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Journal of Education and Training Studies Vol. 4, No. 12; December 2016
Teachers stated that they faced some problems such as cost, usability, lack of experience while using assistive technology (AT) (Flanagan et al., 2013). Teacher had limited information about using AT. Special education teacher may have had AT coursework during their undergraduate or graduate education, besides they can attend workshops or seminars for their professional development to enrich their instruction with AT (Derer, Polsgrove & Rieth, 1996; Lee & Vega, 2005; Ludlow, 2001; Michaels & McDermott, 2003 as cited in Flanagan et al., 2013). Moreover, high purchase and upgrade prices are other problems for teachers while using technology. Usability is another problem that teachers faced with, usable tools make easier for them to use technology in special education.
In their studies Nam et al. (2013) tested the relationships among fundamental elements (facilitating condition, perceived ease of use, computer self-efficacy, result demonstrability, perceived usefulness, and behavioral intention) of AT acceptance. The results of the study supported the effects hypothesized in conceptual model of AT acceptance. According to the results the significant element that was affecting AT usage was perceived usefulness.
1.3 Technology Acceptance Model
Research model based on Technology Acceptance Model (TAM) was designed by Davis (1989). The aim of the TAM was to explain theoretically the determinants factors of acceptance of computer and be efficient to make explanation for wide range of users’ behaviors. The researchers can prefer this model both estimate users’ behaviors and make theoretical explanations about technology acceptance. Therefore, TAM guides researchers to identify reasons for a system that was unacceptable and to assist about corrective interventions (Davis, 1989).
1.4 The Elements of Technology Acceptance Model
Behavioral intention (BI) is a measurement of degree of person’s engaging in a specified behavior. BI can be explained as an individual’s presence for acting a behavior. TAM asserted that it determines individual’s acceptance or rejection to use of information technology, in other words it states that behavioral intention of individual is the primary factor that determine an individual's actual use (Hu, Clark & Ma, 2003; Teo, 2011).
Perceived Usefulness (PU) is a positive or negative idea about performance increase in users’ jobs that they got after using technology (Davis, 1989). The idea of not waiting in a line can be given as an example of tax payer who pays taxes by online without going tax office.
Perceived Ease of Use (PEU) is a degree to which an individual’s beliefs about ease of using particular technology and learning without additional effort. If individuals think that new technology is easy to use, their behavioral intention towards using technology becomes positive. The studies showed that PEU had indirect effect on PU (Davis, 1989). For example, two graphic editors are similar in terms of perceived ease of use; if one of them produces higher quality of graphics than other editor, previous one should be accepted as more useful (Davis, 1989).
Subjective Norm (SN) is a “perceived social pressure to perform or not to perform a behavior” (Hu, Clark & Ma, 2003; Ma, Anderson & Streith, 2005). Individuals can perform behaviors with respect to the decisions of people around them (Fishbein ve Azjen, 1975).
1.5 The Purpose of the Study
The purpose of this research was to determine pre-service special education teachers’ acceptance and usage frequency of technology in their daily lives and lessons. This research analyzed the TAM in order to determine the factors that affect pre-service special education teachers’ ICT acceptance and usage decisions. This research examined pre-service teachers’ perceived usefulness (PU), perceived ease of use (PEU), behavioral intention (BI) and subjective norm on using ICT tools.
1.6 Hypothesis
The general structural model, which included behavioral intention, perceived usefulness, perceived ease of use and subjective norm on technology acceptance of pre-service special education teachers, was developed based on the TAM. Research model was shown in Figure 1.
H1:Pre-service special education teachers’ perceived ease of use of ICT tools affects their perceived usefulness of ICT tools.
H2:Pre-service special education teachers’ perceived ease of use of ICT tools affects their behavioral intention to use ICT tools.
H3:Pre-service special education teachers’ perceived usefulness of ICT tools affects their behavioral intention to use ICT tools.
