Student and in-service teachers’ acceptance of spatial hypermedia in their teaching: The case of HyperSea

George Koutromanos & Georgios Styliaras &
Sotiris Christodoulou
Published online: 15 January 2014
# Springer Science+Business Media New York 2014
Abstract The aimof this study was to use the Technology Acceptance Model (TAM) in
order to investigate the factors that influence student and in-service teachers’ intention to
use a spatial hypermedia application, the HyperSea, in their teaching. HyperSea is a
modern hypermedia environment that takes advantage of space in order to display
content nodes and social media pages that can be dragged from the Internet. In total,
257 student and in-service teachers completed a survey questionnaire, measuring their
responses to four constructs in the TAM. The results of student teachers’ regression
analysis showed that all components of the TAMwere found to predict their intention to
use HyperSea in their teaching. Perceived usefulness was the most important predictor
in their attitude and intention. On the other hand, only attitude towards use had direct
influence on teachers’ intention. In addition, perceived usefulness influenced teachers’
intention. Perceived ease of use in this study failed to emerge as a significant predictor of
teachers’ attitude and perceived usefulness. The results showed that the TAMin general
is useful model for predicting and exploring the factors that influence student and inservice
teachers’ intention to use spatial hypermedia such as the HyperSea in their
teaching in future. Results of the study are discussed in terms of increasing the intention
of student and in service teachers to use spatial hypermedia in their teaching.
Keywords Spatial hypermedia . Student and in-service teachers . TAM. Technology
acceptance
Educ Inf Technol (2015) 20:559–578
DOI 10.1007/s10639-013-9302-8
G. Koutromanos
Faculty of Primary Education, National and Kapodistrian University of Athens,
20 Ippokratous, 10680 Athens, Greece
e-mail: koutro@math.uoa.gr
G. Styliaras (*)
Department of Cultural Heritage Environment and New Technologies, University of Patras,
Seferi 2, Agrinio 30100, Greece
e-mail: gstyl@upatras.gr
S. Christodoulou
Technological Educational Institute of Messolonghi, Nea Ktiria, 30200 Messolonghi, Greece
e-mail: sxristod@teimes.gr
1 Introduction
Nowadays, web-based education systems tend to use traditional web interfaces, such as
simple HTML pages, with hyperlinks and embedded multimedia objects. Established
educational applications and games employ mainly offline-based technologies, which
make them ideal for executing on a personal computer (e.g. DiPaola and Akai 2006;
Conti et al. 2006). On the other hand, recent evolutions in hypermedia applications
have given new opportunities for interface and interaction design (e.g. Jankowski and
Decker 2012; Heinrich et al. 2012). Collaborative and social media capabilities along
with spatial and multimedia features have allowed for more expressive and graphic
representation of content objects and their relations (e.g. Matias 2005; Buchanan and
Owen 2008). Users can annotate, link and display hypermedia content by using simple
interface actions such as drag and drop actions.
Despite the interest in spatial hypermedia applications, there is a lack of studies
investigating teachers’ adoption behaviours of these technologies within educational
context. Previous studies have shown that teacher is the person upon whom the degree
of the ICT’s implementation depends (e.g. Jones 2004). According to Fullan (2007),
“educational change depends on what teachers do and think” (p. 115). In the last 30
years, a number of studies have proposed theories or models to represent the psychological
factors that influence teachers to use ICT in their teaching (e.g. Czerniak et al.
1999; Preston et al. 2000; Salleh and Albion 2004; Shiue 2007; Smarkola 2008; Lee
et al. 2010; Teo 2011). In addition, many studies have used theories or models in order
to identify and develop student teachers’ beliefs and attitudes during their teacher
education programmes to prepare these future educators for the uptake of ICT in their
teaching (e.g. Smarkola 2008; Teo 2009; Teo and Lee 2010). The most commonly used
models and theories are the Theory of Reasoned Action (TRA) (Ajzen and Fishbein
1980; Davis et al. 1989) and its extensions, the Technology Acceptance Model (TAM)
(Davis 1989) as well as the Theory of Planned Behaviour (TPB) (Ajzen 1991, 2006).
Among these models, TAM is the most widely-used and tested model in technology
acceptance studies. More specifically TAM has been used in order to investigate the
factors that influence pre-service and in-service teachers’ intention to use ICT in their
teaching (e.g. Teo 2009; Teo et al. 2008, 2009). Although TAM studies have examined
educators’ attitudes and intentions related to different technologies in general, research
related to hypermedia (e.g. Gao 2005) and to student and in-service teachers’ psychological
factors toward using spatial hypermedia for teaching, is limited.
The aim of this study was to use the TAM in order to investigate the factors that
influence student and in-service teachers’ intention to use spatial hypermedia application
in their teaching. This study used as a case study the example of HyperSea
(Styliaras and Christodoulou 2009), which is a typical spatial hypermedia environment
enhanced by some innovative features as discussed below. This is an environment that
exploits space in order to display content nodes that can be dragged from the Internet.
Users can import, link and organize such content from different sources by allowing
them to further refine or enrich this organization. Users may link and annotate nodes in
a surface, by employing only natural actions such as drag and drop actions and not
requiring complex menu choices and button operations. They need not be aware of the
technical and content structuring details that are hidden behind the spatial interface.
New devices, such as tablets and smartphones, favour this kind of interaction, as drag
560 Educ Inf Technol (2015) 20:559–578
and drop can be easily performed by tapping on touch-sensitive surfaces (e.g.
