RELEVANT REVIEW Demystifying the Millennial Student: A Reassessment in Measures of Character and Engagement in Professional Education Camille DiLullo,1* Patricia McGee,2 Richard M. Kriebel3 1 Department of Anatomy, Philadelphia College of Osteopathic Medicine, Philadelphia, Pennsylvania 2 Department of Educational Psychology, College of Education and Human Development, The University of Texas at San Antonio, San Antonio, Texas 3 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 INTRODUCTION 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 2011. Published online 6 July 2011 in Wiley (wileyonlinelibrary.com). DOI 10.1002/ase.240 © 2011 American Association of Anatomists Anat Sci Educ 4:214–226 (2011) Online Library 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; JULY/AUGUST 2011 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 JULY/AUGUST 2011 MILLENNIAL STUDENT CHARACTERIZATION 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 215 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 based. 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 environment. 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. Multitasking 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 JULY/AUGUST 2011 Reading 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 217 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 218 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 JULY/AUGUST 2011 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. 219 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. FUTURE DIRECTIONS 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 needed. 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 Innovation 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 Function Example Speciﬁc tools Adaptive tools Support individualized cognitive needs that may be at odds with a course management delivery system. 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. Virtual environments 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 programs. A course, program or special C interest group Facebook (2011) or TM Twitter (2011) account can provide an ongoing system of interaction, communication, and sharing. Virtual tutors Content or skill-based programs that encourage self-directed learning. 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 assessment tools Allow learner to improve general learning skills through interaction with content and assessment activities. 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 JULY/AUGUST 2011 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 221 Table 2. Instructional Strategies for Feedback and Interaction Through and with Technology Strategies Function Some examples Delivery methods General communication To inform about class activities, events, changes Announcements; directions; information about course or technology; change in schedule/ activity Course management system (CMS); announcement; e-mail; instant messaging; RSS Instructional directions and guidance To explain, illustrate, direct, scaffold, model, or respond Embedded directions; assignments; demonstrations; lectures Document; video; podcast; chat; discussion; animation; digital presentation Performance feedback 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 Facilitation 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 instruments 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 C 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 222 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. CONCLUSIONS 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 JULY/AUGUST 2011 NOTES ON CONTRIBUTORS CAMILLE DILULLO, Ph.D., is a professor in the Department of Anatomy at the Philadelphia College of Osteopathic Medicine in Philadelphia, Pennsylvania. She teaches anatomy to ﬁrst year medical, physical therapy, and Master of Biomedical Sciences students. She is course director of the Masters of 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 teaches both undergraduate and graduate students in the areas of interdisciplinary studies, instructional technology, learning theory, curriculum, and assessment and evaluation. RICHARD M. KRIEBEL, Ph.D., is a professor and Chair of the Department of Neuroscience, Physiology and Pharmacology at the Philadelphia College of Osteopathic Medicine in Philadelphia, Pennsylvania. He teaches neuroscience to second year medical students and is Senior Associate Dean of Curriculum and Research. LITERATURE CITED ACGME. 2005. Accreditation Council for Graduate Medical Education. ACGME outcome project. An Introduction. ACGME, Chicago, IL. URL: http://www.acgme.org/outcome/project/prohome.asp [accessed 11 May 2011]. Adesunloye BA, Aladesanmi O, Henriques-Forsythe M, Ivonye C. 2008. The preferred learning style among residents and faculty members of an internal medicine residency program. J Natl Med Assoc 100:172–175. Arroway P, Davenport E, Xu G, Updegrove D. 2010. EDUCAUSE Core Data Service; Fiscal Year 2009 Summary Report. Boulder, CO: EDUCAUSE. 