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By design: negotiating flexible learning in the built environment
discipline
Richard Tucker
a,
* and Gayle Morris
b
a
School of Architecture and Building, Waterfront Campus, Deakin University, Geelong,
Australia;
b
Centre for the Enhancement of Teaching and Learning, The University of Hong Kong,
Hong Kong
(Received 19 December 2010; final version received 9 May 2011)
The term ‘flexible education’ is now firmly entrenched within Australian higher
education discourse, yet the term is a contested one imbued with a multiplicity of
meanings. This paper describes a process designed to elucidate how the idea of
flexible education can be translated into teaching models that are informed by the
specific demands of disciplinary contexts. The process uses a flexible learning
‘matching’ tool to articulate the understandings and preferences of students and
academics of the Built Environment to bridge the gap between student
expectations of flexibility and their teacher’s willingness and ability to provide
that flexibility within the limits of the pedagogical context and teaching resources.
The findings suggest an informed starting point for educators in the Built
Environment and other creative disciplines from which to traverse the complex-
ities inherent in negotiating flexibility in an increasingly digital world.
Keywords: flexible learning; flexible delivery
Introduction
In Australia the term ‘flexible education’ is commonly used to incorporate flexible
teaching, flexible learning and other related terms with which it is often used
synonymously (e.g., e-learning, open learning, recourse-based learning, distance
learning and self-directed learning). Most Australian Universities claim flexible
provision as a strategic teaching and learning objective, and the pervasiveness of
the idea of ‘flexibility’ has only been increased by the most recent report to
influence Australian higher education policy and discourse !the 2008 Bradley
Review (Bradley et al.2008). Yet there is no universally accepted definition of what
is meant by flexible education (Casey and Wilson 2005; Kirkpatrick 1997; Ling
et al.2001; Morrison and Pitfield 2006; Nicoll 1998; Normand, Littlejohn, and
Falconer 2008; Nunan 1996; Sappey 2005). Nor is there agreement on how
flexibility (no matter how it is defined) as an institutional objective should be
implemented at the teacher/student interface; where teachers have to provide for
flexibility within budgetary limits often informed by the requirements of more
traditional teaching.
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*Corresponding author: Email: rtucker@deakin.edu
Research in Learning Technology
Vol. 20, 2012
RLT 2012. #2012 R. Tucker and G. Morris. Research in Learning Technology is the journal of the Association for Learning
Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity
number 1063519. http://www.alt.ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons
"Attribution 3.0 Unported (CC BY 3.0)" license (http://creativecommons.org/licenses/by/3.0/) permitting use, reuse, distribution
and transmission, and reproduction in any medium, provided the original work is properly cited.
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Citation: Research in Learning Technology 2012, 20:14404-DOI:10.3402/rlt.v20i0/14404
The project described in this paper aimed to articulate the meaning of ‘flexible’
learning for students and teachers at an Australian school of architecture and
building, where traditionally the dominant teaching form has been studio-based and
thus face-to-face, by developing a ‘matching’process for negotiating student and
teacher competing demands. The matching process aimed to bridge the gap between
student expectations of flexibility and their teacher’s willingness and ability to provide
that flexibility within the limits of pedagogical context and available teaching
resources (finances, technology, staff !see Palmer on these difficulties !(Palmer
2001)). The process in Normand and Littlejohn’s (2006, 22) terms is illustrative of a
bottom-up initiative, enabling a ‘teaching!learning’discourse rather than a ‘manage-
rialist discourse,’and thus providing the foundation for more robust course
development and design.
The project had three primary goals; namely, to:
1. Articulate a context related understanding of ‘flexible’learning that it is
contained within manageable and meaningful boundaries;
2. Advance a negotiation tool for matching students’expectations of flexible
learning to teacher attributes, recourses and pedagogical intent;
3. Create two distinct models of flexible learning to be used as a basis for
informing appropriate flexibility for (1) theory-lecture-based and (2) design-
studio-based learning.
