developing
identities as geoscience students” through practical experiences and emphasis on collaboration
and group work. However, multiple aspects of student identity - such as race/ethnicity and
gender, but also “hidden identities'' such as religiosity, sexual orientation, and generation in
college - may impact a student's willingness to engage in learning activities, and may
consequently impact student learning (Henning et al. 2019). This situation is especially relevant
in UFEs (such as residential programs) where active learning environments and extended
student-student and student-faculty interactions may increase feelings of social isolation for
some (Henning et al. 2019). Identity has a strong social component with issues of power, race,
class, gender, and age influencing how students may feel obligated (or see only one pathway) to
fit in as being a valuable and exemplary member of a particular setting - in this case, the culture
of science, field work, a field station and marine lab (Hughes 2016). However, research has
shown that students given agency to merge their lifeworlds with the world of science (in other
words, bringing in aspects of their other identities and interests into the world of field education),
can successfully develop and strengthen their science identity (Carlone and Johnson 2007, Tan
and Barton 2008, Henning et al 2019). In the future, we need to give students possibilities for
negotiating their identities that are varied, authentic, and inclusive.
Prior Knowledge and Skills
Research has identified prior knowledge as one of the best predictors of subsequent
learning (Hattie 2012). Constructivist learning theories are built on the idea that learners tend to
adjust and add to what they already know rather than remember entirely new ideas. Each learner
in a course or experience is building on their existing knowledge base. Therefore, knowing what
prior knowledge and skills incoming learners have allows educators to begin a course at an
appropriate place. Pre-assessments can play an important role in understanding prior knowledge,
which can vary considerably among students. For example, Dalton (2001) found that in the U.K.
students enter their undergraduate programs having experienced a highly variable number of
academic field days and therefore bring highly variable degrees of field-specific skill and
knowledge. However, Nelson-Barber and Trumbull (2007) present the need for a culturally
relevant assessment in order to address conflicts between scientific and vernacular ways of
knowing, and may prevent the frustrations of students who are not invited to use the knowledge
they already have (Civil 2016). Students may have culturally- or linguistically-bound ways of
knowing about the course content that can provide a rich starting point on which to build future
knowledge (Sánchez Tapia et al. 2018, Bang et al. 2018).
Motivation and Expectations
Motivation, driven largely by one’s self-efficacy, or one’s perceived ability to complete a
task (Bandura 1997), is “a condition that activates and sustains behavior toward a goal,” and is
“critical to learning and achievement” (NASEM 2018). Expectations are the hopes and fears that
students hold about future experiences and their outcomes. Both are relevant to the learning
experience and how students engage in the experience itself, given a student's ability to persist or
cope with unexpected or novel aspects of the UFE (Dykas and Valentino 2018). Scott et al.
(2019) discuss the motivations and expectations of students in choosing among field courses,
finding that students balance a range of “actual and potential costs and benefits.” The students in
the Scott et al. (2019) study discussed barriers to participation such as financial costs (see also:
Maw et al. 2011, Fleischner et al. 2017, Jensen et al. 2021), physical risk, and social costs,
defined as “time spent away from family, work, etc., and pressure to conform to the social
culture of the course,” (Scott et al. 2019, see also: Hall et al. 2002, Cotton 2009, Durrant and
Harman 2015). Positive motivators for participation cited by students focused on the location of
the experience (either because they had visited the location before or because it was a new
location they wanted to visit) and an expectation that the course would benefit them by
enhancing their employability (Scott et al. 2019). In a different study about a program focused on
engaging Native American students in research experiences, Ward et al. (2018) found that 28%
of students applied for the program because it was close to home.
