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Understanding Student Characteristics in the Development of Active Learning Strategies


Abstract and Figures

Student evaluations after non-lecture, active learning sessions at Michigan State University, College of Osteopathic Medicine, have yielded bipartite responses with one group responding favorably and the other group negatively. The purpose of this study was to understand the characteristics, motivation, and learning strategies of medical students that find value in, appreciate, and perceive active learning sessions to be beneficial and those of students that do not. We generated a survey, based on the Motivated Strategies for Learning Questionnaire (MSLQ), that included questions regarding overall student motivations for learning, learning strategies that students employed throughout medical school, and their perceptions of active learning. Following an active learning session on hyperammonemia, we administered the modified MSLQ survey. Using the results of this survey, we validated the modified MSLQ and identified correlations between student characteristics and their perception of the active learning session. We found that, in general, students with high task value, intrinsic goal orientation, self-efficacy for learning and performance, and control of learning beliefs felt more positively about their experience in the active learning session. Understanding the characteristics, motivations, and learning strategies that help students find value in active learning sessions will help medical educators develop future curricular material so that these exercises will better engage and be more effective with a greater number of learners. Supplementary information: The online version contains supplementary material available at 10.1007/s40670-022-01550-9.
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Understanding Student Characteristics intheDevelopment ofActive
Learning Strategies
SeemaMehta1· CaseyP.Schukow1· AmarTakrani1· RaquelP.Ritchie2· CarolA.Wilkins3· MarthaA.Faner1
Accepted: 12 April 2022
© The Author(s) 2022
Student evaluations after non-lecture, active learning sessions at Michigan State University, College of Osteopathic Medi-
cine, have yielded bipartite responses with one group responding favorably and the other group negatively. The purpose of
this study was to understand the characteristics, motivation, and learning strategies of medical students that find value in,
appreciate, and perceive active learning sessions to be beneficial and those of students that do not. We generated a survey,
based on the Motivated Strategies for Learning Questionnaire (MSLQ), that included questions regarding overall student
motivations for learning, learning strategies that students employed throughout medical school, and their perceptions of active
learning. Following an active learning session on hyperammonemia, we administered the modified MSLQ survey. Using the
results of this survey, we validated the modified MSLQ and identified correlations between student characteristics and their
perception of the active learning session. We found that, in general, students with high task value, intrinsic goal orientation,
self-efficacy for learning and performance, and control of learning beliefs felt more positively about their experience in the
active learning session. Understanding the characteristics, motivations, and learning strategies that help students find value
in active learning sessions will help medical educators develop future curricular material so that these exercises will better
engage and be more effective with a greater number of learners.
Keywords Active learning· Medical school· Biochemistry· Motivated strategies for learning questionnaire
Over the past decade, undergraduate medical education
has witnessed a major shift in focus from teacher-centered
education models towards active, learner-centered instruc-
tion [1, 2]. Often presented as a radical change from tra-
ditional instruction, active learning is generally defined as
any instruction that engages students in the learning process
rather than passively taking it in [2]. This form of teaching
allows students to engage in meaningful learning activities
that encourage self-reflection about the content of one’s own
education [3]. Active learning has been found to be effec-
tive in maximizing learning, improving collaboration with
peers, and optimizing the practice of evidence-based medi-
cine [1, 46]. It allows students to remain at the forefront of
the learning process and encourages higher-order thinking
through practices such as problem solving, case-based learn-
ing, and self-monitored learning [7]. Active learning ses-
sions are effective at fostering the self-reflection that medical
students engage in while learning and prepares them for the
management of complex clinical cases through the transla-
tion of biomedical knowledge into clinical practice [8, 9].
In addition, medical professionals are expected to engage
in life-long learning which requires two cardinal tenets of
the active learning process: self-regulation and monitoring
of learning [8]. The Commission on Osteopathic College
Accreditation (COCA) board and the Liaison Committee
on Medical Education (LCME) have standards that include
a requirement for self-directed learning and independent
study, which are both examples of active learning, in their
Seema Mehta, Casey Schukow and Amar Takrani contributed
equally to this work.
* Martha A. Faner
1 College ofOsteopathic Medicine, Detroit Medical Center,
Michigan State University, Detroit, MI48201, USA
2 College ofOsteopathic Medicine, Macomb University Center,
Michigan State University, MI48038ClintonTwp, USA
3 College ofOsteopathic Medicine, Michigan State University,
EastLansing, MI48824, USA
/ Published online: 30 April 2022
Medical Science Educator (2022) 32:615–626
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medical curriculums [10, 11]. As such, reform efforts in
medical education have also acknowledged the importance
of this necessary shift in learning.
Recognizing the value in active learning, the biochemis-
try faculty at Michigan State University College of Osteo-
pathic Medicine (MSU COM) began to design sessions that
use this mode of learning into the primarily lecture-based
pre-clerkship curriculum. After debuting these sessions,
qualitative student feedback was sought. Students’ responses
were dichotomous. Some enjoyed and saw value in the ses-
sions while others disliked and viewed the sessions as a
waste of time. We hypothesized that these groups of students
with opposing viewpoints may have certain characteristics
about them as learners, related to how they felt about these
sessions. If so, a greater understanding of the characteristics,
motivations, and learning strategies of students who find
value in active learning sessions, as well as those who do
not, would be particularly useful for attempts at optimizing
these sessions for all medical students.
The Motivated Strategies for Learning Questionnaire
(MSLQ) is a tool that was developed by Pintrich etal. at
the University of Michigan to measure academic motiva-
tion and learning strategies among students [12]. As a self-
report instrument, the MSLQ has proven to be a reliable and
validated study tool that may be adapted to various student
populations and contexts [13, 14]. It consists of two scales,
including motivation and learning strategies, that are scored
on a 7-point Likert measure [12]. In a study by Soemantri
etal., a systematic search of 401 journal articles revealed
that the MSLQ was the most effective questionnaire for
measuring the reflective learning of medical students [8].
This study uses a modified form of the MSLQ as an
instrument to better understand the motivational beliefs and
learning strategies of medical students at MSU COM and
explore correlations between those attributes and how they
perceived an active learning session. This understanding
will allow medical educators to optimize activity sessions
to engage students that do not perceive active learning ses-
sions to be beneficial, while further encouraging the students
that do. In doing so, this study fills an important gap in the
literature on this topic and provides biomedical educators
with the additional guidance to create an optimal learning
environment in medical school for all learners.
