Article
Preservice Teachers’ Level of Web Pedagogical Content Knowledge: Assessment by Individual Innovativeness
Journal of Educational Computing Research 2017, Vol. 55(1) 70–94 ! The Author(s) 2016 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0735633116642593 journals.sagepub.com/home/jec
S ¸ ahin Go¨kc¸earslan1, Tug˘ra Karademir2, and Agah Tug˘rul Korucu3
Abstract Technological Pedagogical Content Knowledge, one of the frameworks proposed in order to popularize the use of technology in a classroom environment, has been customized and has taken the form of Web Pedagogical Content Knowledge. The Relational Screening Model was used in this study. It aims to determine whether a profile of preservice teachers based on their “individual innovativeness” can be used as a significant predictor in also categorizing them according to their knowledge of web technology, pedagogy, and content. A total of 170 preservice teachers studying at various departments of the Faculty of Education at a public university in Ankara, Turkey participated in the study. This study, in which Discriminant Analysis was used to determine whether this predictor is significant or not, found that teachers in the Early Majority category had high scores in terms of pedagogical web content and general web knowledge, and that “individual innovativeness” was effective in predicting the general web and communicative web categories to which they belonged. Certain conclusion can be made regarding Web Pedagogical Content Knowledge and future studies on this subject, based on results of the study. 1
Department of Informatics, Gazi University, Ankara, Turkey Department of Computer Education and Instructional Technologies, Ankara University, Ankara, Turkey 3 Department of Computer Education and Instructional Technologies, Necmettin Erbakan University, Konya, Turkey 2
Corresponding Author: S¸ ahin Go¨kc¸earslan, Department of Informatics, Gazi University Technical Schools, Besevler, Ankara 06500, Turkey. Email:
[email protected]
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Keywords postsecondary education, improving classroom teaching, pedagogical issues, interactive learning environments, computer-mediated communication
Introduction As the transition into the information age has progressed, technological developments have not only been visible in the high-tech equipment now utilized by teachers in classrooms but also in changes to the necessary competencies expected from teachers. It is important for the future of society that teachers, who have the role of developing individuals able to keep up with the information society, are themselves educated to be creative individuals who can adjust to innovations and who are able to think critically (Fink, 2013; Manik, Qasim, & Shareef, 2014). IT supports the integration of information technology for teachers to train teachers who can use it effectively in programs before they become professional. This process should be grown in a quality manner (Lawless & Pellegrino, 2007; Schlager & Fusco, 2003). Innovative and technology-supported learning environments, and learning by experience the importance and necessity of IT all emphasize the important contribution they make to the integration process (Hughes, 2005; Jonassen, 1999). In today’s world, it is important to provide preservice teachers with the competency to combine technology, pedagogy, and content on a common ground, rather than having teachers who are competent in technology alone. The concept of Pedagogical Content Knowledge developed by Shulman (1986, 1987), which emphasizes the significance of this requirement, was first suggested by Pierson (2001). It was then transformed by Mishra and Kohler (2006) into the concept of Technological Pedagogical Content Knowledge (TPACK) with the addition of technology. As a result of continued developments in the Internet, web tools and their extensive application in learning environments, the opinion that it is necessary to further differentiate TPACK has prevailed, and Lee and Tsai (2010) introduced the concept of Web Pedagogical Content Knowledge (W-PCK) for web environments. The contribution of the teachers to increase the level of effectively using the Internet via education will of course be immense (Mishra & Koehler, 2006). Internet technologies, information and communication technologies have been developing and this development offers innovations to many fields including education (Lim & Ching, 2004; Richards, 2006; Sang, Valcke, Braak, &Tondeur, 2010). Teachers have important roles in adoption, learning, and transferring of these developing technologies (Bo & Ye-mei, 2010; Mandell, Sorge, & Russel, 2002). Training teachers applying different methods to increase the participation of students and putting into practice new abilities by changing their behaviors is important in terms of integration of developing technologies
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into education (Ritchhart, 2004). Below, a theoretical framework is given for research questions on W-PCK and individual innovation. After this, the method and the findings of the current study are given, and discussion and suggestions are presented.