These goals address a need to bridge two distant extremes; those of traditional
modes of higher education delivery, which provide the basis of most teachers’
experience, in contrast to what the modern student demands, which is (as Van den
Brande defines flexible learning (1993, 22)) being able to ‘learn when they want
(frequency, timing, duration), how they want (modes of learning), and what they
want (that is learners can define what constitutes learning to them).’Taking a lead
from the central importance of context in making meaning of flexibility (Casey and
Wilson 2005; Kirkpatrick 1997; Ling et al.2001; Morrison and Pitfield 2006; Sappey
2005), and from the primacy of the individual agency of teachers and students over
institutional rhetoric and policy (Bigum and Rowan 2004; Errington 2004; Nicoll
and Chappell 1998; Normand, Littlejohn and Falconer 2008), our study is under-
pinned by the argument that the precise meaning and value of flexible education can
only be found in the details of the experience of teachers and students engaged in
their specific discipline context.
To articulate student/teachers experiences we have used a model of flexible
learning based on the work of Nikolova and Collis (1998) and Collis and Moonen
(2004), who discuss five basic ‘categories’of flexibility (time, content, access/entry
requirements, instructional approach and delivery), which can be further split into
19 ‘dimensions.’The model supports bottom-up analysis enabling discourse among
those providing and receiving learning flexibility. This is consistent with Kennedy
et al. (2008) who suggest that rather ‘than making assumptions about what students
like !and are like !universitites and their staff must look to the evidence to inform
both policy and practice’(118). The 19 flexibility dimensions are grouped into the
five operationalising categories as follows:
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404
!aspects of time
1. time and date at which module starts and finishes
2. periods of time students are able to participate
3. pace of learning
4. time when assessment occurs
5. sequence in which topics are covered
!aspects of content
6. choice of topics covered
7. amount of learning activities expected to be completed
8. level of difficulty of module content
9. assessment standards
!aspects of access/entry requirements
10. pre-requisites for module participation
!aspects of instructional approach/design
11. social organisation of learning (group or individual)
12. times available for support
13. choice of who decides what modes of flexible learning are available
14. language for communication
!aspects of delivery
15. time and place where support is available
16. methods of obtaining support
17. types of support available
18. places for studying
19. delivery channels (i.e., lectures, tutorials, internet, podcasts)
Before we consider in detail our own analysis of how these 19 dimensions can be
used to identify appropriate pedagogic models, we shall briefly consider the literature
on how the implementation of institutional flexible education policy has been
received by learners and teachers.
Background
The implementation of flexibility
There has been limited research on how flexible education can be appropriately
translated into teaching models informed by the specific demands of other
disciplinary contexts, including the focus of this paper; the Built Environment.
Within the wide range of Telescopia, a project involving the application of a flexible
delivery platform for trans-European tele-learning, Collis, Vingerhoets and Moonen
(1997) suggest that moving from fixed to flexible is more difficult to implement for
some of the 19 dimensions than for others. Thus, Time- and Place-flexibility are
easiest to implement, but offering flexibility on the other dimensions is difficult
because; (1) the costs of realising flexibility are high and, (2) increased flexibility on a
number of the dimensions leads to conflicts between institutional management,
teachers and learners. Collis, Vingerhoets, and Moonen see that ‘the transition
between offering a well-designed and well-supported course, and offering more of a
‘cafeteria’of options will require conceptual changes not only for course providers
but also for the broader society’(Collis et al. 1997).
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Within work that focuses on student experiences, only a few studies engage with
the disciplinary contexts. For example, McShane, Peat, and Masters (2007) have
shown that chemistry undergraduates enroled in a traditional, research-focussed
university continued to expect face-to-face lectures when given online options (26).
They also found that undergraduates, particularly with little previous exposure to
online instruction, preferred on-campus timetable structures and that half of the
surveyed group never accessed the online material made available. Their evidence
suggested that a sudden move to web-based learning alienated less technically
competent students or those without access to adequate computers. McShane et al.
concluded that flexible practices should be introduced from the first year via a staged
and coordinated process (26). In line with these findings, Dobozy (2008) has shown
that first year students do not cope with flexible access provision and suggests
that universities therefore have an ethical obligation to help early students to
improve their engagement levels. Similarly, Samarawickrema (2005) demonstrated
that first-year design students were highly teacher dependent, required better access
to academic staff and that all categories of learners experienced difficulties related to
online study in their first semester. Pillay, Irving, and Tones (2007) investigated a
diagnostic tool for assessing Tertiary students’readiness for online learning. They
found (221) that ‘online students rated flexible pacing, time of study and manage-
ment of conflicts between study time and other commitments as more important, and
social interaction as less important, than classroom students.’
