e-Learning Material Presentation and Visualization Types

and Schemes

Nauris Paulins

, Signe Balina

and Irina Arhipova

Computer System department, Latvia University of Agriculture, Liela Street 2-1, Jelgava, Latvia

Datorzinibu centrs, University of Latvia, Aspazijas Blvd. 5, Riga, Latvia

Keywords: Learning Style, Cognitive Abilities, Learning, Visualisation, Multimodal, Multiple Representations.

Abstract: Multimedia and content visualisation provide ability to transform electronic materials into more dynamic

format. This can provide positive aspect on learning, but also can overload the limited information

processing capacity in human brains. Cognitive load in technology-enhanced learning is closely related to

the learning styles of learners. This study examines interactions between learning styles of students and how

these are related to student’s working memory and cognitive traits. To investigate the learning styles of

learners the Felder- Soloman questionnaire was chosen. It allows analyse students’ learning styles with

respect to the Felder-Silverman learning style model, which is the most appropriate for a web-based

learning. Also the interaciton between cognitive traits and learning styles is analysed. The results of this

analysis prove the importance of multimodal learning in technology-enhanced learning. Also some

relationships between learners with higher working memory capacity and learners with lower working

memory capacity were demonstrated. The results will help to improve students’ model for better adaptivity

of learning materials.

1 INTRODUCTION

The development of information and communication

technologies (ICT) has made the emergence of such

visual media as interactive simulations, animations,

video, and other electronic media more rapid in

educational process.

One of the greatest benefits of electronic media

is its opportunity of adaption, which provides

learners with more flexible usage of learning

material. That makes the learning material more

appropriate for learners’ cognitive style (Chen and

Macredie, 2002; Wang et al., 2000). The analysis on

the adaptivity of e-learning materials has pointed out

the importance of the modelling of learners’

cognitive aspects. One of the instructional designer’s

tasks is to make learning process more effective,

involving the use of new media and visualisation

techniques. The explanation of the positive effect of

visualisation is provided by cognitive load theory

(Bannert, 2002) and cognitive theory of multimedia

learning which is presented by Mayer (Kalyuga,

2011). These theories show the information

processing limitations of our cognitive system in

learning.

Traditional learning does not always allow the

adoption of different learning styles or the adoption

of socio-cultural differences during learning process.

It should be taken into account that each learner has

different learning characteristics, like motivation,

prior knowledge, and learning style, which influence

learning process. This is the reason why some

learners perceive the subject more easily, but others

find the same subject rather difficult. The increase of

multimedia usage in teaching has provided a lot of

possibilities to adapt it for different learning styles,

and also a lot of research has been done to analyse

materials’ adaptivity on learners’ perception. The

essential part of adaptivity nowadays is made

feasible by adaptive virtual learning environments,

which can adapt their content and activities

according to student’s needs. Therefore the learning

systems require implementation of student’s model,

which would allow system understand students’

needs. Basically it is a challenging process, because

students do not possess solely one of the styles –

each student has his own mix of characteristics.

CISCO researchers (Fadel and Lemke, 2008) has

shown that multimodal learning is more effective

than traditional uni-modal learning, but there is a

138

Paulins N., Balina S. and Arhipova I..

e-Learning Material Presentation and Visualization Types and Schemes.

DOI: 10.5220/0004968501380143

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 138-143

ISBN: 978-989-758-029-1

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

lack of evidence-based research. The research is

intuitive and based on educational theories. It shows

that the visualisation in learning process has a

potential to support learner’s needs in learning

process, but interactivity and dynamic animations

are not always the best aids for learning. This

research gives an overview of the relationship

between learner’s cognitive traits and learning styles

thus providing theoretical grounds for instructional

designers.

2 BACKGROUND

To provide the context for understanding the

differences between several aspects of learner’s

cognitive traits and learning styles, this paper briefly

summarizes the key elements of the research about

brain functionality, of how people learn, and the

prior research on cognitive abilities of learners and

how to support them in a learning process.