Theoretical Framework While Internet learning has been one of the most important tools in supporting the teaching process, the competency of teachers themselves with regard to these technologies also has an influence on the usage of web technologies, the learning of teaching processes (Lee & Tsai, 2010), and the usage of web-based learning environments (Lin, Liang, & Tsai, 2010). In addition to research into teachers’ Internet competency, although teachers may have a limited understanding concerning teaching of certain content with new web technologies (Webb & Cox, 2004), research on preservice teachers’ usage of new technologies for educational purposes and for designing courses has been substantial (Kale, 2014; Koh & Chai, 2015; Kumar & Vigil, 2011). Furthermore, it has been suggested that providing courses in the web environment is something that facilitates integration of IT (Goktas, Yildirim, & Yildirım, 2009). It is necessary that teachers, who are responsible for the learning of students using IT and the web as part of their daily life, are as competent in their knowledge of technology and the web as their students. Moreover, pedagogy, content knowledge, and web knowledge should be based on a common foundation. In web-aided teaching, there are common factors, such as the software, the technical infrastructure, and the teacher, all of which interact (Cavanaugh, 2002). The most significant responsibility among these factors belongs to the teacher. According to Gillespie (2014), the teacher’s role is vital in the preparation and development of courses, in order to enable students to access the information they need, and in establishing interaction among these factors. Furthermore, according to Cartwright and Hammond (2003), the integration of IT into the teaching environment is carried out by teachers. Teachers have a significant role in the efficient provision of content developed in a technological form (Southall, 2012). If it is thought that the level of innovation of preservice teachers will directly or indirectly affect the education they give, it will be understood that the studies made in this field are very important in terms of training more qualified teachers and generations (Bo & Ye-mei, 2010). According to this result, situations for improving the innovation of teacher and preservice teachers should be investigated (Bingimlas, 2009). Technologic Pedagogic Content Knowledge is a tool for applying information technologies (computers, projection devices, intelligent boards, etc.) by combining the content knowledge of teachers with these technologies, and utilizing the interaction between technology, students, and the environment (Mishra & Koehler, 2006; Schmidt et al., 2009). While Mishra and Koehler (2006) use
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TPACK as a framework, Graham (2011) states that many studies address TPACK as a framework but it is also considered as a model. In addition, it is stated that it is a strong framework for technology integration. Although there is not a clear distinction about this issue in the field, the experimental studies necessary for considering it as a model are limited. Therefore, it is considered as a framework in this study. W-PCK is described by Lee and Tsai (2010) as the Pedagogic Content Knowledge necessary for teachers to apply the Internet in education as well as for the effective use of a combination of Internet applications. Whereas a determination of teachers’ TPACK competency is important for the efficient preparation of learning content, their W-PCK needs to be determined with regard to their specific preparation of web-based content. Through assessing W-PCK, the teachers’ ability to prepare web-based educational content can be determined. Accordingly, studies emphasize the need to determine the W-PCK levels of preservice teachers in order to address any deficiencies before their professional careers begin (Lee & Tsai, 2010; Tseng, Lien, & Chen, 2016).
Web Pedagogical Content Knowledge (W-PCK) W-PCK is composed of three core components which interact content knowledge, pedagogy, and web knowledge. While one of these components, content knowledge, includes information on the subject that will be taught, pedagogic information includes the process and application of learning and teaching methods, and web knowledge represents general web competencies such as the application of web-related tools, web-based communication, and web-based interaction (Lee & Tsai, 2010). The integration of content knowledge and web knowledge in web technological content knowledge forms the web pedagogical knowledge; the integration of pedagogical knowledge and content knowledge forms the pedagogical content knowledge. The integration of these three main areas, which are pedagogical content knowledge, web knowledge, and content knowledge, forms the pedagogical content knowledge (Lee & Tsai, 2010; Lee, Tsai, & Chang, 2008; Tschannen-Moran & Woolfolk-Hoy, 2001). This is shown in Figure 1. Four areas of competency emerge as a result of the interaction of these three components. They are: 1. Pedagogic Content Knowledge: Application of “Conceptualized Content Knowledge” within a special area, that is, the ability to teach basic content (Shulman, 1986, p. 9). 2. Web Pedagogic Knowledge: Teachers should know which pedagogical strategies utilizing the web should be adopted for specific tasks or responsibilities to achieve the most effective results (Lee & Tsai, 2010, p. 5).
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Figure 1. The framework of web pedagogical content knowledge (Lee et al., 2008). (a) Pedagogical content knowledge and (b) web pedagogical content knowledge.
