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Demystifying the Millennial Student: A Reassessment in
Measures of Character and Engagement in Professional
Camille DiLullo,1* Patricia McGee,2 Richard M. Kriebel3
Department of Anatomy, Philadelphia College of Osteopathic Medicine, Philadelphia, Pennsylvania
Department of Educational Psychology, College of Education and Human Development,
The University of Texas at San Antonio, San Antonio, Texas
Department of Neuroscience, Physiology and Pharmacology, Philadelphia College of Osteopathic Medicine,
Philadelphia, Pennsylvania
The characteristic profile of Millennial Generation students, driving many educational
reforms, can be challenged by research in a number of fields including cognition, learning
style, neurology, and psychology. This evidence suggests that the current aggregate view
of the Millennial student may be less than accurate. Statistics show that Millennial students are considerably diverse in backgrounds, personalities, and learning styles. Data are
presented regarding technological predilection, multitasking, reading, critical thinking,
professional behaviors, and learning styles, which indicate that students in the Millennial
Generation may not be as homogenous in fundamental learning strategies and attitudes
as is regularly proposed. Although their common character traits have implications for
instruction, no available evidence demonstrates that these traits impact their fundamental
process of learning. Many curricular strategies have been implemented to address alleged
changes in the manner by which Millennial students learn. None has clearly shown superior outcomes in academic accomplishments or developing expertise for graduating students and concerns persist related to the successful engagement of Millennial students in
the process of learning. Four factors for consideration in general curricular design are
proposed to address student engagement and optimal knowledge acquisition for 21st century learners. Anat Sci Educ 4: 214–226. © 2011 American Association of Anatomists.
Key words: assessment; competencies; curriculum; learning; Millennial Generation;
generation net; professional identity; multitasking; innovations; technology in education
The optimal way to engage students from the Millennial
Generation in learning has been a major topic of discussion in
*Correspondence to: Dr. Camille DiLullo, Department of Anatomy,
Philadelphia College of Osteopathic Medicine, 336 Evans Hall, 4170
City Avenue, Philadelphia, PA 19131, USA.
E-mail: [email protected]
Received 3 January 2011; Revised 23 May 2011; Accepted 4 June
Published online 6 July 2011 in Wiley
( DOI 10.1002/ase.240
© 2011 American Association of Anatomists
Anat Sci Educ 4:214–226 (2011)
educational communities for over a decade. Countless changes
in all aspects of society including employment, marketing, and
education have been based on postulates put forth in various
writings about this generation. In the book Generations: The
History of America’s Future, 1584 to 2069, written by Washington, D.C. based social historians Strauss and Howe (1991),
the authors showed the patterns of differences among individual generations including the Silent Generation (1925–1942),
Baby Boom Generation (1943–1960), Generation-X (1961–
1981), and the Millennial Generation (1982–2001), a term
which they coined (Strauss and Howe, 1991). A version of
these comparative generational traits in table form often precedes seminars or writings about students from the Millennial
Generation. Subsequent publications by various authors have
contributed to the now popular characterization of Millennial
Generation students (Strauss and Howe, 1991; Tapscott, 1999;
Anatomical Sciences Education
Prensky, 2001; Howe and Strauss, 2003; Oblinger, 2003;
Twenge, 2009). A number of these alleged Millennial student
characteristics are at the heart of ongoing changes in academia.
However, research in a number of fields including cognition,
learning style, neurology, and psychology provides evidence
that the current aggregate view of the Millennial student in
regard to learning may be less than accurate.
The similarities and differences that exist regarding characteristics related to learning among student cohorts have been
delineated in the cognitive and educational literature. Smith
and Ragan (2005) have organized learning characteristics into
four areas: (1) changing similarities, (2) changing differences,
(3) stable similarities, and (4) stable differences. The characteristics included among ‘‘changing similarities and differences’’
are those that will evolve over time for an individual. These
characteristics include personal values and beliefs, personality,
developmental stages, and educational level all of which can
be impacted by personal development, current societal moirés,
overall political climate, and global economics. The one
‘‘changing characteristic’’ related to learning most important
in the design of instruction is educational level as it is directly
relevant to selection of program content complexity. The characteristics included among ‘‘stable similarities and differences’’
are characteristics such as aptitude, ethnicity, learning style,
academic self-concept (self-perception of academic ability),
and ‘‘locus of control’’ (internal locus of control-learner takes
responsibility for their learning; external locus of controllearner expects others to take responsibility for their learning).
Individual learners in any student cohort will have a unique
subset of ‘‘stable characteristics’’ which will vary from individual to individual but remain relatively unchanged for any one
individual over their longitudinal stages of learning. Some stable characteristics may be shared by learners that belong to the
same generational cohort. For example, Millennial students
are often reported to be team-oriented, be collaborative and
value peer opinions. Their ubiquitous use of social media
through a variety of technologies has engendered a desire for
multitasking that is different from previous generations. Millennial students also appear to demonstrate more external
locus of control given their reliance on and connections with
others which is evidenced through their use of social media.
Millennial students live in a world that is clearly different
from that of previous generations and have access to technologies that are revolutionary. While Millennial student expectations, habits, preferences, and beliefs have been shaped by
their environment, analysis of research data suggests that
these students may not be as different from other generations
in the fundamental process of learning as is regularly proposed. It is crucial to accurately assess which specific ‘‘stable
characteristics’’ truly impact the learning process and should
be targeted for consideration in instructional design. Much of
the literature in the area of characteristics related to learning
is interdisciplinary and not specific to professional education.
However, correlations can be made between generalized analyses and trends in professional education. The authors echo
concerns expressed by others (Reeves and Oh, 2007) that
educators should not rely solely on personal observations,
preferences, and student self-assessments as the basis on
which to draw conclusions about differences in generational
characteristics that impact learning style. Relevant learning
styles need to be determined by the analysis of empirical
data. The debate is advanced with a discussion of evidence as
the foundation for a reassessment of Millennial student characteristics related to the process of learning.
