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Creative Education, 2017, 8, 1782-1793
http://www.scirp.org/journal/ce
ISSN Online: 2151-4771
ISSN Print: 2151-4755
DOI:
10.4236/ce.2017.811122 Sep. 20, 2017 1782 Creative Education
The Use of Mind Maps as an Assessment Tool in
a Problem Based Learning Course
Remigio Zvauya*, Shilpa Purandare, Nicola Young, Miranda Pallan
College of Medical and Dental Sciences, Institute of Clinical Sciences, Birmingham University, Birmingham, UK
Abstract
The use of mind maps as an assessment tool is investigated.
The mind map in
the current study represents the student knowledge structure at the beginning
of the student learning curve unlike previous studies in which the maps are
drawn after students have acquired the knowledge already. The study co
m-
pares the inter-rater reliability of two mind map scoring methods and corr
e-
lates the marks from these methods with other end of year outcomes. The
mind maps were scored independently by three examiners using two
mind
map scoring rubrics (MMR): a structural and a holistic qualitative rubric.
The
structural MMR scoring method gave moderate inter-rater reliability with t
o-
tal score ICC values of 0.71 for absolute agreement and 0.57 for consistency
between the three e
xaminers. The qualitative MMR scoring method had poor
inter-
rater reliability with values of 0.33 and 0.32 for absolute agreement and
consistency respectively. The concurrent validity with other end of year a
s-
sessments was poor for both methods. Although t
he mind map scores did not
correlate with other end of year assessments, it is likely that mind maps are
assessing a different aspect of the student knowledge construct not assessed by
traditional assessments. The inter-rater reliability was better for the
structural
MMR than the qualitative MMR.
Keywords
Assessment, Mind Maps, PBL, Medical Course
1. Introduction
The constructivist theory proposes that meaningful learning occurs when prior
knowledge and previous life experience are activated and integrated with new
knowledge being constructed in context (Daley & Torre, 2010; Davies, 2011).
The leaner is actively engaged in the process and collaborates with colleagues in
How to cite this paper:
Zvauya, R., Pu-
randare, S
., Young, N., & Pallan, M. (2017).
The Use of Mind Maps as an Assessment
Tool in a Problem Based Learning Course.
Creative
Education, 8,
1782-1793.
https:
//doi.org/10.4236/ce.2017.811122
Received:
May 27, 2017
Accepted:
September 17, 2017
Published:
September 20, 2017
Copyright © 201
7 by authors and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
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10.4236/ce.2017.811122 1783 Creative Education
order to process, interpret and construct new knowledge. Constructivism im-
plies that previous knowledge is stored in such a way that it can be accessed and
used easily. It is therefore important that the learner is aware of and uses strate-
gies that facilitate activation of prior knowledge and its integration with new
knowledge being acquired (Davies, 2011; Eppler, 2006). Mind maps are
multi-coloured, image-centred, visual, non-linear representations of ideas, and
their relationships which can be used to activate prior knowledge and integrate
new information with previous knowledge (D’Antoni et al., 2010; Noonan,
2012). When creating mind maps, free and spontaneous thinking is required. In
addition associations are made between ideas so that mind maps can assist in
integrating concepts across domains (D’Antoni et al., 2010; Eppler, 2006). The
use of mind maps has been explored in an attempt to move towards a student
centred, cooperative learning environment (Rosciano, 2015). Mind maps have
recently been used in medical education to develop critical thinking (D’Antoni
et al., 2010), assist memory recall (Farrand et al., 2002) and as an assessment tool
(D’Antoni et al., 2009; Evrekli et al., 2010).
Rubrics are scoring guides consisting of specific predetermined criteria used
in making academic judgements in evaluating students work (Mertler, 2001).
Two types of rubric are described in literature, a holistic rubric where an overall
score is given without reference to individual components and an analytic rubric
where component or individual parts of the assignment are scored followed by
summing up to get a total score (Mertler, 2001). A few rubrics used to assess
mind maps have been described in literature (D’Antoni et al., 2009; Evrekli et al.,
2010). These authors’ adapted methods originally used to score concept maps,
which are top to down diagrams presenting information in node link node for-
mat with linking descriptive propositions (West et al., 2002; Tergan et al., 2006).