3. Web Content Knowledge: Teachers should know not only how to teach content but also how to integrate it into the web environment (Lee & Tsai, 2010, p. 5). 4. Web Pedagogic Content Knowledge: This is the combination of all three areas above and means knowing how to effectively carry out all educational activities by integrating both content teaching and web features and activities into the content that is taught (Lee & Tsai, 2010, p. 5). TPACK and W-PCK have common and different aspects. The common aspects of these two concepts are content and pedagogical knowledge. Web knowledge in W-PCK substitutes for the technology in TPACK. Technology and Pedagogy in the TPACK framework is specialized as pedagogical knowledge on the web. Technology and Content knowledge in TPACK framework is specialized as web content knowledge. The measurement of W-PCK includes the components of attitude toward web-based education, general web, communicative web, pedagogical web, and pedagogical web content. Attitude toward webbased education is defined as “measuring the extent of teachers’ agreement regarding the usage of Web-based instruction,” general web is defined as “measuring teachers’ confidence in their knowledge about their use of the Web in general, such as use of Web-related tools,” communicative web is defined as “assessing teachers’ confidence in their knowledge relative to Web-based communication or Web-based interaction,” pedagogical web is defined as
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“evaluating teachers’ confidence in their knowledge about the existence, components and functions of the Web as they are used in educational settings,” and pedagogical web content is defined as “surveying teachers’ confidence in their knowledge about how to identify appropriate online learning activities to fit the needs of a particular course and the practice of appropriate pedagogies to support online activities” (Lee & Tsai, 2010, p. 7–8). Research into the TPACK framework is still ongoing. There are a number of contemporary studies investigating the TPACK for measurement and evaluation (Archambault & Barnett, 2010; Fisser, Voogt, Tondeur, & Van Braak 2015; Mouza, Karchmer-Klein, Nandakumar, Ozden, & Hu, 2014); for developing a framework for the application of TPACK in course designs through web 2.0 tools (Koh & Chai, 2015); for the application of TPACK by preservice teachers in order to develop literacy through cooperative efforts (Boschman, McKenney, & Voogt, 2015); for trend analysis concerning the TPACK (Wu, 2013). Although there are studies investigating W-PCK competency among preservice teachers (Hig˘de, Uc¸ar, & Demir, 2014; Kavanoz, Yu¨ksel, & O¨zcan, 2015), they are rather limited. In addition, it has been suggested that variables that have an influence on the W-PCK should be determined (Kavanoz et al., 2015). Individual characteristics are important variables for web-based learning and designing the learning environments (Alomyan, 2004; Chen & Paul, 2003; Grimley & Riding, 2009; Kurilovas, Kubilinskiene, & Dagiene, 2014). It has been suggested that future studies investigate individual characteristics and W-PCK together. In addition to the fact that one of these individual characteristics, “individual innovativeness,” is a variable attitude of teachers’ regarding the usage of educational technologies (Yilmaz & Bayraktar, 2014), it also affects their TPACK competencies (C¸uhadar, Bu¨lbu¨l, & Ilgaz, 2013; Kocak & Onen, 2013).
Individual Innovativeness One of the researchers focused on innovation and its “extension,” Rogers (2003), produced “innovation profiles” of individuals in his studies in the light of the following definitions. He defines individuals of the 21st century as people who can access necessary information in every situation; solve problems; maintain effective communication; are open to novelties; that is, who exhibits the characteristics of “innovativeness.” Researchers classify individuals into the following groups: “Innovators,” who exhibit willingness to try new opinions and to take risks; “Early Adopters,” who inform other individuals in the environment and lead the way; the “Early Majority,” who adopt a precautious stance toward novelties and who are not willing to take risks; the “Late Majority,” who are skeptical and suspicious toward novelties and who manifest timid behavior (they resist change and have certain prejudices); and “Laggards,” who tend to
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embrace novelties at the end of the process. Several studies indicate that these tendencies of teachers in regard to using new technologies provide accurate classification for individual innovation profiles (Brahier, 2006; Rosen, 2004). According to this classification, since the individuals studied play a significant role in the development of the education process and its quality, determining the category they belong to is important for understanding the adoption of technology by educational institutions and its further “extension” (Davies & Longworth, 2014; Dunne, 2014). “Extension” in this sense means the process by which something new is transmitted among members of a social system through certain channels over time (Rogers, 1995). However, according to the profiles suggested by Rogers, it is evident that individuals vary a great deal in their abilities. If viewed from this angle, individual “innovation profiles” of teachers are significant in revealing how innovation-based web technologies in education will be extended. However, it can be seen that individual innovativeness was examined in the context of new technologies (Brahier, 2006; Rosen, 2004), but it was not examined in the context of web-based technologies which have a significant place for the process of knowledge structuring in the class. But determining teachers’ individual innovativeness is a difficult process. Because of this, researchers have developed different kinds of scales and surveys to determine individual innovativeness categories. One of them is the Innovativeness Scale created by Joseph Hurt and Chester. D. Cook. The scale measures innovativeness in general sense and regards innovativeness in an individual as “willingness to try new things.” The aim of this scale is to determine individuals’ degree of innovativeness and their innovativeness category based on Roger’s innovation profiles (Hurt, Joseph, & Cook, 1977). In terms of studies concerning “individual innovativeness,” the following variables have been investigated: the effect of innovativeness on the adoption of technology (Drent & Meelissen, 2008; Hicks, 2006; Khasawneh, 2008; O¨nen & Koc¸ak, 2014; Rosen, 2004; Teo, 2009); the relationship between the degree of utilization of technology and general computer skills (Sahin & Thompson, 2006); changes in the use of mobile technologies (Hsua, Lub, & Hsuc, 2007); the benefits of compatibility between utilized technologies (Brahier, 2006). In addition, people have researched the individual characteristics that teachers should have (Graham, Borup, & Smith, 2012; Hughes, 2004; Kanuka, 2006; Koehler & Mishra, 2009; Ko¨nings, Gruwel, & Merrienboer, 2007; Lent, Brown, & Hackett, 1994; Peruski & Mishra, 2004; Vanderlinde & Braak, 2011; Wang, Ertmer, & Newby, 2004). In these studies, the significance of “individual innovativeness” regarding technological innovation has been demonstrated. In addition to this, there are studies on the social entrepreneurship characteristics of preservice teachers (Gur-Erdogan, Eksioglu, Zafer-Gunes, & Sezen-Gultekin, 2014), mobile technology use status of preservice teachers (Lu, Yao, & Yu, 2005), the relationship of this with philosophies of education adopted (Ilhan, C¸etin, & Arslan, 2014), and its effect on hedonic and utilitarian