Anatomical Sciences Education
Characteristics Commonly Associated With
the Millennial Student
One of the first notable discrepancies regarding ‘‘facts’’ about
the Millennial Generation, alternatively dubbed the Net Generation, Generation-Y, Echo-boomers, Generation Me, and
Digital Natives, is the range of dates used to bracket the generation. Depending upon the source, generational boundaries set
for the start of the Millennial Generation range anywhere
from 1978 to 1984 with the end proposed to be as early as
1990 but as late as 2001. With the introduction of ubiquitous
computing at the end of the 20th century and a generational
growth spurt (Carlson, 2005), many businesses began the process of analyzing character traits of the new generation. The
intended outcome of these analyses was forecasts in employee
trends and marketing strategies. In 2003, Howe and Strauss,
in Millennials Go to College: Strategies for a New Generation
on Campus, posited seven specific character traits for students
in the Millennial Generation which are (1) special, (2) sheltered, (3) confident, (4) team-oriented, (5) conventional, (6)
pressured, and (7) achieving (Howe and Strauss, 2003). Many
authors concurred with the Millennial characterization offered
by Howe and Strauss and expanded it to include that Millennials are optimistic, intelligent, goal oriented, ambitious, interested in learning, multitaskers, respectful of cultural differences, collaborative, desiring of flexibility, needy for feedback,
and anticipatory of immediate response (Oblinger, 2003;
McGlynn, 2005; Taylor and MacNeil, 2005; Wilson, 2005;
Borges et al., 2006; Goldman and Schmalz, 2006; Pastorino,
2006; Taylor, 2006; Coates, 2007; Roos, 2007; Gleason,
2008; Lower, 2008; Tarr and Palmer, 2008; Wesner and Miller,
2008; Immerwahr, 2009; Moore, 2009; Sweeney, 2009). In
addition to the characterization of personality and behavior,
literature has been generated regarding the learning style of
the Millennial student. Typically these reports show that Millennial students are active learners, work best in small rather
than large groups, and prefer learning via the use of technology (Prensky, 2001; Dede, 2005; Oblinger and Oblinger,
2005; Sweeney, 2006; Sandars and Morrison, 2007; Croasdale, 2008). Confounding these positive characterizations of
the Millennial student are other disquieting traits. Students are
reported to be narcissistic with a feeling of entitlement, unmotivated, impatient, incurious, unprepared for independence,
academically disengaged, and deficient in time management,
media literacy, and critical thinking skills (Newton, 2000;
Kuh, 2003; Weiler, 2004; Spencer, 2006; Westerman, 2006–
2007; Hoover, 2007, 2009; Monaco and Martin, 2007; Sweeney, 2007; UCL, 2008; Considine et al., 2009; Immerwahr,
2009; Twenge, 2009). They have also been described as less
inclined to ‘‘study’’ when compared with students from Generation-X, valuing peer over expert opinions and expecting customization (Raines, 2002; Beard et al., 2008; Dumais, 2009).
The paradoxical traits found among the Millennial student
descriptions call into question the methods being used to characterize these students.
Over the last decade, as the literature on the Millennial
student has proliferated, it has proven that opinions beget
opinions. A scrutiny of the references of a majority of publications and presentations indicates that the ideas being
espoused are fundamentally opinions based on observation
and perception as well as on student personal satisfaction
and preference surveys rather than on evidence-based
research methodologies. The personal mindset and experiences of writers generating the analyses of Millennial students is
likely to influence the opinions drawn from individual observations and perceptions. The extraordinary diversity of the
Millennial student population is also likely to contribute to
the spectrum of character traits with which Millennial students are imbued. Most Millennial student surveys are collected from isolated student populations usually from one or
two similar institutions. It could be argued that the population base generally being surveyed does not reflect the Millennial student body as a whole which includes students from
various races, religions, ethnicities, and socio-economic backgrounds (Kennedy et al., 2006). Specifically, 36% of Millennial students are African American, Hispanic, or Asian and
one-third of this generation has been raised in a single parent
household (Taylor and Keeter, 2009). Statistical research data
derived from well-designed studies and personal preference
information from more dissimilar student collectives would
perhaps provide a more accurate picture of the Millennial
student. The current characterization of the Millennial student population is likely to be over-simplified. Since, at the
heart of ongoing over-arching curricular reform is consideration of many ‘‘typical’’ Millennial student traits, it is imperative to re-examine a number of these traits in the light of
recent research that might highlight what learner characteristics should be considered when designing instruction. Data
from studies on the Millennial students’ engagement with
technology, facility with multitasking, desire to read, ability
for critical thinking, accountability in professional behaviors,
and preference for various learning styles are discussed in
relation to their impact on learning processes and curricular
design. These data reveal the questionable foundation on
which some suppositions regarding the Millennial student are
Engagement and Facility With Technology
One apparent dogma regarding Millennial students is that
instructional content must be delivered digitally because Millennials have grown up having extensive experience using
technology (Tapscott, 1999, 2009; Carlson, 2005; Sweeney,
2006; Sandars and Morrison, 2007; Croasdale, 2008). It is
proposed that Millennials’ technological awareness has
impacted the expectations, preferences, and experiences in
social as well as learning environments for all students in this
generation. In fact, the ‘‘Digital Native’’ moniker given to this
student cohort is meant to accentuate the supposed disparity
of the technological facility of Millennials compared with
members of previous generations who have been dubbed
‘‘Digital Immigrants’’ (Prensky, 2001). It is probable that the
technological abilities of both the Digital Native and Digital
Immigrant have been over-generalized (Kvavik and Caruso,
2005; Bayne and Ross, 2007). Dede (2005) contends that it is
necessary for Digital Immigrants to ‘‘unlearn’’ old teaching
methods before they are receptive to delivering content technologically and to the satisfaction of Digital Natives. However, many digital learning tools and content delivery innovations have been created by Digital Immigrants. No data, published to date, demonstrate that Digital Immigrants have
‘‘unlearned’’ anything for the creation of these. It is rather
more likely that they have experience and an innate aptitude
for digital technology. So while many in the Digital Immigrant population may be uncomfortable with digital technol216
ogy, there are many who are adept at using it in the teaching
Likewise, it has been documented that a spectrum for
both the desire and ability to use digital learning tools exists
among Digital Natives (Murphy et al., 2004; Kvavik, 2005;
Kvavik and Caruso, 2005; Kennedy et al., 2006; Jones et al.,
2010). There may be as gradual a learning curve for some
Digital Natives to use technology for learning as there is for
some Digital Immigrants to employ technology in the educational process. In addition, facility with electronic devices
demonstrated by the Digital Native may not equate to facility
with technology in the educational environment. The predominant Millennial student experience with technology has
been centered on gaming and social media skills (Kennedy
et al., 2006; Margaryan and Littlejohn, 2008; Kolikant,
2010). It has been suggested that gaming skills may not automatically translate into skills requisite for digital learning
(Foerde et al., 2006) in that these skills are situated in a competitive entertainment context, not one in which learning is
the outcome. That, in and of itself, limits the level of technological expertise for learning that should be expected of Millennial students. In addition, it has been proposed that differences in technological expertise among Millennial students
exist as a result of variable exposure to technology due to
factors such as cultural background, socio-economic status,
and discipline specialization (Kvavik and Caruso, 2005; Bennett et al., 2008). Finally, a study commissioned by the
United Kingdom’s Joint Information Systems Committee
(JISC, 2007) included a finding worthy of special note;
Millennial student engagement with technology does not
automatically result in their disengagement with traditional
forms of pedagogy.