Jankowski and Decker 2012; Heinrich et al. 2012) (see more details in the Section 2).
This study contributes to the literature in three ways. Firstly, this study validates the
TAM in two different samples and its findings provide useful information for its use in
educational contexts. Secondly, the contribution of this study is that we assess student
teachers and in-service teachers’ acceptance of a specific educational technology.
Although studies have investigated student teachers and in-service teachers’ intentions
related to technology in general (e.g. Wong et al. 2013), research related to specific
educational technology use is limited (e.g. Lay et al. 2013), specifically related to
student teachers and in-service teachers’ intention to use spatial hypermedia such as the
Hypersea for teaching and learning. Lastly, this study adds to the general literature on
technology acceptance as we investigated the factors affecting technology acceptance
such as spatial hypermedia among student teachers and in-service teachers. A study of
these two groups of teachers will provide a broader view and a better explanation of the
adoption of this technology in schools now as well as in the future.
This paper is organized as follows. In the next section, we present the HyperSea
environment. Then, the theoretical framework of this study is presented. The research
design and data analysis are discussed next. Thereafter, results are presented and
discussed with a number of implications and conclusions regarding student and inservice
teachers’ intention to use HyperSea in their teaching in future. Finally, limitations
of this study and implications for future studies are discussed.
2 HyperSea description
Spatial hypermedia (Shipman et al. 1999) extends classic hypertext and hypermedia in
the following ways: Firstly, it allows new ways of explicit or implicit linking of
multimedia nodes. More specifically, in classic hypermedia, an explicit directed hyperlink
is employed in order to join two nodes, one of those being the anchor or target of
the link. In spatial hypermedia, in addition to this capability, a spatial area is exploited,
in the way that the positioning of some nodes on an empty area may imply some kind
of relation among them depending on their distance. Secondly, nodes may imply
linking based on visual cues. Nodes being nearer and/or using the same border line
or color have stronger relations than others. Finally, spatial hypermedia allows also the
direct or indirect grouping of information. Consequently, by employing spatial hypermedia,
one can express richer relations among some information nodes than just by
hyperlinking them. For example, Visual Knowledge Builder (VKB) (Shipman et al.
2001) is a spatial hypermedia application that has all the above features along with a
history mechanism and has been used in note taking, writing, project management, and
conference organization.
This study uses HyperSea, a spatial hypermedia environment, which extends the
functionality of existing systems such as VKB (Shipman et al. 2004) with some
innovative features, as follows: Firstly, it supports a hierarchical organization of information
in several spaces and the navigation capability among these spaces. Moreover, a
user can import and link new content in the environment from web 2.0 applications, web
pages and local multimedia files. Content is recognized automatically and appropriate
metadata such as title and technical properties (the duration of a video clip) are shown
Educ Inf Technol (2015) 20:559–578 561
and stored. Furthermore, all this content is formalized, structured and stored as an
ontology, which enables its reusability and extension by other users. Last but not least,
all these actions, even the most complex ones such as node deletions and linkings, are
performed by simple mouse or touchscreen actions whereas in VKB for example, the
user should use a menu for this operation. Input by keyboard is required only when text
has to be filled in a property, such as a custom node description. These design choices
minimize the learning effort and required technical skills, while it accelerates the
interaction with linked content as users need only to focus on content and its properties
and relations, than on complex menus and buttons.
More specifically, HyperSea is an environment for collecting, organizing and
presenting web 2.0 content. The environment allows a single user or many users to
organise their information sources in one large space, called Archipelago, which can be
authored and viewed with two-levels of detail. This decision was made in order to keep
the environment simple and inhibit users from creating deep and complex hierarchies of
data. Instead, HyperSea encourages its users to represent deep hierarchies in space.
In the first level of detail, the user sees an archipelago, which is divided into
individual islands. In the second level of detail, all explicit links among nodes and
structs belonging to different islands are also visible. These links are also visible in the
first level of detail as links between the islands, but the end point of each link is placed
inside the island metaphor. Information spaces produced by the HyperSea environment
provide users with alternative ways to comprehend the content and its relations within
this space.
In other words, HyperSea is a spatial hypermedia environment with features aiming
to meet the requirements of web 2.0 users. These features were carefully selected based
on research and design methodologies of spatial environments. Our focus is on
providing simplicity while performing powerful operations and support the efficient
exploitation of the user’s space.
More specifically, we focus on the idea of structured nodes that can be drawn on a
whiteboard and are associated together either explicitly (via linking them) or implicitly
(via proximity or visual cues like colour). Nodes have a common appearance with a
header and basic attributes, so as not to aggravate the user’s learning effort. Node
interactions, such as select and drag lead to respective changes in their status, e.g.
dragging a line from one node to another, results in creating a link among the nodes.
We engage the absence of menus and buttons, in order to allow for more natural
operations (e.g. group some nodes by encircling them, or delete a node by dragging it
out of the whiteboard). Furthermore, all of the HyperSea’s operations, such as clicking
and dragging large items, are designed so as to be easily performed on tablets and
smartphone devices. The nodes’ placement and use of visual cues are also important
design principles. Finally, attention has been given for providing status messages and
allowing moving seamlessly among various environment states. The viewer may
operate the environment in two distinct modes (editing mode and navigation mode).