It is commonly suggested that online learning should augment, rather than
replace, experiential face-to-face learning (Jones and Richardson 2002). Similarly,
Poindexter (2003) warns against incorporating technology in isolation, advocating a
more holistic approach that uses multiple strategies and takes into account the
changing student generation. As with all instructional tools and approaches, the use
of technology needs to grow out of sound learning objectives and resonate with an
evidence base about how students’learn. Given the focus of this study, we might add
that this evidence base ought to include the disciplinary contexts in which we teach !
for different disciplinary cultures have their own discursive practices and their own
ways of thinking, relating and being. Others, such as Shaffer (2004), offer a slightly
different starting point by exploring how the psychological implications of new
media may require a new way of understanding ‘how tools and thoughts contribute
equivalently (though perhaps still not equally) to educational and practical out-
comes’(1). Shaffer raises the possibility that it is the relationship between technology
and cognitive activity that informs social and pedagogical choices in the context of
rapidly emerging technologies.
Teaching/learning flexibility nexus: a matching process
As Nikolova and Collis have explained (1998, 67), at one extreme of the flexibility
continuum are traditional courses with module dates that are fixed, content that is pre-
determined, instructional approaches chosen, learning materials prepared in advance
and course organisation that is pre-defined. The other extreme of the continuum ‘is a
just-in-time, workplace-based, problem-induced learning, about which the learner
makes key choices and which occurs life-long.’We argue that it is somewhere between
the two extremes that appropriately flexible learning exists !at a point that matches
student expectations with teacher willingness, ability and resources. The method used
by us to identify this nexus builds on a process first posited by Nikolova and Collis
R. Tucker and G. Morris
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404
(1998) that maps the 19 flexibility dimensions to generic flexible module profiles to
inform the adaptation for flexibility of a course’s teaching modules. Our method is also
in line with the work of De Boer and Collis (2005) who aimed to operationalise
flexibility by examining the extent to which the 19 dimensions were being offered in
practice by instructors. Only 12 dimensions were considered in the De Boer study; that
is, those seen to represent aspects of flexibility that could be determined by instructors.
The 12 excluded dimension numbers 5, 7, 13 and 16 (although De Boer and Collis have
varied their dimension descriptions from the 19 listed in our introduction).
In common with our own project, a five-stage continuum of flexibility (see
Figure 1) was used by De Boer and Collis to numerate each dimension. This
continuum progresses from a traditional, ‘closed’course tied down in terms of place,
time and content towards more flexible, ‘open,’learning where the design of the
course is shared by students and academics. Once each dimension was given a
flexibility value (1 being the most fixed and 5 being the most flexible), the mean was
calculated. In the De Boer and Collis study, the greatest flexibility was offered in:
Pace of Learning (3.06), Delivery Channels (3.40), Places for Studying (3.28) and
Time and Place where support is available (3.18). De Boer and Collis suggest the
study indicates two types of flexibility can be operationalised by instructors (2005,
46): Planning Flexibility, which maintains more or less the same pedagogy and
teaching and learning programme while offering more flexibility in terms of delivery;
and Interpersonal Flexibility, which implies a change in pedagogy to more ‘student-
centred contributions that relate to the experiences of the individual students and can
be re-used by others as learning resources.’They concluded that the change to
Interpersonal Flexibility is more difficult ‘because instructors need to rethink their
courses in terms of the activities within the course and also the assessment of those
new activities.’
Methodology
In our project, as reported elsewhere (Tucker and Morris 2011), for each of the 19
dimensions a ‘Flexible Learning Nexus’was calculated for the two types of teaching
model that dominate built environment education !design-studio-based and theory-
lecture-based modules. The nexus matched student and teacher expectations at a
half-way point of flexibility. The 19 nexus were then collapsed to the five
operationalising categories of time, content, access/entry requirements, instructional
approach and delivery. The project utilised online questionnaires to profile learners
and teachers and to identify what dimensions of flexibility both groups felt were
appropriate to the two different learning contexts. Once each dimension was given a
flexibility value, the mean was calculated between what students desired and what
teachers felt able to provide.