150 p. URL: http://net.educause.edu/ir/library/pdf/PUB8007.pdf [accessed 22 May 2011]. Baker CM, Pesut DJ, McDaniel AM, Fisher ML. 2007. Evaluating the impact of problem-based learning on learning styles of master’s students in nursing administration. J Prof Nurs 23:214–219. Bayne S, Ross J. 2007. The ‘digital native’ and ‘digital immigrant’: A dangerous opposition. In: Proceedings of Society for Research into Higher Education Annual Research Conference (SRHE 2007). Brighton, Sussex, UK; 2007 Dec 11–13; p 1–6. Society for Research into Higher Education, London, UK. URL: http://www.malts.ed.ac.uk/staff/sian/natives_ﬁnal.pdf [accessed 27 May 2011]. Beard D, Schwieger D, Surendran K. 2008. Preparing the millennial generation for the work place: How can academia help? In: Proceeding of the Special Interest Group on Management Information Systems - Computer Personnel Research Conference (SIGMIS-CPR 2008). Charlottesville, VA; 2008 Apr 3–5; p 102–105. Association for Computing Machinery (ACM), New York, NY. Bennett S, Maton K, Kervin L. 2008. The ‘digital natives’ debate: A critical review of the evidence. Br J Educ Tech 39:775–786. Bergenhenegouwen G. 1987. Hidden curriculum in the university. High Educ 16:535–543. Blue CM. 2009. Do dental hygiene students ﬁt the learning proﬁle of the millennial student? J Dent Educ 73:1372–1378. Böckers A, Jerg-Bretzke L, Lamp C, Brinkmann A, Traue HC, Böckers TM. 2010. The gross anatomy course: An analysis of its importance. Anat Sci Educ 3:3–11. Borges NJ, Manuel RS, Elam CL, Jones BJ. 2006. Comparing millennial and generation X medical students at one medical school. Acad Med 81:571–576. Brown AK, O’Connor PJ, Roberts TE, Wakeﬁeld RJ, Karim Z, Emery P. 2006. Ultrasonography for rheumatologists: The development of speciﬁc competency based educational outcomes. Ann Rheum Dis 65:629–636. Brown JS, Duguid P. 2000. The Social Life of Information. 1st Ed. Boston, MA: Harvard Business School Press. 336 p. Bryan RE, Krych AJ, Carmichael SW, Viggiano TR, Pawlina W. 2005. Assessing professionalism in early medical education: Experience with peer evaluation and self-evaluation in the gross anatomy course. Ann Acad Med Singapore 34:486–491. Carlson S. 2005. The net generation goes to college. Chron High Educ 52:A34. URL: http://chronicle.com/article/The-Net-Generation-Goes-to/12307 [accessed 28 May 2011]. Casner-Lotto J, Barrington L. 2006. Are They Really Ready To Work? Employer’s Perspectives on the Basic Knowledge and Applied Skills of New Entrants to the 21st Century U.S. Workforce. 1st Ed. Washington DC: Conference Board, Inc., Partnership for 21st Century Skills, Corporate Voices for Working 223 Families, Society for Human Resource Management. 64 p. URL: http://p21.org/ documents/FINAL_REPORT_PDF09-29-06.pdf [accessed 28 May 2011]. Cheyne JA, Carriere JS, Smilek D. 2006. Absent-mindedness: Lapses in conscious awareness and everyday cognitive failures. Conscious Cognit 15:578–592. Christen A. 2009. Transforming the classroom for collaborative learning in the 21st century. Tech Connect Educ Careers 84:28–31. Chuang AW, Nuthalapaty FS, Casey PM, Kaczmarczyk JM, Cullimore AJ, Dalrymple JL, Dugoff L, Espey EL, Hammoud MM, Hueppchen NA, Katz NT, Peskin EG; Undergraduate Medical Education Committee, Association of Professors of Gynecology and Obstetrics. 2010. To the point: Reviews in medical education-taking control of the hidden curriculum. Am J Obstet Gynecol 203:316–318. CISCO. 2011. The Cisco Networking Academy. Cisco Systems, Inc., San Jose, CA. URL: http://www.cisco.com/web/learning/netacad/academy/index.html [accessed 24 May 2011]. Coates J. 2007. Generation Y - the millennial generation. In: Coates J. Generational learning styles. 1st Ed. River Falls, WI: Learning Resources Network (LERN) Books. 149 p. URL: http://honolulu.hawaii.edu/intranet/committees/ Fac DevCom/guidebk/teachtip/GenY.htm [accessed 28 May 2011]. Cofﬁeld F, Moseley D, Hall E, Ecclestone K. 2004. Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. 1st Ed. London, UK: Learning and Skills Research Centre. 174 p. URL: http://www.hull.ac.uk/ php/edskas/learning%20styles.pdf [accessed 28 May 2011]. Colucciello ML. 1999. Relationships between critical thinking dispositions and learning styles. J Prof Nurs 15:294–301. Connexions. 