Student Learning Environment

Chapter 32
Student Learning Environment

Sue Roff and Sean McAleer

Center for Medical Education, Dundee University Medical School, UK

Introduction and Definition of Learning Environments

In the past decade there has been a growing appreciation of the importance of student learning environments in medical education. In 2013, the UK General Medical Council (GMC) developed a discussion paper on Approving Educational Environments (GMC, 2013). As part of the GMC’s 2015 revision of this document, it included a definition of an education or learning environment:

The educational or learning environment can be defined in various ways. At its simplest it can mean the physical surroundings within which learning takes place, such as access to library facilities, seminar rooms or simulation equipment. However, references to the environment generally also encompass broader and less tangible notes of educational “climate”, “culture” or “ethos”. (GMC, 2015, p. 3)

This GMC paper also points to the American Medical Association (AMA, 2008) definition of the learning environment as:

A social system that includes the learner (including the external relationships and other factors affecting the learner), the individuals with whom the learner interacts, the setting(s) and purpose(s) of the interaction, and the formal and informal rules/policies/norms governing the interaction. (GMC, 2015, p. 3)

Further, the AMA describes a learning environment as “comprising three broad components in any institution or setting: institutional culture, curriculum (both formal and informal) and educational climate” (GMC, 2015, p. 3).

Some organizations have been more specific in their definition of the learning environment. For example, the GMC’s 2015 publication Promoting Excellence: Standards for Medical Education and Training devotes the first of its five themes to “Learning Environment and Culture” and includes the following standards:

Standard 1: The learning environment is safe for patients and supportive for learners and educators. The culture is caring, compassionate and provides a good standard of care and experience for patients, carers and families.

Standard 2: The learning environment and organisational culture value and support education and training so that learners are able to demonstrate what is expected in good medical practice and to achieve the learning outcomes required by their curriculum. (GMC, 2015, p. 7)

Given these definitions, how can we delineate such a broad and multifaceted aspect of a social organization as its learning environment? Specifically, how can this be done for the learning environments of veterinary medical education?

Social Organizations and Learning Communities

Like all professions, veterinary science is a social organization. In Lave and Wenger’s (1991) terminology, it is a community of practice in which veterinarians acquire the learning, meaning, and identity of a specific profession by participating in its learning communities. In veterinary medical education, these learning communities could include preclinical groups, clinical groups in veterinary teaching hospitals, as well as external placements, e-learning groups, or groups formed through continuing education.

Wenger (1999, p. 4) suggests that participation in learning communities such as these “refers not just to local events of engagement in certain activities with certain people, but to a more encompassing process of being active participants in the practices of social communities and constructing identities in relation to these communities.” Merton and Kitt (1950, p. 81) hypothesized more than 60 years ago that learning communities such as medical schools are social structures, and that it should be possible to delineate “the development of relatively precise, statistical indices of social structure” in such a social organization. In his classic study of the “student-physician,” Merton suggested in 1957:

Learning to be a physician, like complex learning of other kinds, is not only a function of intelligence and aptitude, of motivations and self-images; it is also a function of the social environments in which learning and performance take place.… Learning and performance vary not as the individual qualities of students vary but also as their social environments vary, with their distinctive climates of value and their distinctive organization of relations among students, between students and faculty, and between students and patients. (Merton, 1957, p. 63)

The neo-Mertonian field of analytical social science “endeavours to explicate iterative connections between the properties of a social system and the action of individuals” (Freese, 2009, p. 94). That is, it should be possible to delineate aspects of a social system such as the educational environment or climate through mixed qualitative and quantitative methods in order to describe and analyze them.

Assessment of the Learning Environment

In an effort to apply qualitative and quantitative methods, several psychometric tools have been developed to assess the learning environment. Each of these tools is focused on a specific stage within the professional curriculum.

The Dundee Ready Education Environment Measure (DREEM)

The Dundee Ready Education Environment Measure (DREEM) was developed 20 years ago using a panel of nearly 100 international health professions educators in a grounded theory process, resulting in the generation of 50 items reflecting aspects of the educational environment that the panel considered to be relevant to preclinical learning in the health professions. There are more than 200 published reports of DREEM administrations in the human health professions. The psychometrics reported in these studies are consistently good, but there is some weakness in the factor analysis, particularly in social self-perceptions in some of the studies. While factor analysis of DREEM has been reported more than a dozen times in the last decade, seven studies had fewer respondents than the minimum sample size of 300 recommended by Wetzel (2012). Five studies analyzed data from 323 to 586 respondents. The respondents in these studies included German (n=205), Greek (n=323), and New Zealand (n=176) dental students; Irish (n=239), Swedish (n=395), Pakistani (n=419–586), and Greek (n=487) medical students; Australian (n=245) osteopathy students; and American (n=214) veterinary students. Two studies (one from Germany and one from Spain) had sample sizes of 1119 and 1391, respectively. Yusoff (2012) analyzed four administrations of DREEM to the same cohort of 186 Malaysian medical students. Wetzel (2012, p. 1066) also noted that “Large sample sizes generally produce more stable factor structures and better approximate population parameters,” and there are indications of a trend confirming a more stable factor structure for DREEM with larger sample sizes. There may be an opportunity to test this with the data from a nationwide cohort of more than 9000 Korean medical students reported by Park et al. (2015).