2.1 Memory Systems

The research in cognitive science has shown that

human brain has three types of memory: sensory

memory, working memory, and long-term memory

(Fadel and Lemke, 2008; Mayer, 2001). These types

of memory can be described as follows:

 Sensory Memory – when human senses allow

people receive signals from outside world and

experience various situations, it is said to be

sensory memory. Involuntary signals from

sensory memory are sent to long-term

memory as episodic knowledge. It stays in a

long-term memory if a learner pays attention

to the episodes of sensory memory. In that

case these episodes are loaded into working

memory. If something goes into a learner’s

working memory, then learner can work with

this information accordingly to the common

context;

 Working Memory – this is a main part of a

thinking process. Brain functions are dual

coded with a buffer for storage of verbal and

text elements and also with a buffer for visual

and spatial elements. The main limitation in

human brains is that those buffers can process

approximately four objects of visual

information and seven objects of verbal

information. Working memory is a place

where verbal and visual information work

together, without interference;

 Long-term memory – this is a brain function

which allows humans store information during

lifetime. It acts in parallel with sensory and

working memory. There are too types of long-

term memory – episodic and semantic.

Episodic memory derives directly from a

sensory memory and is involuntary. Semantic

memory receives information from working

memory, and it automatically triggers storage

in a long-term memory.

The main problem in Instructional Design is that

working memory has limited capacity, which can

cause cognitive overload. The Felder -Silverman

model describes student’s characteristics in four

dimensions, pointing out that not always students act

as expected, even if they have strong preferences to

one of the styles. However, taking into account the

learning styles in virtual learning environments

could help to adapt students learning styles and

reduce their cognitive memory system load.

2.2 Cognitive Load Theory and

Instructional Design

Cognitive load theory (CLT) assumes, that the

amount of working memory is limited, but at the

same time it is related with a long-term memory,

which is unlimited. According to CLT the

knowledge in long-term memory is stored in mental

schemas. Learning is possible due to the

construction of schemata. Schema can be treated as a

single element in working memory and functions to

overcome working memory limitations (Hollender et

al., 2010).

There are different types of cognitive load which

can affect learning performance (Sweller et al.,

1998).

The main load which can arise from instructional

design is extraneous cognitive load. It is directly

connected with instructional designer impact on

study process, and the main target is to reduce

extraneous cognitive load in instructional materials.

It is caused by an unnecessary increase in the

number of elements that must be processed

simultaneously in working memory (Wong et al.,

2012).

Intrinsic cognitive load (ICL) refers to the

learning and its level of difficulty. ICL mostly

depends on the interactivity of elements. High ICL

occurs when interactivity of the material is high. For

example, if the interactivity of a learning definition

is low, but some grammar analysis must be learned,

then the interactivity should be made much higher.

e-LearningMaterialPresentationandVisualizationTypesandSchemes

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The last type of cognitive load is a germane

cognitive load which results from active schema

construction process and therefore is beneficial for

learning. Germane cognitive load refers to working

memory resources required to deal with ICL in

learning, as well as working memory resources

which are required to deal with extraneous

resources. If extraneous cognitive load is reduced,

then germane cognitive load can be increased.

The research process in CLT area has resulted in

a range of instructional design guidelines and factors

that impact student’s cognitive load. Firstly, there

should be as low pressure on extraneous cognitive

load as possible. Secondly, it should optimize the

level of germane cognitive load. This is the portion

of load that directly contributes to the learning

process. Furthermore, an efficient training is

characterized by favourable effort-performance

ratio. This is a relatively low mental effort that

results in relatively high performance (Gerven et al.,

2002).

It is also proven that multimodal learning in

which information is presented in multiple modes

such as visual and auditory, is more effective for

electronic environments, and it can provide several

benefits, including:

 promoting learning by providing an external

representations of information;

 deeper processing of information;

 holding learners attention by making the

information more attractive and motivating,

hence making complex information easier to

comprehend (Sankey et al., 2010).