2011. Learning Objects Repository. Connexions Consortium, Rice University, Houston, TX. URL: http://cnx.org/ [accessed 28 May 2011]. Considine D, Horton J, Moorman G. 2009. Teaching and reading the millennial generation through media literacy. J Adolesc Adult Literacy 52:471–481. CPB. 2002.Corporation for Public Broadcasting. Connected to the Future: A Report on Children’s Internet use from the Corporation for Public Broadcasting. Washington DC: Corporation for Public Broadcasting. 8 p. URL: http:// www.cpb.org/stations/reports/connected/connected_report.pdf [accessed 28 May 2011]. Cribb A, Bignold S. 1999. Towards the reﬂexive medical school: The hidden curriculum and medical education research. Stud High Educ 24:195–209. Croasdale M. 2008. Med schools adjusting to millennial students: Educators look for ways to build on new students’ perceived strengths in technology, optimism and team-building. Am Med News 2008: Jan 14. URL: www.ama-assn.org/amednews/2008/01/14/prsd0114.htm [accessed 24 May 2011]. Cross J. 2003. Informal learning – the other 80%. The Internet Time Group, Berkeley, CA. URL: http://www.internettime.com/Learning/The Other 80%.htm [accessed 28 May 2011]. Cruess SR, Cruess RL. 1997. Professionalism must be taught. BMJ 315:1674– 1677. Cusimano MD. 1996. Standard setting in medical education. Acad Med 71: S112–S120. Dede C. 2005. Planning for neomillennial learning styles: Shifts in students’ learning style will prompt a shift to active construction of knowledge through mediated immersion. Educause Q 28:7–12. Delicious. 2011. Delicious, The tastiest bookmarks on the web. Yahoo Inc., Sunnyvale, CA. URL: http://www.delicious.com/ [accessed 28 May 2011]. Diaz V, McGee P. 2007. Learning technology in the future: Connecting today’s innovations to institutional priorities and challenges. In: Proceeding of EDUCAUSE 2007 Annual Conference. Seattle, WA; 2007 Oct 23–26; ID: EDU07181. EDUCAUSE, Boulder, CO. URL: http://www.educause.edu/E07/ Program/11073?Product_CODE5E07/SEM08A [accessed 29 May 2011]. Diigo. 2011. Digest of Internet Information, Groups and Other stuff (DiigoTM). Diigo. Inc., Reno, NV. URL: http://www.diigo.com [accessed 28 May 2011]. DiLullo C, Morris HJ, Kriebel RM. 2009. Clinical competencies and the basic sciences: an online case tutorial paradigm for delivery of integrated clinical and basic science content. Anat Sci Educ 2:238–243. Dotson DL. 2009. Personality/learning styles among dental hygiene students. Internet J Dent Sci 7:2 URL: http://www.ispub.com/journal/the_internet_journal_of_dental_science/volume_7_number_2_20/article/personality-learning-stylesamong-dental-hygiene-students.html [accessed 22 May 2011]. Dumais SA. 2009. The academic attitudes of American teenagers, 1990–2002: Cohort and gender effects on math achievement. Soc Sci Res 38:767–780. Dux PE, Tombu MN, Harrison S, Rogers BP, Tong F, Marois R. 2009. Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron 63:127–138. Dye MW, Green CS, Bavelier D. 2009a. Increasing speed of processing with action video games. Curr Dir Psychol Sci 18:321–326. Dye MW, Green CS, Bavelier D. 2009b. The development of attention skills in action video game players. Neuropsychologia 47:1780–1789. Elizondo-Omaña RE, Guzmán-López S. 2008. The development of clinical reasoning skills: A major objective of the anatomy course. Anat Sci Educ 1:267– 268. Elizondo-Omaña RE, Morales-Gómez JA, Morquecho-Espinoza O, HinojosaAmaya JM, Villarreal-Silva EE, Garcı́a-Rodrı́guez Mde L, Guzmán-López S. 224 2010. Teaching skills to promote clinical reasoning in early basic science courses. Anat Sci Educ 3:267–271. Engels PT, de Gara C. 2010. Learning styles of medical students, general surgery residents, and general surgeons: Implications for surgical education. BMC Med Educ 10:51–56. Epsilen. 2010. Epsilen; Global learning management system, lifelong ePortfolios, and fully integrated collaborative networking. Epsilen L.L.C., Indianapolis, IN. URL: http://www.epsilen.com/ [accessed 28 May 2011]. Erickson KI, Colcombe SJ, Wadhwa R, Bherer L, Peterson MS, Scalf PE, Kim JS, Alvarado M, Kramer AF. 2007. Training-induced functional activation changes in dual-task processing: An fMRI study. Cerebr Cortex 17:192–204. Escobar-Poni B, Poni ES. 2006. The role of gross anatomy in promoting professionalism: A neglected opportunity! Clin Anat 19:461–467. Facebook. 2011. Facebook© . Palo Alto, CA. URL: http://www.facebook.com/ [accessed 28 May 2011]. Fang AL. 2002. Utilization of learning styles in dental curriculum development. N Y State Dent J 68:34–38. Felder RM. 1996. Matters of style. ASEE Prism 6:18–23. Felder RM. 2010. Are learning styles invalid? (Hint: No). On Course Newsletter. On Course Workshop, Monkton, MD. URL: www.oncourseworkshop.com/ Learning046.htm [accessed 22 May 2011]. Felder RM, Spurlin J. 2005. Applications, reliability and validity of the index of learning styles. Int J Eng Educ 21:103–112. Fleming ND, Mills C. 1992. Not another inventory, rather a catalyst for reﬂection. Improve the Academy 11:137–147. Flexner A. 1910. Medical Education in the United States and Canada: A Report to the Carnegie Foundation for the Advancement for Teaching. Carnegie Bulletin 4. New York, NY: The Carnegie Foundation for the Advancement of Teaching. 