The Impact of an E-Learning Strategy on Pedagogical Aspects Pedagogical Aspects of Learning and Implications for E-Learning
Applied Sciences (2000)
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Felix Mödritscher's profile on Mendeley.
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Abstract
Planning and implementing courses no matter whether done for face-to-face education or within e-learning environments deal a lot with pedagogical issues. Learning is influenced by a couple of factors such as attention, motivation, emotions, etc. as well as by learner characteristics like prior knowledge, cognitive and learning styles, intellectual capabilities, constitutional states and the forth. This paper examines learner-centred aspects of e-learning by drawing conclusions from traditional learning in the classroom situation and outlining implications for the process of virtual learning. Furthermore, a case study carried out in the field of adult education tries to point out findings on the impact of different e-learning strategies on the factors influencing learning.
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Available from
Felix Mödritscher's profile on Mendeley.
Page 1
The Impact of an E-Learning Strategy on Pedagogical Aspects Pedagogical Aspects of Learning and Implications for E-Learning
The Impact of an E-Learning Strategy on Pedagogical Aspects
Felix Mödritscher
Institute for Information Systems and Computer Media (IICM), Graz University of Technology,
and CAMPUS 02 – University of Applied Sciences Degree Program in IT and IT-Marketing, Graz, Austria
fmoedrit@iicm.edu and felix.moedritscher@campus02.at
Abstract: Planning and implementing courses – no matter whether done for face-to-face education
or within e-learning environments – deal a lot with pedagogical issues. Learning is influenced by a
couple of factors such as attention, motivation, emotions, etc. as well as by learner characteristics
like prior knowledge, cognitive and learning styles, intellectual capabilities, constitutional states
and the forth. This paper examines learner-centred aspects of e-learning by drawing conclusions
from traditional learning in the classroom situation and outlining implications for the process of
virtual learning. Furthermore, a case study carried out in the field of adult education tries to point
out findings on the impact of different e-learning strategies on the factors influencing learning.
Keywords: pedagogical aspects of e-learning, factors influencing learning, learner characteristics, impact of an e-
learning strategy on virtual learning, case study
1 Introduction
E-learning can be considered to be highly related to learning and teaching as stated in (Jain et al., 2002).
Therefore, pedagogy and didactic are important aspects for all facets of e-learning, reaching from the creation of the
courseware and the application of an e-learning system to the evaluation of the learning progress. Referring to
(Mödritscher et al., 2006), it was already shown that an online course for a certain topic may be implemented in
various ways and each method differs from each other with respect to aspects of the teaching process, such as the
instructional design, the effort for the teacher, the effectiveness of the teaching strategy, or the applicability of an e-
learning platform (in this case the platform used was Moodle).
In this paper, relevant factors of the learning process, e.g. attention, motivation, emotional aspects, students’
characteristics, and the forth are examined according to different e-learning strategies. Therefore, section 2 derives
relevant theoretical aspects for the virtual learning process by examining knowledge transfer within the classroom
situation. In the following, section 3 describes the case study about implementing different e-learning strategies,
which was carried out at the Graz University of Applied Sciences CAMPUS 02 (see Campus02, 2006). In section 4
findings on pedagogical aspects in relation with these e-learning strategies are highlighted.
2 Pedagogical Aspects of Learning and Implications for E-Learning
With respect to (Knowles et al., 1998), education can be understood as “activity undertaken or initiated by
one or more agents that is designed to effect changes in the knowledge, skill, and attitudes of individuals, groups, or
communities”. In contrary, the term “learning” emphasises the person in whom the change occurs or is expected to
occur. Thus, learning comprises “the act or process by which behavioural change, knowledge, skills, and attitudes
are acquired” (see Boyd et al., 1980). Tying up to this definition, the following subsections deal with the learner-
centred aspects of the traditional learning process – such as relevant factors for learning, characteristics of learners,
and further influences on learning – and examine them within the context of e-learning.
2.1 Factors relevant for the Learning Process
Drawing conclusions from (Bransford et al. 2000), four factors can be outlined as significantly important for
the learning process: (1) attention, (2) motivation, (3) emotions, and (4) experiences of the learner.
First of all, the focus of attention determines if a student mentally follows a lecture and, therefore, if the
intended behavioural change affects a learner at all. E-learning particularly requires a strategy for getting and
keeping the learner’s attention. Thus, it is necessary to consider cognitive processes such as the learner’s selection of
incoming data into the sensory memory, organising and integrating this information by building connections in
short-term memory and encoding it by transferring it to long-term memory. Thus, it is recommended to apply certain
principles for instructional design, e.g. the ones by (Fleming & Levie, 1993).
Felix Mödritscher
Institute for Information Systems and Computer Media (IICM), Graz University of Technology,
and CAMPUS 02 – University of Applied Sciences Degree Program in IT and IT-Marketing, Graz, Austria
fmoedrit@iicm.edu and felix.moedritscher@campus02.at
Abstract: Planning and implementing courses – no matter whether done for face-to-face education
or within e-learning environments – deal a lot with pedagogical issues. Learning is influenced by a
couple of factors such as attention, motivation, emotions, etc. as well as by learner characteristics
like prior knowledge, cognitive and learning styles, intellectual capabilities, constitutional states
and the forth. This paper examines learner-centred aspects of e-learning by drawing conclusions
from traditional learning in the classroom situation and outlining implications for the process of
virtual learning. Furthermore, a case study carried out in the field of adult education tries to point
out findings on the impact of different e-learning strategies on the factors influencing learning.
Keywords: pedagogical aspects of e-learning, factors influencing learning, learner characteristics, impact of an e-
learning strategy on virtual learning, case study
1 Introduction
E-learning can be considered to be highly related to learning and teaching as stated in (Jain et al., 2002).
Therefore, pedagogy and didactic are important aspects for all facets of e-learning, reaching from the creation of the
courseware and the application of an e-learning system to the evaluation of the learning progress. Referring to
(Mödritscher et al., 2006), it was already shown that an online course for a certain topic may be implemented in
various ways and each method differs from each other with respect to aspects of the teaching process, such as the
instructional design, the effort for the teacher, the effectiveness of the teaching strategy, or the applicability of an e-
learning platform (in this case the platform used was Moodle).
In this paper, relevant factors of the learning process, e.g. attention, motivation, emotional aspects, students’
characteristics, and the forth are examined according to different e-learning strategies. Therefore, section 2 derives
relevant theoretical aspects for the virtual learning process by examining knowledge transfer within the classroom
situation. In the following, section 3 describes the case study about implementing different e-learning strategies,
which was carried out at the Graz University of Applied Sciences CAMPUS 02 (see Campus02, 2006). In section 4
findings on pedagogical aspects in relation with these e-learning strategies are highlighted.
2 Pedagogical Aspects of Learning and Implications for E-Learning
With respect to (Knowles et al., 1998), education can be understood as “activity undertaken or initiated by
one or more agents that is designed to effect changes in the knowledge, skill, and attitudes of individuals, groups, or
communities”. In contrary, the term “learning” emphasises the person in whom the change occurs or is expected to
occur. Thus, learning comprises “the act or process by which behavioural change, knowledge, skills, and attitudes
are acquired” (see Boyd et al., 1980). Tying up to this definition, the following subsections deal with the learner-
centred aspects of the traditional learning process – such as relevant factors for learning, characteristics of learners,
and further influences on learning – and examine them within the context of e-learning.
2.1 Factors relevant for the Learning Process
Drawing conclusions from (Bransford et al. 2000), four factors can be outlined as significantly important for
the learning process: (1) attention, (2) motivation, (3) emotions, and (4) experiences of the learner.
First of all, the focus of attention determines if a student mentally follows a lecture and, therefore, if the
intended behavioural change affects a learner at all. E-learning particularly requires a strategy for getting and
keeping the learner’s attention. Thus, it is necessary to consider cognitive processes such as the learner’s selection of
incoming data into the sensory memory, organising and integrating this information by building connections in
short-term memory and encoding it by transferring it to long-term memory. Thus, it is recommended to apply certain
principles for instructional design, e.g. the ones by (Fleming & Levie, 1993).
Page 2
Secondly, the motivational states of students are of importance when questioning how the stimuli given by
the teacher promotes the learning process. (Bransford et al., 2000) state that “motivation affects the amount of time
that people are willing to devote to learning”. Yet, this willingness to learn is caused by different motives beginning
with the intention of achieving something over competing against colleagues or helping other people up to
emotional factors like anxiety. (Entwistle, 1981) classified three motivational orientation styles: (a) meaning-
oriented, (b) reproducing-oriented, and (b) achieving-oriented motives. Considering motivational aspects for e-
learning is mainly dependent on the learning content itself, e.g. by pointing out the relevance for an instruction or
including interactive elements such as games and simulations. Furthermore, it is advantageous to create competition
within a learner group and adapt to pre-knowledge in the subject domain to prevent the students from being
unchallenged. For instance, (Astleitner & Keller, 1995) describe a framework for adapting instruction to the
learner’s motivational state in computer-assisted instructional environments.
Thirdly, emotions have, similarly to motivation, a strong impact on the learning process. (Tobias, 1987)
points out findings on students’ performance depending on anxiety, in particular test anxiety, and proposes special
methods for dealing with such problems. On the other side, an emotion – no matter if a negative and positive one –
may influence learning due to its special nature. With respect to (Paulsen, 2005), “emotion is an unconscious
arousal system that alerts us to potential danger and opportunities”. Thus, addressing a learner’s emotional channel
can be seen as a key cognitive process for transferring data into the short- or even long-time memory. Within the e-
learning situation, the improvement of the learning process can be realised through emotions e.g. by storytelling,
provocations, emotional figures and animations, group works, enabling confidence in the learning content, etc.