The online questionnaire was in six sections: the first two profiled the participants
by determining demographics and learning styles, the third considered attitudes to
Figure 1. Five-sage continuum of flexibility.
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404 5
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and learner/teacher confidence in the modules being studied/taught, the fourth
considered awareness and understanding of what flexible education is, and the final
two sections determined for each of the 19 dimensions what degree of flexibility
both groups felt were appropriate to the two different learning contexts. In the fifth
section, questions asked participants to determine an order of preference, for each
dimension, between: 1. inflexible, 2. !intermediate and 3. flexible (see Table 1).
In the sixth section, participants were asked to rate, on a five-point Likert Scale,
six different learning materials, five avenues of obtaining feedback and six learning
spaces, for example, physical locations of study. After the data from the questionnaire
were analysed, the findings were translated into the design of two generic modules !
one lecture-based (delivering theory) and one studio-based (applying that theory to
design practice). The generic modules were used as the basis for redesigning two
specific modules that the students who had answered the entry questionnaires then
participated in. Further research will see these specific modules evaluated through
exit questionnaires and Student Evaluations of Teaching (SET).
Participants
Overall 78 students participated in the study. There was an approximately even split
between male (40, 51.3%) and female (38, 48.7%) participants, with their ages
ranging from 18 to 44 years of age with a mean age of 22. The majority of students
were domestic (77, 98.7%) compared to international students (1, 1.3%). Students
were enroled in three degree programmes: 31 (39.7%) were enroled in Architecture,
eight (10.3%) were enroled in a Construction Management (CM) degree and 39
(50%) of students were enroled in a double degree (Architecture and CM). At the unit
level, students were enroled in a total of 12 undergraduate units in the Architecture,
CM and Archi/CM double degree courses. There were four, 1st year units, four, 2nd
year units, two, 3rd year units and two, 4th year units. The majority of students were
part-time, but all students, regardless of status attended classes on campus. One of
the authors was directly involved in teaching one of the units, however, any
perception of undue influence was mitigated by ensuring strict adherence to a high
standard of research ethics. Participation was voluntary and the data deidentified to
ensure anonymity. It should also be noted in terms of the participants’background,
that the survey did not set out to specifically map students’previous experiencs of
flexible learning, however, consistent with other research, for example, Kennedy et al.
(2008) we would expect that in relation to experience of technologies, as one
manifestation of flexible learning, patterns of access and use of a range of
technologies would be widely disparate (108).
Table 1. Example of order-of-preference question for flexibility dimension.
Who should decide the level of difficulty of unit content?
1st
Choice
2nd
Choice
3rd
Choice
1) Lecturer decides
2) You, the learner, negotiates difficulty level with lecturer
3) You, the learner, decides between basic, intermediate or
advanced
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Thirteen lecturers participated in the study, approximately half (7, 53.8%) were
male and their ages ranged between 29 and 60 with the mean age being 47. The
lecturers ranged in experience from early career researchers in their 30s to senior
academics late in their careers. Seven lecturers taught in all three degree programmes,
one only in Architecture and five only in CM. Their units were selected to cover a
range of online engagement, with some having the most basic level of online presence
to those that had the majority of materials online.
Results
Awareness and understanding of flexible learning
Let us now consider the results of the fourth section of the questionnaire; that is,
awareness and understanding of what flexible education is. There was an even split in
our study between students who were aware that flexible learning was a core
educational goal of their university and those who were not. All of the lecturers who
responded were aware that flexible learning was a core educational goal. A single
sample t-test examined students understanding of flexible learning with results
indicated that students felt they had a significant understanding of flexible learning
(M2.50, SD .82) ([m"3] t (77) #5.29, p"0001). The majority of lecturers who
responded indicated that they understood what flexible learning was (10, 76.9%) and
that flexible learning was a familiar term to them (12, 92.3%). Two questions
examined the general importance of flexible learning; (1) How important to you is it
that your education is flexible? and (2) How flexible should your education be? An
average of these items in a single sample t-test indicated that students felt flexible
learning was significantly important (M1.92, SD .61) ([m"3] t(77) #15.44, p"
0001). A simple regression analysis revealed that ‘Understanding’of flexible learning
significantly predicted ‘Importance’of flexible learning, (b"0.439, t(76) "4.25, pB
0.0001). Understanding of flexible learning also explained a significant proportion
(19.20%) of the variance in importance of flexible learning, (R
2
"0.192, F(1, 77)"
18.11, pB0.001). Students felt that flexible learning was important for three reasons;
educational, personal and economical reasons, and that this flexibility should allow
them to learn how, when and what they wanted (see Table 2).