346 p. URL: http://www.carnegiefoundation.org/ sites/default/ﬁles/elibrary/Carnegie_Flexner_Report.pdf [accessed 28 May 2011]. Foerde K, Knowlton BJ, Poldrack RA. 2006. Modulation of competing memory systems by distraction. Proc Natl Acad Sci USA 103:11778–11783. Gallagher M, Cummings M, Gilman D, McNerney J, Mogil C, Piccinini R, Ryan M. 2003. Report of the Core Competency Task Force. Chicago, IL: American Osteopathic Association. 25 p. URL: http://scs.msu.edu/cc/docs/ AOATaskForceReport.pdf [accessed 21 May 2011]. Gleason P. 2008. Meeting the needs of millennial students. In Touch Newsletter 16:1. Division of Student Services, California State University, Long Beach, CA. URL: www.csulb.edu/divisions/students2/intouch/archives/2007–08/vol16_no1/01. htm [accessed 24 May 2011]. Goldman KD, Schmalz KJ. 2006. Builders, boomers, busters, bridgers: Vive la (generational) difference! Health Promot Pract 7:159–161. Green ML, Holmboe E. 2010. Perspective: The ACGME toolbox: Half empty of half full. Acad Med 85:787–790. Gregory JK, Lachman N, Camp CL, Chen LP, Pawlina W. 2009. Restructuring a basic science course for core competencies: An example from anatomy teaching. Med Teach 31:855–861. Hafferty FW, Franks R. 1994. The hidden curriculum, ethics teaching, and the structure of medical education. Acad Med 69:861–871. Hargittai E, Fullerton L, Menchen-Trevino E, Yates Thomas K. 2010. Trust online: Young adults’ evaluation of web content. Int J Comm 4:468–494. Hoover E. 2007. At college board meeting, researchers challenge views of ‘millennial’ students. Chron High Educ 54:Oct 25. URL: http://chronicle.com/ article/Researchers-Dispute-Views-of/138 [accessed 21 May 2011]. HippoCampus. 2011. HippoCampusTM. Teaching with the Power of Digital Media. Monterey Institute for Technology and Education, Marina, CA. [accessed 28 May 2011]. Hoover E. 2009. The millennial muddle: How stereotyping students became a thriving industry and a bundle of contradictions. Chron High Educ 56: Oct 11. URL: http://chronicle.com/article/The-Millennial-Muddle-How/48772 [accessed 28 May 2011] Howe N, Strauss W. 2003. Millennials Go to College: Strategies for a New Generation on Campus. 2nd Ed. Great Falls, VA: LifeCourse Associate; Washington, DC: American Association of Collegiate Registrars and Admissions Ofﬁcers. 100 p. Howe N, Strauss W, Matson R. 2000. Millennials Rising: The Next Great Generation. 1st Ed. New York, NY: Vintage Books. 432 p. Huddle TS, Heudebert GR. 2007. Taking apart the art: The risk of anatomizing clinical competence. Acad Med 82:536–541. Immerwahr J. 2009. Generational differences. TeachPhilosophy101 (TF101), Villanova University, Villanova, PA. URL: http://www.teachphilosophy101.org/ Default.aspx?tabid570 [accessed 29 May 2011]. Jackson CA, Woolsey JD. 2009. A different set of classrooms: Preparing a new generation of clinicians. Forum on Public Policy Online. 1:1–10. URL: http:// forumonpublicpolicy.com/spring09papers/archivespr09/jackson.pdf [accessed 28 May 2011]. JISC. 2007. Joint Information Systems Committee. Students expectation study: Key ﬁndings from online research and discussion evenings held in June 2007 for the Joint Information Systems Committee. University of Bristol, Bristol, DiLullo et al. UK. URL: http://www.jisc.ac.uk/media/documents/publications/studentexpectations.pdf [accessed 28 May 2011]. Jones C, Ramanau R, Cross S, Healing G. 2010. Net generation or digital natives: Is there a distinct new generation entering university? Comput Educ 54:722–732. Kapranos P. 2007. 21st century teaching & learning. Kolb cycle & reﬂective thinking as part of teaching, creativity, innovation, enterprise and ethics to engineers. In: Proceeding of the International Symposium for Engineering Education (ISEE-07), Dublin, Ireland; 2007 Sep 17–19; p 3–11. Dublin City University, Dublin, Ireland. URL: www.ndlr.ie/mecheng/symp/papers/EPS/Kapra nos_ISEE07.pdf [accessed 21 May 2011]. Kvavik RB. 2005. Convenience, communications, and control: How students use technology. In: Oblinger DG, Oblinger JL (Editors). Educating the Net Generation. 1st Ed. Boulder, CO: EDUCAUSE. p 7.1–7.20. Kvavik RB, Caruso JB. 2005. Students and Information Technology, 2005: Convenience, connection, control, and learning. EDUCAUSE Center for Applied Research (ECAR), Boulder, CO. URL: http://net.educause.edu/ir/ library/pdf/ERS0506/ecm0506.pdf [accessed 24 May 2010]. Kemelgor NH, Johnson SD, Srinivasan S. 2000. Forces driving organizational change: A business school perspective. J Educ Bus 75:133–138. Kennedy GE, Judd TS, Churchward A, Gray K, Krause K-L. 2006. First year students’ experiences with technology: Are they really digital natives? Australas J Educ Tech 24:108–122. Khan Academy. 