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aspects of web use (Hartman & Samra, 2008). However, none of these studies investigated the “individual innovativeness” categories of preservice teachers and their W-PCK knowledge together. Some of the studies indicated contain web and technological variables which include technological innovations and development for learning environment. W-PCK is a framework which brings these variables together. It is thus necessary to evaluate knowledge of W-PCK, which has not yet been properly investigated in the literature by the categories of individual innovativeness. Moreover, evaluating web, content, and pedagogical knowledge within the context of individual innovation is important during the process of the recruitment and development of teachers and preservice teachers (Lee et al., 2008; Vanderlinde & Braak, 2011). On the basis of this study, it is important to examine the indirect relationship between W-PCK and individual innovativeness. In this study, we examined W-PCK with this fifth dimension (Lee et al., 2008), as shown in Figure 2. A visual representation of the hypothesis of this study is given below (Figure 2). H1: Individual innovativeness profiles of preservice teachers are a significant predictor of their General Web knowledge. H2: Individual innovativeness profiles of preservice teachers are a significant predictor of their Communicative Web Knowledge—their ability to communicate using the web. H3: Individual innovativeness profiles of preservice teachers are a significant predictor of their Pedagogical Web Knowledge—their understanding of the web as a pedagogical tool.
Figure 2. Visual presentation of study hypothesis. W-PCK ¼ web pedagogical content knowledge.
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Journal of Educational Computing Research 55(1) H4: Individual innovativeness profiles of preservice teachers are a significant predictor of their W-PCK. H5: Individual innovativeness profiles’ of preservice teachers are a significant predictor of their Attitude Toward Web-based Education in general.
Methodology Research Pattern The Relational Screening Model, one of the quantitative research models, was used in this study. The Relational Screening Model is used for research models which aim to determine the degree and existence of change together among two and more variables (Cohen, Manion, & Morrison, 2000; Gay, 1987).
Study Group The study group was made up of 170 preservice teachers attending a public university in Ankara, Turkey in the academic year 2013 to 2014. The distribution of preservice teachers by department is shown in Table 1. As can be seen from Table 1, 38.8% of the participants were from the elementary school mathematics teaching department, 21.8% were from the preschool teaching department, 15.3% were from the French teaching department, 14.1% were from the visually impaired teaching department, 8.8% were from the social sciences teaching department, 0.6% were from the special education department, and 0.6% were from the primary school teaching department. Preservice teachers were grouped in terms of their individual innovativeness scores.
Table 1. Distribution of the study group based on department. Department Elementary school mathematics teaching Pre-school teaching French language teaching Visually impaired teaching Social sciences teaching Special education Primary school teaching Total
N
%
66 37 26 24 15 1 1 170
38.8 21.8 15.3 14.1 8.8 .6 .6 100.0
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Table 2. Individual innovativeness frequency distribution of pre-service teachers. Individual innovativeness profile Early majority Late majority Laggards Total
n
%
105 63 2 170
61.8 37.1 1.2 100.0
Data Collection and Data Analysis Two different scales were used for data collection. One of them was the Individual Innovativeness Scale (IIS) and the other was the W-PCK Scale.
Individual innovativeness scale In this research, IIS was used for students to determine their individual degree of innovativeness and their innovativeness category (Table 2). The IIS was developed by H. Thomas Hurt, Katherine Joseph, and Chester. D. Cook (1977) in order to determine individuals’ degrees of innovativeness and their innovativeness category. The scale was adapted into Turkish by Kilicer and Odabasi in 2010. The validity and reliability test of this scale, which consists of 20 questions in Turkish, was performed on 343 preservice teacher students and it was found that it had a four-factor structure; factors were valid. It was determined that the first factor, “Resistance to Change,” was comprised of eight items (4, 6, 7, 10, 13, 15, 17, and 20); Prime Mover Originator’ was comprised of five items (1, 8, 9, 11, and 12); “Openness to Experience” consisted of five items (2, 3, 5, 14, and 18), and “Taking Risks” consisted of two items (16 and 19). It can be seen that the subjects gathered under “Resistance to Change” consist of items reflecting concerns of individuals regarding change and innovation, the items gathered under the title of “Prime Mover Originator” consist of items reflecting the qualities separating them from the other individuals in the group which they belong to, the items gathering under “Openness to Experience” consist of subjects reflecting their urge to seek out innovation and experience, the items gathered under “Taking Risk” consist of items reflecting the motivation of individuals to combat obstacles without giving up. Consequently, it can be interpreted that the subjects in the first dimension of the scale test the reaction to innovation and the items in other dimensions reflect reactions related to more private situations. The scale’s internal consistency coefficient was 0.82, and the test–retest reliability was 0.87. Individuals can be classified in terms of innovativeness using the
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scores calculated with the help of the scale. Accordingly, if an individual’s score is over 80, he or she belongs to the “Innovators” category; if an individual’s score is between 69 and 80, he or she belongs to the “Early Adopters” category; if an individual’s score is between 57 and 68, he or she belongs to the “Early Majority” category; if an individual’s score is between 46 and 56, he or she belongs to the “Late Majority” category; if an individual’s score is below 46, he or she belongs to the “Laggards” category. The general innovativeness levels of individuals can also be evaluated using the scores calculated with the help of the scale. Accordingly, while individuals with a score over 68 are considered to be quite innovative, individuals with a score below 64 are considered to have a low level of innovativeness. In the current research, the scale’s reliability, the Cronbach’s alpha internal consistency coefficient, was 0.76.