Pearson correlation analyses (Table 3) indicated that while the importance of
flexibility in ‘How’and ‘What’students learnt significantly correlated, the
importance of ‘When’they learnt was not correlated with the other flexibility
dimensions. In terms of what motivates flexibility, Economy, Education and Personal
reasons all correlated with one another. However, only Economy and Personal
reasons correlated with ‘When,’only Educational and Personal correlated with
‘How,’and none of the flexibility motivators correlated with ‘What.’
Simple regression analyses examined how the importance of each of the flexibility
motivators predicted the importance of the three different areas of flexibility !When,
How and What. The first simple regression examined the ability of the three
motivators (Economy, Personal and Education) to predict the importance of
flexibility in ‘When’students learnt. Results revealed that the model significantly
predicted 50.8% of the variance in the importance of ‘When’(R
2
"0.508, F(3, 74)"
25.42, pB0.0001). However, this finding was driven by the ability of Economy (b"
.285, t(74)"2.89, pB0.005) and Personal (b".534, t(74)"5. 220, pB0.0001) to
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predict ‘When.’Education as a motivator for flexibility in ‘When’students could
learn remained non-significant (b"#.063, t(76)"#.717, pB.475).
The second simple regression examined the ability of the three motivators to
predict the importance of ‘How’student’s learnt. Results revealed that the model
significantly predicted 39.1% of the variance in the importance of flexibility in ‘How’
students learnt (R
2
"0.391, F(3, 74)"15.81, pB0.0001). However, this finding was
driven only by the ability of Education (b".611, t(74) "6.254, pB0.0001) to predict
flexibility in ‘How’students learnt. Both Personal and Economical remained non-
significant in predicting ‘How’(both pB0.53).
The final simple regression examined the ability of the three motivators to predict
the importance of flexibility in ‘What’students learnt. Results revealed that the
model did not significantly predict variance in the importance of ‘What’(R
2
"0.052,
F(3, 74)"1.36, pB0.260).
The following findings can be interpreted from the above analyses of student
understanding of flexible learning:
.Students felt that flexible learning was significantly important.
.As might be expected, students understanding of flexible learning predicted
how important they felt flexible learning to be.
.Flexible learning was important to students for educational, economical and
personal reasons.
.While economic and personal reasons predicted the importance to students of
flexibility in when they learnt, educational reasons predicted the importance to
students of flexibility in how they learnt.
Table 2. One sample t-test.
Mean (SD) [u"3] t(df 77)
Important When 1.73 (.80) #14.003**
Important How 1.71 (.79) #14.452**
Important What 2.10 (.96) #8.244**
Important Economically 1.72(1.03) #10.986**
Important Educationally 1.82 (.89) #11.657**
Important Personally 2.15(1.04) #7.148**
Table 3. Pearson correlations.
Important
When
Important
How
Important
What
Important
Economically
Important
Educationally
Important
Personally
Important
When
1 0.201 0.019 0.568(**) 0.204 0.671(**)
Important
How
0.201 1 0.279(*) 0.215 0.622(**) 0.244(*)
Important
What
0.019 0.279(*) 1 #.088 0.128 0.100
Important
Economically
0.568(**) 0.215 #0.088 1 0.255(*) 0.559(**)
Important
Educationally
0.204 0.622(**) 0.128 0.255(*) 1 0.364(**)
Important
Personally
0.671(**) 0.244(*) 0.100 0.559(**) 0.364(**) 1
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.Students felt that it was important that what, how and when they learnt was
flexible.
However, as we shall see now, these findings were not consistent with what
students told us in detail in the final two sections of the questionnaire about their
preferences regarding the 19 flexibility dimensions. For although this data also
showed a desire for choice in ‘when’and ‘how’students learned, it suggested that
students did not desire to choose ‘what’they learned.