2011. Khan AcademyTM. Watch. Practice. Learn almost anything—for free. Khan Academy, Mountain View, CA. URL: http://www.khan academy.org/ [accessed 28 May 2011]. Kolb DA. 1984. Experiential Learning: Experience as the Source of Learning and Development. 1st Ed. Upper Saddle River, NJ: Prentice Hall, Inc. 256 p. Kolikant YB. 2010. Digital natives, better learners? Students’ beliefs about how the internet inﬂuenced their ability to learn. Comput Hum Behav 26:1384–1391. Kolowich S. 2010. A new battleground for publishers. Inside High Educ 2010:Mar 9. URL: http://www.insidehighered.com/news/2010/03/09/epublishing [accessed 23 May 2011]. Kuh GD. 2003. What we’re learning about student engagement from NSSE. Change 35:24–32. Lachman N, Pawlina W. 2006. Integrating professionalism in early medical education: the theory and application of reﬂective practice in the anatomy curriculum. Clin Anat 19:456–460. Lempp H, Seale C. 2004. The hidden curriculum in undergraduate medical education: Qualitative study of medical students’ perceptions of teaching. BMJ 329:770–773. Lenchus JD. 2010. End of the "see one, do one, teach one" era: The next generation of invasive bedside procedural instruction. J Am Osteopath Assoc 110:340–346. LinkedIn. 2011. LinkedIn professional network. LinkedIn Corporation© , Mountain View, CA. URL: http://www.linkedin.com/ [accessed 24 May 2011]. Lockwood MD, Tucker-Potter S, Sargentini NJ. 2009. Curricular analysis of competency-based osteopathic medical education: Application of a matrix for quality enhancement to a standardized patient encounter example. J Am Osteopath Assoc 109:486–500. Lovas JG, Lovas DA, Lovas PM. 2008. Mindfulness and professionalism in dentistry. J Dent Educ 72:998–1009. Lower J. 2008. Brace yourself here comes generation Y. Crit Care Nurse 28:80–84. Margaryan A, Littlejohn A. 2008. Are digital natives a myth or reality?: Students’ use of technologies for learning. Caledonian Academy, Glasgow Caledonian University, Glasgow, UK. URL: http://www.academy.gcal.ac.uk/anoush/ documents/DigitalNativesMythOrReality-MargaryanAndLittlejohn-draft-111208. pdf [accessed 22 May 2011]. Maudsley G, Strivens J. 2000. ‘cience’, ‘critical thinking’ and ‘competence’ for tomorrow’s doctors. A review of terms and concepts. Med Educ 34:53–60. McGee P. 2007. Designing with the learner in mind. In: Shank P, Carliner S (Editors). The E-learning Handbook: Past Promises, Present Challenges. 1st Ed. San Francisco, CA: Pfeiffer, An Imprint of Wiley. p 401–420. McGlynn AP. 2005. Teaching millennials, our newest cultural cohort. Educ Digest 71:12–16. Medical Mingle. 2011. Medical MingleTM The professional social network for the health care and medical ﬁeld. ONEsite, Inc., Oklahoma City, OK. URL: http://www.medicalmingle.com/ [accessed 28 May 2011]. MERLOT. 2011. Multimedia Educational Resource for Learning and Online Teaching. California State University, Ofﬁce of the Chancellor, Long Beach, CA. URL: http://www.merlot.org/ [accessed 28 May 2011]. Merrit SL, Marshall JC. 1984. Reliability and construct validity of ipsative and normative forms of the learning style inventory. Educ Psychol Meas 44:463–472. Metallidou P, Platsidou M. 2008. Kolb’s learning style inventory-1985: Validity issues and relations with metacognitive knowledge about problem-solving strategies. Learn Indiv Differ 18:114–119. Miller GE. 1990. The assessment of clinical skills/competence/performance. Acad Med 65:S63–S67. Anatomical Sciences Education JULY/AUGUST 2011 MIT. 2011. MIT OpenCourseWare. Massachusetts Institute of Technology, Cambridge, MA. URL: http://ocw.mit.edu [accessed 28 May 2011]. Mitka M. 2010. The Flexner report at the century mark: A wake-up call for reforming medical education. JAMA 303:1465–1466. Monaco M, Martin M. 2007. The millennial student: A new generation of learners. Athletic Train Educ J 2:42–46. Moore A. 2009. They’ve never taken a swim and thought about jaws: Understanding the millennial generation. Coll Univ 82:41–48. Murphy RJ, Gray SA, Straja SR, Bogert MC. 2004. Student learning preferences and teaching implications. J Dent Educ 68:859–866. Naismith L, Lonsdale P, Vavoula G, Sharples M. 2004.Literature review in mobile technologies and learning. Futurelab series, Report 11. London, UK: Futurelab. 