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Personalised learning environments (part 2):a conceptual model for construction
S.M. Syed-Khuzzan and J.S. Goulding
Abstract
Purpose – The purpose of this paper is to present a conceptual model for a PLE prototype, specifically
incorporating learning styles for the UK construction industry.
Design/methodology/approach – The initial research methodology approach adopted for this paper
embraced the distillation of core research material gathered from a detailed literature review. The
literature review encompassed the needs and importance of developing a PLE prototype, and used as a
context learning styles for the UK construction industry. A qualitative approach was used in this
research, as this was considered more suitable for studying social and cultural phenomena. This paper
explores the relationship between pedagogy and technology in the context of the design and
implementation of a PLE. The implementation framework for the PLE adopted the principles of the
‘‘Collaborative System Design’’ approach as identified by the Advanced Distributed Learning (ADL)
Initiative Guidelines.
Findings – This paper describes the development phases of the PLE prototype incorporating learning
styles. This prototype incorporates a learning style inventory – known as the diagnostic questionnaire
which was developed based on the amalgamation of three existing models of learning styles defined
from a detailed synthesis of the literature – namely the Kolb’s model of learning styles, Honey and
Mumford’s model of learning styles and the Felder and Solomon’s model of learning styles.
Originality/value – This paper is a very useful source in developing a learning style inventory and a PLE
prototype incorporating learning styles.
Keywords Learning, Learning styles, Questionnaires
Paper type Conceptual paper
1. Introduction
This paper introduces a conceptual model for a PLE prototype incorporating learning styles
for the UK construction industry. The aim of the research was to develop a PLE prototype that
was able to accommodate individual learning styles for learners; tailored to suit to their own
preference of learning styles.
Learners often have different levels of motivation, different attitudes about teaching and
learning, and different responses to specific classroom environments and instructional
practices. In this context, the more thoroughly instructors understand these differences,
the better chance they have of meeting the diverse needs of their learners (Felder and
Brent, 2005). Furthermore, Karagiannidis and Sampson (2004) noted that there was a
general shortage of evidence to back up the belief that e-learning provided real
advantages – the assumption of which was that the ‘‘traditional’’ mode of instruction
(one-to-many lecturing/one-to-one tutoring) could not fully accommodate the different
learning styles, strategies and preferences of diverse learners. Following this train of
thought, research is now being undertaken on adaptive learning environments that can
personalize the learning experience (Vercoustre and McLean, 2005; Karagiannidis and
Sampson, 2004).
DOI 10.1108/00197850910927769 VOL. 41 NO. 1 2009, pp. 47-56, Q Emerald Group Publishing Limited, ISSN 0019-7858 j INDUSTRIAL AND COMMERCIAL TRAINING j PAGE 47
S.M. Syed-Khuzzan and
J.S. Goulding are both
based at the University of
Salford, Salford, UK.
According to Felder and Silverman (1988) and McCarthy (1990), accommodating learning
styles in a classroom-based environment has been proven to be effective in previous
research; causing the need to explore the possibilities of incorporating learning styles in
e-learning environments.
2. Research methodology
The initial research methodology approach adopted for this paper embraced the distillation
of core research material gathered from a detailed literature review. The literature review
encompassed the needs and importance of developing a PLE prototype, and used as a
context learning styles for the UK construction industry. A qualitative approach was used in
this research, as this was considered more suitable for studying social and cultural
phenomena (Berger and Luckman, 1966). This paper explores the relationship between
pedagogy and technology in the context of the design and implementation of a PLE. The
implementation framework for the PLE adopted the principles of the ‘‘collaborative system
design’’ approach as identified by the Advanced Distributed Learning (ADL) initiative
guidelines (ADL, 2006).
3. Background research
Primarily, the aim of any e-learning program is to help learners achieve the prescribed
learning objectives (Larocque and Faucon, 1997). In this context, in a traditional classroom
environment, the instructor is present to guide the learners towards the objectives through a
variety of teaching strategies and learning activities; which is the opposite of e-learning. Due
to the independent learning involved in e-learning, learners need to be more self-motivated
and self directed in order to achieve the objectives of the course program; thus, the
responsibility for learning is transferred from the instructor to the learner (Martinez, 2002).