Both concept and mind maps promote active, meaningful learning at the meta-
cognitive level and differ only in their structure and organisation of information
(D’Antoni et al., 2009). Concepts maps have been scored using structural and
relational analytical methods (Daley & Torre, 2010; Kassab & Hussain, 2010;
Hung & Lin, 2015; West et al., 2002). The structural method assigns a value to
hierarchal structure, concept-concept link and cross link. Relational methods, on
the other hand are based on the quality of individual links taking into account
the structure of the concept map. The structural methods have been shown to be
sensitive to changes in students evolving knowledge (West et al., 2002).
An adaptation of the concept map scoring methods for mind map scoring is
the addition of the dimensions of colours and figures to the rubric. D’Antoni et
al. (2010) reported good agreement between intra and inter-rater reliability
measurements for total and subtotal scores for the mind map rubric.
The published rubrics have been used to assess knowledge of students who use
mind maps in a limited number of modules. The present study differs in that it ex-
plores the use of mind map assessment rubrics in a cohort of students where mind
maps are an important and integral part of the learning process throughout the
academic year. The mind map in the current study represents the student knowl-
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edge structure at the beginning of the student learning curve unlike previous stud-
ies in which the maps are drawn after students have acquired the knowledge al-
ready.
2. Setting
The study was carried out during the first year at a UK Medical School which
admits life science graduates on a fast track, four year Graduate Entry (GE)
course. The first year uses problem based learning (PBL) as its method of in-
struction with heavy reliance on collaborative and self-directed learning. Stu-
dents are given written and practical guidance on how to generate mind maps at
the beginning of the course.
There are six core modules, each with a set of problems or clinical scenarios
which combine concepts from biological sciences, anatomy, ethics, public health
and behavioural sciences. Students work in groups of 8 - 10 in dedicated PBL
rooms to produce a mind map for each problem. The weekly development of a
mind map by each PBL group is an integral part of exploring each clinical scenario.
The mind map allows the students to deconstruct the scenario presented to them
and explore their collective existing knowledge. The developed mind map is then
used as a basis for identifying focused learning objectives relating for further study.
The course assessment methods include short answer questions (SAQ), multi-
ple choice questions (MCQ), clinical, cognitive and communication skills ex-
aminations. Since mind maps are an important and integral part in the learning
process in the first year, part of the cognitive assessment process involves indi-
vidual students generating a mind map from a given unseen clinical scenario
under examination conditions.
As mind maps are assessed in this way, there is a need for a robust scoring ru-
bric to enable equity of scoring and a vehicle for feeding back to students on
their use of mind maps as a learning tool during PBL. Gasaymeh (2011) dis-
cusses the need to develop rubric criteria that assist in the process of knowledge
acquisition whilst facilitating student’s engagement with the learning environ-
ment. In this study mind maps generated by individual students were scored us-
ing two mind map scoring rubrics (MMR): an analytical, structural MMR and a
holistic, qualitative MMR. The objectives of this study were:
1) To assess mind maps developed by individual students from a given unseen
scenario using a modified analytical structural and a holistic qualitative MMR
scoring method.
2) To compare the inter-rater reliability of the modified analytical structural
and holistic qualitative MMR scoring methods.
3) To assess the concurrent validity of the two scoring methods with respect to
the other end of year examination marks.
3. Method
3.1. Participants
The participants were first year GE medical students with previous life science
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degrees. There were no international students on the cohort. The average age of
the cohort was 24 years. There were 19 male and 35 female students in the co-
hort. All GE first year medical students (n = 54) sat a cognitive assessment as
part of their end of year examinations in the 2011-12 academic year.
3.2. Generation of the Mind Maps
A practice session prior to the examination was organised for students to indi-
vidually draw mind maps under examination conditions. Students were already
familiar with the process of developing mind maps as they used them on a
weekly basis across all modules as part of the problem based learning cycle.
As part of the cognitive assessment students were given a previously unseen
clinical scenario. They were instructed to draw a mind map under examination
conditions on an A3 sheet of paper using different colours. The students were
then required to develop 10 learning objectives with the aid of their mind map.
The time given for this process was 40 minutes. Following this part of the ex-
amination students were examined in a 10 minute oral examination where they
were questioned about how they developed their learning objectives and further
information that they would seek to fulfil these objectives. Separate marks were
awarded for the mind maps, the learning objectives and the oral examination.
These were then combined to give an overall cognitive examination mark. Fig-
ure 1 shows an example of a mind map generated by a student.