48 p. URL: http://archive.futurelab.org.uk/resources/documents/ lit_reviews/Mobile_Review.pdf [accessed 28 May 2011]. NEA. 2004.National Endowment for the Arts. Reading at Risk: A Survey of Literary Reading in America, Research Division Report #46. Washington, DC: National Endowment for the Arts. 47 p. URL: http://www.nea.gov/research/ ReadingAtRisk.pdf [accessed 21 May 2011]. NEA. 2007.National Endowment for the Arts. , To Read or Not to Read: A Question of National Consequence, Research Division Report #47. Washington, DC: National Endowment for the Arts. 99 p. URL: http://www.nea.gov/ research/ToRead.pdf [accessed 21 May 2011]. Newton FB. 2000. The new student. About Campus 5:8–15. Norman G. 2005. Research in clinical reasoning: Past history and current trends. Med Educ 39:418–427. Norman G. 2006. Building on experience—The development of clinical reasoning. N Engl J Med 355:2251–2252. Norris D, Mason J, Lefrere P. 2003. Transforming e-Knowledge: A Revolution in the Sharing of Knowledge. 1st Ed. Ann Arbor, MI: Society of College and University Planning. 164 p. Novak S, Shah S, Wilson JP, Lawson KA, Salzman RD. 2006. Pharmacy students’ learning styles before and after a problem-based learning experience. Am J Pharm Educ 70:74. Oblinger D. 2003. Boomers, Gen-Xers, and Millennials: Understanding the new students. Educause Rev 38:37–47. URL: http://net.educause.edu/ir/library/ pdf/ERM0342.pdf [accessed 24 May 2011]. Oblinger DG, Oblinger JL (Editors). 2005. Educating the Net Generation. 1st Ed. Boulder, CO: EDUCAUSE. 264 p. O’Neill PA, Metcalfe D, David TJ. 1999. The core content of the undergraduate curriculum in Manchester. Med Educ 33:121–129. Ophir E, Nass C, Wagner AD. 2009. Cognitive control in media multitaskers. Proc Natl Acad Sci USA 106:15583–15587. OU. 2011. The Open University, Open Learn. The Open University. Milton Keynes, UK. URL: http://www.open.ac.uk/openlearn/ [accessed 28 May 2011]. P21. 2011. Partnership for 21st Century Skills. A framework for 21st Century learning. Partnership for 21st Century Skills, Washington DC. URL: http:// p21.org/ [accessed 28 May 2011]. Pangaro LN. 2006. A shared professional framework for anatomy and clinical clerkships. Clin Anat 19:419–428. Papadakis MA, Arnold GK, Blank LL, Holmboe ES, Lipner RS. 2008. Performance during internal medicine residency training and subsequent disciplinary action by state licensing boards. Ann Intern Med 148:869–876. Papadakis MA, Hodgson CS, Teherani A, Kohatsu ND. 2004. Unprofessional behavior in medical school is associated with subsequent disciplinary action by a state medical board. Acad Med 79:244–249. Papadakis MA, Teherani A, Banach MA, Knettler TR, Rattner SL, Stern DT, Veloski JJ, Hodgson CS. 2005. Disciplinary action by medical boards and prior behavior in medical school. N Engl J Med 353:2673–2682. Pashler H, McDaniel M, Rohrer D, Bjork R. 2009. Learning styles: Concepts and evidence. Psychol Sci Publ Interest 9:105–119. Pastorino E. 2006. When generations collide in the classroom. In: Saville BK, Zinn TE, Meyers SA, Stowell JR (Editors). E-Xcellence in Teaching 2006, Volume VI. 1st Ed. Providence, RI: Society for the Teaching of Psychology. p 16–19. URL: http://teachpsych.org/resources/e-books/eit2006/eit06–04.pdf [accessed 28 May 2011]. Pawlina W. 2006. Professionalism and anatomy: How do these two terms deﬁne our role? Clin Anat 19:391–392. Pearson. 2003. MasteringTM. A revolutionary online homework and tutoring system. Pearson Education, Inc., Old Tappan, NJ. URL: http://www.pearsoned. co.nz/elearning/mast.asp [accessed 28 May 2011]. Pearson WG Jr, Hoagland TM. 2010. Measuring change in professionalism attitudes during the gross anatomy course. Anat Sci Educ 3:12–16. Picasa. 2011. PicasaTM Web Albums. Warrick Mole’s Gallery. Where to start with a personal learning network. Google Inc., Mountain View, CA. URL: http://picasaweb.google.com/lh/photo/d7eVMpCwwyH8FZP4_XokDA [accessed 28 May 2011]. Prensky M. 2001. Digital natives, digital immigrants. On the Horizon 9(5):1– 6. URL: http://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf [accessed 21 May 2011]. Prensky M. 2009. H. Sapiens digital: From digital immigrants and digital 225 natives to digital wisdom. Innovate 5:1–9. URL: http://innovateonline.info/pdf/ vol5_issue3/H._Sapiens_Digital-_From_Digital_Immigrants_and_Digital_Natives_ to_Digital_Wisdom.pdf [accessed 28 May 2011]. Pungente MD, Wasan KM, Moffett C. 2003. Using learning styles to evaluate ﬁrst-year pharmacy students’ preferences toward different activities associated with problem based learning. Am J Pharm Educ 66:119–124. Raines C. 2002. Managing Millennials. Generations at Work: The Online Home of Claire Raines Associates, Keller, TX. URL: http://www.hreonline. com/pdfs/ManagingMillennials.pdf [accessed 28 May 2011]. RCPSC. 