Fourthly, knowledge transfer can be improved if learners can tie up to prior knowledge either in the same
domain or in a similar context. (Anderson, 1995) states that “interference happens, when information gets mixed up
with, or pushed aside by, other information”. At the beginning, the degree of mastery of the original subject
influences the learning process (see Bransford et al., 2000). In particular, an adequate level of initial learning is
required. Then, learners can construct new understandings by tying up to previous experiences, which may not have
been activated yet. In this way, learners become capable of understanding conceptual changes, adopt knowledge
regarding their culture or everyday life, and even improve meta-cognitive abilities. Research findings have shown
that the higher the level of prior achievement within a domain or a context, the less instructional support is required
to accomplish a task (e.g. see Tobias & Ingber, 1976). Referring to (Tobias, 1994), prior knowledge strongly relates
to interest in the subject. Considering prior knowledge within online courses, the macro-adaptive instructional
approach described in (Park & Lee, 2004) deals with the necessity to determine learning objectives, define
dependencies between instructional units and assess the students’ competencies to grant access to restricted
instructions. These aspects are highly dependent on the learning content so that well-established e-learning standards
– such as the specifications of SCORM (see ADL, 2004) – fulfil these requirements.
2.2 Learner Characteristics
Drawing conclusions from the last subsection, a strong impact on learning is given by the individual
differences of learners as stated e.g. by (Cronbach, 1957). According to literature, each learner differs from each
other by means of the following aspects, so-called learner characteristics:
• First of all, each learner has a unique profile of intellectual capabilities, which can be characterised i.e. by
Gardner’s Multiple Intelligences (see Gardner, 1993) or various types of cognitive abilities described in
(Snow, 1986). Education deals with the theory of multiple intelligences in two ways: On the one side,
teachers devise curricula addressing different intellectual capabilities. On the other side, educators focus on
the development of specific intelligences, e.g. like intra- or interpersonal skills. Although it is rather
unmanageable to consider the learners’ intellectual abilities within the classroom or e-learning situation,
(Kelly & Tangney, 2003) applied Gardner’s theory within an intelligent tutoring system named EDUCE.
• Secondly, learning preferences usually result from predispositions or orientations to learning and can be
seen as influences by the context (see Jarvis & Woodrow, 2001). (Dunn et al., 1989) classify preferences by
four different areas: (a) environmental, (b) emotional, (c) sociological, and (d) physical. Preferences are
considered by many e-learning environments in various ways, e.g. by adapting the language or presentation
of the learning content, group models, etc. Exemplary systems can be found i.e. in (Brusilovsky, 1996).
• Thirdly, researchers in the field of teaching and learning introduced so-called cognitive and learning
styles which are somehow related to intellectual capabilities and preferences. Both kinds of styles try to
provide more practical models for teachers. Cognitive styles, such as field-dependence, reflectivity versus
impulsivity, haptic versus visual, and the forth, characterise modes of perceiving, remembering, thinking
the teacher promotes the learning process. (Bransford et al., 2000) state that “motivation affects the amount of time
that people are willing to devote to learning”. Yet, this willingness to learn is caused by different motives beginning
with the intention of achieving something over competing against colleagues or helping other people up to
emotional factors like anxiety. (Entwistle, 1981) classified three motivational orientation styles: (a) meaning-
oriented, (b) reproducing-oriented, and (b) achieving-oriented motives. Considering motivational aspects for e-
learning is mainly dependent on the learning content itself, e.g. by pointing out the relevance for an instruction or
including interactive elements such as games and simulations. Furthermore, it is advantageous to create competition
within a learner group and adapt to pre-knowledge in the subject domain to prevent the students from being
unchallenged. For instance, (Astleitner & Keller, 1995) describe a framework for adapting instruction to the
learner’s motivational state in computer-assisted instructional environments.
Thirdly, emotions have, similarly to motivation, a strong impact on the learning process. (Tobias, 1987)
points out findings on students’ performance depending on anxiety, in particular test anxiety, and proposes special
methods for dealing with such problems. On the other side, an emotion – no matter if a negative and positive one –
may influence learning due to its special nature. With respect to (Paulsen, 2005), “emotion is an unconscious
arousal system that alerts us to potential danger and opportunities”. Thus, addressing a learner’s emotional channel
can be seen as a key cognitive process for transferring data into the short- or even long-time memory. Within the e-
learning situation, the improvement of the learning process can be realised through emotions e.g. by storytelling,
provocations, emotional figures and animations, group works, enabling confidence in the learning content, etc.
Fourthly, knowledge transfer can be improved if learners can tie up to prior knowledge either in the same
domain or in a similar context. (Anderson, 1995) states that “interference happens, when information gets mixed up
with, or pushed aside by, other information”. At the beginning, the degree of mastery of the original subject
influences the learning process (see Bransford et al., 2000). In particular, an adequate level of initial learning is
required. Then, learners can construct new understandings by tying up to previous experiences, which may not have
been activated yet. In this way, learners become capable of understanding conceptual changes, adopt knowledge
regarding their culture or everyday life, and even improve meta-cognitive abilities. Research findings have shown
that the higher the level of prior achievement within a domain or a context, the less instructional support is required
to accomplish a task (e.g. see Tobias & Ingber, 1976). Referring to (Tobias, 1994), prior knowledge strongly relates
to interest in the subject. Considering prior knowledge within online courses, the macro-adaptive instructional
approach described in (Park & Lee, 2004) deals with the necessity to determine learning objectives, define
dependencies between instructional units and assess the students’ competencies to grant access to restricted
instructions. These aspects are highly dependent on the learning content so that well-established e-learning standards
– such as the specifications of SCORM (see ADL, 2004) – fulfil these requirements.
2.2 Learner Characteristics
Drawing conclusions from the last subsection, a strong impact on learning is given by the individual
differences of learners as stated e.g. by (Cronbach, 1957). According to literature, each learner differs from each
other by means of the following aspects, so-called learner characteristics:
• First of all, each learner has a unique profile of intellectual capabilities, which can be characterised i.e. by
Gardner’s Multiple Intelligences (see Gardner, 1993) or various types of cognitive abilities described in
(Snow, 1986). Education deals with the theory of multiple intelligences in two ways: On the one side,
teachers devise curricula addressing different intellectual capabilities. On the other side, educators focus on
the development of specific intelligences, e.g. like intra- or interpersonal skills. Although it is rather
unmanageable to consider the learners’ intellectual abilities within the classroom or e-learning situation,
(Kelly & Tangney, 2003) applied Gardner’s theory within an intelligent tutoring system named EDUCE.
• Secondly, learning preferences usually result from predispositions or orientations to learning and can be
seen as influences by the context (see Jarvis & Woodrow, 2001). (Dunn et al., 1989) classify preferences by
four different areas: (a) environmental, (b) emotional, (c) sociological, and (d) physical. Preferences are
considered by many e-learning environments in various ways, e.g. by adapting the language or presentation
of the learning content, group models, etc. Exemplary systems can be found i.e. in (Brusilovsky, 1996).
• Thirdly, researchers in the field of teaching and learning introduced so-called cognitive and learning
styles which are somehow related to intellectual capabilities and preferences. Both kinds of styles try to
provide more practical models for teachers. Cognitive styles, such as field-dependence, reflectivity versus
impulsivity, haptic versus visual, and the forth, characterise modes of perceiving, remembering, thinking
Page 3
and decision making. Learning styles like holist versus serialist, perception styles, concept formation
approaches, etc. try to describe the connection between instructional presentation and materials with a
student’s preferences and needs (see Schmeck, 1988). Overall, a lot of practical models like the WAVI
model by (Riding, 1991) – e.g. applied within the AdeLE project (see AdeLE, 2006) – or the learning styles
by (Kolb, 1984) – e.g. realised in the AHA! System (see Stash et al., 2004) – have been developed in the
last decades.
• Fourthly, (Mödritscher et al., 2004) highlight constitutional attributes and states of learners, which may
deal with physical properties of the body like disability, age, amblyopia, etc. as well as with short-term
states of students, such as tiredness, concentration, emotional and motivational states and the like. Both
directions are already well-examined, and various systems try to consider aspects of physical properties –
for instance disabilities as stated in (Sànchez & Flores, 2004) – or constitutional states of learners such as
the learner’s attention (see Ueno, 2004).
• Fifthly, self-efficacy and meta-cognition influence the learner’s achievement in the learning process (see
Bandura, 1982). Self-efficacy comprises a student’s evaluation of the ability to perform a given task
through different senses. Furthermore, meta-cognition stands for the awareness of the process of learning
and consists of two basic processes: (a) monitoring the learning process and (b) adapting the learning
strategy (see Winn & Snyder, 1996). According to (Park & Lee, 2004), meta-cognitive abilities examined
by various researchers within the area of aptitude-treatment interaction (ATI) are highly related to the
learners’ experiences and have an impact on different variables, such as the degree of feedback and
tutoring, the locus of control, personality attributes, and the forth. In particular, various systems in the
scope of adaptive hypermedia – e.g. by methods like adaptive navigation support (see Brusilovsky, 1996) –
focus on learner control.
• Sixthly, the background knowledge of a learner comprising language and computer skills as well as
experience on a related situation by means of a familiar context may also have an impact on learning. For
example, (Campbell et al., 2004) report that students from abroad may have problems with understanding
the language. (Felder & Henriques, 1995) examined learning styles within the scope of language education
and found out connections with learning styles. Thus, (Mödritscher et al., 2005) suggest providing
translations for problematic phrases to support the learning process. Anyway, various approaches in the
field of e-learning focus on experience of work in related areas, the user’s profession, experience of using
the platform (e.g. see Brusilovsky, 1996) as well as on foreign language students (e.g. see EPHRAS, 2005).
Further, (Akhras & Self, 2000) introduce the INCENSE system offering the ability of identifying and
analysing different learning situations and, if necessary, automatically switching among them.