3.3. Mind Map Scoring
The mind maps were scored using both methods independently by three mark-
ers, RZ, NY and SP, who were experienced in mind map generation and PBL fa-
cilitation. The markers had been previously trained in mind map scoring. For
each scoring method, the examiners marked three sample mind maps inde-
pendently and then met to discuss how each marker had arrived at their score.
The marking criteria were further refined at this stage and definitions’ clarified
and agreed among the markers.
3.4. The Analytical, Structural MMR Scoring Method
This was originally developed for concept maps (Srinivasan et al., 2008; West et
al., 2002) and later adapted for mind maps (D’Antoni et al., 2009; Evrekli et al.,
2010). The analytical structural MMR was adapted such that concept links near
the patient at the centre (P-MC) carried more weighting than those lower down
the hierarchal structure as seen in Figure 1 and Table 1. This was because we
identified that these patient-concept (P-MC) links represented the major con-
cepts from the clinical scenario which need further consideration, and also as-
sisted in identification of lower level concept concept (C-C) links as shown in
Figure 1.
In the modified scheme used in this study marks were given to the number of
valid patient-main concept links (P-MC) identified, concept-concept (C-C)
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Figure 1. Mind map from a patient with Irritable Bowel Syndrome: examples of pa-
tient-concept (P-MC) links; concept-concept (C-C) links; relationship links; cross links
and examples, figures and equations are shown.
Table 1. Mind map analytical, structural MMR scoring scheme: examples of the compo-
nents on a mind map are shown in Figure 1.
COMPONENT
MARK for each valid component
Patient-Main concepts (P-MC) link 5
Concepts-Concept links (C-C) 2
List of Examples/Symptoms/Signs (ESS) 2
Picture/Figure/Equation 2
Relationship link 3
Cross Link 10
Colours 1
links, cross links, relationship links, colours, pictures, signs, symptoms and ex-
amples as shown in Figure 1 and Table 1. For each mind map, sub scores and
total scores were computed.
The modifications to the structural MMR were made to allow for the fact that
the purpose of scoring the mind map in this cohort was to assess the process of
logical development of ideas from the clinical case, rather than assessing knowl-
edge per se. Therefore less weighting was given for the accuracy of the factual
information on the mind map, and more weighting given for breadth and inte-
gration of concepts arising from the clinical case.
3.5. The Holistic, Qualitative MMR Scoring Method
It was evident that the structural MMR scoring method was time consuming and
thus resource intensive to use as an assessment tool. We thus developed a
Marie
21
Dr-Pat ien t
Relationship
Pat ien t
Cross link
Relationship link
dysf uncti onal
Social distance
Bowe l
Age difference
Ulcerative Colits
Different from
croh ns
Proximal
spread
Pat chy
Smok ing
beneficial?
granuloma
Mout h to
Anus
opio ds
Distal end
Treatment
Types
Reason s for use
Rehydration
gluc/Na
Swe lli n g
Symptoms
Cause
antidiarrhoeals
Complimentary &
Alternative Medicine
Pain
Different
gender
Default
Consumerist
Exampl es (E) Paternalistic
Exampl es (E)
Mutual
Gas rele ase
Inflammation
Ante rior
Abdominal
Colonic Pulsat ing
Diar rhoe a
Symptoms (S)
auto immun e stress
Experience
Dissatisfaction with
conve ntion al
Water absorption/contro l
Asce nding col on
Transve rse colon
Descending
colo n
Difficult consultation
Concept-concept link (C-C)
Concept-concept link (C-C)
Concept-concept link (C-C)
Pat ien t-concept link
(P-MC link)
Pat ien t-concept link
(P-MC link)Pati ent -concept link
(P-MC link)
Relationship Link
Symptoms (S)
Exampl es (E)
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qualitative MMR method based on the holistic scoring method used for scoring
concept maps by previous workers (McClure et al., 1999; van der Heidt, 2015).
The holistic method described was modified such that more guidance was given
to the marker scoring the mind map. Thus the criteria below were used in as-
sessing each mind map.
1) Identification of triggers in the problem: the degree to which the student is
able to identify the key concepts in the problem.
2) Development of valid concept links: the ability of the students to explore
their knowledge by developing concepts further.
3) Development of hierarchies: the arrangement of concepts in a logical manner
with the more fundamental concepts at the centre and more specific as con-
cepts on the periphery of the map.