Giving students the opportunity to make meaningful choices during instruction, even if
they are small, can strengthen autonomy, self-efficacy, motivation, and ultimately, learning and
achievement (Moller et al. 2006, Patall et al. 2008, Patall et al. 2010). Students report that they
are more motivated when they get to work independently in project groups (Goulder and Scott
2009, Scott et al. 2019). Scott et al. 2019 found that motivation decreased during a field course
when student expectations for independent learning were not met. No degree of academic ability
or knowledge can overcome a student’s lack of self-efficacy or motivation to participate
(Bandura 2010, Dykas and Valentino 2016).
Prior experiences influence interest development (Tanner 1980, Barron 2006, Hidi and
Renninger 2006, Renninger 2009) and novelty space, which refers to a students’ level of
familiarity with three dimensions: cognitive (expectations for learning outcomes in this setting),
psychological (personal comfort and safety concerns), and geographic (where am I) (Orion and
Hofstein 1994, Mogk and Goodwin 2012), and these influences can both support and distract
from intended learning and engagement (Porter and Smithson 2001, Cotton 2009). Lack of early
exposure to positive nature-related opportunities (Floyd 1999, Parker and McDonough 1999,
Floyd and Johnson 2002) and lack of prior experiences in field settings for educational
opportunities are a particular concern for students from groups historically excluded in STEM
(Balcarczyk et al. 2015, Haynes et al. 2015, Giles et al. 2020). In a study of a residential field
course in the UK, far fewer Black and Minority Ethnic (BME) than White British (WB) students
had taken part in a field trip in their previous geography studies, which might disadvantage these
students’ academic achievement in the residential field course (Hughes 2016). In addition,
significantly more WB students than BME students had visited the field trip location at least
once prior to the field trip. This lack of familiarity may explain the greater concerns expressed by
BME students about what the field trip location was like (Hughes 2016).
Individual needs of students such as required supports for disabilities, physical and
mental health conditions, or previously experienced trauma can impact who participates in UFEs
and what their experience is like. Barriers that students with disabilities might experience in
UFEs include physical access to field locations (Hall et al. 2002); attitudes of faculty and other
students (Mol and Atchison 2019); and institutional barriers such as liability concerns on the part
of the institution (Healey et al. 2001). The immersive elements of the field setting (e.g., physical
and mental demands from rugged terrain and isolated settings, extended lack of communication
with and time away from family) that contribute to positive, identity-forming learning
experiences for some, can create barriers for others (Morales et al. 2020, Posselt 2020). Field
settings can create considerable visual and/or auditory processing difficulties for individuals with
autism (Kingsbury et al. 2020). For example, bright sunlight, complex scenery, wind, and
unfamiliar noises can make it challenging for them to hear or take in information while in the
field (Kingsbury et al. 2020). The stressors of physical hardship and real or perceived lack of
safety, as well as stressors linked to group and interpersonal dynamics, can impact mental health
(John and Khan 2018) and therefore limit participation or achievement in UFEs.
Students’ experiences in UFEs, and ultimately, the outcomes they attain, are not only
shaped by student context factors, but also by program design and instructional choices.
Research describing how each presented design factor influences which students are willing and
able to participate in UFEs and which student learning outcomes are possible in UFEs is outlined
here. The list of design factors presented here - setting; timing; immersion and sensory
experience; orientation to the experience and culture; social interactions; choice, control, and
power structures; and instructional models/activities - is supported by research about how people
learn, research about undergraduate STEM education, and research specifically about UFEs.
Like the student context factors, however, these factors do not exist in isolation. Though some
design factors are constrained or fixed (e.g., course enrollment and/or timing may be decided at
the university level), they all influence student learning. Therefore, awareness of design factors,
fixed or not, is essential in intentional program design and implementation, and in interpreting
student outcomes data. Learning in field settings is a complex process and only the design
factors identified by the UFERN community as most salient to UFEs are included in this list. A
list of additional factors that are less salient to UFEs, but still at play, is offered at the end of this
Prioritizing which Design Factors to focus on in designing, implementing, and iteratively
re-designing a UFE will depend on the program or course context which will be unique in every
instance. All Design Factors have an impact on the student experience and must be considered to
better support intended student outcomes. A good starting point for UFE educators to use the
UFERN Model for program improvement may be to identify the factors that are in their control
in their context, prioritize them, and use the literature presented in this paper to consider how the
program design and re-design might influence student learning.