There is no single right way to teach; many instructors naturally confine their teaching to the
method that reflects their own learning style to the exclusion of others (Entwistle, 1981).
Smith and Kolb (1986) argued that students may reject a learning environment that does not
match their learning styles. It has been pointed out in the literature that designing a learning
environment that accommodates learners’ learning style is essential for effective learning.
Hence, since e-learning has influenced a great deal in the field of teaching, training and
development, thus causing a growing number of courses delivered over the web with
increasing numbers of students (Chang, 2001); initiatives to adapt learning styles in
e-Learning are considered essential.
3.1 Importance of incorporating learning style into a PLE
There is no single way to describe learning styles, as a number of definitions appear in the
literature (Sampson and Karagiannidis, 2002). For example, Conner (2005) defines learning
styles as ‘‘. . . the ways you prefer to approach new information’’. Kolb (1976) saw learning
styles as ‘‘the unique learning method presented by the learner during the learning process
and situation’’ while Dunn (1990) described learning styles as ‘‘. . . the way each learner
begins to concentrate, process and retain new and difficult information’’. In addition, Honey
andMumford (1992) define learning styles as ‘‘. . . a description of the attitudes and behavior
which determine an individual’s preferred way of learning’’. Moreover, Felder (1996)
describes learning styles as ‘‘a person’s characteristic strengths and preferences in the
ways they take in and process information’’.
Learning seems to be seen as an integral part of everyday life at work. The skill of knowing
how to learn is considered a must for every worker. It opens doors to all other learning and
facilitates the acquisition of other skills (Blackmoore, 1996). Student learning is a complex
multivariate phenomenon. Some individuals are heavily dominated by one learning style, or
are just particularly weak in one style; so, some learning activities are dominated by explicit
or implicit assumptions about learning styles (Honey and Mumford, 1992). The activity may
be geared to a particular style of learning as to cause a mismatch with any other learners
whose own major styles are different. Furthermore, there are learners whose learning styles
are wide spread, so there are learning activities which contain opportunities to learn in
different ways (Sims, 1990). According to Kim and Chris (2001) and Kolb (1984),
PAGE 48 j INDUSTRIAL AND COMMERCIAL TRAININGj VOL. 41 NO. 1 2009
educational research and practices have demonstrated that learning can be enhanced
when the instructional process accommodates the various learning styles of students.
Learners generally come from different backgrounds and have a great variety of differing
profiles, learning styles, preferences and ‘‘knowledge hooks’’. Learning should be as
personalized as possible (Vincent and Ross, 2001) as a ‘‘one size fits all’’ approach has
been seen to be ineffective (Watson and Hardaker, 2005). However the, incorporation of
learning styles is said to bring an advantage during the development and implementation of
learning environments (Sims, 1990). Thus, the need for both teachers and trainers to take
learning styles into account appears to be greater today than before, due to the increasing
use of technology-aided instruction.
Technology offers a lot of new ‘‘delivery mode’’ options as compared to the traditional
‘‘face-to-face’’ classroom format, including a variety of computer and television-based
delivery mode formats (Buch and Bartley, 2002). The development process based on
individual learning styles and preferences through adaptive technologies has been a
successful approach towards training that enables real-time performance evaluation
through behavioral and attitude measures (Watson and Hardaker, 2005). Furthermore,
O’Conner (1998) noted that technology offers new capabilities to reconstruct learning
environments around specific learning styles. In this context, individuals with specific
learning styles would have a preference for specific training delivery formats (Buch and
Bartley, 2002). Since e-learning has predominantly had a ‘‘one size-fits all’’ approach, the
idea of incorporating learning styles into the learning environment should enable learners to
learn more effectively and also be motivated to learn by building a ‘‘road-map’’ based on
their individual psychological types and learning preferences (Gunasekaren et al., 2002;
Sims, 1990).
Teachers or instructors should therefore:
B know the material well before beginning to teach;
B write objectives and keep them in focus from planning to evaluation;
B let the students know what the objectives are; and
B determine the learning style of students before teaching and educating students
according to their own learning style showing them how to cope (Vincent and Ross,
2001).