2. Method
TAM formed technology by used. SEM is used to test th facilitates ana data collection 2.1 Sampling P The universe universities. T examine cons conducted. A Babin and An sufficient. Ho University’s D 21. 59% of th students, 30% participants w purpose of the at any stage o in approximat 2.2 Data Colle The data coll professional q duration of us second part co intention (5 ite and from ques Table 1. Sourc
Table 1 presen about perceiv Davis (1989) adapted from
the basic fram y pre-service sp a method used heories because alysis of media n is explained
Procedures of the study The sample co struct validity
rule of thumb nderson, 2005 owever, to dea Department of he participants % were second were day stud e study was ex f the study or r tely 20 minute
ection Tool an lection tool us questions on t se, whether the ontained a tota ems) and subje stion sets with ces of the Item Factors Perceiv Perceiv Behavi Subject nts a list of the ved usefulness and had the questions used
mework of thi pecial educatio d to test the rel e it allows con ation effects ( followed by te
consists of p onsisted of stu of the scale fo regarding sam ). Given that al with poten Special Educa s (n: 123) were year students, dents, and 33% xplained to pre
retract their an s.
d Developmen sed in the stu the gender an ey attended IC al of 20 items
ective norms ( high reliability ms Used in the
s
ved Usefulness ( ved Ease of Use ioral Intention (B
tive Norms (SN e scale’s factor
and perceived high reliabilit d by Hu, Clark
Figure 1
s study condu on teachers. To lationship betw nducting multip (Kline 2005; T esting of the hy
re-service tea udents attendin for technology mple size in EFA
the TAM sca ntial loss of d ation. The age e female and 4
30% were thi % were eveni e-service teach nswers. There w
nt of the Scale udy consisted nd age of the CT courses bef
on perceived u (3 items). Item ty figures. Item
Scale
(PU) (PEU) BI) N)
rs and studies d ease of use ty figure of 0 k and Ma (2003
1. TAM Diagra
ucted to identif o examine thes ween observed ple analyses at Teo, 2009). Fi
ypotheses and
chers attendin ng Marmara U y acceptance m FA is that there ale in this stud data, the scale of participants 41% were ma rd year studen ing students. hers, and they w
was no time li
of two main participants, t fore, and whet usefulness (6 i ms on the scale ms in this part w
Adapted Davis (19 Davis (19 Hu, Clark Hu, Clark that were cons were adapted .93. Items on 3).
am
fy the factors se factors, a St d and latent var
t once, takes m rst, the proces a discussion o
ng the special University’s D model, an Expl
should be at l dy has 20 item e was adminis
s varied betwe le (n: 85). 20% nts, and 20% w
Before admin were informed imit for filling
parts. (a) Th the classes th ther they deve items), perceiv were selected were 5-point L
Studies 989) 989)
k & Ma (2003) k & Ma (2003)
sulted when pr d from a quest behavioral in
that affect acc tructural Equat
riables (Kline, measurement e ss of question of results.
education tea epartment of loratory Facto east 5 subjects ms, a sample stered to 208 een 17 and 28, % of the partic were fourth yea
nistering the m d that they cou
out the forms.
he first part co hey attend, ow
eloped any dig ved ease of use from previous Likert type item
reparing the sc tion set that w ntention and s
ceptance and tion Model (SE , 2005). It is fr
rrors into acco nnaire developm
aching departm Special Educa or Analysis (E
s per item (Hai size of 100 sh
students of M and the mean cipants were f ar students. 67 measurement t uld refuse to pa . Forms were f
ontained perso wnership of IC gital materials. e (6 items), be s studies on the ms.
cale. Items in t was originally
subjective norm usage of EM) was equently ount, and ment for
ments of ation. To FA) was ir, Black, hould be Marmara age was first year % of the tool, the articipate filled out
onal and CT tools, (b) The ehavioral e subject,
Journal of Education and Training Studies Vol. 4, No. 12; December 2016
Cronbach’s alpha internal consistency coefficients were calculated to find out how reliable the data would be. As Table 2 shows, all variables had moderate or high levels of reliability, with reliability coefficients equal to or greater than 0.80.
Table 2. Reliability Coefficients
Factors Number of Factors Cronbach’s Alpha Reliability Coefficients
Perceived Ease of Use (PEU) 6 .874
Perceived Usefulness (PU) 6 .923
Subjective Norms (SN) 3 .800
Behavioral Intention (BI) 5 .933
3. Results
3.1 Participants’ Experience with ICT Tools
Table 3 reports the ICT tools owned by participants of the study. Table 3 shows that almost all (94%) participants own smartphones, 88,5% have internet access, and 75,5% own computers. Only 2 participants (1%) said that they did not use ICT tools. Projectors (3,4%), scanners (9,1%) and printers (12%) are among the least owned ICT tools.
Table 3. ICT tools owned
Devices Frequency %
Smartphone 196 94,2
Internet 184 88,5
Computer 157 75,5
Television 117 56,3
Tablet computer 60 28,8
CD/DVD player 45 21,6
Printer 25 12
Scanner 19 9,1
Projection 7 3,4
None of them 2 1
Participants were asked to indicate for how long they have been using ICT tools, and Table 4 reports data on duration of use. Table 4 shows that 38,9% of the participants have been using computers for 8-11 years, 59,6% do not use tablet computers, and 42,3% have been using smartphones for 1-3 years and 41,8% for 4-7 years. No participants reported having used a tablet computer for longer than 7 years.
