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Abstract

Purpose The literature includes several studies that define different critical success factors (CSF) which have to be considered to support the implementation of technology-enhanced learning (TEL) approaches. An analysis of such studies revealed that (1) regional differences seem to determine the CSF for TEL approaches, (2) certain CSF are relevant for TEL approaches in general, and (3) professionals in higher education determine which influential factors they consider when implementing TEL approaches. Thus, the question arises: in general, which influential factors do professionals in Austrian and German institutions of higher education actually consider when implementing TEL approaches? Design/methodology/approach The study is a quantitative research approach based on survey data. Findings The results show that certain influential factors seem to be generally important, such as the factors of respecting learning success or motivation. However, the outcome of the study also indicated that different moderating variables like experiences and personal relevance affect the professionals’ choices. Originality/value The originality and value are in the approach to identify generally important influential factors for the implementation of TEL approaches in Austrian and German institutions of higher education.
Journal of Research in Innovative Teaching & Learning
Influential factors for technology-enhanced learning: professionals’ views
Patrick Schweighofer, Doris Weitlaner, Martin Ebner, Hannes Rothe,
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To cite this document:
Patrick Schweighofer, Doris Weitlaner, Martin Ebner, Hannes Rothe, (2019) "Influential factors for
technology-enhanced learning: professionals’ views", Journal of Research in Innovative Teaching &
Learning, https://doi.org/10.1108/JRIT-09-2017-0023
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Influential factors for
technology-enhanced learning:
professionalsviews
Patrick Schweighofer and Doris Weitlaner
Department of Information Technology & Business Informatics,
University of Applied Science CAMPUS 02, Graz, Austria
Martin Ebner
Department of Educational Technology,
Graz University of Technology, Graz, Austria, and
Hannes Rothe
Department of Educational Technology, Freie Universitat Berlin, Berlin, Germany
Abstract
Purpose The literature includes several studies that define different critical success factors (CSF) which
have to be considered to support the implementation of technology-enhanced learning (TEL) approaches. An
analysis of such studies revealed that (1) regional differences seem to determine the CSF for TEL approaches,
(2) certain CSF are relevant for TEL approaches in general, and (3) professionals in higher education
determine which influential factors they consider when implementing TEL approaches. Thus, the question
arises: in general, which influential factors do professionals in Austrian and German institutions of higher
education actually consider when implementing TEL approaches?
Design/methodology/approach The study is a quantitative research approach based on survey data.
Findings The results show that certain influential factors seem to be generally important, such as the
factors of respecting learning success or motivation. However, the outcome of the study also indicated that
different moderating variables like experiences and personal relevance affect the professionalschoices.
Originality/value The originality and value are in the approach to identify generally important influential
factors for the implementation of TEL approaches in Austrian and German institutions of higher education.
Keywords Influential factors, Technology-enhanced learning, Importance of influential factors,
Moderating effects of influential factors, Opinion of professionals in higher education,
Time exposure for influential factors
Paper type Research paper
Introduction
Different synonyms are used in research to talk about digital technologies that support
human learning (e.g. computer-assisted instruction, educational technology, educational
computing, e-learning, distributed learning and more) (Chan et al., 2006). One of these terms
is technology-enhanced learning (TEL), which Goodyear and Retalis (2010) described as an
attractive, broadly defined term because it includes all technologies that help make learning
more effective, efficient and enjoyable. If this definition is accepted, the list of relevant
technologies becomes quite long and includes technologies that have been developed
for and are intentionally deployed in learning situations (e.g. mobile learning approaches,
game-based learning approaches, interactive learning videos and more). Furthermore, even
Journal of Research in Innovative
Teaching & Learning
Emerald Publishing Limited
2397-7604
DOI 10.1108/JRIT-09-2017-0023
Received 27 September 2017
Revised 5 March 2018
6 September 2018
8 October 2018
Accepted 8 December 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2397-7604.htm
© Patrick Schweighofer, Doris Weitlaner, Martin Ebner and Hannes Rothe. Published in Journal of
Research in Innovative Teaching & Learning. Published by Emerald Publishing Limited. This article is
published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce,
distribute, translate and create derivative works of this article (for both commercial and non-commercial
purposes), subject to full attribution to the original publication and authors. The full terms of this licence
may be seen at http://creativecommons.org/licences/by/4.0/legalcode
The authors declare that they have no conflict of interest.
Influential
factors
for TEL
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software that is needed to present information via WWW, e-mail and mobile phones can
facilitate learning and are, therefore, part of TEL (Dror, 2008).
Goodyear and Retalis (2010) provide a clear overview that describes situations in which
technologies can be used as part of TEL. Technologies can be:
media for accessing and studying learning material;
media for learning through inquiry;
media for learning through communication and collaboration;
media for learning through construction;
used for learnersassessments; and
used to improve digital and multimedia literacy.
Currently, TEL approaches are widespread in higher education and the number of TEL
approaches implemented is still increasing in German-speaking countries, in part because of
the so-called digital natives. Many researchers claim that students born between 1980 and
1994, the digital natives (Prensky, 2001), demand technological support in their learning
scenarios because they are used to using it in their daily lives (Pedró, 2006; Redecker, 2009;
Noguera Fructuoso, 2015; Schweighofer et al., 2015). However, others have criticized this
statement, pointing out the lack of convincing evidence that supports this claim (Bekebrede
et al., 2011; Bullen and Morgan, 2011; Margaryan et al., 2011). Regardless of the reason for
the spread of TEL approaches, it is indisputable that these approaches have become
widespread in higher education. For instance, the results of a survey conducted in Austria
showed that all 49 higher educational institutions that responded use some kind of TEL
approach (Bratengeyer et al., 2016). Therefore, the implementation of TEL approaches needs
to follow an instructional design (ID) (Bullen and Morgan, 2011) since using technology to
help in learning processes is a complex process (Goodyear and Retalis, 2010).
ID or instructional system design (ISD) is a procedure used to develop learning experiences
and environments, applying learning strategies to make the acquisition of knowledge and skill
more efficient, effective and appealing (Merrill et al., 1996). The origin for ID is in behaviorism
and system thinking (Rogers, 2002; Branch and Merrill, 2012). It is related to TEL in that
professionals combine ID and instructional media, which includes various technologies (e.g.
computer, internet, mobile devices and social media), to accomplish their goals (Reiser, 2001). By
now, a wide variety of instructional design models are available (Branch and Merrill, 2012) that
can provide assistance during this design process (e.g. a model created by Dick et al. (2015)).
According to Branch and Merrill (2012), practically all of these models are related to the
ADDIE model, although the origin of this model is unknown (Molenda, 2003). ADDIE stands
for analyze, design, develop, implement and evaluate, which are the process steps of a
systematic product development concept. Therefore, ADDIE itself is not a specific, fully
elaborated model but rather a structure used by taking different approaches (Branch and
Merrill, 2012). These approaches define which influential factors must be considered during
the process steps (cf. Ghirardini, 2011; Lohr, 1998).
However, it is not enough to merely consider relevant, influential factors that are related to
ID; it is necessary to consider critical success factors (CSF) in order to be successful with a
TEL approach. In general, CSF are characteristics, conditions or variables that can decide
whether a company has success or not (Leidecker and Bruno, 1984). In addition, according to
Pinto and Slevin (1987) considering such factors can also improve the chances of success of
different project implementation whereas Cheawjindakarn et al. (2012) furthermore pointed out
that identifying CSF is especially important in educational institutes. They defined CSF for
online distance learning as factors that must be taken care of in order to be successful, more
efficient and more effective. This study applies the same definition of CSF for TEL approaches.
JRIT
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In order to show the variety of CSF for TEL approaches and provide a selection of such
CSF mentioned in literature, a search in Google Scholar using the search term critical
success factorsin combination with the terms technology-enhanced learning,”“e-learning
and online learningwas performed and a selection of studies identifying preferably
different CSF was chosen. Table I contains this brief selection of studies and their identified
CSF. The list is thereby not intended to be exhaustive.
In five of these studies (Frydenberg, 2002; Joo et al., 2011; McGill et al., 2014; Selim, 2007;
Sun et al., 2008), CSF were identified for e-learning and online courses in general.