According to Vincent and Ross (2001), learners need to know what their own learning style is
in order to manage their learning more effectively and efficiently. At the same time, trainers
should also be aware of the learning styles of their students so that they can establish
alternate ways of teaching identical information to students. The Dunn and Dunn model of
learning styles prescribes that all individuals have a specific learning style; this differs from
person to person, and each person has learning style strengths or preferences (Pfeiffer et al.,
2005). The model suggests that it is easier to learn through one’s strengths or learning style
preference. The central aim of the model is that the ‘‘closer the congruence between
students’ learning style and their teachers’ teaching styles’’, the higher the level of
achievement (Pfeiffer et al., 2005). Also on this theme, Alsubaie (2006) suggested that
learning styles should be incorporated in a learning environment to achieve a holistic
environment that appeals to a whole raft of learners.
‘‘ The skill of knowing how to learn is considered a must forevery worker. ’’
VOL. 41 NO. 1 2009 j INDUSTRIAL AND COMMERCIAL TRAININGj PAGE 49
4. The PLE prototype incorporating learning styles: a conceptual model
The development of the PLE prototype is divided into two phases: the development of the
diagnostic questionnaire, which is the learning style inventory used to identify the learners’
styles (phase 1); and the development of the prototype itself (phase 2). This is shown in
Figure 1.
Figure 1 shows the relationship of the diagnostic questionnaire to the development of the
PLE. In respect of the development of the PLE, pedagogy will be mapped with technology
using instructional design (ID) theories. ID theory is ‘‘a theory that offers explicit guidance on
how to help people learn and develop’’ (Reigeluth, 1999). This sets out procedural steps to
systematically design and develop instructional materials (Dick and Carey, 1990; Gagne
et al., 1988; Merrill et al., 1996). Learning objects will be used together with e-learning
standards and interoperability between delivery platforms, reusability of e-learning
materials, etc. A learning object is considered as any resource or content object that is
supplied to a learner by a provider with the intention of meeting the learner’s learning
objective(s) (Vercoustre and McLean, 2005). However, the current focus in the e-learning
community has predominantly been centered upon the development of technical
infrastructures that support reusability, interoperability, durability and accessibility of
Figure 1 PLE prototype incorporating learning styles conceptual model
PAGE 50 j INDUSTRIAL AND COMMERCIAL TRAININGj VOL. 41 NO. 1 2009
learning content (Bannan-Ritland et al., 2002; Hummel et al., 2004). Hence, the key
concepts behind learning objects is that they can be used and reused in different (and
multiple) learning contexts (Dahl and Nygaard, 1966). Put simply, learning objects are
something tangible that is produced by bringing together subject knowledge and
pedagogical expertise (Duncan, 2003). Notwithstanding these issues, the precise
development rubrics applied to PLE’s will be discussed in further works.
4.1 Phase 1 – development of ‘‘diagnostic questionnaire’’
Coffield et al. (2004) noted that it is often difficult to teach students if we do not know what
their learning preferences are. In this context, this questionnaire aimed to identify a learner’s
learning style preference. The questionnaire was formed by amalgamating three models of
learning styles which was determined from the literature; namely Kolb’s model of learning
styles, Honey and Mumford’s model of learning styles, and Felder and Silverman model of
learning styles. It was formed with the basis that a learning style comprises the following
activities:
B perceive and process information (Kolb-LSI) (Kolb, 1984);
B process and organize information (H & M-LSQ) (Honey and Mumford, 2006); and
B process and receive (or remember) information (FS-ILS) (Felder and Silverman, 1988).
For the benefit of the readers, the three-core model of learning styles were identified after a
detailed synthesis of the literature review. These were considered the most suitable for this
research as being the most cited and commonly used in a web-based learning environment;
i.e. INSPIRE (Honey and Mumford model of LS), CS388, LSAS and Tangow (Felder and
Silverman model of LS) (see Stash et al., 2004, for further details). These models have also
been successfully implemented in traditional classroom scenarios.
4.1.1 Process of development of the diagnostic questionnaire for learning styles. Upon
choosing the three models of learning styles, the overall development process of the
Diagnostic Questionnaire was divided into three stages (see Syed-Khuzzan and Goulding,
2008).