• Finally, the last and most relevant characteristic of learners involves the user’s prior knowledge and
experience in the domain. (Vassileva, 1996) differentiates between experience and real knowledge about a
topic, where experience determines the user’s model of a knowledge space, the way of browsing through
and mastering tasks in a domain, etc. Similarly, (Bransford et al., 2000) state that people having developed
expertise in a particular area are able to think effectively about problems in this area and, therefore, differ
from novices. Both factors are relevant for the learning process as shown in the last section. Thus, most
theoretical models – e.g. the macro-adaptive instructional approach dealing with adaptation of the learning
process according to the student’s pre-knowledge and dependencies between instructional units (see Park &
Lee, 2004) – and systems – like AHA, ELM-ART, INTERBOOK, KBS, PT, and the forth (see Henze,
2000) – focus on the prior knowledge of a learner. Experience is considered within the field of various
research fields such as adaptive hypermedia, e.g. by methods like adaptive navigational support for
browsing through a hyperspace (see Brusilovsky, 1996).
Concluding these seven classes of learner characteristics, it can be said that the result of learning is highly
dependent of the learner itself. Therefore, teachers as well as e-learning content creators and online instructors have
to know very much about pedagogical aspects and provide a large set of methods to support different kind of
learners by means of the characteristics depicted above. Nevertheless, there are still more influences on learning as
shown in the upcoming subsection.
2.3 Further Influences on Learning
While the last subsection dealt with learner-dependent attributes affecting the learning process, some further
aspects – given by didactics or related with learner characteristics – may have an impact on learning. Therefore, the
following paragraphs examine various factors influencing learning.
approaches, etc. try to describe the connection between instructional presentation and materials with a
student’s preferences and needs (see Schmeck, 1988). Overall, a lot of practical models like the WAVI
model by (Riding, 1991) – e.g. applied within the AdeLE project (see AdeLE, 2006) – or the learning styles
by (Kolb, 1984) – e.g. realised in the AHA! System (see Stash et al., 2004) – have been developed in the
last decades.
• Fourthly, (Mödritscher et al., 2004) highlight constitutional attributes and states of learners, which may
deal with physical properties of the body like disability, age, amblyopia, etc. as well as with short-term
states of students, such as tiredness, concentration, emotional and motivational states and the like. Both
directions are already well-examined, and various systems try to consider aspects of physical properties –
for instance disabilities as stated in (Sànchez & Flores, 2004) – or constitutional states of learners such as
the learner’s attention (see Ueno, 2004).
• Fifthly, self-efficacy and meta-cognition influence the learner’s achievement in the learning process (see
Bandura, 1982). Self-efficacy comprises a student’s evaluation of the ability to perform a given task
through different senses. Furthermore, meta-cognition stands for the awareness of the process of learning
and consists of two basic processes: (a) monitoring the learning process and (b) adapting the learning
strategy (see Winn & Snyder, 1996). According to (Park & Lee, 2004), meta-cognitive abilities examined
by various researchers within the area of aptitude-treatment interaction (ATI) are highly related to the
learners’ experiences and have an impact on different variables, such as the degree of feedback and
tutoring, the locus of control, personality attributes, and the forth. In particular, various systems in the
scope of adaptive hypermedia – e.g. by methods like adaptive navigation support (see Brusilovsky, 1996) –
focus on learner control.
• Sixthly, the background knowledge of a learner comprising language and computer skills as well as
experience on a related situation by means of a familiar context may also have an impact on learning. For
example, (Campbell et al., 2004) report that students from abroad may have problems with understanding
the language. (Felder & Henriques, 1995) examined learning styles within the scope of language education
and found out connections with learning styles. Thus, (Mödritscher et al., 2005) suggest providing
translations for problematic phrases to support the learning process. Anyway, various approaches in the
field of e-learning focus on experience of work in related areas, the user’s profession, experience of using
the platform (e.g. see Brusilovsky, 1996) as well as on foreign language students (e.g. see EPHRAS, 2005).
Further, (Akhras & Self, 2000) introduce the INCENSE system offering the ability of identifying and
analysing different learning situations and, if necessary, automatically switching among them.
• Finally, the last and most relevant characteristic of learners involves the user’s prior knowledge and
experience in the domain. (Vassileva, 1996) differentiates between experience and real knowledge about a
topic, where experience determines the user’s model of a knowledge space, the way of browsing through
and mastering tasks in a domain, etc. Similarly, (Bransford et al., 2000) state that people having developed
expertise in a particular area are able to think effectively about problems in this area and, therefore, differ
from novices. Both factors are relevant for the learning process as shown in the last section. Thus, most
theoretical models – e.g. the macro-adaptive instructional approach dealing with adaptation of the learning
process according to the student’s pre-knowledge and dependencies between instructional units (see Park &
Lee, 2004) – and systems – like AHA, ELM-ART, INTERBOOK, KBS, PT, and the forth (see Henze,
2000) – focus on the prior knowledge of a learner. Experience is considered within the field of various
research fields such as adaptive hypermedia, e.g. by methods like adaptive navigational support for
browsing through a hyperspace (see Brusilovsky, 1996).
Concluding these seven classes of learner characteristics, it can be said that the result of learning is highly
dependent of the learner itself. Therefore, teachers as well as e-learning content creators and online instructors have
to know very much about pedagogical aspects and provide a large set of methods to support different kind of
learners by means of the characteristics depicted above. Nevertheless, there are still more influences on learning as
shown in the upcoming subsection.
2.3 Further Influences on Learning
While the last subsection dealt with learner-dependent attributes affecting the learning process, some further
aspects – given by didactics or related with learner characteristics – may have an impact on learning. Therefore, the
following paragraphs examine various factors influencing learning.
Page 4
Referring to (Krapp et al., 1992), interest in a certain domain depends on aspects of prior knowledge,
emotions, and motivation, all of them treated in the last two subsections. Thus, it can be assumed that interest results
from expertise enhancing the degree of the learner’s self-confidence as well as former positive experiences with the
subject and can be modelled on bases of learner characteristics by means of inferring a certain amount of interest
derived from these factors. As far as supported by the didactical strategy, e-learning may consider the factor interest
by providing adaptability, e.g. by allowing the learner to choose preferred learning content, implementing statistical
methods to determine instructions which are more interesting for the learner, or applying taxonomies and IR-based
strategies to adapt the learning process on basis of the factor interest. Examples of platforms comprise a wide range
of systems beginning with the ones focusing on motivation or prior knowledge, others dealing with self-organising
hypertext maps like the KnowledgeSea (see Brusilovsky & Rizzo, 2002) or environments supporting constructivistic
learning, e.g. the idea of navigating on basis of a course’s concepts as mentioned in (Mödritscher et al., 2005).
A didactical key issue is about people’s remembering and forgetting. While these processes are highly
related with intellectual capabilities, meta-cognition, prior knowledge and motivation of learners, a teacher might
antagonise the forgetting curve, e.g. the one introduced in (Ebbinghaus, 1964), by regularly repeating relevant
content. To define such key concepts within a course, teachers often use adequate learning objectives and choose
appropriate teaching strategies. Such issues are considered within various e-learning environments or even
specifications for learning content, e.g. by typical concepts of the macro-adaptive instructional approach examined
in (Park & Lee, 2004), by competency-driven strategies realised in the study introduced in this paper or even by the
possibilities of instructional sequencing within the specifications of SCORM (see ADL, 2004).
Another didactical aspect deals with the time to learn or the so-called time on-task. (Bransford et al., 2000)
point out the necessity to give the student enough time to reach an appropriate level of experience within the scope
of a certain domain, in particular for a complex subject matter. Nevertheless, learners are often faced with the
situation in which a teacher tries to cover too many topics too quickly, which hinders an effective knowledge
transfer for different reasons. On the other side, the time on-task should be limited, so that the learner is sufficiently
challenged and self-efficiency is able to increase. Thus, it is particularly important for e-learning to plan the time
allocated for learning and the time really spent on learning. As pointed out in (Dietinger, 2003), it is one of the
advantages of e-learning that learners can go through the course materials at their own pace, deadlines must be set
realistic in order to avoid frustration of the students. The possibility to define and manage deadlines for instructional
units is provided by the commonly known specifications for e-learning content and nearly each learning
management systems, even by open source solutions such as Moodle (see Moodle.org, 2006).
Depending on the given learning objectives, issues like feedback and tutoring might be of relevance for the
learning process. In particular, if a course aims at mediating high-level objectives, skills or a certain behaviour, it is
important for successful learning to give immediate feedback (e.g. see Thorndike, 1913). With respect to (Park &
Lee, 2004), various research areas, such as aptitude-treatment interaction or the micro-adaptive instructional
approach, deals with giving feedback and technical solutions like intelligent tutoring systems (ITS) or techniques for
natural language dialogues. Furthermore, (Mödritscher & Sindler, 2005) suggest to apply methods such as
simulations, games, automatically essay grading, quizzes created with professional authoring software, and the forth.
Finally, learning is also affected by the context in which knowledge transfer takes place. According to
(Bransford et al., 2000), learners might be able to learn in a certain context, but fail to learn in another one or to
transfer the experiences gained to other contexts. Contextualised knowledge is regarded only by few e-learning
environments – one of them is the INCENSE mentioned in the last subsection. As this issue is highly related with
constructivistic theory, new paradigms are of importance nowadays. One idea in this scope is the application of a
dynamic background library to support context-driven learning (see Mödritscher et al., 2005).
Concluding this section, it has to be pointed out that the issues depicted so far comprise just the most relevant
and learner-centred factors of the learning process. A full overview about the complexity of learning can be read
somewhere else, e.g. in (Bransford et al., 2000). Nevertheless, it can be stated that the most critical factor for
successful learning is the learner. The most important difference between the classroom situation and e-learning can
be outlined with the statement that a teacher can adapt the learning process much more effectively by holding a
lecture in the class, since communication in both directions – from the teacher to the learners and vice versa – is
faster and more effective. In contrary, it is much harder to evaluate a factor of the learning process or learner
characteristic and react to it via e-learning platform. Nevertheless, research mainstreams such as adaptive
instructional systems or adaptive e-learning (e.g. see Park & Lee, 2004) deal with aspects of adaptation in e-learning
environments to improve the learning process.