Another factor to consider in the use of digital learning
tools for content delivery is exactly what percentage within a
cohort of students is actually composed of Millennial students
especially in professional classrooms (Pastorino, 2006). Postbaccalaureate classes are unlikely to be comprised solely of
students from a single generation. Therefore, as a result of
both generational and personal preference, a range of desire
for technology and technological expertise will always be
present among professional student populations. The use of
complex digital tools for learning might impede the learning
curve for less tech savvy students from any generation.
In 2005, Dede proposed the next steps in the digital movement in learning would be creation of (1) wireless environments, (2) multipurpose habitats, (3) augmented reality, and
(4) mirroring (Dede, 2005). Most institutions are already
wireless enabled (Arroway et al., 2010). More careful evaluation of the purpose of technology in learning with regard to
actual student needs, desires, and professional applications
should be undertaken before additional time, money, and
resources are invested in more extensive technologies.
Millennial students are lauded for being capable of extraordinary productivity as a result of their ability to multitask.
Prensky states ‘‘. . .the brains of those who interact with technology frequently will be restructured by that interaction’’
(Prensky, 2009). In other words, the use of digital technology
can fundamentally alter neural pathways. This may be
physiologically true but it does not necessarily follow
that such restructuring is beneficial to the learning process.
Recent studies have provided data on video game players,
DiLullo et al.
multimedia users, and multitaskers. In fact, some of these
data indicate that the presumed ‘‘skill’’ of multitasking is
likely to have a negative impact on learning (Foerde et al.,
2006). Life-long learning is required for continued professional success; therefore, any diminution of learning ability is a
potential barrier to successful longitudinal learning.
Several studies have demonstrated that video game playing
fundamentally modified aspects of visual attention. Dye et al.
(2009a,b) showed that ‘‘video game players’’ responded more
quickly and with equal accuracy to tests of visual stimuli
than did ‘‘nonvideo game players.’’ The increase in speed
without sacrifice of accuracy in this study was isolated to
visuospatial cognition. Other studies expanded response selection to include visual as well as auditory stimuli in conjunction with motor response and also demonstrated reduced
response time without the loss of accuracy even in dual task
trials (Erickson et al., 2007; Dux et al., 2009). In essence,
with practice, one can improve reaction times for rapidly presented stimuli. The perceived benefit of practice, in each
study, was not proven to extrapolate to learning in any way.
Other data have led to the postulation that momentary
lapses of conscious awareness have ‘‘pervasive effects on the
efficient, effective conduct of our everyday activity’’ (Cheyne
et al., 2006). Studies designed to measure multiple task efficiency demonstrate cognitive impairment and loss of focus of
the multitasker. Ophir et al. (2009) showed that heavy media
multitaskers (HMMs) approach fundamental informational
processing differently when compared with light media multitaskers (LMMs). HMMs were shown to have greater difficulty filtering out irrelevant stimuli than LMMs. HMMs were
distracted by the multiple streams of media they were consuming, whereas LMMs were more effective at focusing on a
primary task in the face of distractions. Results from other
studies on the use of hands free cell phones during simulated
driving indicated that ‘‘drivers’’ demonstrated inattention
blindness (Strayer et al., 2003). Essentially, they were less
aware of their surroundings than when driving with no cell
phone engagement (Strayer and Drews, 2007).
A study by Watson and Strayer (2010) suggested that there
exists a subpopulation of ‘‘supertaskers.’’ Study participants
were engaged with word and math challenges via a hands
free cell phone during simulated driving. Scores for the driving and mind challenge tasks completed independently were
compared with scores for the tasks when they were
completed concurrently. A little more than 2% of the study
population showed no loss in efficiency of tasks performed
simultaneously compared with independent task engagement.
However, that means greater than 97% of the study population did demonstrate an efficiency cost for one or both tasks
that were performed simultaneously. A separate study demonstrated that despite equivalent performance in single and dual
task trials, declarative knowledge, which is used in higher
order thinking, was less available in subsequent recall tests
(Foerde et al., 2006). In this study, functional magnetic resonance imaging (fMRI) scans of brain activity during execution of dual tasks showed competition for access to neural
pathways. Under dual task conditions, brain activity (i.e. information processing) was directed away from the region
associated with declarative memory and flexible learning to a
region that facilitates habit learning. Students may want to
reconsider dividing their attention while engaged in the learning process particularly given the professional requirement
for continued knowledge and skills acquisition over the
course of their career.