Development stage 1: this stage was used to identify and disaggregate the types of learning
styles for all the three models of learning styles, which also involved the ‘‘typical’’
characteristics and traits exhibited by these styles of learning. These characteristics can be
seen in Tables I, II and III; representing Kolb, Honey andMumford, and Felder and Silverman
respectively.
Development stage 2: this development stage was used to identify the similar
characteristics of each learning styles in the three models by amalgamating them
together, in order to tease out four core themes (identified as learning styles A, B, C and D).
Figure 2 shows the amalgamated/synthesized model, the abstract conceptualization of
which shows the four core themes, LS A, LS B, LS C, and LS D.
Development stage 3: the final stage of this development process was used to formulate the
questions within the four core themes. Six questions were formed for each core theme,
adding up to a total of 24 questions. The process of forming the questions was considered
critical as it needed to accurately crystallize the core ‘‘essence’’ of each learning style. In this
respect, the questionnaire was piloted with five domain experts within the field of learning
styles to provide feedback and views concerning:
B questionnaire content;
B questionnaire validity;
B questionnaire construct;
B questionnaire format; and
B type and level of questions used.
VOL. 41 NO. 1 2009 j INDUSTRIAL AND COMMERCIAL TRAININGj PAGE 51
Subsequently, in measuring the validity and reliability of this questionnaire, it will be tested
with learners alongside with the original models in developing the questionnaire, i.e. Kolb’s
learning style inventory (LSI), Honey and Mumford’s learning style questionnaire (LSQ), and
Felder and Solomon inventory of learning styles (ILS).
4.2 Phase 2 – Development of PLE prototype incorporating learning styles
Upon validation, the diagnostic questionnaire will then be ‘‘mapped’’ into a PLE prototype
(as a strategy to accommodate a combination of different learning styles in an e-Learning
environment) for further research and development work, including the augmentation to
each of the four learning environments (A, B, C and D) (see Figure 1). The precise modus
operandi, development rubrics and technological interdependencies/conformance
requirements will be discussed in further works.
Table I Learning styles characteristics
Model of LS Types of LS Characteristics of each style
Kolb’s model of LS Divergers (concrete experience andreflective observation)
Take experiences and think deeply about themLike to ask ‘‘why?’’Start from detail to constructively work up to the big pictureEnjoy participating and working with otherCalm over conflictsGenerally influenced by other peopleLike to receive constructive feedbacksLike to learn via logical instruction or hands-one exploration withconversation that lead to discovery
Convergers (abstractconceptualisation and activeexperimentation)
Think about thingsTry out ideas to see if it works in practiceLikes to ask ‘‘how?’’Like factsSeek to make things efficient by making small and careful changesPrefer to work by themselvesThink carefully and independentlyLearn through interaction and computer-based learning
Accommodators Most hands-on approachLikes doing rather than thinkingLikes to ask ‘‘what if?’’ and ‘‘why not?’’Do not like routine and repetitionTakes risks to see what happensLikes to explore complexity by direct interactionLearn better by themselvesLikes hands-on or practical learning rather than lectures
Assimilators Have the most cognitive approachPrefer to think rather than actLike to ask ‘‘what is there that I can know?’’Likes organized and structured understandingPrefer lectures for learning (esp. with demonstrations wherepossible)Learn through conversationPrefer logical and thoughtful approachOften have strong control needPrefer clean and simple predictability of internal models to externalmessinessLearns better when lecture starts with high-level concept and workdown to the detailDo not teach by play – they are serious learners
Source: Kolb (1984)
PAGE 52 j INDUSTRIAL AND COMMERCIAL TRAININGj VOL. 41 NO. 1 2009
5. Conclusion
The advances in technology have increased the demand for new and innovative teaching
approaches, prompting the design and development of cost-effective and high quality
e-Learning environments that can efficiently respond to learners’ needs and requirements.
Over the past decade, research has attempted to address key areas in this field, such as the
automation of the learning process, improving the portability of e-learning materials,
pedagogy, learning objects and e-learning standards. The relationship between pedagogy
and technology also appears to be an important aspect in designing educational systems.