Frydenberg (2002) presented a literature review and McGill et al. (2014) described the results
of a survey of authors who had written articles about e-learning initiatives. The remaining
three articles reported the results of surveys in which students identified CSF. The results of
these five studies were quite diverse because there was no shared CSF occurring in all five
studies and a greater consensus only existed among CSF related to acceptance. These CSF
were perceived usefulness and ease of use ( Joo et al., 2011), students like the innovation,
innovation is easy for students to use, innovation is easy for teachers to use (McGill et al.,
2014), perceived usefulness and perceived ease of use (Sun et al., 2008). Regional differences
could be a possible reason that such diverse results were obtained. Richter and Pawlowski
(2015) described differences related to e-learning between Germany and South Korea. For
instance, they pointed out the differences between the teachers role, the value of errors and
the preferred learning style. Furthermore, Paulsen (2003) also discussed regional differences
in Europe with regard to the use of learning management systems.
In the remaining studies, CSF were identified that address more specific TEL approaches
or specific aspects of TEL approaches such as blended learning, m-learning or technical
infrastructure aspects in particular.
As mentioned before, this list of CSF is just a brief selection in order to show the variety of
CSF for TEL approaches. However, prior to this study Schweighofer and Ebner (2015)
conducted a vast literature review, analyzing 4,567 publications to identify factors that were
potentially influential when implementing TEL approaches and clustered these factors into 20
categories with 76 subcategories. Table II shows how the discussed CSF fit into these categories.
Additionally the table includes the subcategories of each category to further define the category
as well as the number of articles in which influential factors of this category were identified.
The categorization revealed that, although the results stated in the publications were
diverse, some similarities could also be identified. For example, most of the CSF identified were
course-related, but other categories show also a high count of CSF. Furthermore, only two
categories (demographic differences and requirements on teachers) did not include CSF with
reference to the provided literature. Therefore, it seems like although different CSF are relevant
for different TEL approaches, some CSF seem to be relevant for TEL approaches in general.
Finally, none of the selected studies investigated the teacherspoints of view, with the
exception of two (Cochrane, 2010; Soong et al., 2001). However, in higher educational
professional practice, the teaching staff or their management decide which TEL
approaches will be implemented and which influential factors will be considered while
doing so. Furthermore, the amount of time needed to consider these factors during the
implementation is also important. If the teaching staff or their management do not believe
that it is important to consider an influential factor or if they do not want to spend time
considering such a factor, the fact that the students consider it a CSF will be irrelevant
since the decision makers will not investigate or consider this factor.
In summary, the insights led to three assumptions and conclusions regarding the
research question:
(1) Regional differences determine the CSF for TEL approaches. Therefore, the research
question should be regionally restricted.
Influential
factors
for TEL
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Source Description CSF
Alsabawy et al. (2016) A survey conducted with 720 students of online courses at
an Australian university to determine the impact of IT
infrastructure services and IT quality on perceptions of
usefulness
(1) IT infrastructure services, (2) system quality,
(3) information quality
Chen et al. (2013) A survey conducted with 306 students of courses using
web-based language learning at a university in Taiwan to test
hypotheses based on the social cognitive theory in order to
determine how different factors influence learnerssatisfaction
(1) system characteristics, (2) possibilities of interaction
Cochrane (2010) Feedback (qualitative and quantitative) from students and
teachers participating in three mobile learning projects at a
higher educational institute in New Zealand to identify CSF
for mobile learning
(1) the importance of the pedagogical integration of the
technology into the course assessment, (2) lecturer modeling
of the pedagogical use of the tools, (3) the need for regular
formative feedback from lecturers to students, (4) the
appropriate choice of mobile devices and software to
support the pedagogical model underlying the course
Frydenberg (2002) A literature review on quality standards for e-learning in
the USA
(1) executive commitment, (2) technological infrastructure,
(3) student services, (4) design and development,
(5) instruction and instructor services, (6) program delivery,
(7) financial health, (8) legal and regulatory requirements,
(9) program evaluation
Henrich and Sieber (2009) Lessons learned from using different approaches to enhance
courses about information retrieval with technology at a
university in Germany to identify CSF for TEL approaches
(1) concept, (2) creation, (3) maintenance, (4) utilization,
(5) participation
Joo et al. (2011) A survey with 709 students of online courses at a
South Korean online university to test hypotheses in order to
determine how different factors influence learning satisfaction
(1) teaching presence, (2) cognitive presence, (3) perceived
usefulness and ease of use
McGill et al. (2014) A survey conducted of 70 authors of articles about
e-learning initiatives, conducted to identify factors which
influence the success, continuation or sustainability of
e-learning initiatives
(1) students like the innovation, (2) innovation is easy for
students to use, (3) innovation is consistent with approach to
teaching, (4) technology is sufciently mature/stable,
(5) management supports e-learning, (6) innovation
improves student learning,
(7) technology is inexpensive, (8) innovation is easy for
teachers to use, (9) technology is up to date
(continued )
Table I.
Selection of studies
identifying CSF
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Source Description CSF
Selim (2007) A survey conducted with 538 students at a university in the
United Arab Emirates to identify important factors for
successful e-learning
(1) instructors attitude toward and control of the technology,
(2) instructors teaching style, (3) student motivation and
technical competency, (4) student interactive collaboration,
(5) e-learning course content and structure, (6) ease of
on-campus internet access, (7) effectiveness of information
technology infrastructure, (8) university support of
e-learning activities
(Soong et al., 2001) Interviews with instructors, a survey conducted with
students and the analysis of archival records (logs) in order
to identify CSF for using online learning resources at an
university in Singapore
(1) human factors pertaining to the instructors, (2) the
instructorsand studentstechnical competency,
(3) the instructorsand studentsmind-set (about learning),
(4) the level of collaboration intrinsic in the course,
(5) the level of perceived IT infrastructure and technical
support
(Stacey and Gerbie, 2008) Based on the literature and personal practices at two
universities in New Zealand and Australia, the article
describes success factors for blended learning
(1) institutional success factors (e.g. needs), (2) success factors
regarding teachers (e.g. workload, fears), (3) success factors
regarding students (e.g. readiness, expectations),
(4) pedagogic considerations (e.g. course design)
(Sun et al., 2008) A survey conducted with 295 students at two universities in
Taiwan to test literature-based hypotheses in order to
determine how different factors influence learners
satisfaction in e-learning courses
(1) learner computer anxiety, (2) instructor attitude toward
e-learning, (3) e-learning course exibility, (4) e-learning
course quality, (5) perceived usefulness, (6) perceived ease of
use, (7) diversity in assessments
Table I.