For every operation mode, simple interface operations suffice to cover the entire
environment’s functionality, such as the creation and the interaction with nodes, and
more complex structs and islands.
During editing mode, the user can drag a multimedia file or a web page and drop it
into the environment in order to create a new node. All nodes have the same size, but
their color, placement and border denote their origin, importance and relationships. By
562 Educ Inf Technol (2015) 20:559–578
single-clicking a node, its attributes appear, some of which were automatically extracted
when this node was dragged-in the HyperSea space. Users can also contribute new
attributes to the node’s description. By double-clicking the node, the underlying content
(web page or multimedia content) is loaded. A node is deleted, by simply dragging it
out of the environment. A node may be cloned in many places of the environment if it
is required by the visual representation of its relationships. All clones are mirrors of the
original node and changes on one clone are reflected on the rest of them. The user can
enclose a set of nodes and form a struct by simply drawing a line around them. By
creating a new struct, the user can enter attributes that describe the common features of
nodes belonging in the struct. A struct is represented as a rectangular with the struct title
at the top. User may move a whole struct in space by clicking (or tapping) on its title
area and dragging it. Users can create annotation nodes by double-clicking on empty
space. Annotation nodes appear as yellow boxes and their size change as users are
typing text in them. Nodes placed near each other are implicitly related as in spatial
hypertext systems. By dragging a line from the first node’s title area to the destination
node’s title area, an explicit link is created.
During navigation mode, users can interact with nodes, move them and view their
content and attributes. They can view, edit and insert new annotations. Thus, they are
able to perceive the interconnected content as proposed by the island creator. Figure 1
shows a screenshot while interacting with the HyperSea environment for linking
content related to the film Avatar. A video-clip showing the current environment
implementation in action while gathering and organizing content for the area of
Kalamata, Greece can be found at http://youtu.be/jXSAso5lecw . In this video, some
Fig. 1 A screenshot of the HyperSea environment
Educ Inf Technol (2015) 20:559–578 563
actions are expressly recurring and/or invalidated in order to show all possible features
of the environment.
3 Theoretical framework
As background to this study, a brief review of the Technology Acceptance Model
(TAM) is provided in this section. The TAM was developed by Davis (1989) to explain
computer-usage behaviour. The theoretical basis of the model was Fishbein and Ajzen’s
Theory of Reasoned Action (TRA) (see Fishbein and Ajzen 1975). The goal of TAM
was “to provide an explanation of the determinants of computer acceptance that is
general, capable of explaining user behaviour across a broad range of the end-user
computing technologies and user populations, while at the same time being both
parsimonious and theoretically justified” (Davis et al. 1989, p. 985).
According to TAM (see Fig. 2), an individual’s technology acceptance decision is
determined by his or her voluntary behavioural intention which is underpinned by his
or her attitude towards the use of technology. Attitude toward the use is the person’s
positive or negative evaluation of performing the behaviour (Fishbein and Ajzen 1975).
Attitude towards use is determined by beliefs towards a technology’s usefulness and
ease of use, as perceived by an individual.
Perceived usefulness is defined as “the degree to which a person believes that using a
particular system would enhance his or her job performance” (Davis 1989, p. 320).
Perceived ease of use is defined as the “the degree towhich a person believes that using a
particular system would be free from effort” (Davis 1989, p. 320). Perceived usefulness
is influenced by perceived ease of use. Furthermore, TAMtheorises that external factors,
such as the task, user characteristics, and organizational contexts are expected to
influence technology acceptance behaviour indirectly by affecting attitudes or intention.
Since the development of TAM in 1989, it has been well supported in its utility and
applicability by a number of studies which refer to wide range of contexts and
technologies. These include ‘traditional task-related’ information systems (e.g., spreadsheet
and word processing), ‘Internet related’ information systems (e.g. online learning,
World Wide Web, digital library, e-Government), e-commerce information systems
Behavioural
intention
to use
Actual
system use
Attitude
toward use
Perceived
usefulness
Perceived
ease of use
External
variables
Fig. 2 The technology acceptance model (Davis et al. 1989, p. 985)
564 Educ Inf Technol (2015) 20:559–578
(e.g. online shopping, mobile banking), and hedonic information systems (e.g. interactive
televisions, PDAs, or mobile phones, online gaming) (see Hsiao and Yang 2011).
In education, the TAM has been used to explain behaviours such as university students’
acceptance of e-learning tools (e.g. WebCT, Moodle) (e.g. Ngai et al. 2007; Sánchez
and Hueros 2010; Tselios et al. 2011), an online course companion site of a textbook
(Gao 2005), an e-portfolio system (Shroff et al. 2011), students’ intention to use a
ubiquitous English vocabulary learning system (Huang et al. 2012), pre-service
teachers’ intentions to use technology (Teo 2009; Teo et al. 2009; Teo and Noyes
2011; Wong et al. 2013), and geography teachers’ acceptance of the use of the
geographic information system in their teaching (Lay et al. 2013).
In the literature review there are studies that expanded TAM (e.g. see Venkatesh and
Davis 2000 for TAM 2; Venkatesh et al. 2003 for the Unified Theory of Acceptance and
Use of Technology; Venkatesh and Bala 2008 for TAM 3) and some researchers
suggested that it should be modified to include and other external and internal variables
in order to provide a broader view and a better explanation of technology adoption.