It appears that the developments and strategic alliances in e-learning could produce a
revolution in the way education and training is delivered in the knowledge-based economy,
particularly increasing the delivery of knowledge globally through the Web. It is widely
accepted that learning through the web (e.g. e-learning) can take place anywhere, at any
time, through any computer and without necessarily the presence of a human tutor. However,
research findings have found that the majority of e-learning applications are rather static and
represent a generic approach to tutoring that does not take into account the individual needs
(e.g. learning styles) of each student that is using the educational application.
The quality of technological delivery and developing effective pedagogies are crucial issues
in shaping the said e-learning future. Hence, this paper briefly introduced the conceptual
model for the development of a PLE (incorporating learning styles) from an educational,
pedagogical, and technological as well as standardization perspective by adopting the
principles of the ‘‘collaborative system design’’ approach, as identified by the Advanced
Distributed Learning (ADL) initiative guidelines (see Alshawi et al., 2006). This conceptual
model has not been tested and the author invites rooms for discussions and comments for
improvement.
Table II Learning styles characteristics
Model of LS Types of LS Characteristics of each style
Honey and Mumford model of LS(Honey and Mumford, 2006)
Activists Like to think independently – like to take direct actionPrefer to have short sessions – like to take direct actionLess interested in the past – interested in the here and nowLikes plenty of varietyLikes to have a go, try things out and participateLikes to be in the center of attention
Reflectors Likes to think in detail before actingPrefer thoughtful approach and thorough preparation (read andread)Likes to research and evaluateGood listeners and prefer to adopt a low profileLikes to make a decision in their own timeLikes to listen and observeWelcome the opportunity to repeat a piece of learning
Theorists Likes to see how things fit into an overall pattern (a global person)Logical (likes logical presentation of ideas) and objective systemspeople – prefer sequential approach to problemsAnalytical – pay attention to details and tend to be perfectionist(likes to feel intellectually stretched)Like structures and clear objectives
Pragmatists Likes to see how things work in practice – to see the relevancy oftheir workPractical – likes to gain practical advantage from learningLikes to solve problems and are down to earthLike credible role modelsLike proven techniquesLike activities to be real
Source: Honey and Mumford (2006)
VOL. 41 NO. 1 2009 j INDUSTRIAL AND COMMERCIAL TRAININGj PAGE 53
Table III Learning styles characteristics
Model of LS Types of LS Characteristics of each style
Felder andSilverman modelof LS (Felder andSilverman)
How learners perceive:Sensing learners Likes to observe
Gather data through the sensesLikes facts, data and experimentation Likes to solve problems by standardmethods – dislikes surprisesPatient with detail and do not like complicationsGood at memorizing factsCareful but may be slowLikes to do hands-on work
Intuitive learners Likes to speculate, imagine and hunchPrefers principles and theoriesBored by detail and welcome complicationsLikes innovation and dislike repetitionGood at grasping new factsQuick but may be careless
Ways learners receive information:Visual Best remember what they see (pictures, diagrams, flow charts, films,
demonstrations)Auditory Remember much of what they hear andmore of what they hear and say (learn
better by discussion, verbal explanation, and by explaining things)Ways learners process information:Active Feels more comfortable when involved in doing something in the external
world with the information learnedDo not like passive learning environments (i.e. lectures)Work well in groupsTend to be experimentalist – evaluate ideas, design, etc.
Reflective Likes examining and manipulating with the information learnedOccasional pauses for thoughtsPrefers materials that are fundamental understandingTheoreticians (the one who can define the problems and propose possiblesolutions)
Ways learners understand:Sequential Likes presentation of materials to be in a logically ordered progression
Follow linear reasoning processCan work with materials when they understand partially or superficiallyStrong in convergent thinking and analysis
Global Learn in fits and startsMake intuitive leaps; and may be unable to explain how they came up with asolution to a problemSometimes learn better by jumping directly to more complex and difficultmaterialStrong in divergent thinking and synthesis
Source: Felder and Silverman (1988)
Figure 2 Synthesized model: abstract conceptualisation
PAGE 54 j INDUSTRIAL AND COMMERCIAL TRAININGj VOL. 41 NO. 1 2009
References
ADL (2006), ‘‘Advanced Distributed Learning initiative’’, available at: www.adlnet.gov/index.cfm
(accessed 1 November 2006).