To examine pedagogical issues within the e-learning situation, the next two sections report about a case study
implementing different e-learning strategies and examine their impact on the learning process.
emotions, and motivation, all of them treated in the last two subsections. Thus, it can be assumed that interest results
from expertise enhancing the degree of the learner’s self-confidence as well as former positive experiences with the
subject and can be modelled on bases of learner characteristics by means of inferring a certain amount of interest
derived from these factors. As far as supported by the didactical strategy, e-learning may consider the factor interest
by providing adaptability, e.g. by allowing the learner to choose preferred learning content, implementing statistical
methods to determine instructions which are more interesting for the learner, or applying taxonomies and IR-based
strategies to adapt the learning process on basis of the factor interest. Examples of platforms comprise a wide range
of systems beginning with the ones focusing on motivation or prior knowledge, others dealing with self-organising
hypertext maps like the KnowledgeSea (see Brusilovsky & Rizzo, 2002) or environments supporting constructivistic
learning, e.g. the idea of navigating on basis of a course’s concepts as mentioned in (Mödritscher et al., 2005).
A didactical key issue is about people’s remembering and forgetting. While these processes are highly
related with intellectual capabilities, meta-cognition, prior knowledge and motivation of learners, a teacher might
antagonise the forgetting curve, e.g. the one introduced in (Ebbinghaus, 1964), by regularly repeating relevant
content. To define such key concepts within a course, teachers often use adequate learning objectives and choose
appropriate teaching strategies. Such issues are considered within various e-learning environments or even
specifications for learning content, e.g. by typical concepts of the macro-adaptive instructional approach examined
in (Park & Lee, 2004), by competency-driven strategies realised in the study introduced in this paper or even by the
possibilities of instructional sequencing within the specifications of SCORM (see ADL, 2004).
Another didactical aspect deals with the time to learn or the so-called time on-task. (Bransford et al., 2000)
point out the necessity to give the student enough time to reach an appropriate level of experience within the scope
of a certain domain, in particular for a complex subject matter. Nevertheless, learners are often faced with the
situation in which a teacher tries to cover too many topics too quickly, which hinders an effective knowledge
transfer for different reasons. On the other side, the time on-task should be limited, so that the learner is sufficiently
challenged and self-efficiency is able to increase. Thus, it is particularly important for e-learning to plan the time
allocated for learning and the time really spent on learning. As pointed out in (Dietinger, 2003), it is one of the
advantages of e-learning that learners can go through the course materials at their own pace, deadlines must be set
realistic in order to avoid frustration of the students. The possibility to define and manage deadlines for instructional
units is provided by the commonly known specifications for e-learning content and nearly each learning
management systems, even by open source solutions such as Moodle (see Moodle.org, 2006).
Depending on the given learning objectives, issues like feedback and tutoring might be of relevance for the
learning process. In particular, if a course aims at mediating high-level objectives, skills or a certain behaviour, it is
important for successful learning to give immediate feedback (e.g. see Thorndike, 1913). With respect to (Park &
Lee, 2004), various research areas, such as aptitude-treatment interaction or the micro-adaptive instructional
approach, deals with giving feedback and technical solutions like intelligent tutoring systems (ITS) or techniques for
natural language dialogues. Furthermore, (Mödritscher & Sindler, 2005) suggest to apply methods such as
simulations, games, automatically essay grading, quizzes created with professional authoring software, and the forth.
Finally, learning is also affected by the context in which knowledge transfer takes place. According to
(Bransford et al., 2000), learners might be able to learn in a certain context, but fail to learn in another one or to
transfer the experiences gained to other contexts. Contextualised knowledge is regarded only by few e-learning
environments – one of them is the INCENSE mentioned in the last subsection. As this issue is highly related with
constructivistic theory, new paradigms are of importance nowadays. One idea in this scope is the application of a
dynamic background library to support context-driven learning (see Mödritscher et al., 2005).
Concluding this section, it has to be pointed out that the issues depicted so far comprise just the most relevant
and learner-centred factors of the learning process. A full overview about the complexity of learning can be read
somewhere else, e.g. in (Bransford et al., 2000). Nevertheless, it can be stated that the most critical factor for
successful learning is the learner. The most important difference between the classroom situation and e-learning can
be outlined with the statement that a teacher can adapt the learning process much more effectively by holding a
lecture in the class, since communication in both directions – from the teacher to the learners and vice versa – is
faster and more effective. In contrary, it is much harder to evaluate a factor of the learning process or learner
characteristic and react to it via e-learning platform. Nevertheless, research mainstreams such as adaptive
instructional systems or adaptive e-learning (e.g. see Park & Lee, 2004) deal with aspects of adaptation in e-learning
environments to improve the learning process.
To examine pedagogical issues within the e-learning situation, the next two sections report about a case study
implementing different e-learning strategies and examine their impact on the learning process.
Page 5
3 Setup of the E-Learning Study
After identifying the need for realising e-learning phases within its educational concept at the Graz
University of Applied Sciences CAMPUS 02 (see Campus02, 2006), an internal project was initiated with the aim to
support lecturers to implement their e-learning strategy within the area of adult education. The study introduced in
this section is one of the project’s outcomes and describes an online course about the topic “document formats”. The
course was carried out over a period of two month and realised according to a full virtual concept using a
customised version of the e-learning platform Moodle.
Although the course can be considered as lecture on the basics of information technology, attempts were
made towards reaching the whole set of competencies and even some higher levels of objectives. Characterising the
e-learning course with respect to (Bloom, 1956), the learning objectives comprise five “Level I”, four “Level II”,
and two “Level III” objectives of the cognitive domain, two “Level III” objectives of the psychomotor domain as
well as one “Level III” objective of the affective domain. To realise different e-learning strategies, three courses
were implemented and each of the 38 students was assigned to one of these courses according to the achievement
levels of a prior topic-related lecture. Referring to (Mödritscher et al., 2006), the three courses can be characterised
in the following ways:
• Course A was planned with respect to Behaviourism, whereat the teacher portioned the learning objectives
and the material into three modules, each of the 14 students assigned by the teacher had to study each
module and finished with an online examination to measure the achievement levels. Furthermore, this
course included some playful activities, such as several attempts in the exam, an increasing difficulty level,
a task to gain a bonus, etc., to keep the learners motivated. The learning process was assessed by typical
behaviouristic elements like multiple-choice questions, assignment tasks or short answers. To examine the
high-level objectives of the psychomotor and affective domain, ITS methods were simulated by the teacher.
• Course B was implemented according to the ideas of Cognitivism. Therefore, the tasks could be
characterised by classical cognitive elements, for instance repeating learning content in different ways,
working out parts of the course within a group work or re-structuring the content. This course was divided
into two phases: Firstly, three groups consisting of four students each had to work out a part of the overall
objectives. In the second phase, the groups were reassembled to four groups with three members, while
each group had to restructure the results of the first phase using a WIKI environment. To motivate the
groups, the best work of the second phase was awarded with a bonus. To assess the learning process, the
results of each phase were graded by the teacher based on the quality and quantity of the students’ work
within the group. The WIKI environment allowed reproducing the student’s effort within the group.
• Course C comprises the idea of constructivistic learning and was realised by simply providing the four
groups consisting of three students with all the materials and the task to create a manuscript for mediating
all the learning objectives of the course. In the second phase, the three members of each group had to
compare the works of the other groups and evaluate them by distributing a certain amount of points and
reasoning this distribution. Again, the group with the best work received a bonus. The group works were
graded by the teacher on basis of the students’ peer reviews.
While the e-learning phase was in progress, students were instructed to document certain aspects, such as the
effort for learning, a self-assessment on reaching the objectives, the learning materials used, etc. Furthermore, an
unannounced and challenging examination as well as a post-questionnaire was carried out in the lecture held after
the e-learning experiment. Based on the whole amount of data retrieved from this e-learning experiment, the next
section examines the impact of the e-learning strategy on pedagogical issues.
4 Findings on pedagogical Aspects
In accordance with section 2, this section deals with two issues of evaluating the e-learning study. On the one
hand, selective learner characteristics are examined in two ways, for all participants and for the students of each
course. On the other hand, the factors relevant for the learning process are analysed for each e-learning strategy. For
both approaches, all data collected during and after the e-learning phase is exploited.
Page 6
4.1 Assessing the Learner Characteristics
The following findings on the learner characteristics are mainly deducted from the post-questionnaire,
where the students had to evaluate and rate certain statements. As the literature manifests that the students’ self-
assessment of learning behaviour is often wrong and psychological tests are more reliable, it has to be said that the
statements of this questionnaire are easy to understand and formulated in the way that the students hardly can infer
one of the learner characteristics. Furthermore, the questionnaire was performed right after the end of the e-learning
phase, so that significant differences between the courses might be found. After all, the results of the post-
questionnaire concerning typical learner characteristics are summarised in Table 1.