The Setting or physical environment in which UFEs occur is typically determined by the
discipline and influences which students are willing and able to participate, and which student
learning outcomes are possible. UFEs are a form of in situ learning (National Research Council
2009) in which the landscape is employed as a pedagogical tool (Giamellaro 2013, Jolley et al.
2018, Oliver et al. 2018), and is the context of research and learning (Vogt and Skop 2017). UFE
educator choices to utilize the physical environment range from deeply authentic interactions
immersed in the complexity of the subject of study (e.g., semester-long collection of data at a
field station in order to publish) to a less complex representation of the subject of study (e.g.,
visits to exemplar field sites) (Jolley et al. 2018). Each design choice regarding the setting will
influence how a student experiences the learning environment. For example, given that field
sites are often in remote locations with difficult terrain, the setting can provide challenges to the
overall physical (and by default social) accessibility for students, thereby influencing the
learner’s experience and outcomes (Atchinson et al. 2019). In addition, a geographically remote
program site could create a barrier to student engagement if students are distracted by not having
phone service to contact friends and family, while this same setting could also create the
opportunity for students to build social and professional connections through a shared experience
in a remote setting (Mogk and Goodwin 2012).
Key aspects of timing include: 1) the overall duration of the experience, 2) the structure
of engagement with the field setting (e.g., long-term vs. punctuated visits to residential, and
immersive programs, or visiting one or many for a period of time, and number of sites visited per
day for traveling programs), and 3) placement of the UFE within the academic calendar (e.g.,
academic year, semester, summer). Each of these aspects may have an impact on students' ability
to participate in the experience, especially students who are first-generation, from groups
historically excluded from STEM, nontraditional, or living on a limited income (NRC 2012,
Balcarczyk et al. 2015, Scott et al. 2019, Jensen et al. 2021). For example, caregiving
responsibilities at home present a barrier to students participating in an extended course, as does
a part-time job (Giles et al. 2020). People with physical and mental health issues also have real
and perceived barriers to the long hours often required in the field (John and Khan 2018, Stokes
et al. 2019). Giles et al. (2020) also raises the needs of those who must schedule prayer breaks, or
are fasting as an important consideration in Timing.
Duration and the time students spend immersed in the experience also has implications
for development of sense of place (Ardoin 2006, Gustafson 2001, Jolley et al. 2018),
instructional time (and types of instructional activity) (Hayes and Gershenson 2016, Jolley et al.
2018), and community-building (Wenger 1998, Mogk and Goodwin 2012, Struele and Craig
2016). Sense of place often strengthens over a long time period within a specific environmental
context or through an intense experience in a shorter time period (Ardoin 2006). Based on Credé
et al. (2010) and Cook et al. (2010) meta-analyses of time on-task learning and academic
success, which show very large effect sizes for this relationship (.95 & 1.25, respectively), we
hypothesize that longer duration UFEs will produce stronger outcomes, but may limit
accessibility for some student populations.
Immersion and sensory experience
Inhabiting the object of study (the natural outdoor environment) offers an immersive
experience that influences which students are willing and able to participate and which student
learning outcomes are possible. Field-based learning occurs in context. It is undertaken to
immerse students within environments that authentically represent and highlight the science
content to be learned and to provide students with multi-sensory experiences with those
environments. As such, field environments are contextualized learning environments that provide
opportunities to use all of the senses to better develop skills and knowledge. In other words,
students are learning in context rather than with context (Giamellaro 2014). Full immersion in
these contextualized learning environments represents a systemic, aesthetic experience (Roth and
Jornet 2014) with the potential to develop not just understanding but also a sense of awe and
wonder (Mogk and Goodwin 2012). The environment provides a perpetual sensory stimulus that
can both support and/or distract from intended learning (Maltese et al. 2013, Mogk and Goodwin
2012, Stokes and Boyle 2009) and lead to individual discovery beyond the intended learning
objectives (Giamellaro 2014). Students often develop a sense of geographical, cognitive,
psychological, and social novelty in these immersive sensory experiences, which can be both
positive and negative (Falk et al. 1978, Orion and Hofstein 1994, Cotton 2009). Immersion is
also associated with student development of sense of place, which is considered essential to
fostering an environmentally conscious and responsive citizenry (Ardoin 2006).