Materials andMethods
This study was conducted at MSU COM, which offers a
4-year medical program across three campuses to a class of
nearly 300 students. The East Lansing campus accommo-
dates approximately 200 students, while the Detroit Medical
Center and Macomb University Center host about 50 stu-
dents each. The pre-clerkship curriculum (Fig.1) is primar-
ily lecture-based with some small-group discussion sessions,
patient presentations, and unguided individual study. At the
end of semester 6, MSU COM students take the Compre-
hensive Osteopathic Medical Licensure Examination Level
One (COMLEX-USA Level 1) evaluation and subsequently
transition into the clerkship curriculum/clinical rotations.
The biochemistry, molecular biology, and genetics curricu-
lum within the pre-clerkship program consists of two main
courses totaling three credits. In addition, there are several
lectures and activities integrated into other courses spanning
the 2years. Overall, the content is delivered primarily via
lecture with some active learning sessions (Table1). Appen-
dix 1 provides more specific information about the types of
active learning activities listed in Table1.
Over the course of several years, MSU COM biochemis-
try faculty added active learning sessions to the curriculum
to improve students’ attitudes toward their learning, reten-
tion of curricular material, critical thinking skills, and self-
direction and collaboration. Most of these newly developed
sessions took place within the two Biochemistry, Molecular
Biology, and Genetics courses (Fig.1, Table1, and Appen-
dix 1). However, student feedback from these sessions early
in the curriculum yielded opposing viewpoints, with some
liking them while others disliking them. To improve these
sessions, we sought to gain some understanding of the char-
acteristics of the students using the MSLQ vehicle. The main
reason for choosing to administer the MSLQ survey follow-
ing the session on hyperammonemia was the latter’s place-
ment late in the series of active learning sessions involving
the biochemistry faculty. The choice was based, therefore,
not so much on the subject matter but simply because it
was one of the last opportunities in which the biochemistry
faculty had a direct encounter with the students in an active
learning session (Fig.1, Table1, and Appendix 1).
The hyperammonemia session took place approximately
2weeks after four lectures on nitrogen metabolism dur-
ing the students’ semester three genitourinary course
(Fig.1). The coverage included digestion and absorption
of proteins and amino acids, nitrogen balance, amino acid
metabolism particularly in terms of the urea cycle and
fate of the carbon skeletons, and inter-organ relationships
of nitrogen fixation or removal. This provided students
time to assimilate the information before applying it to
a patient case. As with most of the other active learning
sessions described in Appendix 1, the class was divided
into six classrooms (~50 students/classroom with one fac-
ulty) and the students worked collaboratively in teams of
eight within their rooms. The session begins with a case
presentation on a patient with hyperammonemia. Students
then collaborate within their teams to identify key words
most appropriate for a literature search on a recent review
616 Medical Science Educator (2022) 32:615–626
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Year 1
Semester 2
Semester 1
Year 2
Semester 5Semester 6
Semester 4
Years 3-4
Clinical Rotaons
Semester 3
Systems Systems Systems
BMB516 BMB528
AcveLearningSession on
Fig. 1 MSU COM curriculum. The 4-year curriculum is broken into 2 years of pre-clerkship and 2years of clinical rotations. The summer semesters (1 and 4) are 10weeks long. Semesters
2, 3, 5, and 6 are approximately 15weeks long and semester 7 is a short transitional semester between pre-clerkship and clerkship. Osteopathic Patient Care (OPC) and Osteopathic Manipula-
tive Medicine (OMM), are two clinical courses that run longitudinally throughout the first 2years. Basic science courses, including two biochemistry and molecular biology courses, occur in
the first two semesters. Biochemistry content is also integrated into systems courses; for example, nitrogen metabolism is covered during lectures in the genitourinary course. Approximately
2weeks after lectures on nitrogen metabolism, the students participate in an active learning session on hyperammonemia
617Medical Science Educator (2022) 32:615–626
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article on hyperammonemia diagnosis and management
(~5min); the classroom then assembles as a whole, and
teams present their work product. Using the common
top five key words, students then break out into teams
again to identify the best review articles (~20min);
the classroom reassembles as a whole, and teams pre-
sent their findings. Within certain set boundaries (e.g.,
article published within the last 5years), the article by
Haberle etal., outlining guidelines for the diagnosis and
management of urea cycle disorders has come up as one
of the top five in most of the selections identified by the
student teams [15]. On this basis, the faculty declares it
as the “gold standard” and students break out into teams
to read the article, guided by a set of questions on the
etiology, symptoms, biochemical basis, diagnosis, and
management of the condition. For this reading (~30min),
students were encouraged to adopt a strategy of “divide-
and-conquer,” with one member of the team responsible
for etiology, another responsible for diagnosis and test-
ing, etc. Team members share their individual findings and
each team then constructs a summary of their collective
work product, which is submitted for grading. Finally, the
classroom reassembles, and teams can present and share
their summary. Despite some of the negative feedback (in
addition to positive) that we received on this exercise, we
have continued to offer it (while continually seeking to
improve it) because it provides a valuable opportunity for
the students to work as a team, synthesize and summarize
scientific literature, and present their findings. These are
all valuable skills for future physicians and are difficult to
teach in a traditional lecture environment.
Data Collection
The participants in this study were first-year osteopathic
medical students (OMS-I) from the 2020, 2021, and 2022
graduating classes. Following completion of the hyperam-
monemia session, students were invited to return a question-
naire (either online or via scantron), which took 15–20min
to complete and was offered over a 2-week period. The
questionnaire informed students of the study, in which par-
ticipation was voluntary and anonymity was guaranteed. Of
the ~900 students (300 in each class) that were invited to
participate, a total of 213 questionnaires were collected and
192 students answered all the questions: 93 questionnaires in
2017, 38 in 2018, and 61 in 2019. No identifying or demo-
graphic information was collected from the participants. No
potential risk or harm to students participating in this study
was expected and approval was obtained from the Michigan
State University Institutional Review Board on Human Sub-
jects (IRB # × 14-185e, February 2014).