Influential
factors
for TEL
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Influential factors identified by Schweighofer and Ebner (2015) Discussed CSF
Acceptance aspects (technology acceptance) (identified in 36 articles) (1) utilization (Henrich and Sieber, 2009), (2) perceived usefulness and ease of use
( Joo et al., 2011), (3) students like the innovation, (4) innovation is easy for students to
use, (5) innovation is easy for teachers to use (McGill et al., 2014), (6) perceived
usefulness, (7) perceived ease of use (Sun et al., 2008)
Business aspects (benefits, organizational influences, cost reduction, difficulties,
effectiveness, value, ethics, quality assurance) (identified in 38 articles)
(1) information quality (Alsabawy et al., 2016), (2) executive commitment, (3)
financial health, (4) legal and regulatory requirements (Frydenberg, 2002), (5)
management supports e-learning, (6) technology is inexpensive (McGill et al., 2014),
(7) institutional success factors (e.g. needs) (Stacey and Gerbie, 2008), (8) e-learning
course quality (Sun et al., 2008)
Cognitive aspects (cognition, attention) (identified in 39 articles) (1) cognitive presence ( Joo et al., 2011)
Course-related aspects (delivery mode, relevance, purpose, provided time, teaching
discipline, course design) (identified in 13 articles)
(1) lecturer modeling of the pedagogical use of the tools (Cochrane, 2010), (2) design
and development, (3) program delivery, (4) program evaluation (Frydenberg, 2002),
(5) concept, (6) maintenance (Henrich and Sieber, 2009), (7) e-learning course content
and structure (Selim, 2007), (8) pedagogic considerations (e.g. course design) (Stacey
and Gerbie, 2008), (9) diversity in assessments, (10) e-learning course exibility, (11)
e-learning course quality (Sun et al., 2008)
Demographic differences (age, gender differences, studentsbackground,
socioeconomic status, cultural background, ethnical background) (identified in
27 articles)
Influence from prior knowledge and experience (experience, knowledge level, digital
competence) (identified in 39 articles)
(1) student motivation and technical competency (Selim, 2007), (2) the instructors
and studentstechnical competency (Soong et al., 2001)
Instruction aspects (instructions effectiveness, instructional strategies, instructional
design, instruction influence) (identified in 20 articles)
(1) instruction and instructor services (Frydenberg, 2002)
Learnerslearning aspects (adaptive learning, approaches, behavior, learning
strategy, goal orientation, out-of-school learning resources, learning process,
participation, interaction) (identified in 102 articles)
(1) possibilities of interaction (Chen et al., 2013), (2) participation (Henrich and Sieber,
2009), (3) student interactive collaboration (Selim, 2007), (4) success factors
regarding students (e.g. readiness, expectations) (Stacey and Gerbie, 2008)
Learnersrequirements (identity issues, learnersreadiness, learnerspreferences)
(identified in 24 articles)
(1) success factors regarding students (e.g. readiness, expectations) (Stacey and
Gerbie, 2008)
Learning success (learning outcomes, learning effectiveness, learning reflection,
learning efficiency) (identified in 186 articles)
(1) innovation improves student learning (McGill et al., 2014)
(continued )
Table II.
Categorization of CSF
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Influential factors identified by Schweighofer and Ebner (2015) Discussed CSF
Mind-set and feelings before TEL (conceptions, expectations, beliefs) (identified in
23 articles)
(1) perceived usefulness and ease of use (Joo et al., 2011), (2) the instructorsand
studentsmind-set (about learning) (Soong et al., 2001), (3) learner computer anxiety,
(4) perceived usefulness, (5) perceived ease of use (Sun et al., 2008)
Mind-set and feelings during TEL (attitude, perceptions, perspectives, satisfaction,
emotions) (identified in 102 articles)
(1) instructors attitude toward and control of the technology (Selim, 2007), (2) learner
computer anxiety, (3) instructor attitude toward e-learning (Sun et al., 2008)
Motivational aspects (intention, engagement, motivation) (identified in 74 articles) (1) student motivation and technical competency (Selim, 2007), (2) the level of
collaboration intrinsic in the course (Soong et al., 2001)
Requirements on teachers (identified in 3 articles)
Self-regulation aspects (self-regulated learning, computer/internet self-efficacy)
(identified in 36 articles)
(1) e-learning course exibility (Sun et al., 2008)
Social aspects (social competence) (identified in 4 articles) (1) the level of collaboration intrinsic in the course (Soong et al., 2001)
Support processes (support, feedback) (identified in 27 articles) (1) the need for regular formative feedback from lecturers to students (Cochrane,
2010), (2) student services (Frydenberg, 2002), (3) university support of e-learning
activities (Selim, 2007)
Teachersteaching aspects (teachersself-reflection, teachers behavior, teaching
style, teaching strategy, teaching performance) (identified in 12 articles)
(1) teaching presence ( Joo et al., 2011), (2) innovation is consistent with approach to
teaching (McGill et al., 2014), (3) instructors teaching style (Selim, 2007), (4) human
factors pertaining to the instructors (Soong et al., 2001), (5) success factors regarding
teachers (e.g. workload, fears) (Stacey and Gerbie, 2008)
Technical infrastructure aspects (accessibility, reliability, time of availability,
available infrastructure, learning environment) (identified in 18 articles)
(1) IT infrastructure services, (2) system quality (Alsabawy et al., 2016), (3) system
characteristics (Chen et al., 2013), (4) technological infrastructure (Frydenberg, 2002),
(5) ease of on-campus internet access, (6) effectiveness of information technology
infrastructure (Selim, 2007), (7) the level of perceived IT infrastructure and technical
support (Soong et al., 2001)
Technology-related aspects (technology integration, technology usage) (identified
in 30 articles)
(1) the appropriate choice of mobile devices and software to support the pedagogical
model underlying the course, (2) the importance of the pedagogical integration of the
technology into the course assessment (Cochrane, 2010), (3) creation (Henrich and Sieber,
2009), (4) technology is sufciently mature/stable, (5) technology is up to date (McGill
et al., 2014), (6) instructors attitude toward and control of the technology (Selim, 2007)
Table II.
Influential
factors
for TEL
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(2) Some CSF are relevant for TEL approaches in general. Therefore, in order to identify
these CSF, the research question should not be restricted to specific TEL approaches.
(3) Professionals in higher education determine which influential factors they will
consider when implementing TEL approaches. Therefore, the research question
should address the opinion of these professionals.
The purpose of this study is bipartite. First, the aim of this study is to broadly show which
influential factors are important for professionals in Austrian and German institutions of
higher education and, additionally, how much time these professionals would spend
considering these factors. This information would support the implementation of TEL
approaches, since it would allow to develop methods, which include important influential
factors that professionals would actually consider during the development process.
Additionally, the study should reveal influential factors that are important to
professionals although they do not want to spend much time considering them.
Methods that are easy to use and do not need much time must be developed for
such factors.
Second, the study should identify influential factors that professionals do not consider as
important. This information can help researchers identify fields in which more explanatory
work is needed to emphasize the importance of these influential factors.
Finally, based on these conclusions and purposes, this study was conducted in an
attempt to answer the following research question:
RQ1. Which influential factors would professionals in Austrian and German institutions
of higher education consider when implementing technology-enhanced
learning approaches?
The remainder of this paper consists of a description of the research method used, followed
by the survey results, a discussion of these results and, finally, a conclusion.
Research design
The overall research project applied a cross-sectional design, which is the most commonly-used
survey design. This means that the units of analysis were studied either at one point in time or
within a short time frame (Straits and Singleton, 2011). Furthermore, at this point it should be
noted that the research project had two different target groups. For both the same survey was
conducted. Although this paper focuses solely on the second group (institutions of higher
education), in order to be accurate the following sections describe the research design for the
overall research project.
Overall description of the study
The study population included: employees of Styrian enterprises operating in either the
manufacturing or services industry (according to the classification of the Austrian statistical
office, Statistik Austria, 2015) or schools ( from secondary level upwards) and university
extensions as well as institutions of higher education located in the German-speaking DACH
region (Germany, Austria and Switzerland). Due to the broad nature of the study population,
an online survey seemed to be an appropriate and resource-saving method that could be used
to collect empirical data. An electronic mailing list and a suitablequestionnaire were prepared.
These were prepared with reference to a marketing database to obtain the Austriandata. This
database includes information obtained from the Austrian commercial register and the
association for the protection of creditors. To obtain data from Germany and Switzerland,
publicly-available lists of German and Swiss institutions of higher education were examined
and contact e-mail addresses were manually identified. These two data sets were
supplemented by information from three of the authorspersonal author contact lists, which
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led to the creation of an electronic mailing list with approximately 19,000 entries. Thereby,
approximately 1,300 entries regarded the group of institutions of higher education located in
the German-speaking DACH region.
Survey
The elaboration of the questionnaire was an iterative process. A first draft of the questionnaire
was prepared based on the 20 categories of influential factors identified by Schweighofer and
Ebner (2015) (see Table II) and the question of interest. After conducting multiple cycles of
linguistic refinement, an online form of the questionnaire (using EFS Survey software) was
created. Pilot tests further improved the questionnaire, which resulted in the creation of an
adapted and shortened version based on the testerscomments. In summary, the final
instrument can be described as follows: the questionnaire contains of three blocks using the 20
categories of influential factors identified by Schweighofer and Ebner (2015) as survey items,
namely categories considered to date along with a satisfaction rating (or reasons for omission
if not considered), a general assessment of each categorys importance and amount of time the
respondents would spend to consider factors of these categories and the top five categories as
ranked by each respondent, based on their considered importance and time investment (see
Appendix). In order to limit the scope of the interpretation, the labels of the 20 categories of
influential factors included keyword descriptions in parentheses (see Table II).