Table 4. Duration of use of ICT tools
Computer Tablet Computer Smartphone
Duration F % F % F %
Not use 12 5,8 124 59,6 6 2,9
Less than 1 year 5 2,4 11 5,3 13 6,3
1-3 years 14 6,7 57 27,4 88 42,3
4-7 years 53 25,5 16 7,7 87 41,8
8-11 years 81 38,9 0 0 12 5,8
More than 11 years 43 20,7 0 0 2 1,0
In addition to duration of use, participants were asked to indicate how frequently they use ICT tools in their daily lives (Table 5). Table 5 shows that smartphone is the ICT tool most frequently used by participants in their daily lives, with 78,4 reporting that they use smartphones on a daily basis. 66,3% stated that they do not use tablet computers in their daily lives at all. 25,5% of participants said they used a computer several days a week, and 24% said they used a computer several hours every day.
Table 5. Frequency of use of ICT in daily lives
Computer Tablet Computer Smartphone
Frequency F % F % F %
Every day, continuously 39 18,8 2 1,0 163 78,4
Several hours every day 50 24,0 16 7,7 32 15,4
Several days a week 53 25,5 20 9,6 5 2,4
Several hours a week 36 17,3 13 6,3 0 0
Several hours a month 19 9,1 19 9,1 2 1,0
None 11 5,3 138 66,3 6 2,9
Table 6. Frequency of use of ICT for studying purposes
Computer Tablet Computer Smart Phone Digital Material
Frequency F % F % F % F %
Every day, continuously 12 5,8 2 1,0 41 19,7 13 6,3
Several hours every day 18 8,7 3 1,4 28 13,5 22 10,6
Several days a week 68 32,7 16 7,7 58 27,9 35 16,8
Several hours a week 54 26,0 9 4,3 35 16,8 20 9,6
Several hours a month 49 23,6 13 6,3 27 13,0 49 23,6
None 7 3,4 165 79,3 19 9,1 69 33,2
92% of participants stated that they took a course on ICT tools, whereas 8% reported not having received any training on this subject. 80% of participants never used ICT tools to develop digital materials for special education, whereas 20% developed digital materials for special education using ICT tools.
3.2 Results of the Factor Loading Analysis
To examine construct validity of the scale, an Exploratory Factor Analysis (EFA) was conducted. Factor loadings for the variables that make up the Technology Acceptance Model were calculated. Results of the exploratory factor analysis of the variables, conducted using Varimax rotation, are reported in Table 7. None of the items on the questionnaire had a factor loading smaller than 0,500.
Table 7. Factor loadings of the variables
Factors (Eigen Values>1)
1 2 3 4
PEU1 ,124 ,881 ,087 -,057 PEU2 ,135 ,894 ,089 -,015 PEU3 ,091 ,862 ,095 ,074 PEU4 ,254 ,697 ,236 ,097 PEU5 ,132 ,608 ,073 ,079 PEU6 ,285 ,689 ,166 ,141 PU1 ,767 ,190 ,211 ,182 PU2 ,780 ,288 ,269 ,075 PU3 ,799 ,064 ,219 ,273 PU4 ,794 ,211 ,225 ,204 PU5 ,739 ,226 ,274 ,133 PU6 ,718 ,229 ,271 ,244 SN1 ,342 ,190 ,129 ,765 SN2 ,225 ,072 ,328 ,642 SN3 ,206 ,081 ,308 ,833 BI1 ,235 ,142 ,791 ,316 BI2 ,213 ,151 ,876 ,227 BI3 ,255 ,170 ,798 ,246 BI4 ,296 ,188 ,847 ,192 BI5 ,388 ,142 ,697 ,003 3.3 Results of the Structural Equation Model Analysis
Journal of Educ
Table 8. Fit cr Fit Criteri X² df X²/df p RMSEA CFI TLI
As the structu measured by with 93%, and also measured items with the tools allows m measured by 5 with 93%, and is measured b has the highes As the structu (SN) around p communicatio behavioral int teachers’ beha
cation and Train
riteria and mod a
ural equation 6 items, but th d “It is easy f d by 6 items, w e highest value me to conduct 5 variables. Of d “I would like by 3 items. The st effect on the ural equation m pre-service spe on technologie tention (BI) to avioral intentio
ning Studies
del fit statistics Recommended 2df< X² ≤3df < 3
≤ 0,08 0,9 ≤ CFI 0,9 ≤ TLI
Figure model path d he two most im for me to use I
with the items es are “Using I t my courses m f those, the mo e ICT tools to b
e item “People e variable with model path diag ecial education es with a signi
use ICT with on (BI) to use
s
Value M
40 16 2,4 0,0 0,0 0,9 0,9
2. Structural E diagram (Figur
mportant item ICT tools” (PE s having simil ICT tools impr more quickly” ost important t be used in my e I care about
89%. gram (Figure 2 n teachers pred ificance level o h a significance ICT with a sig
odel Referen 01,01
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ar levels of in roves my cours ” (PU2) with 8 two are “It is a
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of 54%. Subje e level of 39% gnificance leve
nce
melleh-Engel, M melleh-Engel, M nald and H rugger & Müller 2000), McDona 2000), McDona