Alshawi, M., Goulding, J.S. and Faraj, I. (2006), ‘‘Knowledge-based learning environments for
construction’’, Journal for Education in the Built Environment, Vol. 1 No. 1, pp. 51-72.
Alsubaie, M. (2006), Creating a Personalised Learning Environment Using Learning Objects, School of
Construction and Property Management, University of Salford, Manchester.
Bannan-Ritland, B., Dabbagh, N. and Murphy, K. (2002), ‘‘Learning object systems as constructivist
learning environments: related assumptions, theories, and applications’’, in Wiley, D.A. (Ed.), The
Instructional Use of Learning Objects, Agency for Instructional Technology and Association for
Educational Communications and Technology, Bloomington, IN, pp. 61-97.
Berger, P.L. and Luckman, T. (1966), The Social Construction of Reality: A Treatise in the Sociology of
Knowledge, Double Anchor, New York, NY.
Blackmoore, J. (1996), ‘‘Learning style preferences online’’, Telecommunications for Remote Work and
Learning, available at: www.cyg.net/jblackmo/diglib (accessed 24 September 2006).
Buch, K. and Bartley, S. (2002), ‘‘Learning style and training delivery mode preference’’, Journal of
Workplace Learning, Vol. 14, pp. 5-10.
Chang, C.C. (2001), ‘‘Construction and evaluation of a web-based learning portfolio system: an
electronic assessment tool’’, Innovation in Educations and Training International, Vol. 38 No. 2, pp. 144-5.
Coffield, F.J., Moseley, D.V., Hall, E. and Ecclestone, K. (2004), Learning Styles for Post 16 Learners:
What Do We Know?, Learning and Skills Research Centre/University of Newcastle upon Tyne,
London/Newcastle upon Tyne.
Conner, M.L. (2005), ‘‘What’s your learning style?’’, available at: www.agelesslearner.com/assess/
learningstyle.html (accessed 6 November 2006).
Dahl, O.J. and Nygaard, K. (1966), ‘‘Simula: an AGOL based simulation language’’, Communications of
the ACM, Vol. 9, pp. 671-8.
Dick, W. and Carey, L. (1990), The Systematic Design of Instruction, 3rd ed., Harper Collins, Glenview,
IL.
Duncan, C. (2003), ‘‘The value of managing learning objects: an intrallect white paper’’, available at:
www.intrallect.com/products/intralibrary/papers/value.pdf (accessed 19 November 2006).
Dunn, R. (1990), ‘‘Understanding the Dunn and Dunn learning styles model and the need for individual
diagnosis and prescription’’, Reading, Writing and Learning Disabilities, Vol. 6, pp. 223-47.
Entwistle, N. (1981), Styles of Learning and Teaching, John Wiley & Sons, New York, NY.
Felder, R.M. (1996), ‘‘Matters of style’’, ASEE Prism, Vol. 6, pp. 18-23.
Felder, R.M. and Brent, R. (2005), ‘‘Understanding students’ differences’’, Journal of Engineering
Education, Vol. 94, pp. 57-72.
Felder, R.M. and Silverman, L.K. (1988), ‘‘Learning and teaching styles in engineering education’’, Engr.
Education, Vol. 78 No. 7, pp. 674-81.
Gagne, R.M., Briggs, L.J. and Wager, W.W. (1988), Principles of Instructional Design, Holt, Rinehart and
Winston, New York, NY.
Gunasekaren, A., McNeil, R.D. and Shaun, D. (2002), ‘‘E-learning: research and applications’’, Industrial
and Commercial Training, Vol. 34 No. 2, pp. 44-53.
Honey, P. and Mumford, A. (1992), The Manual of Learning Style, Peter Honey Publications,
Maidenhead.
Honey, P. and Mumford, A. (2006), The Learning Styles Questionnaire 80-item Version, Peter Honey
Publications, Maidenhead.