4) Identification of cross links and relationship links: the ability to show the
meaningful connections between different concepts (cross links) and links
within a concept (relationship link).
5) Use of colours and pictures to enhance the mind map making it visually easy
to follow.
These individual criteria were not given scores, but were set out as a guide for
markers to divide mind maps into very good 75% - 100%, good 65% - 74%, av-
erage 54% - 64%, borderline 48% - 53% and fail below 48%. Thus an overall
percentage mark was then given for each mind map based on the overall quality
of each mind map.
4. Data Analysis
Anonymised end of year assessment data were obtained from university records.
These included mind map scores, scores from the cognitive, clinical, communi-
cation assessment examination and overall knowledge scores, derived from the
written examinations designed to assess knowledge across the different modules
covered in the GE first year. Data were analysed using a statistical software
package (stata v11; StataCorp LP). Descriptive analysis was undertaken to visu-
ally inspect differences in marks between the three markers for each of the scor-
ing rubrics. A two way random ANOVA model was used to calculate inter-rater
reliability and an intraclass correlation coefficient (ICC). We calculated two dif-
ferent ICCs; one to assess consistency between markers; whether the markers
were ranking the students in the same way, but not necessarily awarding similar
value marks, and the other to assess absolute agreement; how similar the value of
the marks given by each of the markers for each student were.
To explore agreement between the two scoring methods, Pearson’s correlation
coefficients were calculated for the two MMRs for each individual marker. Mean
marks for each student were then calculated from the 3 markers’ scores for each
method and correlation coefficients were calculated for these mean scores.
Pearson’s correlation coefficients were also calculated for mind map scores
and other end of year outcomes to assess whether mind map scores correlate
with other assessment outcomes.
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5. Results
Table 2 shows the mean and range values for the marks from the qualitative and
structural MMR methods from the three independent markers, RZ, NY and
SP. For the patient-main concept links (P-MC) using the analytical, structural
MMR scoring method, the mean values were 33.3; 29.8; 30.9 for RZ, NY and
SP respectively.
The concept-concept links (C-C), examples and symptoms were grouped to-
gether as C-C-ESS during the analysis as they had equal weighting. The C-C-
ESS for the MMR structural method had means of 169.6, 157.3 and 171.0 for RZ,
NY and SP respectively.
The total scores for the analytical, structural MMR scoring method had mean
values of 255.5, 212.9 and 259.3 while that for the holistic, qualitative MMR
scoring method were 61.0, 63.2 and 59.3 for RZ, NY and SP respectively.
Table 3 shows the ICC values for the three examiners. The holistic, qualitative
MMR scoring method gave low inter-rater agreement with an ICC (95% CI) of
0.33 (0.16 - 0.51) for consistency and 0.32 (0.15 - 0.49) for absolute agreement.
Conversely the analytical, structural MMR total scores had an ICC (95% CI)
value of 0.71 (0.59 - 0.81) and 0.57 (0.25 - 0.77) for consistency and absolute
agreement respectively.
P-MC links, pictures, C-C-ESS and colours had high ICC values as shown in
Table 3.
The Pearson correlation coefficients for individual markers were all moder-
ately low with the highest value being 0.31 (
p
= 0.03) as shown in Table 4. The
Pearson correlation coefficients for the average scores when comparing the two
methods was moderate with a value of 0.47 (
p
< 0.001).
Table 2. Mean and range of marks from the qualitative and structural MMR methods
from the three independent markers, RZ, NY and SP (N = 54).
RZ
NY
SP
Mean (SD)
Range
Mean (SD)
Range
Mean (SD)
Range
Analytical
,
Structural MMR method
Patient-Main concept
links (P-MC)
33.3 (9.3)
20
- 60
29.8 (7.7)
15
- 55
30.9 (8.1)
15
- 54
Concept-Concept
links, plus examples,
symptoms (C-C-ESS)
169.6 (42.5)
68
- 272
157.3 (44.2)
72
- 258
171.0 (46.3)
88
- 290
Pictures
2.4 (2.9)
0.14
1.8 (3.0)
0
- 16
1.9 (2.7)
0
- 14
Relationship links
2.4 (4.1)
0
- 18
1.2 (2.4)
0
- 9
4.6 (5.1)
0
- 21
Cross
-links
41.3 (21.2)
0
- 90
16.7 (13.1)
0
- 50
44.4 (24.8)
0
- 100
Colours
6.6 (1.9)
2
- 12
6.3 (1.6)
3
- 12
6.3 (1.7)
2
- 10
Total score
255.5 (48.4)
136
- 355
212.9 (48.5)
96
- 321
259.3 (57.0)
117
- 374
Holistic Qualitative MMR method
Total score
(%)
61.0 (8.2)
48
- 80
63.2 (7.4)
50
- 75
59.3 (9.3)
40
- 80
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Table 3. ICC values for consistency and absolute agreement for qualitative marking and
structural MMR scoring methods.