Students’ expectations of flexible learning matched to teacher attributes, recourses and
pedagogical intent
The five collapsed nexus explained in the Method section are recreated below
(Figure 2 - white representing matched flexibility for the learning context of design-
studio-based teaching, and grey representing matched flexibility for lecture-based
teaching).
Figure 2. The Five Collapsed Nexus for Time, Content, Entry Requirements, Instructional
Approaches and Delivery.
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Although students rated at over 3.0 (i.e., ‘flexible’) six dimensions of flexibility in
design-studio-based modules and three dimensions in theory-lecture-based modules,
the collapsed-nexus demonstrated that for both staff and students, in neither learning
context was flexibility in time, content or access/entry requirements seen as
appropriate or desirable. In the fourth operationalising category !Instructional
Approaches !if we remove the dimension of language (for this in most cases was not
a possible variable) the collapsed nexus returns means of: Studio "3.57, Lecture "
3.37. In the fifth operationalising category !Delivery !all five dimensions were
desired as flexible by students, and the collapsed nexus for both learning contexts are
again rated as ‘flexible,’with means of: Studio"3.65, Lecture "3.59. The overall
interpretation for the above five delivery dimensions is straightforward; students
wanted flexibility in delivery and social organisation for both theory-lecture-based
and studio/design modules. These findings are largely consistent with what De Boer
and Collis found to be offered in practice by instructors.
Once the nexus were determined through survey, the flexibility ratings were
translated into learning design implications or frameworks for each of the 19
dimensions !for both lecture-based and studio teaching (Table 4). The outcomes
were arrived at by identifying for each dimension what the most fixed and flexible
outcome might be and then scaling towards the dimension rating. Thus, for example,
for Dimension 1 (time and date module starts and finishes), a 1.0 rating equated to
fixed module start and finish and 5.0 to an elective that could be enrolled in at any
time during the degree programme. With many of the dimensions it was not possible
to provide choice due to the constraints of professional accreditation !which often
dictates the order in which modules can be studied, prerequisites, assessment
standards, and module and course content.
Table 4 sets out what might be usefully considered as prototypes of two of the
main modes of teaching in the built environment, studio design and theory-lecture-
based modules. At a glance it provides academics with a more nuanced reading of
each of the flexibility dimensions. The table tries to illustrate through concrete
examples what it might mean in practice. In doing so, it may expose dimensions
hitherto not recognised, and therefore open flexibility doors where appropriate to do
so. As prototypes, the objective is for academics to see possibilities emerging from the
research findings, which could be used, but are of course open to negotiation and
other interpretations.
Findings in relation to other categorisations of flexible education
There are, of course, others ways of characterising models of online delivery than
the 19-dimension/five-operationalising categorisation of Nikolova and Collis.
For example, Roberts (2002) describes a four model categorisation of online delivery
!the naı
¨ve model (where only lecture notes are provided online), the standard model
(with an email list and other online resources), the evolutionary model (which scores
most highly for flexibility) and the radical model (which is largely based on group
work) (5!6). Using this categorisation it can be seen that the two modules described
in Table 4 are variations on an ‘evolutionary’model, with the lecture/theory module
representing a standard/evolutionary hybrid. How ever, it should be stressed that
both of the two modules are intended to have significant face-to-face components.
The U.S. Department of Education (2009) use an alternative conceptual framework
which identifies three key components when considering online delivery, either
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Table 4. Flexibility dimensions set out against general module characteristics for design- and
theory-lecture-based modules.