The original MSLQ consists of 81 survey items divided into
nine learning strategy subscales and six motivation sub-
scales [12]. Since the MSLQ subscales were designed to
fit the needs of any study, they were appropriately adopted
for our current analysis with modifications to reference the
MSU COM curriculum. Eight additional items were added
to measure student perception of the hyperammonemia ses-
sion and active learning in general (labeled as “Perceptions
of Active Learning” in Fig.2). Seven of these questions spe-
cifically address student perceptions of the hyperammone-
mia session and one asks about inclination to attend class
in-person for active versus lecture style format. The purpose
of these questions was to measure student perception so that
we could investigate if there is a correlation between those
perceptions and the student characteristics measured by the
MSLQ. This study also elected to use the 7-point Likert
scoring scale of the original MSLQ, with 1 corresponding
to “not at all true of me” and 7 indicating “very true of
me.” The modified questionnaire is provided as Appendix
2 of this article and Table2 displays the eight additional
questionnaire items used in the present study along with a
sample of two of the modified MSLQ items. The final sur-
vey was comprised of 89 items that were divided into two
sections: the first assessed student perceptions of the hyper-
ammonemia session and the second assessed student char-
acteristics. The student characteristics section was further
divided to assess motivation and learning strategies each of
which contained subscales as depicted in Fig.2. The value
components subscale measures extrinsic goal orientation
Table 1 Delivery methods of biochemistry, molecular biology, and
medical genetics content in MSU COM pre-clerkship courses
* Active learning consists of any case-based sessions, flipped class-
rooms, or student-driven inquiry session that is not a lecture.Lectures
are traditional in format but may include active components like stu-
dent polling and group problem-solving. Patient presentations involve
a visit to the classroom from a patient who talks about their medical
condition and life-experience with the condition
Course Active
learning*Lecture* Patient
Metabolic Biochemistry 5 10
Molecular Biology &
Medical Genetics
2 28 1
Pathophysiology 8
Endocrine 6
Genitourinary 1 4
Cardiology 2 1
Ethics 1
Osteopathic Patient Care 2 1
618 Medical Science Educator (2022) 32:615–626
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(motivation that stems from external sources), intrinsic goal
orientation (motivation that stems from internal reasons),
and task value (how interesting, useful, or important a task
seems). The expectancy components subscale is made up
of items assessing control beliefs (belief that outcomes
are contingent on one’s own effort) and self-efficacy for
Modified MSLQ Survey
Percepon of Acve
Learning Session
Movaon Learning Strategies
Cognive and
Metacognive Strategies
Crical Thinking
Resource Management
Time and Study
Effort Regulaon
Peer Learning
Help Seeking
Extrinsic Goal
Intrinsic Goal
Task Value
Control Beliefs
Self-Efficacy for
Learning and
Test Anxiety
Fig. 2 The components and associated subscales of the modified
MSLQ survey administered to study participants. The survey con-
tained 89 items divided into two sections: student perception of
the hyperammonemia active learning session (student perception
of active learning sessions such as hyperammonemia?); student
characteristics in terms of motivation and learning strategies. Sub-
scales listed under motivation and learning strategies are described
in Pintrich etal. [12]
Table 2 Sample questions from the modified MSLQ*
* Shown are eight added questionnaire items that measure student perceptions of active learning and two original MSLQ items with that have
been slightly modified to reflect a medical school context
1. This session has improved my ability to search for relevant journal articles
2. This session reinforced important concepts on the topic of nitrogen metabolism (ammonia and urea) previously introduced in [the
genitourinary system course]
3. This session improved my understanding of how basic science content (urea cycle) forms the basis for clinical application (hyperammonemia)
4. Reading the gold standard article as a team was an efficient way to learn the necessary information
5. Writing a summary as a team helped me in understanding content as well as in developing clarity of thought
6. I felt a greater sense of social support from my classmates during this session than in a traditional lecture
7. In general, I feel an active learning session (like the one I experienced today) is a more effective way than lectures to learn the required
8. In general, I would be more inclined to attend class in person if the time was used for group activities or problem solving rather than a lecture
19. The most important thing for me right now is improving my overall chances of a desirable residency match, so my main concern in this
curriculum is getting good grades
Original MSLQ: The most important thing for me right now is improving my overall grade point average, so my main concern in this class is
getting a good grade
38. I want to do well in this curriculum because it is important to show my ability to prospective residency directors
Original MSLQ: I want to do well in this class because it is important to show my ability to my family, friends, employer, or others
619Medical Science Educator (2022) 32:615–626
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learning and performance (self-appraisal of one’s ability to
accomplish a task). The affective components subscale is
made of a single component on test anxiety. The cognitive
and metacognitive strategies subscale measures learning
strategies and is composed of rehearsal (reciting or naming
items from a list), elaboration (paraphrasing, summarizing,
creating analogies), organization (clustering, outlining, and
selecting the main idea), critical thinking (applying pre-
vious knowledge to a new situation), and metacognitive
self-regulation (awareness, knowledge, and control of cog-
nition). The other learning strategies subscale is resource
management strategies and measures time and study envi-
ronment (scheduling, planning, time management), effort
regulation (ability to focus when distracted or uninterested),
peer learning (collaboration with peers), and help seeking
(identify gaps in knowledge and those who can assist). For
a complete description of the subscales, please see Pintrich
etal. [12].
Exploratory Factor Analysis
Using the statistical analysis software SPSS (SPSS
Inc., Chicago, IL), we first applied an exploratory factor
analysis (EFA) on the items intended to measure student
characteristics (9–89) to gain a general understanding of the
patterns in the data [16]. EFA identifies common factors that
help explain a measured variable [16]. The original dataset
was divided into two, and an EFA was performed on half of
the dataset (n =
96), which included half of the data from
each cohort year (the classes of 2020, 2021, and 2022). A
principal axis factor estimator was used to extract the factors
from this dataset and help determine coefficients to relate
factors with multiple variables [16]. An oblique (quartimin)
rotation was chosen for the EFA as the theorized model pre-
dicted a correlation between the instrument’s items. The nine
factors predicted by the EFA and their pattern coefficients
are in Appendix 3. Only items with a pattern coefficient
of > 0.4 were considered.
The EFA analysis, and theoretical considerations (the
original MSLQ sub-scales), informed the construction of the
final seven factor model (Fig.3) that we have named “MSLQ
MSU COM” [12]. All the factors were made up of three or
more questions. All the questions making up the factors were
designed to test that factor in the original MSLQ. For exam-
ple, the task value (TV) factor was made up of four questions
(numbers 18, 31, 34, 35) all designed to measure task value.