W-PCK Scale The W-PCK scale was developed by Lee et al. (2008) and was adapted to Turkish by Horzum (2011) with the name “Web Pedagogic Content Knowledge Survey.” Within the framework of the adaptation, items on the scale were translated into Turkish by the researcher and the translation was revised according to feedback from 10 different experts. The final forms in Turkish and English were filled in by 30 preservice teachers on dates two weeks apart. The correlation between the Turkish and English versions of the forms was found to be 0.87 and the forms were thus accepted as equivalents. The validity and reliability test was performed on 232 preservice teachers. As a result of exploratory factor analysis and confirmatory factor analysis, it was found that the scale had a five-factor structure. Cronbach’s alpha coefficient of internal consistency for the Turkish form of the scale was found to be 0.94. Ultimately, the Turkish form of the scale was found to be valid and reliable for this study group. The scale was given its final form as a five-point Likert type scale consisting of 30 items and the five factors of “General Web,” “Communicative Web,” “Pedagogical Web,” “Web Pedagogical Content,” “Attitude Towards Web-Based Education.” In the current research, the scale’s reliability, Cronbach’s alpha internal consistency coefficient was 0.89. Data collection tools were used simultaneously, but first the results of the IIS were analyzed in order to determine categories, and the total scores of preservice teachers were determined using the formula in the scale (the score determination formula will be discussed later when providing information on the scale). The total scores were used to divide participants into groups based on the scale. Table 2 shows the distribution of preservice teachers according to the categories of “Early Majority,” “Late Majority,” and “Laggards.” The IIS was carried out with a total of 170 preservice teachers. As can be seen from Table 2, it was found that 105 preservice teachers were in the “Early Majority,” 63 preservice teachers were in the “Late Majority,” and 2 preservice
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teachers were in the “Laggards” category. No preservice teacher was in the other two categories. Discriminant analysis was used to determine the relationship between preservice teachers’ individual innovativeness scores and their “General Web,” “Communicative Web,” “Pedagogical Web,” “Web Pedagogical Content,” and “Attitude Towards Web-Based Education” scores (only one discriminant analysis was used for verifying all hypotheses and all findings were reported under the title of findings). To meet the preconditions of the discriminant analysis, first the extreme values were checked and after measuring the mahalanobis distance, the data that showed extreme values were excluded. After removing the extreme values, the number of preservice teachers studied dropped from 170 to 168. As a result of Box-M test, which is conducted for the homogeneity of covariance matrixes, it was measured that covariance matrixes were homogeneous (F(15.68816.742) ¼ 3.525, p > .025). It was concluded that the Condition Indices value was not between 3.922 and 16.785, and Variance Inflation Factors values for every variable were not between –1.524 and 5.259. This shows that these did not demonstrate multicollinearity. In addition to this, it was concluded that absolute correlation value of variables in the study is between .013 and .369, which is low. In the light of this information, it was concluded that the data were appropriate for discriminant analysis. The results of the analysis are given in the “Findings” section.