Orientation to the experience
How students are Oriented or introduced to the UFE and its culture influences which
students are willing and able to participate and which student learning outcomes are possible.
Undergraduate field experiences often occur in complex, off-campus, multi-institution settings
that are unfamiliar to the students, geographically remote, and include unique safety concerns,
with faculty and students sometimes living in close quarters. Orientation to the experience in the
field prepares students for the experience and introduces the resources available to address
personal concerns including health, safety, social dynamics, and academics (Nelson et al. 2017,
John and Khan 2018, Giles et al. 2020). This kind of orientation is one step in providing an
environment where learners feel safe, which is essential because “No significant learning can
occur when students are unsure about where they are, what they are supposed to do, what the
expectations are for learning outcomes, or if they have concerns about their personal comfort and
safety” Mogk and Goodwin (2012). Another step is creating an inclusive learning environment
(Flowers et al. 2021, Zavaleta et al. 2020) where UFE educators and students are both prepared
with strategies in place (Demery and Pipkin 2020) to support field safety. The degree to which
students sense their upcoming experience as familiar and/or welcoming will impact their
outcomes for the UFE.
The Social Interactions, or ways of meeting and engaging with others in the UFE,
influences which students are willing and able to participate and which student learning
outcomes are possible. Much evidence exists for the social benefits of field work (Fuller et al.
2006, Mogk and Goodwin 2012, Stokes and Boyle 2009, Streule and Craig 2016), including the
opportunity to build a professional network (Mogk and Goodwin 2012, Thompson et al. 2016,
Mason et al. 2018), foster a sense of belonging (Morales et al. 2020), develop needed social
skills for collaborative research (Hanauer et al. 2012, Mogk and Goodwin 2012, Jolley et al.
2018), and increase enthusiasm for learning (Posselt 2020). However, social interactions, both
formal and informal across peers and/or mentors, (or lack thereof) may create challenges that
detract from the learning experience such as discomfort when interacting with mentors (Daniels
et al. 2019), workload issues and power conflicts (Anderson et al. 2009, Kortz et al. 2020),
institutionalizing women’s silence (Posselt 2020), and social isolation (John and Khan 2018).
Even in these “highly unstructured learning environments, [where] informality and togetherness
..encourage science as a communal experience,..the field experience requires careful design and
management to minimize risks of harm and to facilitate inclusion and a shared sense of
belonging (Posselt 2020, pp 56-57).”
Choice, Control, and Power structures
The extent to which students have Choice and Control over decisions made in their UFE
can influence learning outcomes. UFEs represent an atypical academic environment in which
products, as well as social and cognitive expectations of students, can range from highly
structured to ill-defined. In any learning experience, decisions are made regarding research
projects, mentoring, partnering, and a constant barrage of situational choices on the process of
work, comfort, and safety. Who makes those decisions can have significant impacts on student
outcomes and engagement (Jolley et al. 2018, Schmidt et al. 2017). For example, in Jolley et al.