Fig. 3 MSLQ-MSU COM. The
seven factors used in our model
based on factor loading as deter-
mined by the EFA as
well as theoretical consid-
erations. The seven factors are
labeled F (for factor) followed
by their respective factor num-
ber. An abbreviation that indi-
cates what the factor measures
is given next to the factor num-
ber and is defined as follows:
F1, Task value; F2, Self-efficacy
for learning & performance;
F3, Control of learning beliefs;
F4, Test anxiety; F5, Extrinsic
goal orientation; F6, Critical
thinking; F7, Metacognitive
self-regulation. The question
numbers thatloaded on a given
factor are indicated in boxes
and connected to their assigned
factor with arrows
620 Medical Science Educator (2022) 32:615–626
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There were other questions that indicated covariance with
items in the final factors that were omitted on the basis that
they did not logically group with the other questions in that
factor. For example, questions 46, 55, 74, and 79 had strong
covariance and were all meant to measure critical thinking.
The EFA also identified questions 60, 64, 68, and 72 as cova-
rying with those questions but they did not measure similar
characteristics (Appendix 3).
Confirmatory Factor Analysis
Based on the results from the EFA, a confirmatory factor
analysis (CFA) was then applied to the other half of the
dataset (n = 96) using the seven-factor model defined from
the EFA (Fig.3). This analysis is performed to validate the
seven-factor model proposed by the EFA by confirming
the relationship between the items measured on the MSLQ
and the factors produced by the EFA [16]. Computations
were completed using the AMOS module of SPSS
(SPSS Inc., Chicago, IL). The CFA was specified using the
seven-factor model, with 30 items. The CFA demonstrated
a reasonable model fit as indicated by CMIN/d.f. = 2.047
(< 3.0 is considered a reasonable fit) and p < 0.05, while the
other indices approached significant validity (χ2 = 800.5,
d.f. = 391, CFI = 0.863, SRMR = 0.074). The comparative
fit index (CFI) compares the fit of the model being tested
with a model in which none of the variables is related. A
CFI > 0.95 indicates that the tested model fits the data well
so our model fell just short of that [16]. The standardized
root-mean-square residual (SRMR) should be < 0.08 to be
acceptable and our model did come in under 0.08 [16].
Using the coefficient (Cronbach’s) alpha, which measures
internal consistency and thus how closely items within a
dataset are related, all seven factors exceeded the 0.70 cutoff
for reasonable acceptance of the factor. Statistically signifi-
cant co-variances were demonstrated between Test Anxi-
ety and two additional factors: Self-Efficacy for Learning &
Performance (TA -0.248, p = 0.02) and Control of Learning
Beliefs (− 0.175, p = 0.049) which was predicted in the EFA.
CFA indices and standard regression weights for each of the
seven factors constructed by the EFA are in Appendix 4.
Similar analyses have been conducted before. Pintrich
etal., the original developers of the MSLQ, applied a
confirmatory factor analysis to the original questionnaire
upon its publication [17]. They determined that the ques-
tionnaire demonstrated good factor structure and that the
MSLQ shows reasonable predictive validity to the actual
course performance of students. A study evaluating the
questionnaire among Iranian high school students found
that it was reasonably reliable, with an EFA determin-
ing strong construct validity [18]. A Turkish study, which
looked at both elementary and high school students,
applied a CFA to the questionnaire items, and deduced
that the models relevant to the motivation scale and learn-
ing strategy subscale demonstrated an acceptable fit [19].
Another study evaluating student perceptions and moti-
vations in asynchronous online learning environments
(AOLE) considered all MSLQ items and determined that,
within AOLE settings, the questionnaire demonstrated
poor factor structure [20]. The original authors of the
MSLQ instrument intended to measure (generally) the
types of learning strategies and academic motivation
used by undergraduate students. Our derived factors sup-
port this goal, but specifically within the context of active
learning environments. This speaks to the importance of
adapting the original MSLQ to reflect specific learning
mediums, and subsequently conducting a factor analysis
on all items to determine instrument validity.
Our overall approach to data analysis included screening
of the datasets from all three class years for missing values
and errors in data entry. Statistical analysis began with an
exploratory factor analysis (EFA) to define valid factors in
our survey. Once we established a model from this analysis,
we performed a confirmatory factor analysis (CFA) to ensure
validity of the model and of the questionnaire’s use. These
analyses provide confidence to the researcher employing the
instrument and serve as markers of validity for future stud-
ies that may utilize the same modified instrument [16]. We
were able to validate the use of our model, MSLQ MSU
COM, to measure task value (TV), self-efficacy for learning
and performance (SELP), control of learning beliefs (CLB),
test anxiety (TA), extrinsic goal orientation (EGO), critical
thinking (CT), and meta-cognitive self-regulation (MSR) in
our students. TV refers to student evaluation of how impor-
tant or useful a task is (“What do I think of this task?”),
while SELP measures one’s ability and confidence to master/
perform a task (“I believe I will do well in this class”) [12].
CLB represents students believing that positive outcomes
are due to their own effort and control within a learning
environment (“If I study in appropriate ways, then I will be
able to learn this material”) [12]. TA has two components:
a worry component with negative thoughts (“When I take a
test, I think about how poorly I will do”), and an emotional
component related to physiologic aspects of anxiety (“I feel
my heart beating fast when taking an exam”) [12]. EGO
involves students’ concerns with malleable ends to their
learning, such as grades, rewards, and financial gain (“I am
most concerned with getting a good grade in this class”)
621Medical Science Educator (2022) 32:615–626
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[12]. CT refers to how students apply previous knowledge to
solve new problems or reach decisions (“I try to apply ideas
of my own when learning about new material”) [12]. Finally,
MSR applies student awareness, knowledge, and control of
cognition through thought planning, monitoring, and regu-
lating (“When learning new material, I make up questions
to help focus my thoughts”) [12]. Following definition and
confirmation of the factors that our instrument could test,
series of bivariate Pearson coefficient and ANOVA analyses
were completed to identify correlations between questions
specific to student motivations, learning strategies, and per-
ceptions of the hyperammonemia active learning session and
active learning in general.
Characteristics ofMSU COM Students onAverage
MSLQ MSU COM was scored for each respondent by
obtaining the mean of the items that make up each factor as
has been described in the use of the original MSLQ [12].