Findings Only one discriminant analysis was used for verifying all hypotheses. Findings were presented using this one analysis. The data obtained regarding the research problems and results of the analysis are given in Table 3. Examining the group statistics given in Table 3, it can be seen that the “General Web Knowledge” score averages of preservice teachers were X ¼ 33.3048 for the “Early Majority” and X ¼ 28.3492 for the “Late Majority.” Regarding “Communicative Web Knowledge,” the average score of the “Early Majority” was found to be X ¼ 17.4952, and the average score of the “Late Majority” was found to be X ¼ 13.5556. Regarding “Pedagogical Web Knowledge,” the average score of the “Early Majority” was found to be X ¼ 22.6571, and the average score of the “Late Majority” was found to be X ¼ 19.7519. Regarding the W-PCK, the average score of the “Early Majority” was found to be X ¼ 35.2952, and the average score of the “Late Majority” was found to be X ¼ 30.1746. Finally, regarding the preservice teachers’ “Attitude Towards Web-based Education,” the average score of the “Early Majority” was found to be X ¼ 26.5905, and the average score of the “Late Majority” was found to be X ¼ 23.0476. When a comparison between
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Table 3. Group statistics. Innovativeness
X
S
N
Early majority General web Communicative web Pedagogical web Web pedagogical content Attitude toward web-based education
33.3048 17.4952 22.6571 35.2952 26.5905
2.78431 3.02275 2.77291 5.01330 3.82978
105 105 105 105 105
Late majority General web Communicative web Pedagogical web Web pedagogical content Attitude toward web-based education
28.3492 13.5556 19.7619 30.1746 23.0476
5.19043 3.55046 4.06287 6.65883 5.16011
63 63 63 63 63
Table 4. Eigenvalues. Function 1
Eigenvalue
Canonical correlation
Wilk’s lambda
SD
p
.603
.613
.624
5
.000
the average scores is made, it can be seen that the “Early Majority” had a higher average score than the “Late Majority” in all six categories. Table 4 shows the Eigenvalues and Wilk’s Lambda value. Wilk’s lambda indicates the significance of the discriminant function and Eigenvalue provides information on each of the discriminate functions (equations) produced (Burns & Burns, 2008). Eigenvalues over .40 can be considered to be “good” (Kalaycı, 2005) and it can be seen (Table 4) that the Eigenvalue of the function in the study was .603 and this means that the function is effective in discriminating the groups. According to the Wilk’s lambda statistic (Table 4), the chi-square value for this function was significant (2(5) ¼ 77.146; p < .01). The discrimination power of the function was significantly high or the groups could be discriminated via a discrimination function. Based upon this, it can be said that subfactors of W-PCK are the meaningful predictors of individual innovative profiles. However, this finding does not show which subfactor has a meaningful contribution to the created function. Significance values of these subfactors in their contribution to individual innovative profiles are given separately in Table 5.
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Table 5. Wilk’s lambda test of equality of group averages. Wilk’s lambda General web Communicative web Pedagogical web Web pedagogical content Attitude toward web-based education
.719 .739 .847 .839 .865
F 64.813 58.580 30.053 31.958 25.830
df1
df2
p
1 1 1 1 1
166 166 166 166 166
.000 .000 .000 .000 .000
Table 6. Standardized coefficients related to the discriminant function. Category
Function
General web Communicative web Pedagogical web Web pedagogical content Attitude toward web-based education
.738 .658 .139 .227 .087
If the significance level of each independent variable given in Table 5 is examined, it can be seen that differences between the “General Web Knowledge” (F(2.218) ¼ 5.694, p < .05), “Communicative Web Knowledge” (F(2.218) ¼ 3.344, p < .05), “Pedagogical Web Knowledge” (F(2.218) ¼ 3.344, p < .05), W-PCK (F(2.218) ¼ 3,344, p < .05), and “Attitude Towards Webbased Education” (F(2.218) ¼ 3.344, p < .05) scores of the groups were significant. Wilk’s lambda values for “General Web Knowledge,” “Communicative Web Knowledge,” “Pedagogical Web Knowledge,” “W-PCK,” and “Attitude Towards Web-based Education” were .719, .739, .847, .839, and .865 respectively. The fact that the Wilk’s lambda values were close to 1 indicates that it was not highly effective in discriminating between subtest groups. Based upon this, it was concluded that the attitudes of preservice teachers toward “General Web Knowledge” (H1 hypothesis was confirmed), “Communicative Web Knowledge” (H2 hypothesis was confirmed), “Pedagogical Web Knowledge” (H3 hypothesis was confirmed), “Web Pedagogical and Content Knowledge” (H4 hypothesis was confirmed), and “Attitude Towards Web-based Education” (H5 hypothesis was confirmed) are predictors of their individual innovative profiles. The contribution levels of each one of them to this prediction equation are understood from the values given in Table 6.
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Table 7. Correct classification rate of the variables. Early majority n Early majority Late majority
89 15
Late majority
% 84.8 23.8
n 16 48
% 15.2 76.2
Total N
%
105 63
100.0 100.0
Note. 81.5% of original grouped cases correctly classified.