Self-assessment of Learner Characteristics Overall (x¯ /σ)
Course A
(x¯ /σ)
Course B
(x¯ /σ)
Course C
(x¯ /σ)
1. Extensive prior knowledge about topic 3.7/1.0 3.7/0.9 3.6/1.3 3.8/0.9
2. Interest in the course’s topic 3.1/1.2 2.8/1.3 3.2/0.9 3.3/1.2
3a. Preferring online learning to reading printouts 1.8/0.9 1.8/1.0 1.9/1.1 1.7/0.9
3b. Preferring online tests to written exams 2.9/1.2 2.8/1.4 2.5/1.2 3.3/1.1
3c. Preferring open-ended to closed questions 3.5/1.1 3.5/1.3 3.2/1.1 3.8/0.9
3d. Preferring learning within a group 3.1/1.1 2.8/1.1 3.5/1.0 3.1/1.0
3e. Preferring interactive elements 3.0/1.1 2.8/1.2 3.2/0.8 2.9/1.3
4a. Acquiring knowledge by typical cognitive
processes (structuring, summarising) 4.0/1.0 4.2/0.7 3.8/1.3 4.0/1.0
4b. Intensively studying complex content 4.2/0.9 4.5/0.7 4.1/1.0 4.0/1.1
4c. Need to practice new skills 3.9/0.9 4.2/0.9 3.9/0.6 3.7/1.0
4d. More wholist than analyser 3.5/1.2 3.7/1.3 4.1/0.9 2.8/1.2
4e. More imager than verbaliser 3.5/1.1 3.7/1.0 3.3/0.8 3.5/1.0
5a. Understanding background by research or
questions (related to Kolb’s “converger”) 2.9/1.0 3.3/1.0 2.7/0.9 2.6/1.1
5b. Meaning-oriented learning of important concepts
(related to Kolb’s “assimilator”) 4.4/0.7 4.5/0.8 4.6/0.5 4.2/0.7
5c. Tying up to own experiences (related to Kolb’s
“diverger”) 3.9/0.9 4.2/0.7 3.7/0.9 3.8/1.1
5d. Finding practical examples for theoretical content
(related to Kolb’s “accommodator”) 4.1/0.8 4.4/0.7 4.2/0.6 3.7/1.0
6. Easy to be motivated by game-based elements or
competitions 3.3/1.3 3.5/1.3 2.9/1.3 3.6/1.2
7. Preferring autonomous to teacher-driven learning 3.1/1.1 3.2/1.2 3.3/0.9 2.7/1.2
Table 1: Results of the Post-questionnaire concerning the Learner Characteristics (each statement rated with
a number between 1 and 5 comprising the range from “absolute disagreement” to “strong agreement”)
Considering prior knowledge (1) about the course’s topic, most students agreed with this statement and
meant to have good experiences and knowledge within the course’s domain. This can be reasoned due to the
students’ experiences from their jobs as well as to former lectures dealing with a few parts of the learning content.
The even distribution across the three courses may be a result of the students’ assignment to the courses described in
section 3. While the overall interest on the online course (2) is rather average, students of behaviourist e-learning
strategy seem to be less interested in the topic than the other students that mastered the course with group tasks.
The students’ preferences (3a-e) underlie some surprises. First of all, students through all courses strongly
dislike learning on the screen. Secondly, their attitudes towards preferring online tests to written exams differ from
disagreement in course B to a slight agreement in course C. Similarly, students of course C more strongly agree on
preferring open-ended to closed questions than students of course B. Furthermore, students of the behaviourist
approach are slightly negative about learning in a group, while the students of the other courses are positive about it.
Applying interactive elements seems to be neutral and evenly distributed amongst the students of all courses.
Summarising the findings on cognitive styles (4a-e), the students’ ratings for the statements were rather
high, which means they agreed about acquiring knowledge by typical processes or the need to practice new skills.
The self-assessment of the so-called WAVI-factors outlines that students consider themselves to be more wholist
The following findings on the learner characteristics are mainly deducted from the post-questionnaire,
where the students had to evaluate and rate certain statements. As the literature manifests that the students’ self-
assessment of learning behaviour is often wrong and psychological tests are more reliable, it has to be said that the
statements of this questionnaire are easy to understand and formulated in the way that the students hardly can infer
one of the learner characteristics. Furthermore, the questionnaire was performed right after the end of the e-learning
phase, so that significant differences between the courses might be found. After all, the results of the post-
questionnaire concerning typical learner characteristics are summarised in Table 1.
Self-assessment of Learner Characteristics Overall (x¯ /σ)
Course A
(x¯ /σ)
Course B
(x¯ /σ)
Course C
(x¯ /σ)
1. Extensive prior knowledge about topic 3.7/1.0 3.7/0.9 3.6/1.3 3.8/0.9
2. Interest in the course’s topic 3.1/1.2 2.8/1.3 3.2/0.9 3.3/1.2
3a. Preferring online learning to reading printouts 1.8/0.9 1.8/1.0 1.9/1.1 1.7/0.9
3b. Preferring online tests to written exams 2.9/1.2 2.8/1.4 2.5/1.2 3.3/1.1
3c. Preferring open-ended to closed questions 3.5/1.1 3.5/1.3 3.2/1.1 3.8/0.9
3d. Preferring learning within a group 3.1/1.1 2.8/1.1 3.5/1.0 3.1/1.0
3e. Preferring interactive elements 3.0/1.1 2.8/1.2 3.2/0.8 2.9/1.3
4a. Acquiring knowledge by typical cognitive
processes (structuring, summarising) 4.0/1.0 4.2/0.7 3.8/1.3 4.0/1.0
4b. Intensively studying complex content 4.2/0.9 4.5/0.7 4.1/1.0 4.0/1.1
4c. Need to practice new skills 3.9/0.9 4.2/0.9 3.9/0.6 3.7/1.0
4d. More wholist than analyser 3.5/1.2 3.7/1.3 4.1/0.9 2.8/1.2
4e. More imager than verbaliser 3.5/1.1 3.7/1.0 3.3/0.8 3.5/1.0
5a. Understanding background by research or
questions (related to Kolb’s “converger”) 2.9/1.0 3.3/1.0 2.7/0.9 2.6/1.1
5b. Meaning-oriented learning of important concepts
(related to Kolb’s “assimilator”) 4.4/0.7 4.5/0.8 4.6/0.5 4.2/0.7
5c. Tying up to own experiences (related to Kolb’s
“diverger”) 3.9/0.9 4.2/0.7 3.7/0.9 3.8/1.1
5d. Finding practical examples for theoretical content
(related to Kolb’s “accommodator”) 4.1/0.8 4.4/0.7 4.2/0.6 3.7/1.0
6. Easy to be motivated by game-based elements or
competitions 3.3/1.3 3.5/1.3 2.9/1.3 3.6/1.2
7. Preferring autonomous to teacher-driven learning 3.1/1.1 3.2/1.2 3.3/0.9 2.7/1.2
Table 1: Results of the Post-questionnaire concerning the Learner Characteristics (each statement rated with
a number between 1 and 5 comprising the range from “absolute disagreement” to “strong agreement”)
Considering prior knowledge (1) about the course’s topic, most students agreed with this statement and
meant to have good experiences and knowledge within the course’s domain. This can be reasoned due to the
students’ experiences from their jobs as well as to former lectures dealing with a few parts of the learning content.
The even distribution across the three courses may be a result of the students’ assignment to the courses described in
section 3. While the overall interest on the online course (2) is rather average, students of behaviourist e-learning
strategy seem to be less interested in the topic than the other students that mastered the course with group tasks.
The students’ preferences (3a-e) underlie some surprises. First of all, students through all courses strongly
dislike learning on the screen. Secondly, their attitudes towards preferring online tests to written exams differ from
disagreement in course B to a slight agreement in course C. Similarly, students of course C more strongly agree on
preferring open-ended to closed questions than students of course B. Furthermore, students of the behaviourist
approach are slightly negative about learning in a group, while the students of the other courses are positive about it.
Applying interactive elements seems to be neutral and evenly distributed amongst the students of all courses.
Summarising the findings on cognitive styles (4a-e), the students’ ratings for the statements were rather
high, which means they agreed about acquiring knowledge by typical processes or the need to practice new skills.
The self-assessment of the so-called WAVI-factors outlines that students consider themselves to be more wholist
Page 7
and visualiser. Particularly, the wholist factor shows strong differences between course A and B to course C.
Nevertheless (Phillips, 2005) states that learners are – for different reasons – not good judges of their style and that
even psychological tests are not fully reliable in assessing cognitive styles.
Referring to Kolb’s learning style inventory, four questions (5a-d) focussed on evaluating the students’
learning behaviour by means of being more active or reflective in the learning process as well as thinking in a more
abstract or pragmatic way. Overall, the students participating in the three courses see themselves as rather reflective
learners, while there is no clear tendency for abstract or pragmatic thinking. Analysing the groups of the three
courses, students of course A seem to rate their attitudes towards diverging and converging higher than the
participants of the other courses. In contrary, students in course C do not consider themselves being as good
accommodators as in the other courses. Generally, these results of the students’ self-assessment are unreliable due to
the reasons stated above for the ratings of the WAVI-factors.
Concluding the results of the post-questionnaire, both game-based elements (6) as well as autonomous
learning (7) are rated neutral by the overall class. Hence, students of course A and C are more convinced of the fact
that game-based elements improve their motivational states slightly. On the other side, self-directed learning is seen
much more positive in course A and B – the two courses mainly driven by the teacher – than in course C, which
implemented a constructivistic approach.
Finally, it has to be stated that background knowledge was considered by the teacher in three ways: Firstly,
the course was given in German, the students’ mother language. Secondly, the teacher introduced the students to the
Moodle platform in the lecture, before the e-learning phase was started. Thirdly, the students attended a technology-
focused study for two and a half year. Overall, the students should not have had problems with the course’s language
as well as with the usage of the system. Analysing the questions about the system’s usability and the quality of the
learning material, these two aspects were – excluding some problems about using the WIKI module in course B –
not mentioned at all by the participants. In contrary, students stated that the Moodle platform can easily and
intuitively be used.
4.2 Analysing the Factors of Learning
Considering the factors influencing the learning process (see subsections 2.1 and 2.3), two sources are of
relevance for the findings of this analysis: (a) the distribution of the students’ activities, and (b) the students’ ratings
of the statements of the post-questionnaire.