Implementation of students characteristics and

students’ model in Cognitive Trait Model (Lin,

2007) include cognitive traits such as working

memory capacity, inductive reasoning ability and

information processing speed. It would help

automate instructional design process and improve

learning environment adaptivity. Cognitive Trait

Model is a domain independent, so it can be used in

different learning environments. As the working

memory has been already evaluated, it needs to

provide the description about reasoning ability,

associative learning and information processing

speed.

2.3 Reasoning Ability and Information

Processing Speed

There are different methods of reasoning which are

mainly distinguished among inductive, deductive

and adductive reasoning. During this research the

main focus is on inductive and deductive reasoning

since they are more related with learning abilities.

Inductive reasoning is one of the most important

abilities in learning process by means of which it is

possible to construct concepts from examples.

During problem analysis, learners look for known

examples to construct internal hypothesis. As a

result cognitive load is reduced and learning process

becomes more efficient. It means that higher

inductive reasoning ability allows build up mental

models of the information learned, which leads to

better learning results.

Deductive reasoning is a process during which

logical consequences are drawn from premises; it is

basically naturalistic decision making process what

people do in real-world situations. Learners with

greater experience can recognize appropriate actions

to take in various situations that might arise, but

learners with less experience almost always perform

random search of alternatives. The problem becomes

more noticeable during complex problem analysis,

where learners often fail to find appropriate solution.

But if a necessary amount of skills is acquired and

learned then it becomes more effective.

Reasoning ability is closely connected with

information processing speed, which determines

how quickly learners can acquire information

correctly. Instructional designers should take this

aspect into account, because learners with low

information processing speed should be presented

with only the important points of material and also a

number of ways should be decreased. In contrast, for

learners with high information processing speed, the

information space can be enlarged by providing

greater amount of information (Lin, 2003).

2.4 Associative Learning

The associative learning is ability to link acquired

knowledge to existing knowledge. It is a mechanism

where behaviours are influenced by experiences. For

instructional designers it means that material for

learning support must assist to the recollection of

learned information, as well as it should clearly

show the relationships of concepts, where the new

knowledge is connected to the existing one. That

means that it is useful to provide some additional

information and links for learners with low

associative learning skills – it would help to

associate one concept with another.

3 METHODOLOGY

To investigate the learning styles of learners the

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research was performed with 150 students

participating. Students where mixed from different

faculties and courses, mostly from bachelor level. To

investigate students’ learning styles Felder and

Soloman questionnaire (Felder and Soloman, 1997)

was chosen. It is 44 question form for identifying

the learning styles according to Felder-Silverman

learning style model (Felder and Silverman, 1998).

There are more learning style models in this research

area, like Kolb’s learning style model (Kolb, 1984)

and Honey and Mumford’s learning style model

(Honey and Mumford, 1982), but Felder-Silverman

learning style model is one of the most appropriate

for web-based learning. It was confirmed during the

comparison of learning style models with respect to

web-based learning systems (Kuljis and Liu, 2005).

The chosen questionnaire which is called Index

of Learning Styles (ILS), with 44 questions is

divided into 4 dimensions, which are expressed by

values between +11 and -11 per dimension, with

steps +/-2, assuming that each learner has personal

preferences for each dimension. Each dimension is

assigned to 11 questions of questionnaire. Table 1

shows all dimensions of this questionnaire. Each

question is answered either with a value +1 (answer

a) or -1 (answer b). Answer a corresponds to the

active, sensing, visual or sequential preference of

dimensions, answer b corresponds reflective,

intuitive, verbal, or global preference of dimensions.

Table 1: Learning style dimensions according to Felder-

Silverman model.

Learning Style

dimensions

Description

Active – Reflective

Active learners like to try the

learned concepts and are tended

to work in groups, but reflective

learners like to work alone.

Sensing – Intuitive

Sensing learners prefer concrete

definitions and practical facts.

Intuitive learners are more tended

on abstract concepts and theories.