Nexus Learning design implications
Flexible learning
dimension
Studio/
design
model
Lecture/
theory
model Studio/design model Lecture/theory model
Time
1. Time and date
module starts and
finishes
2.11 1.60 Fixed Fixed
2. Periods of time
students to
Participate
3.17 2.25 Lectures fixed tutorial
times chosen by students
and varying as assign-
ments required
Lectures fixed Tutorial
times chosen by students
at the beginning of
Semester
3. Pace of learning 3.17 2.69 Project-based assignment
and formative assessment
allows learning pace to
develop within fixed
semester duration limits
Project-based assignment
allows learning pace to
develop within fixed
4-weekly duration limits
4. Time when as-
sessment occurs
2.15 2.08 Fixed Fixed
5. Sequence in which
topics are covered
2.49 2.22 Malleable within
assignment limits
Fixed
Content
6. Choice of topics
covered
3.34 2.59 Students choose between
assignments
Student choose
specialisation
7. Amount of
learning activities
completed
2.92 2.12 Fixed Fixed
8. Level of difficulty
of module
content
2.89 2.5 Students determine
project complexity
Fixed
9. Assessment
standards
2.19 1.93 Fixed Fixed
Access/entry
requirements
10. Pre-requisites for
module
participation
2.31 2.40 Fixed Fixed
Pedagogy
11. Social
organization
(group or
individual)
3.47 3.39 Student has choice unless
‘teamwork’skills are a
focus options for those
students that have to
work alone
Student has choice unless
‘teamwork’skills are a
focus options for those
students that have to
work alone
12. Times available
for support
3.18 2.85 Face-to-face fixed within
6-hour studio sessions
Face-to-face fixed to
hourly tutorials but
feedback available
regularly via online
discussion boards
13. Who chooses
modes of flexible
learning
2.84 2.67 Predetermined by
matching process
Predetermined by
matching process
14. Language for
communication
1.77 1.46 Fixed Fixed
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404 11
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Table 4 (Continued)
Nexus Learning design implications
Flexible learning
dimension
Studio/
design
model
Lecture/
theory
model Studio/design model Lecture/theory model
Delivery
15. Time and place
where support is
available
2.93 2.70 Face-to-face fixed within
6-hour studio sessions
Face-to-face fixed to
hourly tutorials but
feedback available
regularly via online
discussion boards
16. Methods of
obtaining
support
3.46 3.23 Predominantly face-to-
face ‘sign-up’tutorials
with some on-line
feedback
Heavy online support
available via unit web-
page discussion thread
Email Supporting
face-to-face
17. Types of support
available for
3.52 3.26 Predominantly face-to-
face
Peer-support and peer-
feedback opportunities
Online
Email
Face-to-face
Discussion board for
reflective discussions
18. Places for
studying
3.15 2.96 Studio Lecture theatre Lecture theatre
Tutorial rooms
Online via unit webpage
Mobile devices
19. Delivery
channels
3.81 3.62 A range of electronic
resources linked to from
the course home page
Studio workshops
Paper course notes
Lecture slides in Power
Point format
Assignment marking
guidelines
Full contact details of all
instructors
Pre-recorded audio lec-
tures available from the
web for key lectures
‘Live’lectures
A range of electronic
resources linked to from
the course home page
Electronic copies of all
printed course materials
Lecture slides in Power-
Point format
Assignment marking
guidelines
Full contact details of all
instructors Copies of
past examinations for the
cours
Hints and tips for the
current examination
Model answers
An electronic course
discussion list
Pre-recorded audio lec-
tures available from the
unit webpage for all
lectures
Animations to explain
many of the concepts
‘Live’lectures
Web-based archives of
mailing list discussions
from previous semesters
Electronic assignments
submission, recording
marking and return
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404
‘wholly online’or as part of a blended approach, ‘(1) whether the activity served as a
replacement for or an enhancement to conventional face-to-face instruction, (2) the
type of learning experience (pedagogical approach) and (3) whether communication
was primarily synchronous or asynchronous’(3). Each component represents what
might be conceived of as a key decision point where the response opens up or limits
the way in which technology might be incorporated.
Discussion
The generic modules represented in Table 4 are not intended to be prescriptions or
templates but, rather, to offer academics an informed starting point from which to
traverse the complexities inherent in negotiating flexibility. In that sense they
represent one way of representing what typical design-studio-based and theory-
lecture-based fexible learning modules might look like. Both types of flexible module
could be said to be consistent with De Boer and Collis’s Planning Flexibility mode of
flexible teaching in that they maintain more or less the same pedagogy, while offering
more flexibility in terms of delivery. Academics are often unclear about how to
incorporate aspects of flexibility and the appropriation of technology is often more
by ‘feel’rather than an theoretically informed perspective. The generic modules may
ease that gap by providing academics a template from which to consider the
pedagogical benefits of and the changes required to provide for flexible learning.