The survey was available online as two versions: a personalized version and an
anonymous version. In this way, it was possible to handle personal contacts individually and
to use the systems bulk mail function. Informative letters were directly mailed that contained
a web link to the personalized version of the questionnaire, stated the research purpose
and assuring the confidentiality of the personal contact information. In the remaining cases,
the contacts were additionally asked to forward the message to teachers, lecturers and other
interested parties. Furthermore, the web link to the anonymized version of the questionnaire
was replaced, with the risk that individuals could respond multiple times and bias the results.
Since it was possible to monitor the response behavior of each personal contact, reminders to
non-respondents were sent one month after the initial mail was sent out. Of course, these
measures could not be used in the other cases as it was not possible to either track who
responded or determine whether the contacts forwarded the invitation.
Once the questionnaire responses had been collected, the data provided on 319 fully
completed questionnaires between March and April 2015 was analyzed. The data were
pre-processed during which incomplete data sets and data from schools, university
extensions and Switzerland due to insufficient group sizes were excluded. The final data set
included 276 records. As described, this paper emphasizes on the subpopulation from the
field of higher education, which further reduced the sample set to 120 usable questionnaires.
Table III shows the descriptive statistics for this data set; this information was entered at
the top of the questionnaire by the respondents. These descriptive statistics and the
following calculations were performed using IBM SPSS Statistics 22.0.
Results
In the following section, the results of the analyses performed are presented based on the data
obtained through the survey. The outcome shows which influential factors are considered to
be important by professionals in higher education and how much time they would invest to
consider these factors during the implementation. Therefore, the survey included the
following six questions that directly addressed these issues (also see Appendix).
Q1. How important (unrelated to your position) is the consideration of the following aspects
during the development of TEL approaches or when using technology to support learning
and teaching for you?
Influential
factors
for TEL
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Q2. How much time would you spend (in your position) when considering the following
aspects during the development of TEL approaches or when using technology to support
learning and teaching?
Q3. Please choose those five aspects from the following list which are most important for
you (unrelated to your position) during the development of TEL approaches or when using
technology to support learning and teaching.
Q4. Now arrange these aspects in descending order based on their priority.
Q5. Please choose those five aspects from the following list on which you (in your
position) would spend the most time during the development of TEL approaches or when
using technology to support learning and teaching.
Q6. Now arrange these aspects in descending order based on the expenditure of time.
The combined results for all questions about the importance of the influential factors
appear in Tables IV and V, and the results about the time invested to consider these factors
appear in Tables VI and VII. The relevant frequencies and medians for the data are shown
in these tables, because the median is considered the best measure of central tendency when
using ordinal scaled variables and is also usually not as strongly affected by outliers and
skewed data (Quatember, 2011).
Item Category n%
Age (years) o25 1 0.8
2530 10 8.3
3140 33 27.5
4150 35 29.2
W50 41 34.2
Organization University of applied science 59 49.2
University 45 37.5
College of education 16 13.3
Country/state Germany 28 23.3
Austria (only Styria) 62 51.7
Austria (without Styria) 30 25.0
Function in university of applied science Head of degree program 6 5.0
Full-time lecturer/teacher 24 20.0
Part-time lecturer/teacher 16 13.3
Responsible for online learning and/or didactics 9 7.5
Other function 4 3.3
Function in university Rector, faculty director, head of institute 6 5.0
Professor, assistant professor, lecturer 19 15.8
Responsible for online learning and/or didactics 7 5.8
Other function 13 10.8
Function in college of education Rector, faculty director, head of institute 3 2.5
Professor, assistant professor, lecturer 11 9.2
Responsible for online learning and/or didactics 1 0.8
Other function 1 0.8
Time in position (years) o1 5 4.2
13 25 20.8
46 31 25.8
710 21 17.5
W10 38 31.7
Field of work Natural science 15 12.5
Engineering and technology 43 37.5
Agricultural, medical and health science 6 5.0
Social sciences 51 42.5
Humanities 36 30.0
Experiences with TEL in teaching Yes 97 80.8
No 23 19.2
Table III.
Overview of
descriptive statistics
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As can be seen in Tables IV and V, the categories learning success and teachersteaching
aspects appear to include the most important influential factors on the basis of the results
of this study (median ¼4.0). In addition, more than 90 percent of the participants
considered the categories motivational aspects, learnerslearning aspects, learners
requirements, course-related aspects and requirements on teachers to be (very)
important[1]. The fact that these categories were most frequently ranked among the top
five categories in terms of importance (with the exception of requirements on teachers)
confirms these results. More than 70 percent of the respondents ranked the category
learning success among the top five results. However, at the same time, at least 5 percent
of the respondents considered these categories to be (very) unimportant. The category of
influential factors with the least importance is social aspects (median ¼2.0) according to
this survey. More than 50 percent of the respondents considered this category to be (very)
unimportant. In addition, at least a third of the respondents deemed the categories
business aspects, mind-set and feelings before TEL and demographic differences to be
(very) unimportant. Nevertheless, at least some respondents ranked these four categories
among the top five most important categories and, at least once, each was ranked first.
More than 14 percent of the respondents thought that these categories were very
important as well.
With regard to time investment, Tables VI and VII show that the respondents would
spend the most time considering the influential factors of the category learning success.
Specifically, 82.5 percent of the respondents thought they would spend much of even very
much time considering influential factors in this category. At least two-thirds of the
respondents also thought they would spend the same amount of time on the categories
motivational aspects, support processes, learnerslearning aspects, course-related aspects,
requirements on teachers and teachersteaching aspects. Again, the results showed that
all these categories were ranked among the top five categories except requirements on
Very
unimportant
(1)
Unimportant
(2)
Important
(3)
Very
important
(4)
Category n%n%n%n% Median
Acceptance aspects 2 1.7 25 20.8 60 50.0 33 27.5 3.0
Business aspects 14 11.7 37 30.8 51 42.5 18 15.0 3.0
Cognitive aspects 2 1.7 14 11.7 64 53.3 40 33.3 3.0
Course-related aspects 1 0.8 9 7.5 55 45.8 55 45.8 3.0
Demographic differences 12 10.0 41 34.2 45 37.5 22 18.3 3.0
Influence from prior knowledge and
experience 3 2.5 33 27.5 60 50.0 24 20.0 3.0
Instruction aspects 4 3.3 28 23.3 50 41.7 38 31.7 3.0
Learnerslearning aspects 1 0.8 5 4.2 62 51.7 52 43.3 3.0
Learnersrequirements 2 1.7 7 5.8 66 55.0 45 37.5 3.0
Learning success 2 1.7 6 5.0 40 33.3 72 60.0 4.0
Mind-set and feelings before TEL 8 6.7 45 37.5 50 41.7 17 14.2 3.0
Mind-set and feelings during TEL 6 5.0 30 25.0 56 46.7 28 23.3 3.0
Motivational aspects 2 1.7 8 6.7 52 43.3 58 48.3 3.0
Requirements on teachers 1 0.8 9 7.5 63 52.5 47 39.2 3.0
Self-regulation aspects 2 1.7 26 21.7 57 47.5 35 29.2 3.0
Social aspects 14 11.7 48 40.0 33 27.5 25 20.8 2.0
Support processes 2 1.7 13 10.8 51 42.5 54 45.0 3.0
Teachersteaching aspects 2 1.7 7 5.8 50 41.7 61 50.8 4.0
Technical infrastructure aspects 3 2.5 10 8.3 56 46.7 51 42.5 3.0
Technology-related aspects 5 4.2 34 28.3 52 43.3 29 24.2 3.0
Table IV.