Among these variables incorporated into TAM are training (e.g., Igbaria et al. 1995),
compatibility, computer anxiety, self-efficacy, enjoyment, computing support, and
experience (e.g., Chau and Tam 1997), personal innovativeness (see Agarwal and
Prasad 1998), trust (Gefen et al. 2003), perceived playfulness (see Moon and Kim
2001) and perceived risk (Pavlou 2003).
From 2003 to 2012, there are more than ten meta-analysis studies which attempt to
consolidate the results obtained from TAM studies (see Legris et al. 2003; Lee et al.
2003; Ma and Liu 2004; King and He 2006; Yousafzai et al. 2007a, b; Schepers and
Wetzels 2007; Wu and Lederer 2009; Turner et al. 2010; Šumak et al. 2011; Wu et al.
2011; Zhang et al. 2012). Most of these meta-analysis studies provided strong evidence
to support TAM as a model for predicting technology usage behavior (e.g. King and He
2006; Schepers and Wetzels 2007).
Nevertheless, there are criticisms among some researchers regarding the accuracy of
the TAM to predict the actual use. For instance, in an earlier meta-analysis, Ma and Liu
(2004) found that “the relationship between ease of use and acceptance is weak, and its
significance does not pass the fail test” (p. 59). However, they concluded that their
finding regarding this relationship was uncertain and therefore they suggested that more
studies are needed in order to resolve this uncertainty. In addition, a meta-analysis of 73
studies, from 1989 to 2006, conducted by Turner et al. (2010), found that behavioural
intention “is likely to be correlated with actual usage. However, the TAM variables
perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated
with actual usage” (p. 463). They argue that a possible explanation for this finding is “that
PU explains part of the variation in BI and BI explains part of the variation in actual
usage, but they could each explain a different part of the variation, meaning that it cannot
be assumed that there is an association between PU and actual usage” (p. 470).
Despite the criticisms, results from three more recent meta-analysis studies show that
TAMis still a valid model. Šumak et al. (2011) examined 42 empirical studies published
up to the end of 2010 and found that “the perceived ease of use and the perceived
usefulness tend to be the factors that can influence the attitudes of users toward using an
e-learning technology in equal measure for different user types and types of e-learning
technology settings” (p. 2067). Wu et al. (2011) examined through 128 TAM studies,
from 1992 to 2010, the role of trust in user’s online behavior, especially in the e-
Educ Inf Technol (2015) 20:559–578 565
commerce context. They found large correlations between perceived usefulness, perceived
ease of use and attitude as well as attitude and behavioural intention. Finally,
Zhang et al. (2012) meta-analysis regarding the mobile commerce adoption and the
moderating effect of culture also demonstrated the validity of the TAM.
Based on the above discussion, in order to investigate and compare the factors that
influence student and in-service teachers’ intention to use the HyperSea in their teaching
we used the original TAMfor the following three reasons. Firstly, despite the criticismof
the TAM, for the last two decades it has proven to be an efficient model in different
technologies including technologies in education. Secondly, the variables of TAM (i.e.
perceived usefulness, perceived ease of use) are the core for a number of revised and
extended TAMs. These variables are found to be important for the prediction of
acceptance of technologies such as the use of Face-book (Sánchez et al. 2014),
YouTube (Lee and Lehto 2013) and virtual communities (Hung and Cheng 2013).
This suggests that these variables are still valid in an understanding of technology use.
Thirdly, it is worth mentioning that teachers have a large degree of autonomy during
teaching including the choice of technologies they use (see Teo et al. 2009). For
example, earlier study by Preston et al. (2000) showed that “…teachers use ICT in their
teaching not because they are obliged to, due to school policy, their professional role,
etc., but because they find it useful and beneficial for both themand their pupils” (p. 37).
Also, their study showed that those teachers who experienced difficulties using software
were less likely to find it easy to think of ways of using ICT. Taking this issue into
consideration, the HyperSea has been designed to make its use and operation simple and
practical in an educational environment. It has also been designed to make lessons more
diverse and interesting for teachers and students (e.g. dragging multimedia files, web
pages). Therefore, we considered that the main factors for student and in-service
teachers’ intention to accept a voluntary spatial hypermedia system such as the
HyperSea are attitude, perceived usefulness and perceived ease of use. Other factors
which were included in revised and extended TAMs (e.g. subjective norm) were not
considered appropriate for the purpose of this study.
The research hypotheses based on the TAMmodel in the context of the HyperSea are:
H1: Perceived ease of use has a significant effect on the perceived usefulness of the
HyperSea.
H2: Perceived ease of use has a significant effect on attitude towards using the HyperSea.
H3: Perceived usefulness has a significant effect on attitude towards using the HyperSea.
H4: Perceived usefulness has a significant effect on intention to use the HyperSea.
H5: Attitude towards using has a significant effect on intention to use the HyperSea.
4 Method
4.1 Participants
The sample (n=257) of this study included both student (n=151) and in-service
teachers (n=106) studying at the University of Athens (Faculty of Primary Education).
Among the in-service teachers, 33 were postgraduate students and 73 were enrolled at
the university in order to attend a specific training programme. The student teachers were
566 Educ Inf Technol (2015) 20:559–578
in the 4th year of their studies. They had many hours of teaching experience in primary
schools, initially observing their cooperating teachers in their classrooms and then taking
full teaching responsibilities for one or more days every week. This experience occurred
in the fall and spring semesters. In addition, the sample was enrolled in the course
“Information Communication Technology in Education” that was one of the core
curriculum courses required for their studies at the University of Athens. Finally, all of
them owned a computer at home. All teachers used ICT in their teaching. Therefore, the
majority of the sample had considerable computer and teaching experience. Table 1
summarizes the demographic profile and descriptive statistics of the respondents.