Anatomical Sciences Education
This generation more than any other has been influenced by
the disaggregation of knowledge through digital media
(DVDs, eBooks, the Internet, and mobile technologies) that
has reduced narratives to sound bites and resulted in diminished attention spans and a shift from reading as literacy to
media literacy (Considine et al., 2009). It is often put forward
as a corollary to the Millennial student preference for learning with digital resources that they do not read (Oblinger and
Oblinger, 2005; Sweeney, 2006). Yet, reading is fundamental
to learning and requisite for informed current and future professional success. Statistics from the 2004 National Endowment for the Arts (NEA) report on reading in America are often referenced to support the claim that Millenial students do
not read. The NEA report contained data that revealed a
decline in literary reading in the Millennial population (NEA,
2004). The basis of the report was a survey that included
questions on the amount of time devoted to literary reading
in the preceding 12 months. Literary reading was defined as
reading limited to novels, short stories, plays, or poetry.
Respondents were directed to exclude any reading devoted to
school or work. While the data showed a 10% decline in
reading for the Millennial Generation, it showed an equal
10% decline in reading for every generational cohort. An
increase in the use of audio-books or an overall decrease in
the amount of free time available for leisure reading are plausible explanations for the overall 10% decrease in literary
reading. As the survey focused exclusively on literary reading,
the data did not actually support the conclusion that Millennial students do not use educational texts in the process of
learning. In fact, subsequently, the 2007 report on reading
from the NEA took compulsory reading, defined as that
required for educational assignments, into consideration.
Notably, data showed that overall the number of pages read
per day in school and for homework by Millennial students
has actually increased (NEA, 2007).
Given the amount of readable content available via digital
media, how and what is available now for students to access
differs from that which was available for students in the
learning environment a decade ago. A British study that
tracked the use of internet searches to obtain information
implied that students used the internet for information
searches but did not read the content of the resources that
were collected (UCL, 2008). This conclusion was based on
data that tracked the limited amount of time students spent
at a stream of individual web sites. The study did not address
the possibility that these students printed and read the digital
content after the searching session was completed. Another
British study reported that students felt with ‘‘an assignment
amounting to just a few pages. . .the internet is sufficient’’ for
research (Kolikant, 2010). However, when doing research for
more ‘‘serious’’ assignments, they searched for information
using a combination of online and text book resources. With
these accumulated findings taken into consideration, it is
likely an overstatement to claim that students from the Millennial Generation are resistant to including textual reading
among their learning strategies.
Critical Thinking
Critical thinking involves analyzing information, seeking evidence, and making well founded decisions or conclusions.
For example, the integration of anatomical knowledge into
professional practice analysis, assessments, and plans involves
critical thinking. The ability for critical thinking is key for
professional success in the 21st century workplace (P21,
2011). It can be argued that students of the Millennial Generation are probably the first generation to have experienced a
wide range of teaching methods given the rapid advancement
of learning theories in the end of the 20th century, particularly given trends of serving needs of specialized populations
and individualized instruction, and they are possibly the most
tested generation in US history. Such testing has been suggested as a cause for reduced critical thinking, a primary concern for 21st century employers (Spellings, 2006). In 2006, a
consortium of businesses surveyed 400 employers nationwide
to ascertain their perspective on the preparedness of Millennial Generation graduates to enter the workplace (CasnerLotto and Barrington, 2006). Their report included information about subpopulations of students who completed high
school as well as two- and four-year colleges. Only 24% of
employer respondents indicated that four-year college graduates had excellent preparation for the workplace, whereas
65% felt these students had only adequate preparation and
9% had deficient preparation. Specifically, when surveyed
about critical thinking/problem solving skills, just 28% of
employer respondents indicated that four-year college graduates had excellent preparation. Only adequate preparation for
critical thinking was observed by 63% of respondents and deficient preparation was observed by 9%.
The process of critical thinking is linked to the process of
reasoning. For the expert, clinical reasoning involves inductive
reasoning in which pattern recognition of relevant knowledge
along with previous experience facilitates rapid assessment of
typical conditions (Maudsley and Strivens, 2000). These patterns of recognition, also referred to as ‘‘schemas’’ (Sweller,
1988), constitute chunks of information. The more advanced
the expert, the larger the chunk of relevant information that
can be accessed (Sweller, 1998). For experts, when inductive
reasoning fails problem solving involves switching between inductive and deductive reasoning (Maudsley and Strivens,
2000). The novice lacks the foundational knowledge and experience to create schemas. They use hypothetico-deductive reasoning to work backward from a hypothesis. Providing the
novice with predetermined schemas for problem solving may
limit their ability to independently synthesize appropriate
knowledge and experience for the development of their own
expert critical thinking. Training should provide opportunity
for the novice to gain critical thinking skills.
Ongoing advances in health care necessitate that practicing
professionals, to remain current in their field, must be capable
of independently ascertaining the most advanced knowledge.
Verifying the validity of this knowledge requires critical analysis of primary literature. It has been reported that media literacy skills within the Millennial student population are
weak (Weiler, 2004; UCL, 2008; Considine et al., 2009; Hargittai et al., 2010; Kolikant, 2010). Millennials have grown
up with astonishing exposure to unvetted internet resources
exemplified by WikipediaTM (Wikimedia Foundation Inc.,
San Francisco, CA) and YouTubeTM (YouTube, LLC, San
Bruno, CA). The predilection for Millennial students is to
make big gains quickly and with minimal effort (Newton,
2000; Kuh, 2003) which has conditioned them to select the
first or most easily available information resource. In large
measure, Millennials appear to lack the skill to critically analyze information and determine what is and is not valid
(Selwyn, 2008; Considine et al., 2009) which suggests that
strategic curricular strategies should be considered to develop
these skills.
Unprofessional Behaviors
Regardless of which character traits are determined to be accurate for the Millennial student, once they choose to progress to
professional education and training they must recognize their
obligation to maintain the behavioral standards defined by
their chosen profession. Every professional field is expected to
maintain standards (Cusimano, 1996; Lovas et al., 2008) and
no profession should compromise their standards to accommodate the personality of one student or a cohort of students
from any generation. Each profession must identify the behavioral standards that fulfill the needs of those they serve.