Visual – Verbal

Visual learners are tended on

pictures, diagrams and flow

charts, bet verbal learners are

tended on written and spoken

explanations.

Sequential –

Global

Sequential learners like processes

where the linear link can be

clearly distinguished with small

steps, but global learner likes

holistic thinking and large leaps.

During the research the balanced value is

calculated – it shows values in dimension from +3 to

-3 from the survey. This result is due to the factor

that a lot of learners did not show a strong

preference for one of the dimensions.

Another part of the research was to empirically

study students’ learning behaviour and derive the

required information from their behaviour. This

study was based on the Cognitive Trait Model (Lin,

2007) to profile learners according to their cognitive

traits. The Cognitive Trait Model’s (CTM) four

cognitive traits – working memory capacity,

inductive reasoning ability, processing speed and

associative learning skills – are addressed in CTM.

Various patterns or manifests of traits are defined for

each cognitive trait, as well as the identification of

cognitive traits is based on the behaviour of learners

within the system or learning process.

4 FINDINGS AND DISCUSSIONS

Firstly, the overall distribution of learners in each

dimension was analysed.

Table 2: Learning dimensions between students.

Active Balanced Reflective

28% 58% 14%

Sensing Balanced Intuitive

34% 49% 17%

Visual Balanced Verbal

68% 26% 6%

Sequential Balanced Global

18% 65% 17%

The analysis shows that for the learners with

active learning style the ability to practically try

learned concepts has more impact on memorising

and obtaining knowledge than for the reflective

learners, but reflective learners have more relevance

to social behaviour. These learners are more tended

to inductive reasoning and low associative ability,

which shows the importance of giving them the

opportunity to work individually. These dimensions

can be related also with field-dependant and field-

independent learners (Witkin et al., 1997). These

dimensions are grouped in low working memory and

high working memory, which allow making

relations to active and reflective learners’ working

memory capacity.

It was discovered that sensing and intuitive

dimensions also have some relationships with field-

independent and field-dependent learners. According

to Chen,

S.Y. et al (Chen 2002) field-dependent

learners prefer concrete materials and small learning

steps; they could get very good results in

cooperation with sensing learners, but they would be

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less successful in cooperation with intuitive learners,

who prefer abstract materials.

The research’s results also show that students

with low working memory capacity are more tended

to visual learning style, but this doesn’t mean that

learners with visual learning style have low working

memory capacity. It could be explained by dual

coding theory of multimedia learning.

The sensing learning style requires concrete and

specified learning materials. The sensing learners are

more careful and attentive during a learning process,

but intuitive learners are more tended to abstract

materials and they have tendency of not being

patient and careful.

The results of sequential/global dimension shows

that sequential learners are tended to understand the

concepts by building them from smaller parts to the

whole solution. We also noticed rather close relation

between sensing/intuitive and sequential/global

learning dimensions. These dimensions correlated

with each other.

5 CONCLUSIONS

This paper analyses the learners’ cognitive traits and

learning styles. Felder-Silverman learning style

model and cognitive trait model was used for the

research analysis.

Considering the relationship between cognitive

traits and learning styles it is possible to obtain

additional information about a learner, which could

improve the overall students’ mode. The research

shows that students with active, sensing visual and

global learning style have lower memory than

reflective, intuitive and sequential. This could help

to support learner’s cognitive load with appropriate

instructional design automation and integration in

learning systems.

Within the research the use of electronic learning

materials at high schools was analysed, and it was

concluded that most of the materials do not meet the

necessary requirements for supporting students’

cognitive traits and learning styles.

Learning styles can improve identification of

cognitive traits; if the learning style is already

detected then it will improve indication of cognitive

traits. But at the same time cognitive traits can also

help to identify learning styles. Such interaction can

better show students working characteristics and

provide the analyses of not only learning styles but

also the cognitive traits of students. This analysis

can lead to more accurate representation of materials

which will give ability to provide learning without

cognitive overload. Such analysis can improve

pedagogical models to provide more adaptive

learning, with better effect.