4.2 Research instrument
A questionnaire instrument was developed for this study. First of all, the questionnaire
was developed in English and then translated into Greek. The translation was made by
three certified translators in order to confirm the adequacy of the translation.
Additionally, five experts in ICT in education were invited to review the questionnaire.
Later, the questionnaire was pilot tested with 30 Greek student and in-service teachers.
These were excluded from the final data collection. The questionnaire consisted of
three sections. The first section required participants to provide their demographic
information such as gender, age, access to a computer at home, years of experience
with computers and the Internet, as well as frequency of Internet use. Regarding
frequency of Internet use, we used a five point scale (never, around 1 h per month,
around 1 h per week, several hours per week, more than 1 h per day), which was used
in earlier research (e.g., Preston et al. 2000).
Table 1 Demographic profile and descriptive statistics of sample
Item Number Percent
Gender
Male 42 16.3
Female 215 83.7
Age
<25 191 74.3
26–35 35 13.6
36+ 31 12.1
Level
Student teachers 151 58.8
In service-teachers 106 41.2
Frequency of internet use
More than 1 h per day 167 65
Several hours per week 77 30
Around 1 h per week 12 4.7
Around 1 h per month 1 0.4
Never 0 0
Mean years of Internet usage 4.92 (SD=3.579)
Educ Inf Technol (2015) 20:559–578 567
The second section of the questionnaire contained items designed to measure the
variables of the TAM except the actual HyperSea use. The items of this section were
adapted from previously validated instruments and modified to fit the HyperSea environment
of the present study. Specifically, items measuring perceived ease of use and
perceived usefulness were adapted from Davis (1989), whereas items measuring attitude
towards behavior and intention were taken from Ajzen and Fishbein (1980) and Ajzen
(2006). All items, except the attitudes items, were measured using a five point Likerttype
scale, ranging from “strongly disagree” to “strongly agree”. Attitudes’ items were
based on five-point semantic differential scales. Table 2 lists the items of this section.
The third section consisted of two open-ended questions, to enable examination of
sample’s perceptions of factors which support the use of HyperSea in their teaching in
future. Their responses were used to construct a list of the most commonly held beliefs,
which are presented in Table 5.
4.3 Procedure
The procedure of this study consisted of three stages. At the first stage, a brief
instruction on how to operate HyperSea was given. In the second stage the participants
were required to interact with HyperSea following the instructions of the scenario as
shown in the Appendix. This scenario concerned a course of Geography for primary
education. Upon the completion of the aforementioned two stages, each participant was
asked to fill out the questionnaire about their perception toward the use of HyperSea
(Stage 3). The duration of this procedure ranged from 20 to 30 min for each participant.
Data were collected during the academic year 2011–2012. The whole procedure took
place in a computer room of the Faculty of Primary Education, University of Athens.
4.4 Data analysis
All statistical analyses were performed using SPSS (version 20). Data analysis consisted
of the following four methods: First, descriptive statistics were used (means, standard
deviations) for all the components of the questionnaire. In addition, Cronbach’s alpha
was calculated for each scale of the TAM. As shown in Table 2, nearly all TAM scales
exhibited an α-value greater than 0.7 (see Nunnally 1978). Analysis of data from student
teachers for behavioural intention showed α-value lower than but close to 0.7. Second,
relationships between the components of the TAM were determined by using Pearson
correlation coefficients. Third, regression analysis was used to determine the components
that significantly explained the variance in samples’ intention to use HyperSea. Finally, in
addition to above analysis, an independent samples t-test was performed, to document if
there were any significant differences between student and in-service teachers’ intention,
attitude, perceived usefulness and perceived ease of use mean scores.
5 Results
Table 2 presents descriptive statistics (means, standard deviations) among the variables
and items of the TAM. All means scores were greater than 4. These results of the
descriptive analysis show that most of the student and in-service teachers had positive
568 Educ Inf Technol (2015) 20:559–578
Table 2 Means (M), Standard Deviations (SD) and Cronbach alpha for variables of the TAM
Construct and scale items Overall sample (n=257) Student teachers
(n=151)
In-service teachers
(n=106)
M SD Cronbach
a
M SD Cronbach
a
M SD Cronbach
a
Behavioural intention 4.29 0.494 0.714 4.25 0.469 0.665 4.34 0.526 0.769
I intend to use HyperSea in
the following school year
in my teaching.
4.42 0.621 4.37 0.639 4.49 0.590
I will try to use HyperSea in
the following school year
in my teaching.
4.26 0.609 4.25 0.556 4.26 0.680
I plan to use HyperSea in
the following school year
in my teaching.
4.19 0.628 4.14 0.622 4.25 0.633
Attitude 4.31 0.458 0.763 4.28 0.434 0.732 4.34 0.489 0.799
For me to use HyperSea in the following school year in my teaching would be:
Harmful/beneficial 4.28 0.571 4.26 0.538 4.29 0.617
Pleasant/unpleasant 4.39 0.635 4.36 0.626 4.43 0.648
Good/bad 4.38 0.588 4.38 0.586 4.39 0.666
Worthless/valuable 4.27 0.614 4.27 0.577 4.26 0.666
Enjoyable/unenjoyable 4.21 0.767 4.13 0.772 4.32 0.750
Perceived usefulness 4.17 0.476 0.885 4.19 0.470 0.821 4.14 0.485 0.853
Using HyperSea will enable
me to accomplish tasks
more quickly.