In the field of medicine, a 1999 report (ACGME, 2005)
had already identified deficient professional behaviors as the
root of the problem for a significant increase in both patient
dissatisfaction and increased medical errors. Other reports
indicate that unprofessional behaviors are associated with subsequent disciplinary action by medical licensing boards (Papadakis et al., 2004, 2005, 2008; Teherani et al., 2005). How
did the acquisition of appropriate professional behaviors get
lost in the process of training? Eighteenth century medical education in the United States incorporated training in professional behaviors through an ‘‘Apprenticeship’’ model which was
part of the formal curriculum (Rothstein, 1987). Each student
shadowed an individual physician for a significant portion of
their training. The physician apprentice was purposefully
guided in appropriate patient/physician interactions as well as
professional behaviors, ethics, and morals. The 1910 Flexner
report, which standardized medical education, also lengthened
the time devoted to the delivery of scientific knowledge
(Flexner, 1910; Rothstein, 1987; Mitka, 2010). The essentially
‘‘Apprenticeship’’ model of medical training morphed to a
model that was basically a ‘‘Two plus Two’’ model. Approximately, the first two years of training were designated predominantly for the acquisition of foundational knowledge with a
concomitant decrease in the time devoted to ‘‘Apprenticeship’’
training in which students had the opportunity to learn appropriate professional behaviors through observation of physician/
patient interactions. Interactions with physicians in clinical
training were largely relegated to the remaining two years of
training. As the 19th century progressed, modernization of
healthcare moved treatment from primarily an office/home setting to one that was hospital based (Rothstein, 1987). This
dramatically changed the environment in which students
received clinical exposure and further diminished apprenticeship training. The majority of student/physician interactions
were limited to the hospital setting. Thus, the undergraduate
medical curriculum evolved in a way that minimized formal
training in professional behaviors.
It has been proposed that in the current medical education
system, students receive most of their input regarding professional behaviors and the development of their ‘‘Professional
Identity’’ (Cribb and Bignold, 1999) through the ‘‘hidden curriculum’’ (Hafferty and Franks, 1994; Turbes et al., 2002;
Lempp and Seale, 2004; Chuang et al., 2010) rather than
through explicitly delineated program requirements within
the formal curriculum. In contrast to ‘‘explicit education,’’ the
hidden curriculum has been defined as ‘‘implicit education’’ in
which students must independently discern informal or
unstated demands related to skills and qualities that are considered necessary for successful competency performance
DiLullo et al.
(Bergenhenegouwen, 1987). Not all the personality traits of
any particular generation are guaranteed to ally with the
standards of behavior expected in individual professions. For
student behaviors that may be considered contentious but that
do not negatively impact professional standards, changes in
the educational environment could be considered to accommodate them. However, a student or student cohort exhibiting
traits which will prevent them from attaining success in rigorous professional assessment must be guided to retool their
motivations or habits. Training students in behavioral standards, or in acquiring their ‘‘Professional Identity,’’ should be as
integral a component in the formal curriculum as is delivery of
requisite knowledge (Cruess and Cruess, 1997). The anatomy
course has been identified as one possible vehicle through
which to introduce elements of ‘‘Professional Identity’’ training
in the early medical curriculum (Escobar-Poni and Poni, 2006;
Lachman and Pawlina, 2006; Pangaro, 2006; Pawlina, 2006;
Swartz, 2006; Swick, 2006; Warner and Rizzolo, 2006; Böckers et al., 2010; Pearson and Hoagland, 2010).
To improve training in professional behaviors accrediting
organizations, exemplified within the field of medicine, introduced processes that included assessment of behavioral standards in conjunction with the assessment of knowledge (Gallagher et al., 2003; ACGME, 2005; RCPSC, 2011). These
standards are now referred to as ‘‘Competencies.’’ The thrust
of competency assessment has moved from assessing the
structures and processes of training to assessing the outcomes
of training. Since their introduction, ‘‘Competencies’’ have
been incorporated into graduate (Brown et al., 2006; Rousseau et al., 2007; Spector et al., 2008; Lenchus, 2010) and
undergraduate medical education (O’Neill et al., 1999; Bryan
et al., 2005; Lovas et al., 2008; DiLullo et al., 2009; Gregory
et al., 2009; Lockwood et al., 2009) as well as training in
other health care professions.
In many fields, there is evidence that using outcomes alone
for competency assessment has not fulfilled its promise and
has become a cycle of ever increasing clarification of defined
standards (Wolf, 2001). Recently, a case has been made that,
although in the field of medicine adequate competency assessment instruments exist, there is inconsistent interpretation and
use of them (Green and Holmboe, 2010). Others caution that
relying solely on assessing lists of outcomes focuses on testing
knowledge and skills (Wass et al., 2001; Whitcomb, 2007)
and in the case of medicine eliminates what should really be
assessed which is ‘‘critical judgments in context’’ (Huddle and
Heudebert, 2007). Under the former ‘‘Apprenticeship’’ model
of education, trainees were assessed on their acquisition of
‘‘knows,’’ ‘‘knows how,’’ and ‘‘shows how’’ knowledge and
skills. The ‘‘Apprenticeship’’ model, however, also provided
substantial opportunities for the assessment of trainees’ critical
thinking skills. Mentors worked with students in the process
of treating patients and assessed student understanding of
how to integrate their knowledge, skills, and behaviors in
practice. Assessment of ‘‘knows,’’ ‘‘knows how,’’ and ‘‘shows
how’’ knowledge and skills can be easily duplicated with competency assessments focused on outcomes (Miller, 1990).
However, competency assessment based on demonstration of
knowledge and skills alone, at any level of education, essentially neglects the evaluation of the students’ critical thinking
skills—a skill already shown to be weak in Millennial students. The development of critical thinking and clinical reasoning should be incorporated as a major objective especially
in professional education (Norman, 2005, 2006; ElizondoOmaña and Guzmán-López, 2008; Elizondo-Omaña et al.,
Anatomical Sciences Education
2010). Using a process known as ‘‘teaching around the cycle,’’
students are sequentially engaged in abstract conceptualization, concrete experience, reflective observation, and active
experimentation in the acquisition of targeted knowledge and
skills (Kolb, 1984; Kapranos, 2007). This cumulative learning
process provides students a framework in which they can develop critical thinking.