2011. Royal College of Physicians and Surgeons of Canada. The CanMEDS 2005 Physician Competency Framework. Ottawa, ON, Canada. URL: http://rcpsc.medical.org/canmeds/CanMEDS2005/index.php [accessed 28 May 2011]. Reeves TC, Oh E. 2007. Generational differences. In: Spector JM, Merrill MD, van Merriënboer J, Driscoll MP. Handbook of Research on Educational Communication and Technology. 3rd Ed. New York, NY: Lawrence Erlbaum Associates. p 295–304. Reynolds M. 1997. Learning styles: A critique. Manag Learn 28:115–133. Roos D. 2007. How net generation students work. HowStuffWorks.com, A Discovery Company, Atlanta, GA. URL: http://communication.howstuffworks. com/how-net-generation-students-work.htm [accessed 27 May 2011]. Rothstein WG. 1987. American Medical Schools and the Practice of Medicine: A History. 1st Ed. New York, NY: Oxford University Press, Inc. 408 p. Rousseau A, Saucier D, Côté L. 2007. Introduction to core competencies in residency: A description of an intensive, integrated, multispecialty teaching program. Acad Med 82:563–568. Rubin DC, Greenberg DL. 2003. The role of narrative in recollection: A view from cognitive psychology and neuropsychology. In: Fireman GD, McVay TE, Flanagan OJ (Editors). Narrative and Consciousness: Literature, Psychology and the Brain. 1st Ed. New York, NY: Oxford University Press. Inc. p 53–85. Sandars J, Morrison C. 2007. What is the net generation? The challenge for future medical education. Med Teach 29:85–88. Schwarz T. 2008. Generation Y responds: Respect differences, admit similarities, says Gen Y nurse. Crit Care Nurse 28(5):83–84. Selwyn N. 2008. An investigation of differences in undergraduates’ academic use of the internet. Active Learn High Educ 9:11–22. Skiba DJ, Barton AJ. 2006. Adapting your teaching to accommodate the net generation of learners. Online J Issues Nurs 11:1–9. Smith PL, Ragan TJ. 2005. Instructional Design. 3rd Ed. Hoboken, NJ: Wiley/ Jossey-Bass Education. 400 p. Spector M, Holmes DC, Doering JV. 2008. Correlation of quantity of dental students’ clinical experiences with faculty evaluation of overall clinical competence: A twenty-two-year retrospective investigation. J Dent Educ 72:1465–1471. Spellings M. 2006. A Test of Leadership: Charting the Future of U.S. Higher Education. A Report of the Commission Appointed by Secretary of Education Margaret Spelling. 1st Ed. Washington, DC: U.S. Department of Education. 51 p. URL: http://www.ed.gov/about/bdscomm/list/hiedfuture/reports/pre-pub-report. pdf [accessed 28 May 2011]. Spencer E. 2006. Student affairs administrator shares research on millennial generation. Division of Student Affairs, Virginia Polytechnic Institute and State University, Blacksburg, VA. URL: http://www.studentprograms.vt.edu/publica tions/millennials.php [accessed 28 May 2011]. Stickle JE, Lloyd J, Waldo FK, Cherney E. 1999. Learning styles in veterinary medicine: Relation to progression through the professional curriculum and integration into the profession. J Vet Med Educ 26:9–12. Strauss W, Howe N. 1991. Generations: The History of America’s Future, 1584 to 2069. 1st Ed. New York, NY: William Morrow and Company, Inc. 544 p. Strayer DL, Drews FA. 2007. Cell-phone-induced driver distraction. Curr Dir Psychol Sci 16:128–131. Strayer DL, Drews FA, Johnston WA. 2003. Cell phone-induced failures of visual attention during stimulated driving. J Exp Psychol Appl 9:23–32. Swartz WJ. 2006. Using gross anatomy to teach and assess professionalism in the ﬁrst year of medical school. Clin Anat 19:437–441. Sweeney R. 2006. Millennials behaviors & demographics. New Jersey Institute of Technology, Newark NJ. URL: http://library1.njit.edu/staff-folders/sweeney/ Millennials/Article-Millennial-Behaviors.doc [accessed 21 May 2011]. Sweeney R. 2007. How the new generation of well-wired multitaskers is changing campus culture. Chron High Educ 53:B10–B15. Sweeney R. 2009. Millennial behaviors and higher education focus group results. How are Millennials different from previous generations at the same age? New Jersey Institute of Technology, Newark NJ. URL: http://library1.njit. edu/staff-folders/sweeney/Millennials/Millennial-Summary-Handout.doc [accessed 21 May 2011]. Sweller J. 1988. Cognitive load during problem solving: Effects on learning. Cognit Sci 12:257–285. Swick HM. 2006, Medical professionalism and the clinical anatomist. Clin Anat 19:393–402. Symbaloo. 2011. Symbaloo | Access your bookmarks anywhere. Symbaloo BV, Delft, Netherlands. URL: http://www.symbaloo.com/ [accessed 28 May 2011]. 226 Tapscott D. 1999. Growing up Digital: The Rise of the Net Generation. 1st Ed. New York, NY: McGraw-Hill. 352 p. Tapscott D. 2009. Growing-up Digital. How the Net Generation is Changing your World. 1st Ed. New York, NY: McGraw-Hill. 384 p. URL: http://dontap scott.com/books/grown-up-digital/ [accessed 28 May 2011]. Tapscott D, Williams AD. 2009. Wikinomics: How Mass Collaboration Changes Everything. 