4.30 0.523 4.36 0.521 4.22 0.516
Using HyperSea will
enhance the quality of
my teaching.
4.05 0.648 4.03 0.626 4.07 0.680
Using HyperSea will make it
easier to do my teaching.
4.21 0.590 4.22 0.576 4.21 0.613
Using HyperSea will
enhance my effectiveness
of my teaching.
4.05 0.697 4.08 0.717 4.01 0.669
Overall, HyperSea will be
useful in my teaching.
4.24 0.596 4.26 0.619 4.21 0.619
Perceived ease of use 4.42 0.498 0.834 4.38 0.494 0.871 4.48 0.501 0.902
Learning to use HyperSea
is easy for me.
4.46 0.572 4.41 0.569 4.54 0.572
My interaction with
HyperSea is clear and
understandable.
4.25 0.669 4.21 0.676 4.32 0.655
I find HyperSea to be
flexible to interact with.
4.36 0.635 4.32 0.647 4.42 0.635
I find it easy for me to
become skilful in
using HyperSea.
4.50 0.560 4.47 0.563 4.54 0.555
I find HyperSea easy to use. 4.51 0.567 4.48 0.575 4.57 0.552
Educ Inf Technol (2015) 20:559–578 569
scores for the TAM variables. The intention to use the HyperSea in their teaching was
strong in all groups. In addition, respondents had favorable attitude toward using
HyperSea and perceived high level of usefulness and ease of use of HyperSea.
This study also investigated if there were any differences between student and inservice
teachers and their intention, attitude, perceived usefulness and perceived ease of
use as well as between their demographic characteristics. Based on t-test results, no
significant differences (p>0.05) were found between student and in-service teachers for
each variable of the TAM.
Table 3 shows the Pearson correlations for student teachers and Table 4 for inservice
teachers. Table 3 shows that the student teachers’ correlation results among the
TAM variables were positive. The correlations ranged from 0.329 to 0.565. Attitude
toward use (r=+0.462, p<0.01) and perceived usefulness (r=+0.523, p<0.01) positively
correlated with intention. Perceived usefulness (r=+0.565, p<0.01) had the
strongest correlation with attitude, followed by perceived ease of use (r=+0.346,
p<0.01). Finally, perceived ease of use (r=+0.329, p<0.01) was correlated with
perceived usefulness.
The correlations of the in-service teachers’ results in Table 3 show that attitude (r=+
0.514, p<0.01) and perceived usefulness (r=+0.436, p<0.01) were correlated positively
with intention. In addition, attitude correlated positively with perceived usefulness
(r=+0.625, p<0.01). However, the perceived ease of use was not correlated with
attitude (r=+0.100, p>0.01) and perceived usefulness (r=+0.136, p>0.01).
Table 5 show the results of the regression analyses for student teachers’ intention to
use the HyperSea in their teaching. All of the hypotheses were confirmed by the data.
The Adjusted R2 values show that perceived ease of use explained 10.2 % of the
variance in perceived usefulness (F=18.115, p=0.000), while perceived ease of use and
perceived usefulness together explained 34 % of the variance in attitude towards using
HyperSea (F=39.683, p=0.000). Perceived usefulness was the most important
predictor in attitude (beta = 0.505, p=0.000) and perceived ease of use the second
(beta = 0.183, p=0.010). Finally, attitude and perceived usefulness accounted for
30.5 % of the variance in student teachers’ intention to use HyperSea in their
teaching (F=33.873, p=0.000). Perceived usefulness was the strongest predictor of
intention (beta = 0.385, p=0.000).
Table 6 shows the results of the regression analyses for in-service teachers’ intention
to use the HyperSea in their teaching. Hypotheses 1, 2 and 4 were not supported. As
can be seen from this table, perceived ease of use was not a significant predictor. On the
other hand, perceived usefulness (beta = 0.625, p=0.000), explained 38.5 % of the
Table 3 Pearson correlations for the student teachers’ results
BI A PU PEOU
Behavioural intention (BI) 1 0.462a 0.523a 0.327a
Attitude (A) 1 0.565a 0.349a
Perceived usefulness (PU) 1 0.329a
Perceived ease of use (PEOU) 1
a Correlation is significant at the 0.01 level (2-tailed)
570 Educ Inf Technol (2015) 20:559–578
variance in in-service teachers’ intention to use the HyperSea in their teaching (F=
66.776, p=0.000). Finally, attitude and perceived usefulness explained 27.2 % of the
variance of in-service teachers’ intention (F=20.654, p=0.000). The beta coefficients
in Table 6 shows that only attitude was a significant predictor of in-service teachers
intention (beta = 0.396, p=0.000).
Table 7 presents the factors to support respondents to use HyperSea and were
identified from the open-ended survey data. Among the most important factors were
the availability of color change of icons and background, zoom and shrink icons and a
help button.