To get a feel for the characteristics of MSU COM students,
the respondents’ scale means and standard deviations were
calculated (Fig.4). Students scored on the higher end of the
scale (closer to very true of me) on the scales measuring
task value, self-efficacy for learning and performance, con-
trol of learning beliefs, extrinsic goal orientation, and meta-
cognitive self-regulation. Students scored on the middle of
the scale for test anxiety and critical thinking. This indi-
cates that our students may need help with critical thinking.
Active learning sessions like the hyperammonemia session
should help them practice using prior knowledge to solve
Pearson Correlations
To evaluate whether the way that students answered ques-
tions on the student characteristics portion of the survey had
any correlation to how they answered the questions on the
perception of active learning portion, we calculated Pearson
correlation coefficients. The coefficient analysis was per-
formed for all eight of the active learning questions with the
seven factors from the MSLQ MSU COM model. In addi-
tion, we performed the analysis for the eight active learn-
ing questions with the student characteristic questions that
did not co-vary with other items to comprise a factor. Even
though those items did not group into a factor, they can still
provide some insight as individual questionnaire items.
Pearson correlations and p-values were calculated to
assess associations between the seven factors and eight
active learning items (Fig.5 and Appendix 5). The list
of the eight questions on active learning can be found in
Table2. A moderate strength of association is presumed to
have a Pearson coefficient value between 0.3 and 0.5, while
a small strength of association is presumed to have a value
between 0.1 and 0.3. We focused on results with moder-
ately positive strengths of association and found those to be
present between task value (factor 1) and questions 1, 2, 3,
5, and 7, self-efficacy for learning and performance (factor
2) and questions 1, 2, and 3, and control of learning beliefs
(factor 3) and questions 1 and 3. This means that students
who rated high on those characteristic scales also rated the
active learning questions highly. We also found that some
individual questions from the student characteristics portion
of the survey had medium strength correlations with some
of the questions on active learning. Question 9 measured
intrinsic goal orientation and had a medium strength corre-
lation with all eight of the active learning questions. Ques-
tion 12 measured task value and had a medium correlation
with questions 1 and 3. Question 30 measured intrinsic goal
orientation and had a medium correlation with questions 1,
3, and 5. Question 32 measured intrinsic goal orientation
and had a medium correlation with question 1. Question 56
measured effort regulation and had a medium correlation
with question 3. All p-values for associations demonstrating
medium strength were far below a cutoff of < 0.05, therefore
demonstrating significance. No significant negative correla-
tions were found.
One key result of our survey indicates that students who
find value in the curriculum (task value, F1, Q12) recognize
that several important objectives of the hyperammonemia
active learning session were met, as indicated by their more
Fig. 4 Characteristics of MSU COM Students for the seven factors
measured in the MSLQ MSU COM survey. The data are expressed as
means (height of the bar) and standard deviations (error bars and val-
ues above them) of students’ responses for the seven factors: TV, Task
value; SELP, Self-efficacy for learning & performance; CLB, Control
of learning beliefs; TA, Test anxiety; EGO, Extrinsic goal orientation;
CT, Critical thinking; MSR, Meta-cognitive self-regulation
622 Medical Science Educator (2022) 32:615–626
1 3
favorable Likert scale choices (Fig.5). Students who believe
the overall MSU COM curriculum is interesting, impor-
tant, and useful also attribute these values to the hyperam-
monemia session. These students, measuring high on the
task value scale, further agree that active learning is more
effective in achieving content understanding than through
lectures. While task value may be innate to certain students,
other students might struggle to find value in active learn-
ing sessions for several reasons. In a curriculum prioritiz-
ing lecture-based delivery, the underlying message some
students may perceive is that lectures represent the optimal
mode of content delivery. Thus, some students may identify
active learning sessions as outliers and struggle to recognize
the value of these sessions for their learning. Other students
may simply be used to lectures and have difficulty realiz-
ing the value of other modes of delivery. We propose that
this task value characteristic can be acquired or enhanced
through more effective communication with students, both at
the session level and at the curriculum level. Detailed com-
munication at the session level of the objectives and mode
of delivery (active learning versus lecture) will enable stu-
dents to appreciate the importance and utility of the session
towards their ability to apply concepts. Thoughtful design of
the session such that the students will be solving an authen-
tic problem has been shown to promote student interest [21].
Clear communication at the curriculum level conveys the
importance of the active learning process in acquiring or
enhancing the necessary skills of a physician.
Students with high self-efficacy for learning and per-
formance recognized that the hyperammonemia session
improved their ability to search the literature, reinforced
nitrogen metabolism, and helped connect basic and clinical
sciences (Fig.5). These students, rating high on the self-
efficacy scale, are confident in their skills to perform a given
task. They are assured in their ability to learn content using
a different methodology. This suggests that if we can help
improve students’ confidence in their skills to perform ses-
sion tasks, we may also help to improve their perceptions
of active learning sessions. To improve student confidence
in task performance, it is important to have clear objectives
and instructions for the session and to communicate them
in advance. Students will then know what tasks will be
expected of them during the session. To make this connec-
tion, faculty knowledge of the sequence of skills acquired
by students as they progress through the curriculum is also
important to prevent or minimize incidences in which stu-
dents are expected to perform unfamiliar tasks. For example,
if most students are not experienced in executing applicable
literature searches, session design would need to provide
appropriate background prior to or during the session to
ensure their ability to complete the session tasks. In fact,
it has been shown that to enhance self-efficacy the learning
environment should allow for collaboration and autonomy
and should not make too many demands on learners which
might result in negative emotions [22].
This study also found students who believe their efforts
will result in positive outcomes (control of learning beliefs)
indicated that the session improved their literature search-
ing skills and helped them make connections between basic
and clinical sciences (Fig.5). These are students who take
responsibility for their own learning, rather then rely on
external factors like their teacher. This is the type of student
one would expect to embrace active learning, which is inher-
ently more student driven, rather than receiving content via
Fig. 5 Correlation between
perceptions of active learning
session and student charac-
teristics. Pearson correlations
and p-values were calculated
to assess associations between
the eight active learning items
(Table2) with seven factors
from the MSLQ MSU COM
model (in boxes); and student
characteristic questions that did
not co-vary with other items to
comprise a factor (in ovals). TV,
Task value; SELP, Self-efficacy
for learning & performance;
CLB, Control of learning
beliefs; IGO, Intrinsic goal ori-
entation; ER, Effort regulation.