el Path Diagram the variable o asy for me to l %. The variabl nfluence varyin se performanc 85%. The vari a very good ide ) with 93%. Th ng ICT tools i
e of hypothese deas about the ective norms ( %. Perceived U el of 38%. Per
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ease of use” ( use ICT tools” ed usefulness” 5% and 78%. h 84%, and “U vioral intention
tools in course subjective nor is a good idea
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mber 2016
ngel,
(PEU) is ” (PEU1) ” (PU) is The two sing ICT n (BI) is es” (BI2) rms (SN) a” (SN3)
to predict teachers’ ideas about the usefulness (PU) of information and communication technologies with a significance level of 29%.
Table 9. Hypothesis rejection / acceptance table
Hypothesis Path Direction Coefficient Values T value P Accept /Reject
H1 PEUPU ,29 4,51 <,001 Accepted
H2 PEU BI ,08 1,28 ,202 Rejected
H3 PUBI ,38 4,59 <,001 Accepted
H4 SNPU ,54 7,23 <,001 Accepted
H5 SNBI ,39 4,86 <,001 Accepted
Results mostly supported the Technology Acceptance Model (TAM), with one exception: the theoretical expectation that perceived ease of use (PEU) would affect behavioral intention (BI) to use technology was not empirically supported (Table 9).
4. Discussion
Studies on the effects of information and communication technologies show that their use improves instructors’ effectiveness and efficiency when teaching (Kirkup & Kirkwood, 2005). New technologies strengthen cooperation and communication in education and training, and increase the flexibility and convenience of educational activities. The technology supported learning methods can be better adapted for students if teachers’ and students’ reactions to technology supported learning systems are understood well (Turan & Colakoglu, 2008). To utilize the full potential of information and communication technologies, complementary and descriptive studies are needed.
This study aimed to identify the factors that affect an individual’s decision to accept and use information and communication technologies, using the framework of TAM and working with pre-service special education teachers. First, data were collected on the duration and frequency of use of information and communication technologies by pre-service special education teachers. It was found that pre-service special education teachers used ICT tools frequently in their daily lives and a large majority of participants received training on ICT tools, but the duration and frequency of use for studying purposes were much lower. The findings of this study can be important for identifying the factors behind this behavior and taking necessary measures.
Special education teachers’ perceptions of usefulness (PU) of information and communication technologies, as well as opinions of people (SN) they care about regarding use of technology, shape their behavioral intentions (BI) towards information and communication technologies. In addition, special education teachers’ perceptions regarding the ease of use (PEU) of information and communication technologies, and opinions of people (SN) they care about regarding use of technology, predict their perceptions of usefulness (PU) of these technologies. Perceived ease of use (PEU) was not found to have a statistically significant effect on behavioral intention (BI). In another study that used technology acceptance model, perceived usefulness was found as a dominant factor affecting assistive technology usage (Nam et al, 2013).
Proper and effective use of technology by special education professionals goes hand in hand with their frequency of use of ICT tools in class, and competency in ICT supported education (Flanagan, Bouck & Richardson, 2013). If pre-service teachers make more active use of ICT tools for studying purposes during their college education, this would help them make more use of technology after graduation when they conduct sessions with special education students. This is because pre-service teachers’ intention to accept and use ICT would improve if they were introduced to technology-supported practices in the courses they take and found these practices to be useful and effective (Avcu & Gokdas, 2012). Therefore, during their university education, pre-service teachers should be provided periodic training regarding the effects of ICT practices on the learning/teaching process, and they should be encouraged to participate in these programs.
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