Marking scheme
ICC consistency*
(95% Confidence Interval)
ICC agreement**
(95% Confidence
Interval)
Analytical structural MMR
0.71 (0.59
- 0.81)
0.57 (0.25
- 0.77)
Patient-Main concept links
(P-MC)
0.74 (0.62
- 0.83)
0.71 (0.57
- 0.81)
Concept-Concept links, plus examples,
symptoms (C-C-ESS)
0.87 (0.80
- 0.92)
0.85 (0.75, 0.91)
Pictures
0.89 (0.83
- 0.93)
0.88 (0.82
- 0.93)
Relationship links
0.44 (0.28
- 0.60)
0.38 (0.19
-0.56)
Cross
-links
0.39 (0.22
- 0.56)
0.25 (0.04
- 0.47)
Colours
0.74 (0.63
- 0.83)
0.74 (0.62
- 0.83)
Holistic
,
qualitative MMR
0.33 (0.16
- 0.51)
0.32 (0.15
- 0.49)
*ICC consistency assesses whether the markers are ranking the students in the same way, but not necessarily
awarding similar value marks; **ICC agreement assesses how similar the value.
Table 4. Agreement between the analytical, structural MMR and holistic, qualitative
MMR marking methods using Pearson’s correlation coefficient.
Marker Pearson’s correlation coefficient p value
RZ 0.31 0.03
NY 0.26 0.07
SP 0.29 0.04
Average quantitative and qualitative scores 0.47 p < 0.001
We also explored whether the mind map scores awarded to students as part of
their end of year assessment correlated with scores achieved by students in other
aspects of their end of year assessment. Pearson’s correlation coefficients were
calculated for mind map scores and other end of year outcomes (overall cogni-
tive assessment score, overall knowledge score, communication skills score, and
community based medicine clinical skills score). Correlation coefficients are
shown in Table 5 (correlation coefficient values can range from 0 to 1). The low
correlation coefficients indicate that mind map scores obtained by both methods
correlated poorly with scores in other end of the year assessments. Most correla-
tion coefficients were also non-significant, as indicated by their p values.
6. Discussion
This study investigates the use of two methods of mind map scoring as part of a
cognitive assessment at the end of the first year of a graduate entry medicine
PBL programme. This entails two aspects: inter-rater reliability of the two scor-
ing methods, and consistency and agreement between the two methods.
Of the two methods used to score the mind maps, the analytical, structural
MMR scoring method had a higher inter-rater reliability than the holistic,
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Table 5. Correlations between average analytical, quantitative and holistic, qualitative MMR method scores and other end of year
exam scores using Pearson’s correlation coefficient.
Cognitive
exam
Overall knowledge
score Communication skills
Community based
Medicine:
clinical skills
Correlation
coefficient
p value
Correlation
coefficient
p value
Correlation
coefficient
p value
Correlation
coefficient
p value
Qualitative mind map
score
0.17 0.22
0.38
<0.01
0.00 0.99 0.09 0.54
Quantitative mind map
score
−0.01 0.96 0.1 0.50 −0.06 0.68 −0.04 0.76
qualitative MMR method. The ICC for consistency (0.71) indicates that agree-
ment between the three markers in terms of how they ranked the students in the
same order was good. The ICC for absolute agreement between examiners for
individual student scores was lower (0.57), but still indicates moderate agree-
ment. These values are similar to the values obtained by previous researchers
who assessed mind maps generated by other cohorts of students (D’Antoni et al.,
2009). The qualitative MMR method on the other hand gave low ICC values for
both consistency and agreement indicating that the examiners neither ranked
the mind maps in the same way nor were the marks from the three markers for
individual students similar. The qualitative MMR scoring method possibly pre-
sents a cognitive challenge, requiring the marker to make simultaneous evalua-
tions of various aspects of the mind map. This is likely to make heavy demands
on the markers working memory. This may result in each individual marker
tackling the mind map complexity differently which would in turn affect exam-
iners consistency and agreement and hence the reliability of this scoring system.