Frequency
distributions
of importance
Influential
factors
for TEL
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Voted into top five Rank 1 (1) Rank 2 (2) Rank 3 (3) Rank 4 (4) Rank 5 (5)
Category n%n% %T5 n% %T5 n% %T5 n% %T5 n% %T5 Median
Acceptance aspects 22 18.3 4 3.3 18.2 1 0.8 4.5 5 4.2 22.7 5 4.2 22.7 7 5.8 31.8 4.0
Business aspects 14 11.7 2 1.7 14.3 0 0.0 0.0 4 3.3 28.6 4 3.3 28.6 4 3.3 28.6 4.0
Cognitive aspects 21 17.5 1 0.8 4.8 5 4.2 23.8 6 5.0 28.6 3 2.5 14.3 6 5.0 28.6 3.0
Course-related aspects 47 39.2 13 10.8 27.7 7 5.8 14.9 11 9.2 23.4 9 7.5 19.1 7 5.8 14.9 3.0
Demographic differences 13 10.8 4 3.3 30.8 0 0.0 0.0 2 1.7 15.4 3 2.5 23.1 4 3.3 30.8 4.0
Influence from prior knowledge and experience 11 9.2 2 1.7 18.2 2 1.7 18.2 4 3.3 36.4 1 0.8 9.1 2 1.7 18.2 3.0
Instruction aspects 18 15.0 2 1.7 11.1 4 3.3 22.2 2 1.7 11.1 1 0.8 5.6 9 7.5 50.0 4.5
Learnerslearning aspects 62 51.7 16 13.3 25.8 20 16.7 32.3 8 6.7 12.9 12 10.0 19.4 6 5.0 9.7 2.0
Learnersrequirements 40 33.3 3 2.5 7.5 12 10.0 30.0 11 9.2 27.5 7 5.8 17.5 7 5.8 17.5 3.0
Learning success 85 70.8 34 28.3 40.0 19 15.8 22.4 15 12.5 17.6 12 10.0 14.1 5 4.2 5.9 2.0
Mind-set & feelings before TEL 9 7.5 1 0.8 11.1 2 1.7 22.2 1 0.8 11.1 2 1.7 22.2 3 2.5 33.3 4.0
Mind-set and feelings during TEL 12 10.0 0 0.0 0.0 1 0.8 8.3 2 1.7 16.7 5 4.2 41.7 4 3.3 33.3 4.0
Motivational aspects 51 42.5 16 13.3 31.4 14 11.7 27.5 8 6.7 15.7 12 10.0 23.5 1 0.8 2.0 2.0
Requirements on teachers 30 25.0 6 5.0 20.0 2 1.7 6.7 5 4.2 16.7 10 8.3 33.3 7 5.8 23.3 4.0
Self-regulation aspects 21 17.5 3 2.5 14.3 5 4.2 23.8 4 3.3 19.0 3 2.5 14.3 6 5.0 28.6 3.0
Social aspects 17 14.2 3 2.5 17.6 2 1.7 11.8 5 4.2 29.4 5 4.2 29.4 2 1.7 11.8 3.0
Support processes 38 31.7 2 1.7 5.3 9 7.5 23.7 7 5.8 18.4 8 6.7 21.1 12 10.0 31.6 4.0
Teachersteaching aspects 38 31.7 4 3.3 11.4 3 2.5 8.6 5 4.2 14.3 9 7.5 25.7 14 11.7 40.0 4.0
Technical infrastructure aspects 35 29.2 4 3.3 10.5 8 6.7 21.1 11 9.2 28.9 8 6.7 21.1 7 5.8 18.4 3.0
Technology-related aspects 16 13.3 0 0.0 0.0 4 3.3 25.0 4 3.3 25.0 1 0.8 6.3 7 5.8 43.8 3.5
Note: For the columns labeled %T5,nis the number of respondents who ranked the category among the top five (column 2)
Table V.
Frequency distributions
of rank among the top
five categories
regarding importance
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teachers, which confirms these findings. The respondents indicated that they thought
they would spend the most time considering factors in the category learning success
(41.8 percent ranked this category in the first place). However, at least 5 percent of the
respondents indicated they would spend very little time considering these categories.
According to the data, the respondents stated they would spend the least amount of time
(more than two-thirds said they would spend only (very) little time) considering factors in
the categories mind-set and feelings before TEL and demographic differences. Despite
these results, these categories were also ranked among the top five by a few participants,
each was ranked in first place at least once and more than 6 percent of the respondents
indicatedtheywouldspendverymuchtimeconsidering factors in these categories.
In general, the results indicate that all categories seem to be important for professionals
in higher education since, for each category (except social aspects), more than 50 percent of
the respondents and, in part, much more than 50 percent stated that the category was
(very) important. In addition, more than 50 percent of the respondents indicated they would
invest (a great deal of ) time considering factors in most categories. However, less than a
third would spend (a great deal of) time considering factors in the categories mind-set and
feelings before TEL and demographic differences.
Discussion
Importance and time investment
The results of the present study show that influential factors of the categories learning
success, teachersteaching aspects, motivational aspects, learnerslearning aspects, course-
related aspects and requirements on teachers were identified as the most important by the 120
respondents. The respondents tended to spend more time considering factors in these
categories. CSF in five of these categories also appear in the literature investigated (see Table
II). Thus, it can be concluded that professionals in higher education considered influential
factors in the six categories mentioned as generally important when implementing TEL
Very little (1) Little (2) Much (3) Very much (4)
Category n%n%n%n% Median
Acceptance aspects 22 18.3 43 35.8 37 30.8 18 15.0 2.0
Business aspects 42 35.0 37 30.8 29 24.4 12 10.0 2.0
Cognitive aspects 13 10.8 41 34.2 47 39.2 19 15.8 3.0
Course-related aspects 7 5.8 17 14.2 47 39.2 19 15.8 3.0
Demographic differences 44 36.7 55 45.8 11 9.2 10 8.3 2.0
Influence from prior knowledge and experience 25 20.8 54 45.0 28 23.3 13 10.8 2.0
Instruction aspects 12 10.0 37 30.8 44 36.7 27 22.5 3.0
Learnerslearning aspects 10 8.3 27 22.5 59 49.2 24 20.0 3.0
Learnersrequirements 12 10.0 43 35.8 41 34.2 24 20.0 3.0
Learning success 6 5.0 15 12.5 50 41.7 49 40.8 3.0
Mind-set and feelings before TEL 34 28.8 52 43.3 26 21.7 8 6.7 2.0
Mind-set and feelings during TEL 23 19.2 53 44.2 32 26.7 12 10.0 2.0
Motivational aspects 11 9.2 26 21.7 49 40.8 34 28.3 3.0
Requirements on teachers 9 7.5 31 25.8 50 41.7 30 25.0 3.0
Self-regulation aspects 21 17.5 49 40.8 34 28.3 16 13.3 2.0
Social aspects 25 20.8 48 40.0 38 31.7 9 7.5 2.0
Support processes 14 11.7 18 15.0 59 49.2 29 24.2 3.0
Teachersteaching aspects 6 5.0 22 18.3 48 40.0 44 36.7 3.0
Technical infrastructure aspects 25 20.8 27 22.5 46 38.3 22 18.3 3.0
Technology-related aspects 25 20.8 40 33.3 44 36.7 11 9.2 2.0
Table VI.