(2018), the field trip module that “allowed opportunity for students to be autonomous, navigate,
and make a range of decisions in the field without immediate or constant instructor
feedback...appeared to promote high levels of engagement from the students (p. 661).” The
collaborative nature of field work can increase students’ sense of agency (Stokes and Boyle
2009), however, intentional empowerment of all students through democratic decision-making
and skill building is an important consideration to account for real or perceived power structures,
even among students (Posselt 2020, Wieselman et al. 2021). In the field environment, students
are studying phenomena in open, unconstrained, dynamic, and complex systems (Mogk and
Goodwin 2012). The experiences foster a sense of exploration and discovery in which students
can choose where to direct their attention, but the experiences also require enough guidance to
mitigate the potential lack of student understanding. Students must use or develop self-regulation
and self-monitoring skills as they adapt to variable physical conditions (Mogk and Goodwin
2012). More choice is not always better for learning and students appreciate some prescription to
maintain the flow of a course (Harmer and Stokes 2016).
The Instructional Models and Activities or the selection, sequencing, and
implementation of activities influences which students are willing and able to participate and
which student learning outcomes are possible. Sequencing activities within the program and
selecting methods to support student understanding and skill development may be the most
familiar aspects of course and program planning. Instructional models are also the most obvious
place in which all of the other student and design factors interact to impact actual student
outcomes. Despite this emphasis, limited research exists on field-specific instructional models
and a reliance on what Mogk and Goodwin (2012) refer to as “practitioner’s wisdom” has not
been objectively tested. The need for student-centered learning is recognized, even if that does
not always materialize in the field (Peasland et al. 2019). Peasland et al. (2019) propose plotting
field instruction models on a matrix that shows a student-centered to teacher-led spectrum on one
axis and passive student observation to active participation on the other to capture the variety of
approaches currently being used and to provide some comparability. In the associated study, they
found that when staff-centered instructional models were used in the field, students were more
likely to report the technical skills they had developed and when student-centered models were
used they were more likely to report the transferable knowledge they had developed (Peasland et
Learning in field settings is a complex process and only the design factors identified by
the UFERN community as most salient to UFEs are included in Figure 1 and Table 2. Other
design factors that influence which students are willing and able to participate and which student
learning outcomes are possible include: broader relevance or the degree to which students' work
is of interest to a community beyond the classroom, which can manifest as authorship on a
scientific paper or presentations or reports to stakeholders (Engle et al. 2011, Auchincloss et al.
2014, Cooper et al. 2019); curriculum or the learning plan (e.g., guide for content delivery,
syllabus) for the course or experience (Balliet et al. 2015, Jolley et al. 2018); subsequent
reinforcing events or the idea that learning is not an instantaneous phenomenon, but a cumulative
process where experiences occurring after the initial learning experience in part determine what
is “learned” (Falk and Storksdieck 2005a, Falk and Storksdieck 2005b). Depending on the UFE,
these and other design factors may also be important to consider.
The approach we used to develop the UFERN Model follows closely from Auchincloss et
al. (2014) and also combined aspects of “critical review” and “narrative review” (Grant & Booth
2009). We did not conduct a completely systematic literature review to design the UFERN
Model, so the possibility of bias (e.g., missing factors) exists. However, the UFERN Model as
presented has enough of a grounding in the literature that establishes a legitimacy for the model.
Similar to Auchincloss et al. (2014), we relied on feedback from the broad range of experts in the
UFERN community to help ensure comprehensiveness and rigor and to make sure we included
factors that were undeniably important in field learning experiences. In addition, we made simple
quality appraisals of the literature we included with a focus on papers that presented empirical
evidence (vs. opinion pieces) and used reviews (e.g., NASEM reports) whenever possible. No
one right way exists to organize the factors. Ultimately, the authors listed on this paper made
final conceptual and organizational decisions. The UFERN Model is not yet mechanistic or
predictive, but it is our hope that it will inspire and facilitate mechanistic studies in the future.