Characteristic Course A Course B Course C
Students’ self-assessment of average effort [in h] 12.2 9.4 7.6
Students’ self-assessment of mastering objectives 92.9% 46.8% 74.3%
Students’ self-assessment of the necessity for the lecturers notes 68.8% 31.3% 62.1%
Students’ self-assessment of using external material 14.3% 43.3% 29%
Students’ self-assessment of learning alone rather than in a group 96.9% 69.2% 98.8%
Results of the concluding exam 54.8% 37.4% 43.2%
Overall number of the students’ activities 2696 8037 3162
Table 2: Characteristics of the three Courses based on the Students’ ongoing Documentation about Learning
and raw Database Queries within the Moodle System
Analysing the students’ attention within the online courses, the distribution of the course activities and
certain characteristics is useful, but does not cover all findings concerning attention. Particularly, it is not possible to
infer something about the students’ behaviour while learning offline. Nevertheless, Table 2 (the courses’
characteristics) as well as Figure 1 (overall number of students’ activities) document that students of course B had to
work much more with the Moodle system than the participants of the other courses. Interpreting the results of the
concluding examination in this context, the high degree of learning within the Moodle platform does not really
imply a high degree of attention, because students of course B performed worse than others. Further, several
students of this course stated that the WIKI module lacks of good usability and they had to concentrate more on the
tool than on the learning content. Thus, good usability of a learning management system is an important factor for a
high degree of the students’ attention and adequate results in learning.
Emotional states of the students can be summaries by three observations: Firstly, the instruction to upload
a photo of each student as profile avatar causes a lot of activities in the discussion groups of all three courses (see
Nevertheless (Phillips, 2005) states that learners are – for different reasons – not good judges of their style and that
even psychological tests are not fully reliable in assessing cognitive styles.
Referring to Kolb’s learning style inventory, four questions (5a-d) focussed on evaluating the students’
learning behaviour by means of being more active or reflective in the learning process as well as thinking in a more
abstract or pragmatic way. Overall, the students participating in the three courses see themselves as rather reflective
learners, while there is no clear tendency for abstract or pragmatic thinking. Analysing the groups of the three
courses, students of course A seem to rate their attitudes towards diverging and converging higher than the
participants of the other courses. In contrary, students in course C do not consider themselves being as good
accommodators as in the other courses. Generally, these results of the students’ self-assessment are unreliable due to
the reasons stated above for the ratings of the WAVI-factors.
Concluding the results of the post-questionnaire, both game-based elements (6) as well as autonomous
learning (7) are rated neutral by the overall class. Hence, students of course A and C are more convinced of the fact
that game-based elements improve their motivational states slightly. On the other side, self-directed learning is seen
much more positive in course A and B – the two courses mainly driven by the teacher – than in course C, which
implemented a constructivistic approach.
Finally, it has to be stated that background knowledge was considered by the teacher in three ways: Firstly,
the course was given in German, the students’ mother language. Secondly, the teacher introduced the students to the
Moodle platform in the lecture, before the e-learning phase was started. Thirdly, the students attended a technology-
focused study for two and a half year. Overall, the students should not have had problems with the course’s language
as well as with the usage of the system. Analysing the questions about the system’s usability and the quality of the
learning material, these two aspects were – excluding some problems about using the WIKI module in course B –
not mentioned at all by the participants. In contrary, students stated that the Moodle platform can easily and
intuitively be used.
4.2 Analysing the Factors of Learning
Considering the factors influencing the learning process (see subsections 2.1 and 2.3), two sources are of
relevance for the findings of this analysis: (a) the distribution of the students’ activities, and (b) the students’ ratings
of the statements of the post-questionnaire.
Characteristic Course A Course B Course C
Students’ self-assessment of average effort [in h] 12.2 9.4 7.6
Students’ self-assessment of mastering objectives 92.9% 46.8% 74.3%
Students’ self-assessment of the necessity for the lecturers notes 68.8% 31.3% 62.1%
Students’ self-assessment of using external material 14.3% 43.3% 29%
Students’ self-assessment of learning alone rather than in a group 96.9% 69.2% 98.8%
Results of the concluding exam 54.8% 37.4% 43.2%
Overall number of the students’ activities 2696 8037 3162
Table 2: Characteristics of the three Courses based on the Students’ ongoing Documentation about Learning
and raw Database Queries within the Moodle System
Analysing the students’ attention within the online courses, the distribution of the course activities and
certain characteristics is useful, but does not cover all findings concerning attention. Particularly, it is not possible to
infer something about the students’ behaviour while learning offline. Nevertheless, Table 2 (the courses’
characteristics) as well as Figure 1 (overall number of students’ activities) document that students of course B had to
work much more with the Moodle system than the participants of the other courses. Interpreting the results of the
concluding examination in this context, the high degree of learning within the Moodle platform does not really
imply a high degree of attention, because students of course B performed worse than others. Further, several
students of this course stated that the WIKI module lacks of good usability and they had to concentrate more on the
tool than on the learning content. Thus, good usability of a learning management system is an important factor for a
high degree of the students’ attention and adequate results in learning.
Emotional states of the students can be summaries by three observations: Firstly, the instruction to upload
a photo of each student as profile avatar causes a lot of activities in the discussion groups of all three courses (see
Page 8
lines in Figure 1 around the 5th of April). Students considered this to hurt their privacy; some started emotionally
driven discussion threads, while others refused to upload a valid photo. Secondly, the evaluation of course B was
worse than the one of the other courses due to the usability problems and the higher effort resulting already depicted.
Thus, several rather emotional comments such as “I hate e-learning”, “The task is senseless”, etc. were written down
in the post-questionnaire, while students in the other two courses were not that offensive and negative. Further, the
higher effort compared with the other courses was criticised (see Table 2). Thirdly, the curiosity about the learning
content was rather neutral and evenly distributed over the three courses (see statement 1 in Table 3).
0
100
200
300
400
500
600
700
800
900
01
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05
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05
31
.05
.20
05
03
.06
.20
05
Periode of E-Learning Phase
N
u
m
b
er
o
f A
ct
iv
iti
es
Figure 1: Distribution of the students’ activities for the courses A (green), B (yellow), and C (blue)
The students’ motivation for learning is examined by the following three findings: Firstly, in course A the
deadlines (see the green line in Figure 1 around the 21st April, the 19th May, and the 2nd June) are more obvious than
in the courses implementing group works (see the yellow line around the 6th May and the 2nd June as well as the blue
line around the 13th May and the 2nd June). Due to the increasing activities ahead of the deadlines, it seems that a
behaviouristic approach enforces students to accomplish the examination right before the module ends. Secondly,
each course included some motivational elements as stated in section 3 as well as some statements about the
necessity to reach the objectives. Yet, students of the courses A and C meant to master the objectives better than the
participants of course B, which highly correlates with the results of the concluding exam (see Table 2). Thirdly, the
motivation to complete the tasks was, according to the questionnaire’s statement 2 (see Table 3), average in course
A and slightly negative in the courses B and C.
Self-assessment of Learner Characteristics Overall (x¯ /σ)
Course A
(x¯ /σ)
Course B
(x¯ /σ)
Course C
(x¯ /σ)
1. Curiosity about learning content 3.1/1.1 3.2/1.3 3.0/1.2 3.2/1.0
2. Strong motivation to accomplish the tasks 2.6/1.0 2.9/1.0 2.5/1.0 2.4/1.1
Table 3: Results of the Post-questionnaire concerning the Factors relevant for Learning (each statement rated
with a number between 1 and 5 comprising the range from “absolute disagreement” to “strong agreement”)
Aspects of prior knowledge were already examined in the last subsection. Moreover, the teacher considered
the tying up to prior knowledge by giving practical and well-known examples for theoretical learning content as
driven discussion threads, while others refused to upload a valid photo. Secondly, the evaluation of course B was
worse than the one of the other courses due to the usability problems and the higher effort resulting already depicted.
Thus, several rather emotional comments such as “I hate e-learning”, “The task is senseless”, etc. were written down
in the post-questionnaire, while students in the other two courses were not that offensive and negative. Further, the
higher effort compared with the other courses was criticised (see Table 2). Thirdly, the curiosity about the learning
content was rather neutral and evenly distributed over the three courses (see statement 1 in Table 3).
0
100
200
300
400
500
600
700
800
900
01
.04
.20
05
04
.04
.20
05
07
.04
.20
05
10
.04
.20
05
13
.04
.20
05
16
.04
.20
05
19
.04
.20
05
22
.04
.20
05
25
.04
.20
05
28
.04
.20
05
01
.05
.20
05
04
.05
.20
05
07
.05
.20
05
10
.05
.20
05
13
.05
.20
05
16
.05
.20
05
19
.05
.20
05
22
.05
.20
05
25
.05
.20
05
28
.05
.20
05
31
.05
.20
05
03
.06
.20
05
Periode of E-Learning Phase
N
u
m
b
er
o
f A
ct
iv
iti
es
Figure 1: Distribution of the students’ activities for the courses A (green), B (yellow), and C (blue)
The students’ motivation for learning is examined by the following three findings: Firstly, in course A the
deadlines (see the green line in Figure 1 around the 21st April, the 19th May, and the 2nd June) are more obvious than
in the courses implementing group works (see the yellow line around the 6th May and the 2nd June as well as the blue
line around the 13th May and the 2nd June). Due to the increasing activities ahead of the deadlines, it seems that a
behaviouristic approach enforces students to accomplish the examination right before the module ends. Secondly,
each course included some motivational elements as stated in section 3 as well as some statements about the
necessity to reach the objectives. Yet, students of the courses A and C meant to master the objectives better than the
participants of course B, which highly correlates with the results of the concluding exam (see Table 2). Thirdly, the
motivation to complete the tasks was, according to the questionnaire’s statement 2 (see Table 3), average in course
A and slightly negative in the courses B and C.