Pearson correlation coefficients
are noted next to the arrows.
Only correlations with medium
strength were noted. All correla-
tions shown had p-values < 0.05
623Medical Science Educator (2022) 32:615–626
1 3
a lecturer. Students who measure low in control of learning
beliefs may be holding on to undergraduate study attitudes, in
which students tend to be more reliant on their instructors in
learning content. In a primarily lecture-based medical school
curriculum, students may find it easier to continue to rely on
instructors for their knowledge access. Increasing the num-
ber of active learning opportunities in all the pre-clerkship
courses, along with clear communication upon their entry
to medical school as to how active learning helps students
acquire and enhance their skills as a physician, could help
make the transition to control of their own learning.
As mentioned previously, our statistical model did not
support factor grouping for many questions. When we per-
formed correlation analysis using the questions that did not
group into factors with the active learning survey questions,
we identified some medium-strength correlations. One ques-
tion (Q56), which measured effort regulation, had a medium-
strength correlation with the question indicating the active
learning session helped connect basic and clinical sciences.
Certain students, even when they are uninterested or dis-
tracted, are still able to put out their best effort because they
see the purpose of the active learning session in making
the connection between those two domains. Three of those
questions (Q9, Q30, Q32) that did not group into factors
were intended to measure intrinsic goal orientation. In fact,
Q9 had a medium correlation with all the active learning
questions and, overall, had some of the strongest correlation
coefficients that we observed in this study. Students who
measure high in intrinsic goal orientation participate in the
curriculum for reasons like challenge, curiosity, and mas-
tery rather than to pass the course and their board exams,
or become a physician to make money. Attending a session
that is different from what they are used to and perceived
as more difficult would not be as daunting to these students
and, therefore, may allow them to enjoy and see value in
such a session. Intrinsic goal orientation can be fostered in
students by providing three psychological needs on which this
type of motivation is dependent [23]. One strategy that
appeals to these needs is to balance giving students more
responsibility for their learning (autonomy) and providing
structure and guidance (competence and relatedness). A
well-designed session with clear expectations and opportu-
nity for active participation can help achieve that balance.
Additionally, focusing on the ideals and skills underlying a
good physician is important to foster intrinsic goal orienta-
tion in our students. This appeals to their need for autonomy
or that one is carrying out a task of their own choice since
most medical students would say that they want to be good
doctors. However, this focus often gets overshadowed by the
stress of performing well on multiple choice examinations.
Throughout their academic careers, many students have been
learning based on their goal of excellent test performance.
This goal does not change upon entry into medical school.
However, changes in medical school programs are com-
ing, with a shift to competency-based education and with
USMLE Step 1 and NBOME COMLEX Level 1 moving
to a pass/fail system. It will be of interest to see how these
changes affect medical school curricula, and in turn, the way
students learn. In part, this shift is occurring to align perfor-
mance evaluation more closely with residency and beyond,
where in addition to standardized tests (USMLE/COMLEX
Step 3, in-service exams, licensure renewal exams), physi-
cians are also evaluated through consistent faculty evalu-
ations on clinical competency, medical knowledge, and
physician–patient communication. Since the competencies
and performance of physicians continue to be measured
throughout their career, it is vital that their foundational
characteristics, competencies, and skills be developed early
on during medical school training.
Perhaps the greatest limitation in this study is the over-
all number of questionnaires we collected and deemed to be
sufficient for the purposes of data gathering (n = 192/213).
Approximately 300 new students attend MSU COM each
year. Thus, we did not collect data on about 700 students,
which could have provided a greater overall strength to the
study [24]. One possible contributing factor is the time con-
straints that medical students face. The students were given
a 2-week time frame to fill out this 20-min optional survey
(either on paper or digitally), which may not seem like a major
task from a medical educator’s standpoint. However, given the
difficulty of the medical school curriculum and the time con-
straints imposed on them from classes, labs, etc., students may
not have seen any value in filling out the survey as compared
to completing their other required educational endeavors. Fur-
thermore, students may have chosen not to complete the sur-
vey as it would not directly benefit them. Although there have
been many improvements addressing the issue of time con-
straints in medical education, additional reforms may still be
needed to optimize time utilization for both medical students
and their educators [25]. Associated with this limited number
of surveys collected is self-selection: those students who did
respond to the survey may not accurately reflect the popula-
tion of the students in the class and the school. In fact, it has
been shown that students who participate in education trials
may be better students in several aspects [26]. Despite these
limitations, all but one statistical parameter demonstrated a
reasonable model fit: CFI fell just short of
0.95 at 0.8623.
On that basis, we believe that our conclusions are meaningful.
The decision to conduct the MSLQ survey immedi-
ately following the hyperammonemia session was based
not so much on the subject matter but more so on the basis
of its placement late in the series of active learning ses-
sions involving the biochemistry faculty (Fig.1, Table1,
and Appendix 1). Although we have added some hyperam-
monemia session-specific questions to the survey, the stu-
dent characteristics identified should be applicable to other
624 Medical Science Educator (2022) 32:615–626
1 3
types of active learning sessions of Table1 and Appendix 1.
This notion is consistent with qualitative comments obtained
from student evaluation of the various courses regarding
active learning sessions. In this connection, it may be worthy
to note that we have made attempts to determine whether the
active learning session made a difference in student learn-
ing/retention of content by using “corresponding” questions
(variations of the same question) in exams prior to (in the
genitourinary course) and after (in the osteopathic patient
care course) the hyperammonemia session. Preliminary
analysis indicated no statistically significant difference in
the % Correct in five such questions (ranging from 85 to 93
in % Correct). It should be noted, however, there is litera-
ture showing that passively obtained bits of information are
easily lost over time [27, 28]. Moreover, our own studies on
another active learning session (Appendix 1: V.-A. “Hey,
Doc, can I safely eat this genetically modified salmon?”)
suggest an attrition of knowledge on the general course con-
cepts over a 1-month period [29]. The fact that we found
knowledge on nitrogen metabolism and hyperammonemia
to be retained (in that student performance was about the
same within statistical norms from the first testing to the
repeat testing 1month later) suggests that the active learn-
ing exercise helped students in internalizing the concepts
and transferring them from temporarily memorized ideas to
longer-term application.