The UFERN model provides a holistic picture of learning and personal development in
UFEs. Using specific research on UFEs, general research on STEM education, and the expertise
and experience of both educators and researchers in the UFERN community, we created a model
that describes the impact of student context factors and program design factors on student
outcomes in a variety of formats of UFEs. The UFERN Model disentangles the complexity of
field learning programs. It also provides a common language to connect educators who design
and implement UFEs with education researchers who study the impacts of UFEs on student
outcomes to collaboratively create more inclusive and impactful field learning experiences that
will attract and retain a more diverse population of students to field science.
The UFERN Model is relevant for a diversity of types of programs - from short field labs
as part of traditional on-campus university courses to weeks-long field research experiences. As
such, the broad flexibility of the model allows the field science community to consider the full
range of ways that students can engage with “the field” when designing inclusive and welcoming
programs and courses. We propose that all the factors are important in understanding student
outcomes and that a diversity of programs that address the various factors in unique ways will
yield positive outcomes for a broader diversity of students.
The UFERN Model is just the beginning. Further research is needed to identify causal
mechanisms leading to the efficacy of these experiences and explore which elements of these
experiences lead to which intended student outcomes for which students across UFE formats and
disciplines (Rickinson et al. 2004, Corwin et al. 2015, NASEM 2017, Beltran et al. 2020). We
suggest that the UFERN Model may be used to guide the investigation of these important
questions in virtual field experiences. We also suggest that the UFERN Model may be used to
guide evidence-based discussions about “what constitutes the field” as a way to increase access
and inclusion in the field sciences as called for by Fleishner et al. (2017) and Morales et al.
(2020). The UFERN Model is a valuable resource to guide design of student-centered, inclusive
UFEs, evaluate student outcomes of UFEs, and catalyze research about field learning.
We could not have developed the UFERN Model without the invaluable contributions from the
UFERN community. We appreciate Ian Billick who led the working group in the first UFERN
Network Meeting in coming up with an initial list of factors. We also appreciate Mike De Luca,
Erin Dolan, Alicia Farmer, Peggy Fong, Jan Hodder, Alison Jolley, Sandra Laursen, Andrew
McDevitt, Dave Mogk, Nia Morales, Teresa Mourad, Ryan Petterson, Julie Risien, Jennifer
Seavey, Steve Semken, Stephanie Shaulskiy, Martin Storksdieck, Sara Syswarda, Emily
Geraghty Ward, and Danielle Zoellner for thoughtful feedback on earlier versions of the UFERN
Model, either as part of that working group, in individual meetings, or as part of the April 2019
UFERN webinar. We also thank the attendees to the 2nd UFERN meeting for giving feedback
about an almost final version of the model. In addition to those already listed, these attendees
include: Chris Atchison, Aly Busse, Itchung Cheung, Carol Colanino, Damon Gannon, Demian
Hommell, Joe Lamanna, Todd Lookingbill, Chris Lorentz, George Middendorf, Andy Rost, Erin
Shortlidge, Jill Zaretsky. We are grateful to Jessica Sawyer who improved the readability of the
manuscript through her detailed copy-editing, and cleaning up of figures, tables, and references.
This work was supported by the National Science Foundation under RCN-UBE grant #1730756
to K. O’Connell, A.R. Berkowitz, G. Bowser, and J. Branchaw.
Author Biographical Narrative
Kari B. O’Connell is a Senior Researcher at the STEM Research Center at Oregon State
University. Kelly L. Hoke is a Researcher at the STEM Research Center at Oregon State
University. Michael Giamellaro is an Associate Professor of Science Education at Oregon State
University, Cascades Campus. Alan R. Berkowitz is Head of Education at Cary Institute of
Ecosystem Studies. Janet Branchaw is Associate Professor of Kinesiology in the School of
Education, and Director of Wisconsin Institute for Science Education and Community
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Table 1. UFERN Model student context factors.
Abbreviated Citation List
The students’ philosophy of life or conception of the world,
including language, values, ethics, politics, and socioeconomic
status. This factor refers to the set of rules the student uses for
interpreting the world.