Learning Style
Many institutions provide students with personality or learning style assessments to help them determine which learning
strategies would be most effective for optimal knowledge acquisition. Understanding the nature of the learner and those
characteristics most critical for learning is paramount. The
majority of these assessment instruments are based on having
the respondent self-report toward which pole of a pair of opposite behaviors they are most inclined (Felder, 1996; Reynolds, 1997; Coffield et al., 2004). Examples of polar pairings
include extrovert/introvert, sensing/intuition, assimilators/
explorers, and verbalizers/imagers (Coffield et al., 2004). One
learning style assessment instrument uses a questionnaire asking students to self-report how they use visual, aural, read/
write, and kinesthetic strategies for information acquisition in
learning (Fleming and Mills, 1992). The information generated by these assessments is not absolute (Coffield et al.,
2004). Rather, there is commonly a range of styles over
which learning is approached. Debate is ongoing as to
whether learning style assessments are useful in designing
instructional methods (Merrit and Marshall, 1984; Felder,
1996, 2010; Felder and Spurlin, 2005) or are not yet validated sufficiently to be used for instructional design (Reynolds, 1997; Metallidou and Platsidou, 2008; Pashler et al.,
2009; Young, 2010). Data that would demonstrate whether
clearly improved outcomes result when student learning style
preferences match content delivery might help resolve the relative value of learning style assessments.
Published data on learning style assessments demonstrate
there is diversity within Millennial student subpopulations in
regard to which learning strategies they choose (Murphy
et al., 2004). Learning style differences among students may
reflect individual experiences with a variety of learning environments, resources, or discipline content. Consider that particular professions often attract individuals with similar core
personality traits. Conceivably, there may be a unique learning predisposition among students in each of the health care
professions of dentistry (Fang, 2002; Murphy et al., 2004;
Blue, 2009; Dotson, 2009), medicine (Vaughn and Baker,
2001; Borges et al., 2006; Adesunloye et al., 2008; Engels
and de Gara, 2010), nursing (Colucciello, 1999; Skiba and
Barton, 2006; Walker et al., 2006; Baker et al., 2007;
Schwarz, 2008), pharmacy (Pungente et al., 2003; Novak
et al., 2006), and veterinary medicine (Stickle et al., 1999). In
other words, there may be unique preferences regarding
learning styles among individual subpopulations of students.
This would, by extrapolation, be true for Millennial students
as well as students in other generational populations. Knowing the distribution of learning styles within a specific professional student subpopulation would allow curricular design
to be best aligned with the specific learning styles held by
that subpopulation of students. Regardless of whether or not
learning style assessments are used to dictate curricular delivery methods, these assessments may still be useful to direct
students in determining their individual learning styles.
Knowing their personal learning style preferences may aid
students in recognizing how to approach various content
delivery methods. The more aware the students are of their
preferences, the more likely they are to consciously plan their
study strategy. However, care should be taken that students
do not begin to feel entitled to be taught solely in their preferred style. Rather, students need to take personal responsibility for their own learning. The use of variety in content
delivery can provide students a complete spectrum of learning
strategies. The mixture of content delivery methods should
include traditional and innovative approaches using both face
to face and technology based content delivery. Students will
probably opt to be engaged with a style that matches their
preferred learning paradigm which may both enhance their
interest in learning and facilitate the learning process. However, once professional status is attained, new information
will be encountered in a variety of formats including text, auditory as well as visual which will require that students have
multimodal information processing skills (Felder, 1996). Professionals must be capable of processing all manner of information with equal facility. Allowing students to learn in their
preferred style only, will be insufficient to fully prepare them
for life-long learning. Confrontation in the context of education with learning styles which are a mismatch to a students’
preferred learning paradigm will provide them opportunity to
practice and gain facilitation with information inputs they
find the most challenging. Student exposure to a mixture of
teaching styles can both facilitate and expand their ability to
learn as well as improve learning skills for strategies with
which they are less well matched.
Issues Impacting Instructional Redesign
The process of learning and the instructional design that supports it have been evolving under the influence of countless
generations and technological advances since the time of illustrated manuscripts. Over the last two decades, the rapid pace
of developing web and digital resources has driven instructional redesign more quickly and perhaps somewhat haphazardly compared with educational changes over the last two
centuries. The rapid change in technology is the focus of
much of the current literature that treats Millennial Generation learners as different from learners in other generations.
While Millennial Generation students do demonstrate generational differences culturally they may not be as fundamentally
different from other generations when it comes to learning. It
may be that evolving learning styles is less a result of the
uniqueness of being a member of the Millennial Generation
or anything inherently different about the brains of Millennials but more a factor of the resources that are currently available that were absent for learners in previous generations.
Interactions through the web and social media have modified
some of the learning styles of Millennial Generation students.
However, learners from previous generations live in the same
world in which the Millennials have grown up. While
older generation learners did not grow up completely
immersed in technology, they have the same access to and
many have the same desire to use available technology. Students of previous generational cohorts who interact through
the web and social media may have modified learning characteristics similar to those seen in Millennial Generation stu220
dents although little research exists on this point. Studies to
look at the overall impact and efficacy of newly developed
educational methods on learners of all generations are
Educators are often unsure how best to use technology to
support learning in this new environment. Successful applications of technology in professional learning are often a result
of partnerships or vested interests of those who own content.
Christen (2009) points out that there is an increasing disconnect between the technology immersed world in which children and young adults spend their time and the often technology sterile environment in which they are expected to learn
through instruction by older generations. He argues for
increased synchronization of personal and professional technology use, substantiated through the development of information dependent professional skills. He also recommends
that workforce partnerships can provide an authentic framework in which students can recognize the value and placement of these requisite skills.