Hummel, H., Manderveld, J., Tattersall, C. and Koper, R. (2004), ‘‘Educational modelling language and
learning design: new opportunities for instructional reusability and personalised learning’’, International
Journal of Learning Technology, Vol. 1 No. 1, pp. 111-26.
VOL. 41 NO. 1 2009 j INDUSTRIAL AND COMMERCIAL TRAININGj PAGE 55
Karagiannidis, C. and Sampson, D. (2004), ‘‘Adaptation rules relating learning style research and
learning objects meta-data’’, in Magoulas, G.D. and Chen, S.Y. (Eds), Proceedings of International
Conference on Adaptive Hypermedia and Adaptive Web-based Systems, The Netherlands,
August 23-26.
Kim, B. and Chris, S. (2001), ‘‘Accommodating diverse learning style in the design and delivery of
on-line learning experiences’’, International Journal of Engineering.
Kolb, D.A. (1976), Learning Style Inventory Technical Manual, McBer & Company, Boston, MA.
Kolb, D.A. (1984), Experiential Learning: Experience as the Source of Learning and Development,
Prentice-Hall, Upper Saddle River, NJ.
Larocque, D. and Faucon, N. (1997), ‘‘Me, myself and . . . you? Collaborative learning: why bother?’’,
available at: http://leahi.kcc.hawaii.edu/org/tcc_conf97/pres/larocque.html (accessed 23 October
2007).
McCarthy, B. (1990), ‘‘Using the 4MAT system to bring learning styles to schools’’, Educational
Leadership, Vol. 48 No. 2, pp. 31-6.
Martinez, M. (2002), ‘‘What is personalised learning?’’, The e-Learning Developers’ Journal – Design
Strategies, May 7.
Merrill, M.D., Drake, L., Lacy, M. and Pratt, J.A. (1996), ‘‘Reclaiming instructional design’’, Journal of
Educational Technology, Vol. 36 No. 5, pp. 5-7.
O’Conner, T. (1998), ‘‘Using learning style to adapt technology for higher education’’, available at: www.
indstate.edu/ctl/styles/learning.html (accessed 29 September 2006).
Pfeiffer, G., Holley, D. and Andrew, D. (2005), ‘‘Developing thoughtful students: using learning styles in
an HE context’’, Education and Training, Vol. 47, pp. 422-31.
Reigeluth, C. (Ed.) (1999), Instructional Design Theories and Models: A new Paradigm of Instructional
Theory, Lawrence Erlbaum Associates, Hillsdale, NJ.
Sampson, D. and Karagiannidis, C. (2002), ‘‘Personalised learning: educational, technological and
standardisation perspective’’, Interactive Educational Multimedia, Vol. 4, pp. 24-39.
Sims, R.R. (1990), ‘‘Adapting training to trainee learning styles’’, Journal of European Industrial Training,
Vol. 14 No. 2, pp. 17-22.
Smith, D.M. and Kolb, D. (1986), Users’ Guide for the Learning Style Inventory: A Manual for Teachers
and Trainers, McBer, Boston, MA.
Stash, N., Cristea, A. and De Bra, P. (2004), ‘‘Authoring of learning styles in adaptive hypermedia:
problems and solutions’’, paper presented at the World Wide Web Conference, May 17-22, 2004, New
York, NY.
Syed-Khuzzan, S.M. and Goulding, J.S. (2008), ‘‘Personalised learning environments: a diagnostic
questionnaire for construction’’, 8th BuHu International Postgraduate Research Conference, Prague
2008 (forthcoming).
Vercoustre, A.M. and Mclean, A. (2005), ‘‘Reusing educational material for teaching and learning:
current approaches and directions’’, International Journal on E-learning, Vol. 4, pp. 57-68.
Vincent, A. and Ross, D. (2001), ‘‘Personalise training: determine learning style, personality types and
multiple intelligence’’, The Learning Organisation, Vol. 8, pp. 36-43.
Watson, J. and Hardaker, G. (2005), ‘‘Steps towards personalised learner management system (LMS):
SCORM implementation’’, Campus-Wide Information Systems, Vol. 22, pp. 56-70.
PAGE 56 j INDUSTRIAL AND COMMERCIAL TRAININGj VOL. 41 NO. 1 2009
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