1st Ed. New York, NY: Penguin Group Inc. 320 p. Tarr TA, Palmer MM. 2008. Understanding and motivating millennial students. Institute for Learning and Teaching Excellence, Indiana University SouthEast, New Albany, IN. URL: http://ilte.ius.edu/pdf/millennials_presenta tion.pdf [accessed 20 May 2011]. Taylor AD. 2006.Here come the millennial generation and their helicopter parents - are you ready? In: Proceedings of the 17th National Meeting of National Association of Advisors for the Health Professions (NAAHP 2006). Balancing Humanism and the Numbers: The Advising Challenge. Portland OR; 2006 Jun 25–29. National Association of Advisors for the Health Professions, Inc., Champaign, IL. Taylor P, Keeter S. 2009. Millennials: A portrait of generation next. Pew Research Center, Washington, DC. URL: http://pewresearch.org/pubs/ 1437/millennials-proﬁle [accessed 28 May 2011]. Taylor S, MacNeil N. 2005. Understanding the millennial student. In: Proceedings of 38th Annual Mississippi Association of Student Financial Aid Administrators Conference (MASFAA 2005). Biloxi, MS; 2005 May 18–20. Mississippi Association of Student Financial Aid Administrators, Jackson, MS. Teherani A, Hodgson CS, Banach M, Papadakis MA. 2005. Domains of unprofessional behavior during medical school associated with future disciplinary action by a state medical board. Acad Med 80:S17–S20. Turbes S, Krebs E, Axtell S. 2002. The hidden curriculum in multicultural medical education: The role of case examples. Acad Med 77:209–216. Twenge JM. 2009. Generational changes and their impact in the classroom: Teaching generation Me. Med Educ 43:398–405. Twitter. 2011. TwitterTM. Twitter, Inc., San Francisco, CA. URL: http://twitter. com/ [accessed 28 May 2011]. UCL. 2008. University College London. CIBER brieﬁng paper: Information behavior of the researcher of the future. CIBER group, University College London. London, UK. URL: http://www.ucl.ac.uk/infostudies/research/ciber/ downloads/ggexecutive.pdf [accessed 20 May 2011]. Vaughn L, Baker R. 2001. Teaching in the medical setting: Balancing teaching styles, learning styles and teaching methods. Med Teach 6:610–612. VisualCV. 2011. Get a better resume, online. VisualCV, Inc., Reston, VA. URL: http://www.visualcv.com [accessed 24 May 2011]. VP. 2010.Virtual Professors© . Free College Online Courses. VirtualProfessors.com. URL: http://www.virtualprofessors.com/ [accessed 28 May 2011]. Walker JT, Martin T, White J, Elliott R, Norwood A, Mangum C, Haynie L. 2006. Generational (age) differences in nursing students’ preferences for teaching methods. J Nurs Educ 45:371–374. Warner JH, Rizzolo LJ. 2006. Anatomical instruction and training for professionalism from the 19th to the 21st centuries. Clin Anat 19:403–414. Wass V, Van der Vleuten C, Shatzer J, Jones R. 2001. Assessment of clinical competence. Lancet 357:945–949. Watson JM, Strayer DL. 2010. Supertaskers: Proﬁles in extraordinary multitaking ability. Psychonomic Bull Rev 17:479–485. Weiler A. 2004. Information-seeking behavior in generation Y students: Motivation, critical thinking, and learning theory. J Acad Librarian 31:46–53. Wesner MS, Miller T. 2008. Boomers and Millennials have much in common. Organ Dev J 26:89–96. Westerman J. 2006–2007.When motivation generation Y in the classroom. Essays on Teaching Excellence. Toward the Best in the Academy 18:1. URL: http://teaching.uchicago.edu/ete/06–07/Westerman.html [accessed 28 May 2011]. Whitcomb ME. 2007. Redirecting the assessment of clinical competence. Acad Med 82:527–528. White S, Liccardi I. 2006. Harnessing insight into disciplinary differences to reﬁne e-learning design. In: Proceedings of the 36th Annual Frontiers in Education Conference, Borders: International, Social and Cultural. (FIE 2006). San Diego, CA; 2006 Oct 27–31; p 5–10. American Society for Engineering Education (ASEE), Washington, DC./ Institute of Electrical and Electronics Engineers (IEEE), New York, NY. Wilson LO. 2005. Teaching millennial students. Faculty Alliance for Creating and Enhancing Teaching Strategies. University of Wisconsin-Stevens Point, School of Education, Stevens Point, WI. URL: http://www.uwsp.edu/education/ facets/links_resources/Millennial%20Speciﬁcs.pdf [accessed 22 May 2011]. Wolf A. 2001. Competence-based assessment. In: Raven J, Stephenson J (Editors). Competence in Learning Society. 1st Ed. New York, NY: Peter Lang Publishing Inc. p 453–466. Yang HH, Yuen SC-Y. 2010. Collective Intelligence and E-learning 2.0: Implications of Web-based Communities and Networking. 1st Ed. Hershey, PA: IGI Global Publishers. 326 p. Young T. 2010. How valid and useful is the notion of learning style? A multicultural investigation. Procedia Soc Behav Sci 2:427–433. DiLullo et al.