The modules also provide an organisational framework that brings coherence to
elements of flexible learning and conventional models of delivery.
One of the limitations of the tool presented here is that it may appear to offer up a
prescription, or a kind of ‘one size fits all’solution that is driven purely by student
want and not a considered position grounded in robust instructional design or
learning theory. As noted previously, the intention is to offer academics a way forward
in terms of brokering several key dimensions of flexibility based on local evidence.
The key here is in ‘brokering;’for as with any mode of delivery, it is incumbent to be
guided by an evidence base about how students learn, and the corresponding kinds of
learning experiences we wish our students to have. But, as Shaffer’s work reveals, these
positions too are contested as new theories emerge on cognition and technology that
challenge traditional notions of how we learn.
Conclusions
Using a flexible learning ‘matching’tool, this project was able to articulate the
understandings and preferences of students and academics of the Built Environment
to bridge the gap between student expectations of flexibility and their teacher’s
willingness and ability to provide that flexibility within the limits of the pedagogical
context and available teaching resources. The findings suggest an informed starting
point from which to navigate the pedagogical complexities inherent in negotiating
flexibility in an increasingly digital world. With that in mind, there are a number of
implications for thinking about flexible learning within the Built Environment
disciplines and a number of conclusions that can be drawn about teachers and
learners in this specificic discipliniary context. These implications and conclusions
are:
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.For studio modules, only in content did students demand less flexibility than
teachers offered, whereas for lecture modules students in all cases demanded
greater flexibility than teachers. This demonstrates that, for Built Environment
students, and perhaps too for students in other creative disciplines, learning
can be more flexible than it is at present. In particular, the demand by students
for more flexibility than their teachers are providing is greatest for the
operationalising category of content delivery. Thus, as we have argued
elsewhere (Tucker and Morris 2011) what our Built Environment students
desired of teaching, and what their ‘teachers were able to provide, were
multiple mediums of knowledge delivery that allowed students flexibility in
when and where they could learn.’
.Multiple mediums of delivery (types of learning material) were seen by
students as more important for theory-lecture-based modules than for design-
studio-based modules. This we suggest is largely due to the preference for face-
to-face feedback in design. This preference is because much of design learning
is experiential and thus feedback is usually individualised to the artefact
created as a demonstration of that learning !artefact which, in the case of
design is different for every student.
.For the categories of Time, Content and Acess/Entry requirements, students
did not demand flexibilty. This is perhaps because in such an amorphous,
creative learning context as design, students desire a learning structure that is
clear and unshifting. In other words, when designing artefacts (such as
buildings), students may not wish to be designing their education.
.Thus, the desire for fixidity in most dimensions, in a context where learning
outcomes are less fixed, might be said to be related to the paradox of choice
that sees learners in general experience a decrease in confidence when there
is an increase in options. Indeed, this effect on learner confidence might be
even greater where, as is the case for students learning how to design, there
is uncertainty in the learning outcome and where assignment solutions are
infinite.
The research that underpins this project was a direct attempt to engage with
students and academics about the meaning and value of flexible education !as a lived
practice in their local discipline contexts. It is not without limitations. The sample
size was limited and was restricted to the built environment disciplines. Moreover, it is
likely that each new cohort of students’preferences will be shaped by their particular
experiences and so the extent to which our findings can be extrapolated to future
cohorts and different disciplinary contexts requires further investigation. Future
research will endeavour to map students’learning preferences to those expressed in
the flexible learning survey to explore any significant patterns. As the growth in
flexible learning continues within higher education, there is a corresponding need to
find new ways of understanding and measuring qualitatively different learning
experiences of students in specific learning contexts. Disciplines by their nature
emphasise some skills, ways of knowing and being over others which, as our findings
suggest, leads to appropriating technologies for different purposes. It is our hope that
research of this nature will add to our understandings of student learning and enable
a more authentic basis from which to negotiate flexible learning in an increasingly
digital world.
R. Tucker and G. Morris
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Citation: Research in Learning Technology 2012; 20: 14404 - DOI: 10.3402/rlt.v20i0/14404
Acknowledgements
Input from our diligent research assistant, Catherine Reynolds is acknowledged.
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