Self-assessment of Learner Characteristics Overall (x¯ /σ)
Course A
(x¯ /σ)
Course B
(x¯ /σ)
Course C
(x¯ /σ)
1. Curiosity about learning content 3.1/1.1 3.2/1.3 3.0/1.2 3.2/1.0
2. Strong motivation to accomplish the tasks 2.6/1.0 2.9/1.0 2.5/1.0 2.4/1.1
Table 3: Results of the Post-questionnaire concerning the Factors relevant for Learning (each statement rated
with a number between 1 and 5 comprising the range from “absolute disagreement” to “strong agreement”)
Aspects of prior knowledge were already examined in the last subsection. Moreover, the teacher considered
the tying up to prior knowledge by giving practical and well-known examples for theoretical learning content as
Page 9
well as cross-references to other lecturers. According to cognitivism, issues concerning remembering and
forgetting were realised only in course B. Yet, these considerations seemed to have failed due to the bad learning
results of this course. Finally, feedback and tutoring for each course differed depending on the three learning
theories: While it was necessary to actively stimulate the participants’ learning in course A, students of course B
required immediate feedback about the completed tasks. In course C, the teacher had to suppress any comment on
the works submitted. Overall, the three courses as well as the learning outcomes differed in several aspects of the
learning process as shown in this section.
5 Conclusions and Recommendations
To conclude this paper, I have to outline that learning is a very complex process depending of various
factors and influences with each of them concerning especially the learners themselves. Thus, an instruction
implementing online courses has to consider two relevant aspects: On the one side, the target group of a course
needs to be analysed by means of the learner characteristics. If required, the teacher might also apply some
classroom assessment methods to assess certain information about the learner within the learning process. On the
other side, it is necessary to adapt the learning process towards the findings about the learners. Yet, it is not worth
evaluating all characteristics, whether the effort to assess one is too high or the relevance or applicability to serve as
a basis for adapting of the learning process is missing.
Summarising the study, it was shown that each e-learning strategy following one of the commonly known
learning theories – the Behaviourism, the Cognitivism, and the Constructivism – is realisable for an online course
within the scope of adult education. Further, each didactical strategy has a more or less strong impact on the factors
influencing the learning process and the self-assessing of learner characteristics. For applying one of the e-learning
strategies, it is recommended to analyse certain learner characteristics, particularly prior knowledge and motivation,
to prevent the learners from failing to finish the course and to assess various characteristics to optimise the learning
process. Nevertheless, there are also some didactical aspects to be considered for realising an e-learning strategy.
Issues such as the impact of the learning objectives determined or the assessment strategy implemented have to be
examined as part of my future work.
Acknowledgements
I have to thank my employer, the Institute for Information Systems and Computer Media (IICM) at Graz
University of Technology, for arousing my interest in hypermedia and e-learning as well as the Austrian ministries
BMVIT and BMBWK for founding my research work through the FHplus impulse programme. Furthermore, I have
to acknowledge the support of the Graz University of Applied Sciences Campus02 for offering me competencies
and the playground for this case study as well as the 38 students that had to participate in this experiment.
References
AdeLE (2006). Research project AdeLE. http://adele.fh-joanneum.at (2006-03-12).
ADL (2004). Sharable Content Object Reference Model (SCORM) 2004 2nd Edition. Advanced Distributed
Learning. http://www.adlnet.org/downloads/70.cfm (2005-08-04).
Akhras, F.N., & Self, J.A. (2000). System intelligence in constructivist learning. International Journal of Artificial
Intelligence in Education, 11, 344–376.
Anderson, J.R. (1995). Learning and memory: An integrated approach. New York: Wiley.
Astleitner, H., & Keller, J.M. (1995). A model for motivationally adaptive computer-assisted instruction. Journal of
Research on Computing in Education, 27 (3), 270–280.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.
Bloom, B.S. (1956). Taxonomy of Education Objectives, Book 1: Cognitive Domain. New York: Longman.
forgetting were realised only in course B. Yet, these considerations seemed to have failed due to the bad learning
results of this course. Finally, feedback and tutoring for each course differed depending on the three learning
theories: While it was necessary to actively stimulate the participants’ learning in course A, students of course B
required immediate feedback about the completed tasks. In course C, the teacher had to suppress any comment on
the works submitted. Overall, the three courses as well as the learning outcomes differed in several aspects of the
learning process as shown in this section.
5 Conclusions and Recommendations
To conclude this paper, I have to outline that learning is a very complex process depending of various
factors and influences with each of them concerning especially the learners themselves. Thus, an instruction
implementing online courses has to consider two relevant aspects: On the one side, the target group of a course
needs to be analysed by means of the learner characteristics. If required, the teacher might also apply some
classroom assessment methods to assess certain information about the learner within the learning process. On the
other side, it is necessary to adapt the learning process towards the findings about the learners. Yet, it is not worth
evaluating all characteristics, whether the effort to assess one is too high or the relevance or applicability to serve as
a basis for adapting of the learning process is missing.
Summarising the study, it was shown that each e-learning strategy following one of the commonly known
learning theories – the Behaviourism, the Cognitivism, and the Constructivism – is realisable for an online course
within the scope of adult education. Further, each didactical strategy has a more or less strong impact on the factors
influencing the learning process and the self-assessing of learner characteristics. For applying one of the e-learning
strategies, it is recommended to analyse certain learner characteristics, particularly prior knowledge and motivation,
to prevent the learners from failing to finish the course and to assess various characteristics to optimise the learning
process. Nevertheless, there are also some didactical aspects to be considered for realising an e-learning strategy.
Issues such as the impact of the learning objectives determined or the assessment strategy implemented have to be
examined as part of my future work.
Acknowledgements
I have to thank my employer, the Institute for Information Systems and Computer Media (IICM) at Graz
University of Technology, for arousing my interest in hypermedia and e-learning as well as the Austrian ministries
BMVIT and BMBWK for founding my research work through the FHplus impulse programme. Furthermore, I have
to acknowledge the support of the Graz University of Applied Sciences Campus02 for offering me competencies
and the playground for this case study as well as the 38 students that had to participate in this experiment.
References
AdeLE (2006). Research project AdeLE. http://adele.fh-joanneum.at (2006-03-12).
ADL (2004). Sharable Content Object Reference Model (SCORM) 2004 2nd Edition. Advanced Distributed
Learning. http://www.adlnet.org/downloads/70.cfm (2005-08-04).
Akhras, F.N., & Self, J.A. (2000). System intelligence in constructivist learning. International Journal of Artificial
Intelligence in Education, 11, 344–376.
Anderson, J.R. (1995). Learning and memory: An integrated approach. New York: Wiley.
Astleitner, H., & Keller, J.M. (1995). A model for motivationally adaptive computer-assisted instruction. Journal of
Research on Computing in Education, 27 (3), 270–280.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.
Bloom, B.S. (1956). Taxonomy of Education Objectives, Book 1: Cognitive Domain. New York: Longman.
Page 10
Boyd, R., & Apps, J. (1980). Redefining the discipline of adult education. San Francisco, CA: Jossey-Bass.
Bransford, J.D., Brown, A.L. & Cocking, R.R. (2000). How people learn (expanded edition): Brain, mind,
experience, and school. Washington DC: National Academy Press.
Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User Adapted
Interaction, 6 (2-3), 87–129.
Brusilovsky, P. & Rizzo, R. (2002). Map-based horizontal navigation in educational hypertext. Proceedings of
Hypertext 2002, 1–10.
Campbell, M., Goold, A., and Goward, P. (2004). Using Online Technologies: Does Culture Matter? Proceedings of
E-Learn 2004. Chesapeake, VA: AACE, 2300–2307.
Campus02 (2006). Graz University of Applied Sciences CAMPUS 02. http://www.campus02.at (2006-03-01).
Cronbach, L.J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684.
Dunn, R.S., Dunn, K.J., & Price, G.E. (1989). Learning styles inventory. Lawrence, KS: Price Systems.
Ebbinghaus, H. (1964). Memory: A contribution to experimental psychology. New York: Dover.
Entwistle, N. (1981). Styles of learning and teaching. Chichester: Wiley.
EPHRAS (2005). Research project EPHRAS. http://www.ephras.org/ (2005-12-23).
Felder, R.M. & Henriques, E.R. (1995). Learning and teaching styles in foreign and second language education.
Foreign Language Annals, 28 (1), 21–31.
Fleming, M., & Levie, W.H. (1993). Instructional message design: Principles from the behavioral and cognitive
sciences (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications.
Gardner, H. (1993). Multiple intelligences: The theory in practice. New York: Basic Books.
Henze, N. (2000). Adaptive Hyperbooks: Adaptation for Project-based Learning Resources. Dissertation, University
of Hannover, 2000.
Jain, L.C., Howlett, R.J., Ischalkaranje, N.S., & Tonfoni, G. (2002). Virtual environments for teaching & learning.
Series of Innovative Intelligence, 1, preface.
Jarvis, J., & Woodrow, D. (2001). Learning preferences in relation to subjects of study of students in higher
education. Proceedings of the British Educational Research Association Annual Conference, 2001.
Kelly, D., & Tangney, B. (2003). A framework for using multiple intelligences in an ITS. Proceedings of ED-
MEDIA 2003. Chesapeake, VA: AACE, 2423–2430.
Knowls, M.S., Holton, E.F., & Swanson, R.A. (1998). The adult learner: The definitive classic in adult education
and human resource development (5th ed.). Houston: Butterworth-Heinemann.
Kolb, D.A. (1984). Experiential learning: Experience as a source of learning and development. Englewood Cliffs,
NJ: Prentice Hall.
Krapp, A., Hidi, S., & Renninger, K.A. (1992). Interest, learning, and development. In K.A. Renninger, S. Hidi, &
A. Krapp (Eds.), The Role of Interest in Learning and Development. Hillsdale, NJ: Lawrence Erlbaum, 3–26.
Bransford, J.D., Brown, A.L. & Cocking, R.R. (2000). How people learn (expanded edition): Brain, mind,
experience, and school. Washington DC: National Academy Press.
Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User Adapted
Interaction, 6 (2-3), 87–129.
Brusilovsky, P. & Rizzo, R. (2002). Map-based horizontal navigation in educational hypertext. Proceedings of
Hypertext 2002, 1–10.
Campbell, M., Goold, A., and Goward, P. (2004). Using Online Technologies: Does Culture Matter? Proceedings of
E-Learn 2004. Chesapeake, VA: AACE, 2300–2307.