Frequency
distributions according
to time invested
Influential
factors
for TEL
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Voted into top five Rank 1 (1) Rank 2 (2) Rank 3 (3) Rank 4 (4) Rank 5 (5)
Category n%n% %T5 n% %T5 n% %T5 n%%T5n% %T5 Median
Acceptance aspects 23 19.2 2 1.7 8.7 6 5.0 26.1 1 0.8 4.3 5 4.2 21.7 9 7.5 39.1 4.0
Business aspects 13 10.8 1 0.8 7.7 2 1.7 15.4 2 1.7 15.4 3 2.5 23.1 6 5.0 46.2 4.0
Cognitive aspects 20 16.7 1 0.8 5.0 2 1.7 10.0 5 4.2 25.0 9 7.5 45.0 3 2.5 15.0 4.0
Course-related aspects 41 34.2 8 6.7 19.5 12 10.0 29.3 10 8.3 24.4 8 6.7 19.5 3 2.5 7.3 3.0
Demographic differences 11 9.2 4 3.3 36.4 1 0.8 9.1 2 1.7 18.2 2 1.7 18.2 2 1.7 18.2 3.0
Influence from prior knowledge and experience 12 10.0 2 1.7 16.7 4 3.3 33.3 2 1.7 16.7 1 0.8 8.3 3 2.5 25.0 2.5
Instruction aspects 22 18.3 4 3.3 18.2 2 1.7 9.1 6 5.0 27.3 5 4.2 22.7 5 4.2 22.7 3.0
Learnerslearning aspects 63 52.5 17 14.2 27.0 16 13.3 25.4 10 8.3 15.9 11 9.2 17.5 9 7.5 14.3 2.0
Learnersrequirements 40 33.3 5 4.2 12.5 15 12.5 37.5 11 9.2 27.5 3 2.5 7.5 6 5.0 15.0 2.5
Learning success 79 65.8 33 27.5 41.8 17 14.2 21.5 9 7.5 11.4 5 4.2 6.3 15 12.5 19.0 2.0
Mind-set & feelings before TEL 8 6.7 1 0.8 12.5 1 0.8 12.5 3 2.5 37.5 3 2.5 37.5 0 0.0 0.0 3.0
Mind-set and feelings during TEL 15 12.5 1 0.8 6.7 2 1.7 13.3 4 3.3 26.7 2 1.7 13.3 6 5.0 40.0 4.0
Motivational aspects 41 34.2 12 10.0 29.3 8 6.7 19.5 8 6.7 19.5 7 5.8 17.1 6 5.0 14.6 3.0
Requirements on teachers 28 23.3 7 5.8 25.0 3 2.5 10.7 5 4.2 17.9 9 7.5 32.1 4 3.3 14.3 3.0
Self-regulation aspects 22 18.3 0 0.0 0.0 3 2.5 13.6 6 5.0 27.3 10 8.3 45.5 3 2.5 13.6 4.0
Social aspects 18 15.0 2 1.7 11.1 2 1.7 11.1 6 5.0 33.3 5 4.2 27.8 3 2.5 16.7 3.0
Support processes 42 35.0 5 4.2 11.9 7 5.8 16.7 11 9.2 26.2 8 6.7 19.0 11 9.2 26.2 3.0
Teachersteaching aspects 55 45.8 9 7.5 16.4 14 11.7 25.5 11 9.2 20.0 16 13.3 29.1 5 4.2 9.1 3.0
Technical infrastructure aspects 30 25.0 3 2.5 10.0 3 2.5 10.0 4 3.3 13.3 5 4.2 16.7 15 12.5 50.0 4.5
Technology-related aspects 17 14.2 3 2.5 17.6 1 0.8 5.9 4 3.3 23.5 3 2.5 17.6 6 5.0 35.3 4.0
Note: For the columns labeled %T5,nis the number of respondents who ranked the category into the top five (column 2)
Table VII.
Frequency
distributions of rank
among the top five
categories regarding
time investment
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approaches, independent of the approach itself. Therefore, in general it can be suggested to
consider the following questions when implementing TEL approaches:
How does the approach increase learning success, effectiveness and efficiency?
How does the approach fit to teachersteaching style?
How does the approach increase learnersmotivation and engagement?
How does the approach fit to learnerslearning behavior?
How does the approach fit the purpose of the course and the teaching discipline?
What are the requirements on teachers to use the approach?
It must be noted, however, that the reviewed literature did not mention influential factors in
the category requirements on teachers and a factor in the category learning success
appeared only once. Although the literature review was not exhaustive, especially with
regard to factors in the category learning success, this result was surprising since this
category was identified as the most important one in the present study. In addition,
Schweighofer and Ebner (2015) emphasized the importance of this category because it
contained by far the largest number of factors identified in their literature review.
Although the six categories mentioned above appear to be most important, in general, all
categories were ranked as important by a majority of the respondents. Even categories like
demographic differences or social aspects, which showed the weakest average results, were
deemed very important by at least a small group of the respondents.
Blooms Taxonomy provides one possible explanation for the category social aspects
(Bloom et al., 1956) in that it describes different levels of knowledge acquisition. Thereby,
collaborative learning scenarios, like collaborative tagging, can be used to attain higher levels
of applied and metacognitive knowledge in the hierarchy (Bateman et al., 2007). It seems like
factors in the category social aspects that are especially relevant in collaborative learning
scenarios (Schweighofer and Ebner, 2015) are, therefore, more important in courses that
address these higher levels. The majority of the respondents may not have addressed these
high levels because they do not use collaborative learning scenarios, leading to the result of
the low (on average) importance assigned to the category social aspects. However, those who
rated this category as very important might have addressed such levels and may use such
scenarios. In order to verify this assumption, further research is necessary.
A possible explanation for the varying results in the category demographic differences is
the fact that these factors are only relevant if there are any demographic differences to be
considered. It is obviously not important to consider demographic differences in courses
that have a homogeneous group of students (with similar age, socioeconomic status and
cultural and/or ethnic backgrounds). Therefore, it can be assumed that these factors were
relevant to only a minority of the respondents. To confirm this assertion, additional data
needs to be gathered, and further research has to be conducted.
In summary, it can be concluded that, in addition to factors in the six most important
categories, depending on varying circumstances such as the course, its goals and students,
different influential factors are relevant and should be considered when implementing TEL
approaches. This was also emphasized by White (2007), who claimed that CSF vary and
local circumstances need to be identified in order to use existing strengths.
Influence on importance and time investments
On the basis of the last conclusion, exploring which circumstances could affect the
considerations of importance and time investment in the given data set was necessary.
Consequently, the initially proposed analysis was extended and an additional aspect in
order to consider potential moderators was added. It was hypothesized that the importance
Influential
factors
for TEL
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of the influential factors was ranked according to the personality of the respondent.
This idea is based on that of Keller and Karau (2013), who conducted a survey with
250 students to investigate how the Big Fivepersonality dimensions (extraversion,
agreeableness, conscientiousness, emotional stability and openness to experience)
influenced five specific types of online course impressions (engagement, value to career,
overall evaluation, anxiety/frustration and preference for online courses).
Whether professionals in higher education consider a factor to be influential seems to be
a personal decision. Several moderators affect such decisions. First, on the one hand,
positive experiences based on decisions made in the past may have influenced subsequent
decisions in similar situations ( Juliusson et al., 2005). On the other hand, negative
experiences can also affect future decisions, since people try to avoid making decisions that
led to negative experiences (Sagi and Friedland, 2007).
Another moderator that potentially affects decisions is personal commitment, because
people tend to invest more time and effort if they feel personally committed to a decision
( Juliusson et al., 2005). Furthermore, according to Acevedo and Krueger (2004), feelings of
personal relevance affects decisions as well. For example, if people believe something
matters, they tend to invest more time on it.
Finally, demographic characteristics and environmental circumstances such as age or
socioeconomic status can be important moderators of the decision process as well. This is in
line with the influence of experiences because, for instance, people have had different
experiences depending on their age and/or socioeconomic status which, as stated before, can
affect their decisions (de Bruin et al., 2007; Finucane et al., 2005).
In conclusion, a personal decision such as whether an influential factor will be considered
can be affected by experiences, personal commitment and relevance and demographic
characteristics and environmental circumstances. Based on these deliberations, various
correlation analyses were conducted. In order to determine whether a correlation was strong,
the classification scheme of Brosius (1999) was used: 0 to 0.199 very weak, 0.2 to 0.399 weak,
0.4 to 0.599 substantial, 0.6 to 0.799 strong and 0.8 to 1 very strong. The following three
sections show the results of these analyses. It should be noted that the use of a multiple
regression would have been preferable but the (sub-)sample size and the conditions for
the regression analysis with regard to predictors and the criteria prevented its use.
Influence of experiences
As part of the survey, the survey asked the respondents whether they had had experience
with TEL in teaching. As can be seen in Table III, 97 respondents had had such experience.
The survey also asked these 97 respondents whether they had already considered the factors
belonging to the 20 categories. Based on the answers received, Cramérs V was calculated to
determine the influence on the importance of the categories and the time people would spend
considering factors in these categories. Cramérs V is a measure of the degree of association
between two nominal variables that have two or more levels, which is equal to the
Phi coefficient in the case of a 2×2 contingency table. The study used this in the analysis
because it is robust where nominal and ordinal data are present.