In conclusion, our study indicates students who perceive
active learning sessions, such as the hyperammonemia ses-
sion detailed in this report, to be an effective and desirable
mode of learning possess certain characteristics (task value,
intrinsic goal orientation, self-efficacy for learning and per-
formance, control of learning beliefs, and effort regulation).
These characteristics are likely a combination of the stu-
dent’s innate attributes and the fostering of these attributes
over the course of their educational development. Should
we, as educators, encourage those traits that make active
learning a “success,” or should we design modalities that
better suit the students we have in medical schools? We
believe that all students have growth potential; and thus,
we believe that these characteristics can be acquired or
enhanced such that all students gain the skills and knowl-
edge provided to them through the active learning process.
We also believe three levels of strategies are important in
this endeavor: (a) communicating to incoming students, via
institutional or curriculum leadership, how active learning
skills can contribute to their becoming a successful physi-
cian; (b) deliberate introduction of active learning early in
the curriculum and consistent use throughout the curriculum
will help students’ confidence in their abilities to navigate
the process; and (c) clear and advanced communication of
learning objectives, session tasks, and knowledge gained for
any particular active learning exercise.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s40670- 022- 01550-9.
Funding Michigan State University College of Osteopathic Medicine.
Data Availability The datasets generated during and/or analyzed dur-
ing the current study are available in the Harvard Dataverse repository,
https:// doi. org/ 10. 7910/ DVN/ 5AJPFE.
Conflict of Interest The authors declare no competing interests.
Ethical Approval The Institutional Review Board at Michigan State
University has approved this project (IRB# × 14-185e).
Informed Consent.
Consent was implied by completion of the voluntary and anonymous
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need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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jurisdictional claims in published maps and institutional affiliations.
626 Medical Science Educator (2022) 32:615–626
... One survey study, which examined both student and faculty perspectives, found that although students perceived that lecture and passive learning was more effective than active learning, faculty perceived active learning to be more effective [5]. Other groups have developed and used validated survey tools to quantitatively evaluate student engagement [16][17][18]. One of these groups found that student ratings of active learning exercises was dependent on characteristics of the students themselves, including their levels of self-efficacy in learning and intrinsic goal orientation, while another found a general preference for an active learning activity over traditional lecture [17,18]. ...
... Other groups have developed and used validated survey tools to quantitatively evaluate student engagement [16][17][18]. One of these groups found that student ratings of active learning exercises was dependent on characteristics of the students themselves, including their levels of self-efficacy in learning and intrinsic goal orientation, while another found a general preference for an active learning activity over traditional lecture [17,18]. ...
... Additionally, studies found that students choose to opt-out of active learning activities because not all activities are well-designed and some limit students from working at their own pace, and that students view active learning as disconnected from course grades and preparation for Step exams [26,27]. What few of these aforementioned studies [11,17,27] elucidate, however, are the details of students' thought processes as they engage with each other and with course material during team-based active learning exercises, and how these details contribute to their learning. ...
IntroductionResearch shows active learning is an effective teaching method. However, few qualitative studies explore medical student perceptions of the active learning process. The present study explored what students thought about while completing paper puzzles, an active learning tool used at the University of Utah School of Medicine, to understand what and how medical students think while engaged in active learning.Materials and Methods To investigate second-year medical students’ attitudes toward these active learning exercises, three Zoom-based focused groups were held and recorded throughout the course. Recordings were transcribed and coded using thematic analysis.ResultsStudents reported that peer interactions were of high value, and that while some interactions and thought processes were action-oriented, others were more metacognitive. Other benefits of the activity included promotion of learning, provision of structure, and designation of high-yield concepts. Challenges included feelings of confusion, problems with timing or difficulty of the tasks, and low utility without adequate preparation.DiscussionThese findings reflect student-acknowledged pros and cons of active learning described in education literature and add further insight into the thoughts and conversations students have during active learning activities. These include practicing metacognitive skills, triaging information, and learning from peers.Conclusions These data further elucidate student perceptions of active learning activities in medical education. Though focused on a specific activity, the data can help medical educators understand what students appreciate about active learning and what they think about while engaged in such activities.
Purpose This study intends to examine whether the reasons learners like or dislike a learning environment can help explain the differences in the characteristics of the learner and whether learner characteristics can influence a learner's preference for a learning environment. Design/methodology/approach This study adopted an exploratory sequential mixed methods research design. The study first conducted focus groups with university students to uncover their learner characteristics by examining the reasons they liked or disliked a learning environment. This was followed by a questionnaire survey to explore how these learner characteristics influenced learner inclination for a learning environment. The survey data were analysed using exploratory and confirmatory factor analysis (partial least squares structural equation modelling). Findings The findings showed that two types of learner characteristics, i.e. online learner characteristics and classroom learner characteristics, significantly affected learner inclination for a learning environment. Analyses also indicated that learner demographics had no major moderating effect between learner characteristics and learner inclination for a learning environment. Practical implications The findings can be useful for education institutions, learning designers and academics to design engaging learning activities to better support different learning needs. Originality/value This study makes a novel attempt to distinguish learner characteristics based on the reasons learners like or dislike a learning environment and establishes that individual learners' characteristics play a role in influencing their preference for a specific learning environment.
Full-text available
Background Reflection on learning is an essential component of effective learning. Deconstructing the components of reflection on learning using a self-regulated learning (SRL) framework, allows the assessment of students’ ability to reflect on their learning. The aim of this study was to validate an instrument to measure medical students’ reflection on their learning. Methods A systematic search was conducted to identify the most suitable instrument to measure students’ reflection on their learning based on the theoretical framework of SRL. The search identified the Motivated Strategies for Learning Questionnaire (MSLQ) which contained five subscales: internal goal orientation, self-efficacy, critical thinking, metacognitive/self-regulation, help seeking and peer learning. Using the original MSLQ as the foundation, we carried out three phases of a research program to develop a useful set of items: an expert panel’s review of items, a substantial pilot study, and a factor analysis of ratings of a modified set of items by preclinical and final year medical students. Results The factor analysis of the Modified MSLQ extracted four subscales with reasonable internal consistency: self-orientation, critical thinking, self-regulation and feedback-seeking. Each subscale correlates highly with the Modified MSLQ score, with modest inter-correlations between the subscales suggesting that they are measuring different components of the total score. Conclusion Medical students and their educators need to be able to monitor their learning in their complex academic and clinical environments. The Modified MSLQ provides a means of investigating and tracking individual medical students’ reflections on their learning.