6 Discussion and conclusions
The aim of this study was to use the TAM in order to investigate the factors that
influence student and in-service teachers’ intention to use Hypersea, a spatial hypermedia
environment in their teaching. The findings of this study showed differences
between the two groups regarding the factors that influence their intention. Most
specifically, the results of student teachers’ regression analysis showed that all components
of the TAM were found to predict their intention to use HyperSea in their
teaching. On the other hand, regression analysis showed that only some of the variables
of the TAM contributed to the explanation of teachers’ intention.
Student teachers’ intention was significantly influenced directly by attitude and
perceived usefulness. Perceived usefulness was the most important predictor in student
teachers’ intention. Similar findings have also been found in other TAM studies which
Table 4 Pearson correlations for the teachers’ results
BI A PU PEOU
Behavioural intention (BI) 1 0.514a 0.436a 0.100 ns
Attitude (A) 1 0.625a 0.100 ns
Perceived usefulness (PU) 1 0.136 ns
Perceived ease of use (PEOU) 1
a Correlation is significant at the 0.01 level (2-tailed)
Table 5 Regression analysis of the TAM variables on student teachers’ intention
Dependent variable Adjusted R2 Independent variable Beta t Significance level
Perceived usefulness 0.102 Perceived ease of use 0.329 4.256 0.000a
Attitude 0.340 Perceived ease of use 0.183 2.600 0.010a
Perceived usefulness 0.505 7.190 0.000a
Intention 0.305 Attitude 0.244 2.955 0.004a
Perceived usefulness 0.385 4.668 0.000a
a Significant (p<0.05)
Educ Inf Technol (2015) 20:559–578 571
examined student teachers’ acceptance of technology (e.g. Teo 2009; Teo et al. 2008).
These results suggest that student teachers’ intention is influenced firstly by having a
positive perception of the usefulness of HyperSea in their teaching and secondly by
having positive attitudes towards the use of HyperSea in teaching. On the other hand,
only attitude towards use had direct influence on teachers’ intention. The finding that
attitude was the only significant predictor in teachers’ intention, supports the results of
other studies that used the TAM model (e.g. Davis 1993).
Another finding of this study was that the students’ and in-service teachers’ attitude
towards the use of HyperSea in teaching was significantly influenced by the perceived
usefulness of HyperSea in teaching. In contrast to findings from previous TAM studies
(e.g. Davis 1993; Hu et al. 2003; Legris et al. 2003; Li et al. 2008) perceived ease of use
in this study failed to emerge as a significant predictor of teachers’ attitude and
perceived usefulness.
The above differences between the two groups regarding the influence of perceived
ease of use could be attributed to the differences in computer and teaching experience
between the student teachers and the in-service teachers. Previous studies (e.g. Angeli
2004; Lei 2009) have shown that students’ teachers, compared to experienced teachers,
have little classroom teaching experience and therefore they lack of concrete experience
of using ICT for teaching and learning. In addition, student teachers, compared to
experienced teachers, do not feel that they have the skills and experiences to integrate
technology into classrooms (Russell et al. 2013). Therefore, it is reasonable to expect
that student teachers are more likely to use the HyperSea in teaching when it is easy to
use and has a specific educational purpose.
Table 6 Regression analysis of the TAM variables on teachers’ intention
Dependent variable Adjusted R2 Independent variable Beta t Significance level
Perceived usefulness ns Perceived ease of use Ns ns ns
Attitude 0.385 Perceived ease of use ns ns ns
Perceived usefulness 0.625 8.172 0.000a
Intention 0.272 Attitude 0.396 3.717 0.000a
Perceived usefulness 0.189 1.768 0.080
a Significant (p<0.05)
Table 7 Factors to support respondents to use HyperSea in their teaching
Number Percent
1 Color change of icons and background. 58 39,7
2 Icon zoom and shrink. 32 21,9
3 Help button. 28 19,2
4 Font face and size change. 18 12,3
5 More modern interface. 10 6,8
572 Educ Inf Technol (2015) 20:559–578
On the other hand, previous studies have shown that the teachers who are already
regular users of ICT have confidence in using ICT (see Preston et al. 2000; Angeli
2004). In addition these teachers are willing to overcome barriers relating to technical
problems and a lack of technical support. This means that as computer experience
increases, concerns about perceive ease of use of ICT maybe decrease. The in-service
teachers in the present study used ICT in their teaching and therefore were experienced
ICT users. These in-service teachers were familiar and comfortable with technology
and have been exposed to different applications of technology in their teaching.
Therefore teachers’ intention to use HyperSea in this study may not depend on whether
or not teachers believe that it is easy to use. It is possible that the measures of perceived
ease of use will be more appropriate for explaining the behaviour of those teachers who
do not use ICT or have less teaching experiences with ICT.
The findings presented in this study have various implications, for the use of
HyperSea in teaching. The practical implication that can be drawn from students’
findings is that successful use of HyperSea in schools in the future should need not
only student teachers to have very positive attitudes towards these, but also they need to
believe that these are useful in teaching and ease to use. This means that in order for a
student teacher to use the HyperSea in future, the usefulness and the ease of use of the
HyperSea will be both taken into consideration. In particular, student teachers need to
be convinced with specific examples that HyperSea will be beneficial for them and
their students. In addition, in order to support student teachers in using HyperSea in
their teaching in future, there needs to be provision for the conditions and opportunities
that influence their perceived ease of use. Therefore, increasing student teachers’
feelings of ease of use and usefulness of ICT in universities’ programmes are likely
to generate the greatest increase in their intention and attitudes towards the use of
HyperSea in their teaching.