The evidence indicates that, regardless of a few exceptions, significant weak or substantial
correlations were observed between the importance of a category and whether someone had
already considered the influential factors in this category. All these correlations indicate that
someone who has already considered factors of a category believes it is still important to
consider these influential factors. In this study, the highest correlations observed were:
business aspects (r¼0.520, po0.01), social aspects (r¼0.445, po0.01) and cognitive aspects
(r¼0.440, po0.01).
In addition, the calculation revealed many significant weak or substantial correlations
between the amounts of time the respondents would currently spend considering factors
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according to their considerations of factors in the past. Again, the findings indicate that
someone who had already considered factors of a category would spend more time considering
factors in this category. The only exception was seen in the category mind-set and feelings
before TEL (r¼0.408, po0.01). In this category, the findings revealed that respondents who
had already considered the category in the past would spend less time to do so now.
Overall, the findings indicate that experiences affect human behavior such that the
factors that have been considered previously are judged to be more important, and
more time is invested during the consideration of these factors. Moreover, both positive
( Juliusson et al., 2005) and negative (Sagi and Friedland, 2007) experiences in the past had
the potential to determine personal decisions such as whether an influential factor would be
important in the future. Based on these findings and the correlations described above, the
respondents seem to have had predominantly positive experiences while considering
influential factors in the past and, hence, indicated that they would be willing to invest time
to consider these factors in the future. In contrast, with regard to the category mind-set and
feelings before TEL, it could be inferred that the respondents could have had negative
experiences in the past while considering these factors and, as a result, indicated that they
did not want to invest much time to consider these either now or in the future. However, the
strength of this claim requires further investigation. First, additional research on this topic
could prove whether the assumptions are accurate. Second, if these assumptions are
supported by evidence, additional research could identify positive and negative experiences
and provide professionals in higher education with better methods that they can use to
consider these factors in the future.
Influence of personal commitment and relevance
To determine whether personal commitment and judged relevance influenced the results,
Spearmans rank correlation was calculated between the importance of a category of
influential factors and the time people would spend considering factors in this category.
Furthermore, this analysis made it additionally possible to evaluate whether the importance
of a category affected the time invested considering factors in other categories.
The results revealed that significant weak to strong positive correlations existed
between the importance of a category and the time people were willing to spend considering
factors in this category. This is valid for the entire set, meaning that the more important a
category was considered to be, the more time the person was willing to spend considering
factors in this category. In this context, the highest correlations observed were: demographic
differences (r¼0.646, po0.01), mind-set and feelings before TEL (r¼0.616, po0.01) and
social aspects (r¼0.605, po0.01).
These three categories also displayed significant and substantially strong correlations
among each other. The more important the category demographic differences were ranked,
the more time the respondents were willing to spend considering the categories mind-set
and feelings before TEL (r¼0.501, po0.01) and social aspects (r¼0.412, po0.01).
Also, the more important the category mind-set and feelings before TEL was ranked, the
more time the respondents were willing to spend considering the categories demographic
differences (r¼0.499, po0.01) and social aspects (r¼0.448, po0.01). Finally, the more
important social aspects were ranked, the more time the respondents were willing to spend
considering the category demographic differences (r¼0.448, po0.01).
Overall, many of the substantial to strong correlations that were identified reveal that the
more important a category was ranked by the respondent, the more time the respondent was
willing to spend considering factors in this category. Therefore, it can be inferred that
personal commitment and judged relevance influence the results.
Furthermore, the evidence indicated that the categories that were considered to be
important only to a small group seem to be interrelated. For instance, respondents who
Influential
factors
for TEL
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thought that it was important to consider factors in the category demographic differences
also tended to spend more time considering factors in the category social aspects.
This supposition needed to be investigated; however, it can be assumed that collaborative
learning scenarios are often used in courses with demographic differences, whereby the
factors in the category social aspects would become more relevant.
Influence of demographic characteristics and environmental circumstances
Finally, Spearmans rank correlation (only for ordinal scales) and Cramérs V were used to
analyze whether the descriptive data (see Table III) affect the importance of each category
and the time respondents would spend considering factors in each category.
Concerning the importance, correlations between the descriptive variables and the
importance of each of the categories (Q1), the choice of the top five categories (Q3) and the
ranking within the top five categories (Q4) were calculated. The analysis results showed that
the first two areas of interest were related by only a few significant but weak to very weak
correlations. With regard to the ranking within the top five, the analysis allowed the
identification of stronger correlations but none were significant because the relevant data
set for these high correlations was too small (n¼222).
With regard to the time invested, the calculation revealed similar correlations. It can be
concluded that the tested demographic characteristics and environmental circumstances did
not affect the ranked importance of categories and the time respondents would be willing to
spend considering factors in these categories. It seems as though other characteristics
(e.g. the course subject, the level of knowledge acquisition and the heterogeneity of the
students) should be tested in the future to verify whether these circumstances result in an
influence on relevance and, therefore, inferred importance.
Limitations of the study
This contribution has several recognizable limitations. The subsample of 120 respondents
included in this study represented only a small fraction of the defined subpopulation.
High non-response rates can bias the data set (Frohlich, 2002) which can jeopardize
the generalizability and validity of the results of the study. However, it is difficult to
test for the presence of this bias, as this would have requiredthe collection of a comprehensive
data set from all professionals from all Austrian and German institutions of higher education.
Such information theoretically might have been obtained from marketing databases or
through manual searches. In the present context, however, commercial databases display
certain weaknesses: they cannot guarantee the absolute accuracy of the data because
complete coverage of the institutions defined in the population does not exist and the number
of employees indicated includes administrative, scientific and teaching staff but frequently
does not include lecturers that give classes and are employed on a part-time basis. For these
reasons, it was not economically possible to precisely calculate the size of the (sub)population
or to compare the basic characteristics of the populations with those of the present sample in
this study. Because an electronic mailing strategy to disseminate the questionnaires was used,
the composition of the sample may have shifted in favor of the paper authorscontact
coverage. Moreover, because the survey was carried out in part anonymously, the possibility
that certain respondents responded multiple times cannot completely be rule out, although no
apparent abnormalities in the data set were detected. Finally, surveys are typical types of
studies that rely, according to their definition, on the participants ability to read questions and
select responses on the questionnaire by themselves without interference from the researcher.
To that effect, the empirical data may have suffered from different cognitive biases
such as acquiescence or social desirability. This especially concerns the interpretation
of the 20 categories, which is why the questionnaire provided additional keyword descriptions
for them.
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Conclusions and future work
The purpose of this study was to identify which influential factors professionals in Austrian
and German institutions of higher education considered important and how much time these
professionals would invest to consider these factors when implementing TEL approaches.
Thereby, intentionally the broadly defined term TEL was used to target influential factors
that were considered important, independent of the approach taken.
The results revealed that influential factors in the six categories learning success, teachers
teaching aspects, motivational aspects, learnerslearning aspects, course-related aspects and
requirements on teachers were generally considered as important by 120 professionals. In
addition, respectively, at least a small group of these respondents considered the remaining
factors as important too, and different circumstances seemed to affect this importance.
Therefore, the given data set was analyzed again to identify potential moderating effects.
The findings revealed that experiences and personal relevance seemed to strongly affect
how professionals in higher education chose which influential factors they were willing to
consider. However, the tested descriptive variables (i.e. age, type of organization, country,
function in organization, time in position, field of work and experience with TEL in general)
did not influence these choices.
For this reason, future research should focus on the relationships between
different moderating variables and relevant influential factors. The research should
identify which factors are relevant under given circumstances. A qualitative research
approach using interviews with several experts as well as teachers seems to be an
appropriate research method for this purpose. This approach can also address some
limitations of the given study like the possibility of different interpretations regarding the
survey items. Revealed relevant circumstances with an impact on the relevance of
influential factors could include the course subject, the addressed level of Blooms
Taxonomy, the available technical infrastructure, experience and knowledge with TEL
approaches, the number of students and much more.