Full-text available
Expectancy-value theory of achievement motivation predicts that students’ task values, which include their interest in and enjoyment of a task, their perceptions of the usefulness of a task (utility value), and their perceptions of the costs of engaging in the task (e.g., extra effort, anxiety), influence their achievement and academic-related choices. Further, these task values are theorized to be informed by students’ sociocultural background. Although biology students are often considered to be math-averse, there is little empirical evidence of students’ values of mathematics in the context of biology (math-biology task values). To fill this gap in knowledge, we sought to determine 1) life science majors’ math-biology task values, 2) how math-biology task values differ according to students’ sociocultural background, and 3) whether math-biology task values predict students’ likelihood of taking quantitative biology courses. We surveyed life science majors about their likelihood of choosing to take quantitative biology courses and their interest in using mathematics to understand biology, the utility value of mathematics for their life science career, and the cost of doing mathematics in biology courses. Students on average reported some cost associated with doing mathematics in biology; however, they also reported high utility value and were more interested in using mathematics to understand biology than previously believed. Women and first-generation students reported more negative math-biology task values than men and continuing-generation students. Finally, students’ math-biology task values predicted their likelihood of taking biomodeling and biostatistics courses. Instructional strategies promoting positive math-biology task values could be particularly beneficial for women and first-generation students, increasing the likelihood that students would choose to take advanced quantitative biology courses.
Full-text available
Background Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.
Full-text available
The purpose of this study was to investigate the factor validity of the Motivated Strategies for Learning Questionnaire (MSLQ) in asynchronous online learning environments (AOLE). In order to check the factor validity, confirmatory factor analysis (CFA) was conducted with 193 cases. Using CFA, it was found that the original measurement model fit for motivation and learning strategies scales of the MSLQ was not satisfactory in AOLE. Exploratory factor analysis (EFA) was conducted to find alternative factors of the MSLQ in AOLE. EFA with motivation and learning strategies scales of the MSLQ revealed five and four latent factors respectively. Finally, issues and implications to improve the factor validity of the MSLQ for AOLE were discussed.
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Motivated Strategies for Learning is a complex construct that has inspired innumerable research in recent years. The present study aim to investigation validity and reliability of the motivated strategies for learning questionnaire in Iranian students. A sample of 504 students (boys & girls) was chosen by multistage sampling. The MSLQ is an 81-item, self-report Likert-type questionnaire was completed by students. The results of study show that the questionnaire was reasonably reliable (alpha was .958). The construct validity of questionnaire was evaluated by exploratory factor analysis. Six factors were obtained that explained 40.95% of total variance. The findings support that the MSLQ is a useful tool for assessing the motivated strategies for learning in Iranian students.
The use of digital environments in nursing education offers new opportunities for nursing students' medical mathematics learning. The aim of this study was to investigate the effects of Digital Learning Materials (DLMs) on nursing students' mathematics learning, self-efficacy, and task value. A pre-test/post-test control group design was used. Students were assigned to the DLMs group (experimental condition) or the face-to-face group (control condition). Students in both conditions completed the same assignments and discussed these with their peers and the (online) teacher via the discussion board or in the classroom setting. The results showed that the mathematics learning of students undergoing DLMs training and of those undergoing face-to-face training improved from the pretest to the post-test, but no significant differences were found between the two conditions. A significant interaction effect between condition and self-efficacy was reported, producing a large reduction in the self-efficacy of students in the DLMs condition and a small reduction in the self-efficacy of students in the face-to-face condition. No significant differences were found for students' task value. The study offers new insights for the future design of mathematics training with DLMs, focusing on students’ appreciation of DLMs features, considering students with low and high learning abilities separately.
Across all sciences, the quality of measurements is important. Survey measurements are only appropriate for use when researchers have validity evidence within their particular context. Yet, this step is frequently skipped or is not reported in educational research. This article briefly reviews the aspects of validity that researchers should consider when using surveys. It then focuses on factor analysis, a statistical method that can be used to collect an important type of validity evidence. Factor analysis helps researchers explore or confirm the relationships between survey items and identify the total number of dimensions represented on the survey. The essential steps to conduct and interpret a factor analysis are described. This use of factor analysis is illustrated throughout by a validation of Diekman and colleagues' goal endorsement instrument for use with first-year undergraduate science, technology, engineering, and mathematics students. We provide example data, annotated code, and output for analyses in R, an open-source programming language and software environment for statistical computing. For education researchers using surveys, understanding the theoretical and statistical underpinnings of survey validity is fundamental for implementing rigorous education research.
Significant developments in medical education are necessary if medical schools are to respond to the pressures from advances in medicine, changes in health care delivery, and patient and public expectations. This article describes 10 key features of the medical school of the future: the move from the ivory tower to the real world, from just-in-case learning to just-in-time learning, from the basic science clinical divide to full integration, from undervalued teaching and the teacher to recognition of their importance, from the student as a client to the student as partner, from a mystery tour to a mapped journey, from standard uniform practice to an adaptive curriculum, from a failure to exploit learning technology to its effective and creative use, from assessment of learning to assessment for learning, and from working in isolation to greater collaboration. A move in the directions specified is necessary and possible. With some of the changes proposed already happening, it is not an impossible dream.
We report a curricular exercise with two principal goals: (a) training medical students to integrate several key elements derived from basic science content to answer patient’s question and (b) enhancing students’ understanding and retention of scientific content. In this assignment, students are presented with a scenario in which a hypothetical patient asks the question, “Hey, Doc, can I safely eat the genetically modified salmon?” The students are expected to construct a cogent and succinct response, applying principles learned in class to address the question posed by the patient. In order to determine whether completion of the homework assignment enhanced the students’ understanding and retention of scientific content, performance on pre- and post-exercise examinations were compared on (a) “project” questions that tested key concepts reinforced by the assignment and (b) “control” questions that tested concepts covered in class but not explicitly reinforced by the exercise. We found that the degree of difficulty for the control questions increased by about 15% over a period of 1 month while the degree of difficulty for the project questions held steady over the same period. These results suggest that the assignment aided students in internalizing key concepts, transferring them from temporarily memorized knowledge to longer-term ideas that could be applied to new problems.