Blending academic learning with real world connections
naturally and authentically helps the learner situate new
knowledge with cognitive schemas that can be more easily
recalled when they are needed. Jackson and Woolsey (2009)
found positive outcomes in the use of technology to provide
the learner informal learning activities through peer mentoring, interactive cases, and problem-based inquiries. A wealth
of digital resources that have been peer reviewed by experts
and can help to foster innovative instructional design are
freely available in a searchable referatory, MERLOT (2011)
which is supported by individual, institutional, corporate,
and editorial board partnerships. The Cisco Networking
Academy (CISCO, 2011) is one model offering online
courses, interactive tools, and various instructional activities
that support professional learning outcomes to a variety of
audiences, including schools, companies, and organizations
with over 900,000 students involved worldwide. Textbook
publishers have also tapped into the opportunities afforded
students outside the brick and mortar classroom and offer a
variety of interactive learning and assessment tools that supplement textbook information affording connections to professional knowledge (Kolowich, 2010).
The multimodal characteristics of Web 2.0 enhance learning further. Imagery enhances memory, in that it assists the
learners’ ability to manipulate data, add and encode details
(particularly personal details) and recall information or relive
personal experiences (Rubin and Greenberg, 2003). Digital
content that combines imagery or allows the viewer/listener
to add imagery or personal interpretations enhances meaning
and is more likely to personalize the learning experience,
engage the learner, and support transfer of learning. The deep
personalization that is offered through interactive Web 2.0
tools (Diaz and McGee, 2007) may afford some advantages
for both teaching and learning (Tapscott, 1999; Brown and
Duguid, 2000; Howe et al., 2000; CPB, 2002; Oblinger,
2003; Dede, 2005; Tapscott and Williams, 2009).
Advancing Learning With Technological
The desire for ubiquitous technologies which Millennial
learners embrace challenges educators to identify and use
instructional technologies that can be accessed freely
(Kemelgor et al., 2000; Cross, 2003; Norris et al., 2003;
Oblinger, 2003) including mobile systems that have shown
DiLullo et al.
Table 1.
Types of Technology Tools that Support Digital Learning (McGee, 2007)
Type of tool
Specific tools
Support individualized cognitive
needs that may be at odds with a
course management delivery
Tagging or bookmarking images,
websites, and people so that they
can be easily located allow
learners to collect and manage
resources in their own space for
their own convenience.
SymbalooTM (2011) allows users to
group and label resource according
to one’s own language and system;
bookmarking tools such as DiigoTM
(2011) or Delicious (2011) allows the
instructor or students to collect and
annotate useful references.
Allow learners to collaborate,
practice and experiment outside of
class meetings and with whom
they have determined best
supports their learning.
Personal learning networks,
including social networks such as
FacebookTM (2011), sustain
learning across courses and
A course, program or special
interest group Facebook (2011) or
Twitter (2011) account can
provide an ongoing system of
interaction, communication, and
Virtual tutors
Content or skill-based programs
that encourage self-directed
Free courses or modules that
support informal learning through
which students can review
material, supplement course
learning, or learn new content at
their own pace.
Virtual Professors (VP, 2010); Open
Learn at the Open University (OU,
2011); MIT Online (MIT, 2011);
HippoCampusTM (2011) or Khan
AcademyTM (2011).
Virtual study
aids and
Allow learner to improve general
learning skills through interaction
with content and assessment
Textbook and news publisher tools
can be optional or required
supplements to course learning
offering options to how and when
students study.
Pearson’s MasteryTM (2003) series;
Rice University’s Connexions1
(2011), a learning object repository.
significant learning gains when utilized to support specific
objectives (Naismith et al., 2004). Generational differences
alone do not provide enough information to make decisions
about tools and resources that can support learning, particularly for the novice in professional training. Given established
successful practices of learning with technology, how do we
make decisions for our courses? Students in the applied sciences
seek out specific well-vetted strategies, processes, and critical
thinking activities that contribute to their professional development (White and Liccardi, 2006). While faculty preferences
must also be considered, there are four general drivers for technology integration that can support the 21st century learner as
they develop professional skills and knowledge. On the basis of
learner preferences, disciplinary predilections, and developing
expertise, the following recommendations are offered.
First, technology can assist to prevent decay of learning
through rehearsal and practice. Much learning, if not most of
it, occurs outside the classroom through informal interactions
between learners with each other, content, and available
resources. Students may not be prepared or skilled in how to
learn on their own or where to locate resources that can support their developing expertise. Providing directions to
resources and offering alternatives to print text and class
notes greatly expands the likelihood that students will take it
on themselves to learn on their own time, a habit of mind
that will sustain them over their professional career. Providing opportunities for debating, problem solving, research, and
even competition outside of class can allow students to apply
and reflect on their developing knowledge. Table 1 illustrates
how different types of tools support the learner with examples from freely available internet resources.
Anatomical Sciences Education
Second, technology can allow students to document, own,
and reveal what they have learned. Much of what students
learn may be fragmented as a result of program organization
(fractured programs, courses taken out of order, etc.). Students rent or sell back their texts to bookstores, lose flash
drives, and misplace notebooks. Personal learning networks
and electronic portfolios are systematic strategies that can
assist learners in keeping track not only of what they produce
and accomplish but also the professional networks of peers
which can be accessed in ways that are natural for the Millennials. Fostering professional identity reinforces the reality
of learning to do as publicized through professional networks
such as LinkedInTM (2011) or Medical MingleTM (2011) created for health care professionals. Public resumes provide an
opportunity not only to document achievements but also to
publicize skills and abilities to a nonacademic audience
through professional resumes (VisualCV, 2011).
Third, technology, particularly Web 2.0 tools, can help students interact and communicate with peers, instructors, and
experts. Millennial students have a ubiquitous connection with
each other through their mobile phones and unlike any other
generation they get more feedback from their peers to verify
what they know, how they are doing, or what others are doing
(Brown and Duguid, 2000). They also have an expectation for
feedback and without it may have misconceptions about their
progress or achievements. Instructors can provide feedback
and interaction through technology in ways that can potentially make the work of teaching more efficient and increase
learner’s engagement (Table 2). Peers and experts can provide
valuable feedback through many of these strategies validating
student knowledge in an authentic and public manner. Social
Table 2.