Examining Table 6 shows that among predictor variables the most useful variable for discriminating between groups was “General Web Knowledge,” followed by “Communicative Web Knowledge” and “Pedagogical Web Knowledge.” It was also found that “W-PCK” was in fourth place in terms of its contribution to discriminating between the groups and in a negative direction. “Attitude Towards Web-based Education” was in the last place and in a negative direction. Table 7 shows the correct classification rate of the variables. In Table 7, the correct classification percentages of preservice teachers who participated in the study are given according to their “Web Pedagogical Content Knowledge” Scale scores. Examining the classification results given in Table 7 shows that out of 105 preservice teachers in the “Early Majority” group, 89 of them were correctly classified in accordance with scores for “W-PCK” of preservice teachers that is, 84.8%. Out of 63 preservice teachers in the “Late Majority” group, 48 of them were correctly classified in accordance with scores for “W-PCK” of preservice teachers that is, 76.2%. The total correct classification rate of the analysis was 81.5%. The fact that this value is higher than 50% shows that the correct classification rate is above the chance criterion that is, the classification was carried out correctly. Based upon this, it can be said that W-PCK scores are not only meaningful for the classification of preservice teachers according to their individual innovative profiles but also have a high accuracy rate. Based upon this, it can be said that not only W-PCK scores are meaningful for the classification of preservice teachers according to their individual innovative profiles but also classification rate has a high accuracy. However when examined in terms of the degree of prediction, it can be seen that “General Web,” “Communicative Web,” and “Pedagogical Web” sub factors have the highest prediction percentage. On the basis of this, it can be said that making an inference about individual innovative profiles of preservice teachers by examining their “General Web,” “Communicative Web,” and “Pedagogical Web” scores is possible. Moreover, it can be seen that the contribution of the “Attitude Towards Web-based Education” category to the classification is limited but meaningful.
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Discussion The rapid change in technological tools has had effects on the school and classroom environments. In addition to computers, a range of mobile devices, smart boards, and projectors have begun to be used in classroom environments. As discussions and studies on the place of computers in the classrooms have taken place, the roles of pedagogy and content have been included in these discussions and an integrated perspective that sees technology as more than just a tool has been attempted. The degree of access to technology has gained in importance and subjects such as how technology is used in education, integration between information and communication technologies and pedagogical methods, and technology’s place in educational outputs have begun to be discussed (Livingstone, 2012; So & Kim, 2009). The International Society for Technology in Education has developed standards in order to improve teachers’ knowledge, skills, learning and teaching abilities and has emphasized field, technological and pedagogical knowledge (ISTE, 2014). In addition to the changes that information, communication and telecommunication technologies have created in the release, use, access, storage, and sharing of information, the multimedia opportunities that these changes have provided have brought different applications along with them. Integrating tools such as search engines, chat tools, and online newsletters into classroom environments have gained in importance. Considering that these features provided by the Internet are different than other technologies, the subject area of Internet and pedagogical knowledge has been configured differently by and through the process of technology integration (Horzum, 2011). When the place of the Internet in learning environments is considered, W-PCK, specialized for web site, has significance for integration of these technologies. W-PCK has positive relations with attitudes for carrying out web-based technologies (Kavanoz et al., 2015). There are limited studies revealing the self-sufficiency regarding W-PCK of teachers and preservice teachers (Akayuure, Nabie, & Sofo, 2013; Horzum & Canan Gungoren, 2012; Kavanoz et al., 2015; Lee & Tsai, 2010; Lee et al., 2008). Also, there has not been any study dealing with both individual innovation and W-PCK. This study has researched the predictive relationship between W-PCK levels, which are important in the integration process, and the “individual innovativeness profile,” which is one measure of an individual’s attributes. It was determined that the preservice teachers participating in this study belonged predominantly to the “Early Majority” and “Late Majority” categories when the categories available were “Innovators,” “Early Adopters,” “Early Majority,” “Late Majority,” and “Laggards.” Cuhadar, Bulbul, and Ilgaz (2013) found that preservice teachers were included in the “Early Majority” category. The fact that there were no preservice teachers in the “Innovators” or “Early Adopters” categories is not a good result considering that innovative individuals tend to be more open to change during the integration of new technologies
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into education. Similarly, the low number of “Laggards” leads to the idea that resistance against change will be weak. According to the results of Yates’ study, teachers are aware of their central position in this regard and are open to change. Among preservice teachers in the “Early Majority” and “Late Majority” categories, the W-PCK score of the “Early Majority” was found to be higher than all other innovativeness profiles. In a study related to the adaptation of a curriculum about the media to a school, a similar result about “Early Majority” was found (Yates, 2001). Also, as well as stating that the level of individual innovation explains perception and intention toward the use of IT-based tools, a parallel result about the individuals in the “Early Majority” category was obtained (Yi, Fiedler, & Park, 2006). The individuals in the “Early Majority” category adopt innovations slowly but they are quicker than the “Late Majority” (Rogers, 2003; Yi et al., 2006). W-PCK and General Web Knowledge scores of the “Early Majority” and “Late Majority” groups were found to be high. The Communicative Web Knowledge and Pedagogical Web Knowledge scores of both groups were low. In another study performed with preservice teachers, it was found that there was an association between the individual innovativeness level and components of preservice teachers’ selfsufficiency such as their motivation, teaching abilities, and guidance (Celikli, 2013). Another study found that there was a positive, medium-level association between preservice teachers’ individual innovativeness levels and their technopedagogical content knowledge proficiencies (Cuhadar et al., 2013). In a further study on acceptance of virtual learning environments, it was found that individual innovativeness related to information technologies directly affected their perceived ease-of-use (Van Raaij & Schepers, 2008). In terms of differentiating W-PCK categories according to individual innovativeness profiles, General Web Knowledge, Communicative Web Knowledge, Pedagogical Web Knowledge, W-PCK, and Attitude Toward Web-based Education all have significant effects. In a study performed with teachers, it was concluded that the individual innovativeness level should be discussed together with openness to experience, ability to take the lead in expressing opinions, risk-taking, and resistance to change (Lee & Tsai, 2010). Another study found that the individual innovativeness level was associated with awareness of Web 2.0 technologies (Bayraktar, 2012). In this context, the innovativeness profiles play an important role in the adoption of web innovations. While General Web and Communicative Web Knowledge were positive and high level predictors, W-PCK and Attitude Toward Web-based Education had negative and low effects in terms of being able to discriminate between the groups. Attitude Toward Web-based Education was the least effective variable in terms of discriminating between the groups. According to the analysis performed to find the variables’ correct classification rate, it was concluded that the chance criterion had been largely disabled. Considering the predictive relationship of the innovativeness profiles with regard to General Web and Communicative Web
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Knowledge, it can be said that this relationship could be the starting point for creating an information society through access to information and communication. Teachers who are at a high level of innovativeness will tend to follow innovations much more quickly.