Campus02 (2006). Graz University of Applied Sciences CAMPUS 02. http://www.campus02.at (2006-03-01).
Cronbach, L.J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684.
Dunn, R.S., Dunn, K.J., & Price, G.E. (1989). Learning styles inventory. Lawrence, KS: Price Systems.
Ebbinghaus, H. (1964). Memory: A contribution to experimental psychology. New York: Dover.
Entwistle, N. (1981). Styles of learning and teaching. Chichester: Wiley.
EPHRAS (2005). Research project EPHRAS. http://www.ephras.org/ (2005-12-23).
Felder, R.M. & Henriques, E.R. (1995). Learning and teaching styles in foreign and second language education.
Foreign Language Annals, 28 (1), 21–31.
Fleming, M., & Levie, W.H. (1993). Instructional message design: Principles from the behavioral and cognitive
sciences (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications.
Gardner, H. (1993). Multiple intelligences: The theory in practice. New York: Basic Books.
Henze, N. (2000). Adaptive Hyperbooks: Adaptation for Project-based Learning Resources. Dissertation, University
of Hannover, 2000.
Jain, L.C., Howlett, R.J., Ischalkaranje, N.S., & Tonfoni, G. (2002). Virtual environments for teaching & learning.
Series of Innovative Intelligence, 1, preface.
Jarvis, J., & Woodrow, D. (2001). Learning preferences in relation to subjects of study of students in higher
education. Proceedings of the British Educational Research Association Annual Conference, 2001.
Kelly, D., & Tangney, B. (2003). A framework for using multiple intelligences in an ITS. Proceedings of ED-
MEDIA 2003. Chesapeake, VA: AACE, 2423–2430.
Knowls, M.S., Holton, E.F., & Swanson, R.A. (1998). The adult learner: The definitive classic in adult education
and human resource development (5th ed.). Houston: Butterworth-Heinemann.
Kolb, D.A. (1984). Experiential learning: Experience as a source of learning and development. Englewood Cliffs,
NJ: Prentice Hall.
Krapp, A., Hidi, S., & Renninger, K.A. (1992). Interest, learning, and development. In K.A. Renninger, S. Hidi, &
A. Krapp (Eds.), The Role of Interest in Learning and Development. Hillsdale, NJ: Lawrence Erlbaum, 3–26.
Page 11
Mödritscher, F., García-Barrios, V.M., and Maurer, H. (2005). The use of a dynamic background library within the
scope of adaptive e-learning. Proceedings E-Learn 2005. Chesapeake, VA: AACE, 3045–3052.
Mödritscher, F., Gütl, C., García-Barrios, V.M., & Maurer, H. (2004). Why do standards in the field of e-learning
not fully support learner-centred aspects of adaptivity? Proceedings of ED-MEDIA 2004. Chesapeake, VA:
AACE, 2034–2039.
Mödritscher, F., & Sindler, A. (2005). Quizzes are not enough to reach high-level learning objectives. Proceedings
of ED-MEDIA 2005. Chesapeake, VA: AACE, 3275–3278.
Mödritscher, F., Spiel, S., & García-Barrios, V.M. (2006). Assessment in E-Learning Environments: A Comparison
of three Methods. Proceedings of SITE 2006, Chesapeake, VA: AACE, 108-113.
Park, O., & Lee, J. (2004). Adaptive Instructional Systems. In D.H. Jonassen (Ed.), Handbook of research on
educational communications and technology (2nd ed.). Mahwah, NJ: Lawrence Erlbaum, 651–684.
Phillips, M. (2005). Cognitive Style’s influence on Media Preference: Does it matter or do they know? Proceedings
of ED-MEDIA 2005. Chesapeake, VA: AACE, 1023–1028.
Paulsen, T. (2005). Emotion, memory, and stories. In B. Hoffman (Ed.), Encyclopedia of Educational Technology.
http://coe.sdsu.edu/eet/articles/storyemotions/index.htm (2006-03-05).
Riding, R. (1991). Cognitive style analysis. Birmingham: Learning and Training Technology.
Sánchez J., & Flores, H. (2004). AudioMath: Blind children learning mathematics through audio. Proceedings of
ICDVRAT 2004, Oxford, United Kingdom, 183–189.
Stash, N., Cristea, A., & De Bra, P. (2004). Authoring of learning styles in adaptive hypermedia: Problems and
solutions. Proceedings of WWW 2004, 114–123.
Thorndike, E.L. (1913). Educational Psychology (Vol. 2). New York: Columbia University Press.
Tobias, S. (1987). Learner characteristics. In R.M. Gagné, (Ed.), Instructional Technology: Foundations. Hillsdale,
NJ: Lawrence Erlbaum.
Tobias, S. (1994). Interest, prior knowledge, and learning. Review of Educational Research, 64 (1), 37–54.
Tobias, S., & Ingber, T (1976). Achievement-treatment interactions in programmed instruction. Journal of
Educational Psychology, 68, 43–47.
Ueno, M. (2004). Animated agent to maintain learner’s attention in e-learning. Proceedings of E-Learn 2004.
Chesapeake, VA: AACE, 194-201.
Vassileva, J. (1996). A task-centered approach for user modeling in a hypermedia office documentation system.
User Modeling and User-Adapted Interaction, 6 (2-3), 185–224.
Winn, W., & Snyder, D. (1996). Cognitive perspectives in psychology. In D.H. Jonassen (Ed.), Handbook of
research for educational communications and technology. New York: Macmillan, 115–122.
scope of adaptive e-learning. Proceedings E-Learn 2005. Chesapeake, VA: AACE, 3045–3052.
Mödritscher, F., Gütl, C., García-Barrios, V.M., & Maurer, H. (2004). Why do standards in the field of e-learning
not fully support learner-centred aspects of adaptivity? Proceedings of ED-MEDIA 2004. Chesapeake, VA:
AACE, 2034–2039.
Mödritscher, F., & Sindler, A. (2005). Quizzes are not enough to reach high-level learning objectives. Proceedings
of ED-MEDIA 2005. Chesapeake, VA: AACE, 3275–3278.
Mödritscher, F., Spiel, S., & García-Barrios, V.M. (2006). Assessment in E-Learning Environments: A Comparison
of three Methods. Proceedings of SITE 2006, Chesapeake, VA: AACE, 108-113.
Park, O., & Lee, J. (2004). Adaptive Instructional Systems. In D.H. Jonassen (Ed.), Handbook of research on
educational communications and technology (2nd ed.). Mahwah, NJ: Lawrence Erlbaum, 651–684.
Phillips, M. (2005). Cognitive Style’s influence on Media Preference: Does it matter or do they know? Proceedings
of ED-MEDIA 2005. Chesapeake, VA: AACE, 1023–1028.
Paulsen, T. (2005). Emotion, memory, and stories. In B. Hoffman (Ed.), Encyclopedia of Educational Technology.
http://coe.sdsu.edu/eet/articles/storyemotions/index.htm (2006-03-05).
Riding, R. (1991). Cognitive style analysis. Birmingham: Learning and Training Technology.
Sánchez J., & Flores, H. (2004). AudioMath: Blind children learning mathematics through audio. Proceedings of
ICDVRAT 2004, Oxford, United Kingdom, 183–189.
Stash, N., Cristea, A., & De Bra, P. (2004). Authoring of learning styles in adaptive hypermedia: Problems and
solutions. Proceedings of WWW 2004, 114–123.
Thorndike, E.L. (1913). Educational Psychology (Vol. 2). New York: Columbia University Press.
Tobias, S. (1987). Learner characteristics. In R.M. Gagné, (Ed.), Instructional Technology: Foundations. Hillsdale,
NJ: Lawrence Erlbaum.
Tobias, S. (1994). Interest, prior knowledge, and learning. Review of Educational Research, 64 (1), 37–54.
Tobias, S., & Ingber, T (1976). Achievement-treatment interactions in programmed instruction. Journal of
Educational Psychology, 68, 43–47.
Ueno, M. (2004). Animated agent to maintain learner’s attention in e-learning. Proceedings of E-Learn 2004.
Chesapeake, VA: AACE, 194-201.
Vassileva, J. (1996). A task-centered approach for user modeling in a hypermedia office documentation system.
User Modeling and User-Adapted Interaction, 6 (2-3), 185–224.
Winn, W., & Snyder, D. (1996). Cognitive perspectives in psychology. In D.H. Jonassen (Ed.), Handbook of
research for educational communications and technology. New York: Macmillan, 115–122.
Page 12
About the Author
Felix Mödritscher is researcher, software engineer, and project manager at the Institute
for Information Systems and Computer Media (IICM) at the Graz University of
Technology. Further, he is lecturer on the courses information design and knowledge
management at the Graz University of Applied Sciences Degree Program in IT and IT-
Marketing (CAMPUS 02).
He has a M.Sc. (Dipl.-Ing.) in Computer Technics (Telematics). His research interests
focus on pedagogical, didactical, and technological issues of e-learning as well as
knowledge management.
Contact address:
Felix Mödritscher
Institute for Information Systems and Computer Media (IICM)
Inffeldgasse 16c
8010 Graz
Austria
Email: fmoedrit@iicm.edu
Phone: +43 316 873 5622
Felix Mödritscher is researcher, software engineer, and project manager at the Institute
for Information Systems and Computer Media (IICM) at the Graz University of
Technology. Further, he is lecturer on the courses information design and knowledge
management at the Graz University of Applied Sciences Degree Program in IT and IT-
Marketing (CAMPUS 02).
He has a M.Sc. (Dipl.-Ing.) in Computer Technics (Telematics). His research interests
focus on pedagogical, didactical, and technological issues of e-learning as well as
knowledge management.
Contact address:
Felix Mödritscher
Institute for Information Systems and Computer Media (IICM)
Inffeldgasse 16c
8010 Graz
Austria
Email: fmoedrit@iicm.edu
Phone: +43 316 873 5622
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