Instructional Strategies for Feedback and Interaction Through and with Technology
Some examples
Delivery methods
To inform about class
activities, events, changes
Announcements; directions;
information about course or
technology; change in schedule/
Course management system (CMS);
announcement; e-mail; instant
messaging; RSS
directions and
To explain, illustrate, direct,
scaffold, model, or respond
Embedded directions; assignments;
demonstrations; lectures
Document; video; podcast; chat;
discussion; animation; digital
To give information regarding
accomplishment of objectives,
participation, communication
Assessment; critiques;
encouragements; congratulations;
recognitions of special
accomplishments or abilities
CMS quiz/tests; discussions/chats/
wiki/blog posts; individual rubrics;
e-mails; instant messaging
To provide just-in-time or justin-need feedback or oversight
of learner actions
Discussion moderations; participation
in group work; periodic ‘visits’ with
individuals or groups
Recorded activities (chats, IM,
VOIP, etc.); polls/surveys to
determine status of work; formal
RSS: really simple syndication; CMS: course management system; IM: instant messaging; VOIP: voice over internet protocol.
networking provides a way for students to assess their ideas in
the context of a broader dialogue. Collaborative note taking
[through tools such as NoteMeshTM (Insidecase, LLC)], Google DocsTM (Google Inc., Mountain View, CA), Zoho WriterTM (ZOHO Corp., Pleasanton, CA), or JotSpotTM (Google
Inc., Mountain View, CA) requires that students not just share
what they think they understand but also corroborate with the
thinking of others as they negotiate understanding. Given the
social presence of experts via the internet, it is also possible
for students to test their ideas with experts in the form of
more experienced peers, practitioners, or authorities. Electronic
portfolios, blogs, presentation sharing sites, and video streams
allow others to make comments and review publicly available
student work which can contribute to the learners’ understanding about how others view their knowledge constructs.
Fourth, and finally, technology—particularly online
tools—can help the learner organize information in ways they
are more likely to access it. Learners already self-organize
their personal lives using digital and social media building
upon their style and personal preferences. Helping them to
organize their digital materials, accomplishments, and connections supports transfer of learning beyond the classroom
and course activities. Creating personal learning networks
(PLNs) taps into the Millennial students’ predilection for
ubiquitous technology access. PLNs are distributed, interconnected people, and services not mediated by a management
system, course, or authority (Yang and Yuen, 2010). At their
simplest, PLNs are a collection of tools that are loosely connected in some centralized manner (Fig. 1). Most importantly,
they are owned by their creators and remain with them long
after they leave the institution. By using tools with which students are familiar and comfortable, it is more likely that student communication, interaction, and subsequently learning
will be enhanced. Many students use Facebook (2001) as
their PLN, but other services offer different strategies for
helping to collect, assemble, and organize tools and resources
that are regularly used. For example, EpsilenTM allows users
to create portfolios, join communities of practice, interact
with learning objects, and access both formal and informal
learning courses (Epsilen, 2010). Tools such as SymbalooTM
(2011) provide an intuitive drag and drop web page that visually organizes tools and resources that can easily be modified or shared. Engaging the student in the use of their PLN
can reinforce the students’ academic accomplishments while
making transparent their developing expertise.
Fundamental Factors to Consider in Reframing
the Structure of Education
Millennial learning has resulted in much discussion about
the form and format of 21st century education. Given that
subsequent generations will experience even greater access to
Figure 1.
Personal Learning Network (PLN) toolkit (reprinted with permission from
PicasaTM, 2011).
DiLullo et al.
innovative technologies and social change, educators must be
prepared to plan with change on the horizon. When contemplating changes in the advancement of instructional design
four factors should be considered.
Student learning styles for acquisition of knowledge.
While individuals have unique sets of learning style preferences, students with similar learning characteristics may be
commonly attracted to particular fields of study or educational programs. Know the learning characteristics of your
specific student cohort and focus on those for instructional
innovation to facilitate learning.
Student aptitude for learning with technology. Students
from every generation, including the Millennial Generation,
indicate via self-assessment a fairly large disparity in the ability to use various types of educational technology. Recognize
that students in any cohort have unequal skill with regard to
the use of educational technology. Provide hands on training
or at a minimum online instruction for the use of general
educational programs, specialized software, and technological
innovations. This will facilitate learning from unfamiliar
paradigms for students that are less technologically proficient.
Student ability for critical thinking. Students must understand how to identify, acquire, and assemble reliable, vetted
information and subsequently how to critically analyze the
collected information, thoughts, and data. Students should be
assessed early and often for their ability to analyze and integrate what they have learned. Online exercises can be useful
to train students in this regard; however, face to face interaction is likely to be most effective in determining how well a
student has met this goal. For those weak in this area, training could prove critical to success in their field.
Student behaviors for development of ‘‘Professional
Identity’’. Explicit delineation of ‘‘Professional Identity’’
behaviors that are expected by the field of study for which a
student is being trained should be incorporated into the formal
curriculum. Students should be provided feedback through
professional and peer evaluation to identify behavioral areas in
which they are weak as well as training in appropriate behaviors to give them opportunity for improvement.
A disservice is done to any student cohort when they are
globally defined with a single set of character traits. Within
any generation, there is diversity and in the Millennial Generation there is considerable diversity in background, personality, and learning style. In the literature, references have already been made to the next generation whose generational
boundary has tentatively been set from 2001 to 2022. Educators should encourage curricular change that will positively
impact the learning process in a way that will be meaningful
not just for a single generation but that will have fundamental application for a broad spectrum of learners. Efforts in
instructional redesign that are focused on addressing learning
styles which are evolving as a result of new technologies and
resources should be useful to students of all generations and
have lasting impact for future generations of learners. If
efforts are concentrated on providing education that focuses
on the knowledge and competencies which students need to
be successful in their chosen profession using teaching methods and techniques designed to accommodate all learning
styles, we are sure to be prepared for the next generation despite their evolving personality quirks.
Anatomical Sciences Education
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Medicine in Philadelphia, Pennsylvania. She teaches anatomy
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Biomedical Sciences program Gross Anatomy course.
PATRICIA MCGEE, Ph.D., is an associate professor of
instructional technology in the Department of Educational Psychology at the College of Education and Human Development,
University of Texas at San Antonio, San Antonio, Texas. She
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RICHARD M. KRIEBEL, Ph.D., is a professor and Chair
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