Similar results have been reported for concept maps (McClure et al., 1999).
Whilst the analytical, structural MMR method gave more structure and guidance
to the examiners, there were problems in defining what constitutes concepts,
examples, signs and symptoms on the mind maps and hence what constitutes
concept-concept links. This again could have affected the agreement between
markers for the analytical, structural MMR scoring method and hence reliability.
Marker training is important in scoring mind maps for assessments. To mini-
mise the effect of training the markers were trained and had discussions on how
to grade three mind maps prior to embarking on the scoring exercise. Marker
training is important in scoring mind maps for assessments.
The quality of a mind map is determined by the template used in the con-
struction of a mind map. Previous workers have used key words as template
when constructing maps (McClure et al., 1999). If an unconstrained template is
used to construct the mind map, this may lead to variation in the nature and
quality of the resultant mind maps generated by individual students. The uncon-
strained nature of the template used in this study, an unseen clinical scenario
may have led to some variation. Students’ prior knowledge in the subject also af-
fects the quality of the mind maps. Since students in the present study had dif-
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ferent educational backgrounds in life sciences, this may have affected the qual-
ity of the mind maps generated.
The low Pearson correlation coefficients between the structural and the quali-
tative MMR scoring methods suggests that the two methods maybe assessing
different aspects of the mind maps. We are not aware of studies comparing dif-
ferent scoring methods for mind maps in similar settings; however such studies
have been carried out for concept maps (West et al., 2002; von der Heidt, 2015).
The Pearson correlation coefficients obtained by previous researchers (McClure
et al., 1999) of r = 0.193 to 0.608 (
p
< 0.01) in their studies comparing various
methods of scoring concept maps are similar to those calculated in the present
study with mind maps (r = 0.31,
p
= 0.03).
Our results indicate that mind map scores did not correlate with other end of
year outcomes irrespective of the MMR scoring method used. However the ho-
listic, qualitative MMR method moderately correlated with overall end of year
scores compared to the structural method. The mind maps were thus assessing a
different aspect of the student knowledge construct from that assessed in other
examinations. Although we could not find any previous reports on the concur-
rent validity of mind maps, our finding is in agreement with previous reports of
studies with concept maps (West et al., 2002).
The use of minds maps described here is different from that described in pre-
vious studies (D’Antoni, Zipp and Olson, 2009; D’Antoni et al., 2010; Evrekli,
Inel and Ali, 2010) in which the maps are drawn after students have acquired the
knowledge already. In the present study mind maps are used to brain storm
ideas from given clinical scenario, reactivate prior knowledge and decide what
learning issues require further study. The mind map thus represents the student
knowledge structure at the beginning of the student learning curve. Future re-
search could correlate the mind map scores to learning objective scores. The de-
velopment of a combined scoring rubric for mind maps and learning objective is
a potential area for further study.
7. Conclusion
Mind maps could be used as part of an overall assessment strategy in a course
using a PBL instructional method. Our results indicate that the structural MMR
scoring method had a high inter-rater reliability. The poor correlation with other
end of year assessments suggests that the mind maps could be assessing different
constructs of student knowledge. However, this study is limited to one institu-
tion and a specific context, and further work on the use of mind maps in assess-
ments in medical education is required.
Acknowledgements and Declaration of Interest
The authors report no conflict of interest during the study. We would like to
thank Dr. C Taylor for reading the manuscript. The study was approved by the
University Ethics Committee ERN_12-1369.
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Notes on Contributors
REMIGIO ZVAUYA BSc, MSc, PhD is currently the Graduate Entry Course MB
ChB Phase 1 lead and Senior PBL facilitator at the College of Medical and Dental
Sciences, University of Birmingham.
SHILPA PURANDARE, MBBS, MMedSc, MRCOG, MRCGP is currently a
PBL Facilitator and a Senior Clinical Tutor at the College of Medical and Dental
Sciences, University of Birmingham.
NICOLA YOUNG, BSC PhD, FHEA is currently a PBL facilitator at the Col-
lege of Medical and Dental Sciences, University of Birmingham.
MIRANDA PALLAN BSc, MB ChB, PhD was a PBL facilitator and is a Senior
Clinical Research Fellow in Public Health at the College of Medical and Dental
Sciences, University Birmingham.
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