Conclusion Determining teachers’ level of W-PCK and finding the variables that affect this level are important steps toward the effective planning of activities related to use of technology in education. The FATIH project in Turkey, which is the “Movement for Enhancing Opportunities and Improving Technology,” continues to make investments in technology and especially Internet infrastructure and also provides teachers with in-service training on the subject. Determining the impact of innovativeness profiles, an important variable in the process of integration and the dissemination of technology, is important for shaping inservice training in the future. Educating creative, productive students who are critical thinkers, researchers, and self-learners in the process of lifelong learning is only possible by producing teachers who have these abilities themselves. Within this framework, teachers’ W-PCK and perspectives on innovation that is, their individual innovativeness profiles, are gaining importance. As the results of this study show, W-PCK and individual innovativeness profiles are interactive variables. Based on this conclusion, it is necessary to take teachers’ individual innovativeness profiles into account while increasing their W-PCK to support their use of technology. During the education of preservice teachers in the faculty, activities providing an increase in levels of individual innovation were needed. The individuals in the “Late Majority” category were suspicious and careful in adopting the innovation. Preservice teachers, included in subcategory for adopting innovations, should be supported to integrate them into web technology learning environments. This study has certain limitations. It would be beneficial to repeat the study with a larger study group in order to generalize the study results, since this study was performed with a limited study group made up of freshmen preservice teachers attending a public university in Ankara, Turkey. It is suggested that the number of teachers and preservice teachers in the study be increased and participants from other departments be included. The fact that the participants of the study were concentrated within two individual innovativeness categories was another limitation of the study. Teachers’ use of the web in classroom activities must be supported with studies. The W-PCK level of teachers is important in their use of educational web tools during classes and further studies on this subject will contribute to educational processes. This study found that Web Pedagogical Knowledge and Attitude Toward Web-based Education were only affected to a very low degree by individual innovativeness profiles. In spite of that General Web,
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Communicative Web, and Pedagogical Web Knowledge had higher levels. It would be beneficial to find variables that do affect or are affected by these variables in future studies. Studies may be conducted on the relationship between preservice teachers’ W-PCK and other individual attributes, their attitudes toward technology and the Internet, their gender and their level of vocational satisfaction. It is offered that experimental studies should focus on how to increase individual innovation level of teachers and preservice teachers. In addition to this, it would be useful to research individual innovation levels to make the students a part of the process. It is suggested that studies should focus on how technology investments change the teacher’s individual innovativeness profiles and the classroom environment. The results of Internet related in-service training in the classroom environment and their effects on educational outputs may also be studied. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
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Author Biographies S¸ahin Go¨kc¸earslan graduated from Computer Education and Instructional Technology (CEIT), Department of Hacettepe University, Turkey. Go¨kc¸earslan
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got master of science degree at the same program, the same University. In 2013, he got his PhD degree in the field of CEIT, Educational Technology Program, at Ankara University, Turkey. His research interest is mainly about new web technologies, learning communities, computer programming, and technology integration. Currently working in Gazi University Informatics Department and Distance Education R & D Center. Tug˘ra Karademir is a Research Assistant and PhD student at University of Ankara, Turkey. Her main department is Computer Education and Instructional Technology (CEIT). She completed her master’s degree in 2012 on learning object and teachers’ competencies. She works on dissemination of technology on education, teacher’s competence and professional development and digital learning materials. Agah Tug˘rul Korucu is a computer engineer and instructional technologists and works as an Assistant Professor Doctor at Necmettin Erbakan University. Also, he serves as an academic consultant for various companies. He has studied about student and educator engagement, dynamic web technologies, academic achievement, collaborative technologies, ICT integration, augmented reality, fuzzy logic, and their impacts on teaching.