Based on this research, the development of a methodology that better support the
implementation of TEL approaches is possible. This methodology should include two
steps: the identification of relevant circumstances and the identification of relevant
influential factors that should be considered under the given circumstances. Using such a
methodology should be more efficient, since only relevant influential factors will be
considered during the implementation of TEL approaches. Additionally, the implemented
TEL approaches should be more successful too, since CSF would have been considered.
For example, the results of this study indicated it is generally not very important to
consider demographic differences in German or Austrian institutions of higher education,
but if a course would involve a highly heterogeneous group of learners, this factor might
become a CSF and should be considered.
Finally, with several case studies the new methodology should be tested at different
higher educational institutions in Germany and Austria in order to verify the value of
this approach.
Note
1. Includes important and very important.
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Appendix. Structure of questionnaire (excerpt) Influential
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About the authors
Patrick Schweighofer currently works as Deputy Head of the degree program Information Technology
& Business Informatics at the University of Applied Science CAMPUS 02 in Graz, Austria. In February
2012, he received a Masters Degree in Information Technology and IT-Marketing and, since June 2013,
he has been working toward his dissertation on the subject of structured implementation of
technology-enhanced learning approaches at the Graz University of Technology. Patrick Schweighofer
is the corresponding author and can be contacted at: patrick.schweighofer@campus02.at
Doris Weitlaner earned her MSc in Software Development Economy at the Graz University of
Technology and is currently employed as Research and Teaching Assistant at the Information
Technologies & Business Informatics department of the CAMPUS 02 University of Applied
Sciences in Graz, Austria. She mainly works on projects that address the topics of service
engineering and data science with a special focus on quantitative research, multivariate data
analyses and process management.
Martin Ebner is currently head of the Department Educational Technology at the Graz University
of Technology and, therefore, is responsible for all university-wide e-learning activities. He hold
Associate Professor in media informatics and also works at the Institute for Information System
Computer Media as a senior researcher. His research focuses strongly on e-learning, mobile learning,
learning analytics, social media and the use of Web 2.0 technologies for teaching and learning. Martin
gives a number of lectures in this area as well as workshops and talks at international conferences.
To view publications as well as information about further research activities please visit his website:
http://martinebner.at
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Hannes Rothe is currently postdoctoral Research Associate at the Department Information
Systems at the Freie Universität Berlin. Since 2013, he coordinates Entrepreneurship Education
through the project Entrepreneurial Network University at the Freie Universität Berlin and
Charité Universitätsmedizin. He holds Doctoral Degree in business administration and performs
research on the development of IT-services. His research has been published in international
conference proceedings and journals in the areas of service engineering and educational technologies.
To view publications and information about additional research, please visit his website:
http://hannesrothe.de
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
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... Various alternative words are used in research to speak about information technologies used in higher education (e.g., educational technology, computer-assisted instruction, elearning, distributed learning, educational computing, etc.) [22]. Goodyear and Retalis [23] used the phrase technology-enhanced learning and explained that the phrase includes all technologies that make learning more efficient, effective and enjoyable. ...
... Technology-enhanced learning methods are popular in higher education today, and the number of approaches used is still growing. Many researchers claim that recent generations of students, so-called digital natives [25], require technical assistance in their different learning situations as they are using that technology on a daily basis [22,26,27]. ...
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Purpose The purpose of this study is to explore the role individual innovativeness along with e-learning self-efficacy play in predicting the e-learning readiness of first- and second-year students of an open and distance education institutions in an African context. Design/methodology/approach Therefore, building on previous related research in this area, a quantitative approach was adopted to address the research questions and to establish whether a statistically significant relationship existed between individual innovativeness, e-learning self-efficacy, the independent variables; and e-learning readiness, the dependent variable. In total, 476 first- and second-years students of the university participated in the four-Likert-type scale survey. The research instrument which comprises 74 survey items was completed by 217 of the students. Statistical tools used for analysing data included both Pearson Product Moment Correlation coefficients and t -tests. Findings It was discovered that a strong positive and significant relationship was observed between individual innovativeness and e-learning readiness of first- and second-year students of the Yenagoa Study Centre of the National Open University of Nigeria (NOUN); a statistically significant relationship was also found between e-learning self-efficacy scores and the e-learning readiness of the first- and second-year students of the Yenagoa Study Centre of NOUN; there was a statistically significant joint relationship between the three variables under investigation; findings equally revealed that male respondents had higher e-learning readiness than their female counterparts. Research limitations/implications Like every other study of this nature, this one also suffers some limitations. First, NOUN is a very large university with over half a million students spread across almost 78 study centres. This means that observation from just one study centre amounts to a very small sample size. This according to Schweighofer, Weitlaner, Ebner and Rothe (2019) jeopardises the generalisability and validity of study results. The authors also maintain that empirical data generated from surveys that usually rely participants' abilities to read and select responses without further interpretation by the researchers suffer from cognitive biases like social desirability. To address the above limitations, detailed studies involving all studies centres of NOUN be undertaken and other qualitative and or mixed research methodologies be adopted in the future. Practical implications The implications for this study are that people who are innately innovative will willingly accept technology and by extension, learning in technology-rich environments like those found in like NOUN whose mode of study is blended learning inherently found in open and distance learning (ODL) institution. Therefore, this study is significant as it will provide relevant information to the management and administrators of NOUN, policymakers and regulatory institutions for the development, deployment and implementation of e-learning strategies. Findings will also benefit e-learning initiatives undertaken by similar institutions that adopt the ODL mode of education in Nigeria and other developing countries. Originality/value Even though, studies on the antecedents of e-learning readiness have been widely conducted across diverse contexts, studies exploring the associations between individual innovativeness, e-learning self-efficacy and e-learning readiness are relatively hard to come by. The above two variables as predicting the e-learning readiness in the study context are comparatively new. This study thus focuses on the relationships between the individual innovativeness levels, e-learning self-efficacy beliefs of students and their e-learning readiness which ultimately determines their ability to sustain studies in an ODL institution.
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This article reports progress in an ongoing research program examining older adults' decision-making competence (DMC). Using a theoretical framework that emphasizes the person-task fit in assessing DMC, the authors report the results of a study comparing older versus younger adults' decision performance on simple and complex tasks about health, finance, and nutrition. The authors hypothesized and found that increasing age and task complexity were related to greater comprehension errors and inconsistency in decision making. Hierarchical regression analyses showed that a large amount of age-related variance in performance on the decision tasks could be accounted for by exogenous social variables, health measures, basic cognitive skills, and attitudinal measures. The discussion emphasizes several directions for future research, including the need to validate the meaning of performance on these tasks for real-life decision processes.
Chapter
If you are a practicing teacher at any level—primary, secondary, or higher education—you already know quite a lot about designing instruction. Your work, prior to teaching a course, includes finding out what your students already know when they walk into the first day of class and determining what knowledge you hope they will gain by the end of the course. You design activities that enhance their new knowledge and allow them to practice with it. You plan tests that help the students demonstrate their newfound understanding. Every time you teach the course, and even at some points during the course, you make changes based on “how things are going” and later on you think about “what happened” throughout the course. The next time you teach the course, it is (hopefully!) much improved. That is, in essence, exactly what instructional design is all about. But instructional design practices proceed from a more formal and systematic way of thinking about the teaching and learning process. Such systematic thinking helps designers focus on each component of the design process that ensures a successful design for learning. Purchase this chapter to continue reading all 17 pages >
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E-learning systems are increasingly essential in universities, schools, government departments and other organizations that provide an education or training service. The objective for adopting e-learning systems is to provide students with educational services via electronic channels. The focus of this study is on the impact of IT infrastructure services and IT quality on perceptions of usefulness of e-learning systems. A model is proposed which includes five constructs: IT infrastructure services, system quality, information quality, service delivery quality, and perceived usefulness. A quantitative study was conducted at an Australian university with 720 survey responses from students who were enrolled in online courses. The results suggest that IT infrastructure services play a critical role in generating information with high quality, enhancing the aspects of e-learning system quality, and improving service delivery quality. The impact of IT infrastructure services, system quality, and information quality on perceived usefulness is fully mediated by service delivery quality. Universities need to be aware of the critical impact of IT infrastructure services and consider how investment in these services could improve system and information quality, service delivery quality, and the usefulness and success of e-learning systems.