Bang et al., 2018; Barnes et al., 2017;
Redford & Hoyer, 2017; Nelson et al., 2017;
Storksdieck et al., 2005
A psychological state that, in later phases of development, is
also a predisposition to re-engage content
Haynes et al., 2015, LaDue & Pacheco, 2013; Hidi
& Renninger, 2006; Barron, 2006; Bell et al., 2019
The way an individual sees themselves, and the way an
individual is recognized by others.
Bell et al., 2018; Carlone & Johnson, 2007; Streule
& Craig, 2016; Henning et al, 2019; Hughes, 2016
The knowledge and skills that the students bring that can be
built upon in the experience for the development of new
knowledge and skills.
Dalton 2001; Trumbull 2017; Civil, 2016;
Bang, et al., 2018
Motivation is a condition that activates and sustains behavior
toward a goal. Expectations are the hopes and fears that
students hold about future experiences and their outcomes.
Scott et al., 2019; Peasland et al., 2019; Dykas &
Valentino, 2018; NASEM, 2018; Bandura, 1997
The experiences that students have that prepared and
predisposed them to interact with elements of the course or
Haynes et al., 2015, Hughes 2019, Cotton &
Cotton 2009, Orion & Hofstein 1994, Barron, 2006
Individual needs of students such as physical, developmental,
and learning disabilities, short-term or long-term health needs,
cognitive and emotional needs, and financial needs.
Hall et al., 2002; Mol & Atchison, 2019; John &
Khan 2018, Kingsbury et al., 2020.
Table 2. UFERN Model design factors.
The physical environment, including where the students reside.
NRC 2009; Giamellaro, 2013; Jolley et al.,
2018; Mogk & Goodwin, 2012: Petcovic et
Duration of experience, structure of engagement with the field
setting and timing of experience within the academic calendar.
Scott et al., 2019; Jensen et al., 2021; Streule
& Craig 2016; Jolley et al., 2018; Cook et al.,
2010; Ardoin, 2006.
The immersion of students within environments that
authentically represent and highlight science content and that
provide opportunities to use all of the senses to better connect
with the content associated with that environment.
Ardoin, 2006; Cotton & Cotton, 2009;
Giamellaro, 2014; Mogk & Goodwin, 2012;
Stokes & Boyle, 2009
Orientation to the
The introduction of students to the experience and the
resources available to address concerns about health, safety,
culture, social dynamics, and academics.
Nelson, et al., 2017; John & Khan, 2018;
Scott et al., 2019
Both intentional and unintentional social interactions, including
student-to-student, student-to-instructor/mentor, and student –
Mogk & Goodwin, 2012; Streule & Craig,
2016; Stokes & Boyle, 2009; NASEM, 2016;
Fuller et al., 2006
Who has power or control in key decisions about student
safety, research, mentoring and collaborating, and what
processes or structures support making these decisions.
Harmer & Stokes, 2016; Schmidt et al., 2017;
Stokes & Boyle 2009; Wieselman et al., 2020
Considering what activities to do with students, what methods
to use to disseminate knowledge, and to lead students through
the development of understanding.
Mogk & Goodwin 2012; Peasland et al.,
2019; Scott 2012; Maskall & Stokes 2008;
Jolley et al. 2018
Additional Design Factors include: Broader relevance,
Curriculum, Subsequent reinforcing events
Auchincloss et al. 2014, Cooper et al., 2019;
Balliet, et al., 2015; Falk & Storksdieck
Figure 1. The UFERN Model illustrates that actual student outcomes of an UFE are a function of the experience each student has, and that this
experience results from an interaction between the contexts that frame who the student is (student context factors) and how the experience was
designed and implemented (UFE design factors). Intended student outcomes, in turn, influence both the student context and design factors. The
recruitment and selection of students is an opportunity to know and consider those student context factors while program design and instructional
choices are opportunities to optimize those students’ experiences.