This study showed that attitudes towards the use of HyperSea in teaching were
found to be important in explaining teachers’ intention. The findings of this study
suggest that teachers will not use HyperSea in their teaching unless they have very
positive attitudes towards using HyperSea in teaching. These findings imply that a way
to influence teachers’ intention to use HyperSea in their teaching could be by
supporting the development of teachers’ positive attitudes towards the use HyperSea
in teaching. In order to achieve this, previous studies found that this requires more
training and more knowledge and skills about ICT (Preston et al. 2000).
Moreover, other implications of the findings of this study are connected with the
teachers’ perceived usefulness. This variable related to attitudes towards the use of
HyperSea. These results show that teachers would use more HyperSea in their schools
if they believe that HyperSea would be beneficial for their teaching. The importance of
these beliefs is in line with earlier studies in ICT in education. For example, Preston
et al. (2000) found that teachers believed that using ICT would help and improve
pupils’ learning as well as help them to improve their teaching.
This study used the TAM in two different samples and therefore there are many
implications regarding its predictive validity. The results showed that the TAM in
general is useful model for predicting and exploring the factors that influence student
and in-service teachers’ intention to use HyperSea in their teaching in future.
Approximately 30.5 % of the variance in student teachers’ intention as well as 34 %
of the variance of attitude in the present study was explained by the regression model. In
Educ Inf Technol (2015) 20:559–578 573
addition, some of the TAMvariables accounted for 27.2%and 38.5%of the variance in
teachers’ intention and attitude, respectively. These are satisfactory percentages compared
to the range of other intentions and attitudes explained in the previous studies of
the TAM (see Yousafzai et al. 2007b). However, this finding means that a significant
percentage of the variance, on average, remains to be explained. Previous TAM(e.g. Al-
Gahtani and King 1999; Karahanna and Straub 1999) as well as TRA (see Ajzen and
Fishbein 1980) and TPB studies (e.g. Ajzen 1991; Armitage and Conner 2001), have
shown that exist some other additional psychological or external variables (e.g. demographic
characteristics, training support, personality traits, i.e. authoritarianism, need for
achievement, institutions and computer experience and computer accessibility) that also
influence individuals’ intention or behaviour, thus increasing the prediction validity of
the research model. The present study did not consider those specific or other variables.
Therefore, further research is necessary, to investigate the potential of other variables
that may also affect the attitudes and intention to use HyperSea in teaching. Finally,
student teachers’ behavioural intention subscale showed a Cronbach’s α-value lower
than but close to 0.7. Therefore, this subscale was the weakest component of the TAM
scales and could be modified before being used in further research studies.
As shown in Section 6, appearance, functionality and attractiveness of the environment’s
nodes were found to be the most critical factors that will support respondents to
use HyperSea in their teaching in future. These findings have led us to prepare a new
version of the environment. This version will offer more robust and predictable
functionality of the nodes, and in the same time, will provide more formatting and
help options to users. In this way, we will be able to evaluate the use of HyperSea in
class. Simultaneously, as this environment has been designed to provide fully functional
interaction with just click and drag actions, we are preparing a version suitable
for smartphones and touch screens in general, by using new technologies, like HTML5,
CSS3 and JQuery Mobile. Users will have thus the opportunity to employ the same,
familiar environment and functionality in all devices.
Appendix
Scenario
In order to evaluate the use of the HyperSea environment, we have designed the
following scenario that every evaluator should implement. The scenario regards getting
to know the area of Kalamata, Greece.
1. Save on the desktop two sound files with traditional dances from the area of
Kalamata.
2. Start HyperSea environment
3. Enter new user credentials
4. Drag and drop the two sound files from the desktop on the environment’s area.
Two respective nodes are created.
5. Encircle the two files, which creates automatically a group
6. Click on the group, which shows the group’s properties
7. Set the title “Kalamata dances” for the group
574 Educ Inf Technol (2015) 20:559–578
8. Double click on any sound node in order to initiate sound playback
9. Go to YouTube on a browser and search videos for Kalamata
10. Download on the desktop a video concerning Kalamata
11. Use Google for finding images concerning Kalamata and save some of these
pictures on the desktop
12. Repeat the same process for finding and saving a map of Kalamata
13. In Wikipedia, find the article concerning Kalamata and we are dragging it on the
environment. A node is created in this way, which represents the Wikipedia link.
Name the node “Kalamata”
14. Repeat the same process for finding and dragging articles about Messinia and
Pylos
15. Encirlce Messinia and Pylos and name “Messinia” the created group
16. By pressing Shift, create a link from Kalamata to Messinia, which denotes that
Kalamata is a part of Messinia
17. Repeat the same process for creating a link between Kalamata and Kalamata
dances
18. Drag the images, the video and the map from the desktop to the environment and
name the created nodes appropriately by clicking on them
19. Encircle the images and video in order to create a group to be named
“Multimedia”
20. Create two links: Kalamata-Multimedia and Kalamata-Map
21. Observe that nodes are colored based on their content, eg image nodes are
coloured differently from sound nodes
22. Observe that when moving nodes on the environment, links that originate or point
to a node are also moved appropriately
23. Observe that when moving groups, nodes belonging to the group are also moved
appropriately
24. Double click on an image or video node to check that the respective multimedia
file playbacks
Repeat the above process for any other course.
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