DREEM has been used in a variety of contexts to evaluate the learning environment (see Box 32.2).

DREEM and Veterinary Medical Education

Two studies have begun to explore the validity and reliability of adapting the widely used DREEM tool to veterinary medical education in the United States and Scotland.

Pelzer, Hodgson, and Werre (2014) administered a slightly adapted version of the 50-item DREEM with its 5 subscales to 224 (53%) of the 419 students in a mostly graduate-entry four-year veterinary medicine program in the United States, which comprises three years of preclinical training and a final year of workplace training in a variety of clinical settings, including a veterinary teaching hospital. Cronbach’s alpha for the overall score was .93 and for the five subscales was as follows: perceptions of learning .85, perceptions of faculty .79, perceptions of atmosphere .81, academic self-perceptions .68, and social self-perceptions .72. Construct validity was determined to be acceptable (p<0.001) and all items contributed to the overall validity of DREEM. The overall DREEM score was 128.9/200. Four individual items of concern were identified by students, originating from four of the five subscales, but all related to workload. The researchers concluded that in this setting DREEM was a reliable and valid tool to measure veterinary students’ perceptions of their learning environment.

Hughes (2015) administered a slightly adapted DREEM (see Box 32.3) to 452 (60%) of 750 students in a UK veterinary school where the curriculum includes a number of transition points when the type of teaching changes. There is also an additional four-year graduate-entry program (GEP) at the school, which presents a combined first and second year in a separate class prior to merging the students into the third year of the program. Statistical analysis of the collected data determined the internal consistency (Cronbach’s alpha) for the overall score to be .79, and for the five subscales perceptions of learning .81, perceptions of teachers .76, academic self-perceptions .80, perceptions of atmosphere .80, and social self-perceptions .62. All of these values were interpreted as acceptable to good, indicating a good level of internal consistency. Confirmatory factor analysis of the construct validity also indicated an adequate model fit.

Hughes (2015, p. 2) reported:

Overall the results of the DREEM were positive from the majority of the students however there was a significant minority of students in all years who held a more negative view of certain aspects of their experience. Additionally, student perceptions were found to be less positive overall in certain areas at key transition points such as entering the first year, moving from pre-clinical to clinical teaching and into final year rotations. In particular the themes “Perceptions of learning” and “Academic self-perception” had up to 30% of respondents in a single year rating them on the negative end of the scale.

A number of results from DREEM allowed this program to make changes to its curriculum. For example, the results

led to a discussion about the issues students face with transitions and what could be done better to support them. In particular, the teaching staff were surprised at just how unprepared final year students felt for the move from classroom teaching to rotations on clinics and using clinical reasoning. Following this discussion a number of initiatives have been put in place to address this: there will now be a clinical reasoning thread through the earlier years and problem-based learning sessions in other courses; the final year preparation course has been re-structured to help students get ready for final year including a track on problem-based cases, and there will also be tours of the hospital for 4th year students by final years to help them feel more comfortable with what happens there and less intimidated when they start final year. There will also be on-line resources with tasks to undertake on rotation to point to what is important as students go through. In addition to support for the final year transition, there is a pre-entry support project in progress and the school is discussing ways to support the pre-clinical to clinical transition. (Hughes, 2015, p. 7)

Manchester Clinical Placement Index (MCPI)

Dornan et al. (2012) developed the Manchester Clinical Placement Index (MCPI) and suggested that its eight items could be sufficient to measure the 50 aspects of educational environment encompassed by DREEM. However, DREEM is not intended for clinical placement learning but for preclinical environments, as indicated by its item descriptors. Strand et al. (2013, p. 1015) comment that MCPI

consists of eight items mapping out various aspects of experiential learning, support and training in undergraduate clinical placements, complemented by open-ended questions. The tool, a valuable short inventory, is based on experiential learning theory and community of practice theory. However, the eight items are limited in their capacity to address the social and emotional dimensions of the learning climate and the quality of pedagogical strategies, which are aspects of learning emphasized in contemporary workplace learning models.

Undergraduate Clinical Environment Education Measure (UCEEM)

Strand et al. (2013, p. 1014) saw the need to develop an Undergraduate Clinical Environment Education Measure (UCEEM), noting that “In medical and health professions education, a significant part of student education takes place in the clinical workplace environment and through workplace experience.” UCEEM is a 25-item instrument with two overarching dimensions, experiential learning and social participation, and four subscales that coincide well with theory and empirical findings: opportunities to learn in and through work and quality of supervision; preparedness for student entry; workplace interaction patterns and student inclusion; and equal treatment. Evidence from various sources supports the content validity, construct validity, and reliability of the instrument (see Box 32.4 for the instrument).

Oct 15, 2017 | Posted by in GENERAL | Comments Off on Student Learning Environment

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