The further work is necessary on the statistical

analyses of survey results which could allow analyse

the correlation between different learning

dimensions. It would also be useful to make more

explicit analyses on students’ behaviour and to find

the ability to detect automatically the learning style

from the student’s behaviour in learning system.

Definitely more research on learning styles and

cognitive traits should be made, to provide more

adaptive electronic learning materials.

ACKNOWLEDGEMENTS

This research is part of a project „Competence

Centre of Information and Communication

Technologies” run by “IT kompetences centrs” Ltd,

contract No. L-KC-11-0003, co-financed by

European Regional Development Fund.

REFERENCES

Bannert, M., 2002. Managing cognitive load — recent

trends in cognitive load theory. Learn. Instr. 12, 139–

146.

Chen, S.Y., Macredie, R.D., 2002. Cognitive styles and

hypermedia navigation: Development of a learning

model. J. Am. Soc. Inf. Sci. Technol. 53, 3–15.

Fadel, C., Lemke, C., 2008. Multimodal Learning Through

Media : What the Research Says. Cisco Syst. 1–24.

Felder, R.M., Silverman, L.K., 1998. LEARNING AND

TEACHING STYLES. Engr. Educ. 78, 674–681.

Felder, R.M., Soloman, B.A., 1997. Index of Learning

Styles questionaire [WWW Document]. URL http://

www.engr.ncsu.edu/learningstyles/ (accessed 9.20.13).

Gerven, P.W.M. Van, Paas, F.G.W.C., Merrie, J.J.G. Van,

2002. Cognitive load theory and aging : effects of

worked examples on training efficiency. Learn. Instr.

12, 87–105.

Hollender, N., Hofmann, C., Deneke, M., Schmitz, B.,

2010. Integrating cognitive load theory and concepts

of human–computer interaction. Comput. Human

Behav. 26, 1278–1288.

Honey, P., Mumford, A., 1982. The Manual of Learning

Styles. P. Honey & L. Mumford.

Kalyuga, S., 2011. Effects of information transiency in

multimedia learning. Procedia - Soc. Behav. Sci. 30,

307–311.

Kolb, D.., 1984. Experiential learning : experience as the

source of learning and development. Engelwood

Cliffs, NJ: Prentice Hall.

Kuljis, J., Liu, F., 2005. A Comparison of Learning Style

Theories on the Suitability for elearning. Web

ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

142

Technol. Appl. Serv. 191–197.

Lin, T.-Y., 2003. User exploration based adaption in

adaptive learning systems. Int.J.Inform.Syst.Educ. 1,

22–31.

Lin, T.-Y., 2007. Cognitive Trait Model for Adaptive

Learning Environments. Massey University, New

Zeland.

Mayer, R.E., 2001. Multimedia learning. UK: Cambridge

University Press, Cambridge.

Sankey, M., Birch, D., Gardiner, M., 2010. Engaging

students through multimodal learning environments :

The journey continues. PCurriculum, Technol.

Transform. an Unkn. Futur. Proc. ascilite Sydney 2010

852–863.

Sweller, J., Merrienboer, J.J.G. Van, Paas, F.G.W.C.,

1998. Cognitive Architecture and Instructional Design.

Educ. Psyhology 10, 251–296.

Wang, P., Hawk, W.B., Tenopir, C., 2000. Users’

interaction with World Wide Web resources: an

exploratory study using a holistic approach. Inf.

Process. Manag. 36, 229–251.

Witkin, H. A., Moore, C. A., Goodenough, D. R., Cox, P.

W., 1997. Field dependent and field independent

cognetive style and their deucaiton implications. Rev.

Educ. Res. 47, 1–64.

Wong, A., Leahy, W., Marcus, N., Sweller, J., 2012.

Cognitive load theory , the transient information effect

and e-learning. Learn. Instr. 22, 449–457.

e-LearningMaterialPresentationandVisualizationTypesandSchemes

143