Technology use can improve student learning. Relevant studies concluded that using technology in educational settings benefits students (Gulbahar, 2007; Kim & Hannafin, 2011). Many governments worldwide have invested money in constructing environments that increase technology access in elementary and secondary school classrooms. Taiwan's government has also funded projects that promote innovative teaching with technology. However, many studies that include Taiwanese have indicated that technology integration in the classroom by teachers is insufficient (Chen, 2008; Gorder, 2008; Hermans, Tondeur, van Braak, & Valcke, 2008). This lack of technology integration is reflected in preservice teacher education. The importance of developing the ability of preservice teachers to integrate technology has been widely recognized.
The National Council for Accreditation of Teacher Education (NCATE) developed the National Education Technology Standards for Teachers (2008) and Interstate New Teacher Assessment and Support Consortium standards (2003) as teacher accreditation requirements. These standards require that teachers use technology in their classrooms, and design learning environments and experiences that support teaching, learning, and curricula. These standards have also led teacher education institutes to acknowledge shortcomings in teacher preparation for using technology as an effective instructional tool.
Teacher education institutes are natural places for training teachers in how to integrate technology into daily classroom learning. Although numerous institutes have allocated considerable effort to develop thoughtful technology-based programs, only a few studies have evaluated these programs (Kay, 2006). Additionally, empirical evidence indicates that teacher education programs have not taught new teachers how to use technology effectively (Maddux, & Cummings, 2004); that is, preservice teachers still lack the ability and knowledge needed to teach successfully with technology (Angeli, & Valanides, 2008).
Although teacher education courses related to technology integration were inadequate, a learning opportunity arising from school-based field practice for preservice teachers has been acknowledged. To attain sufficient experience in technology integration, preservice teachers can interact with mentors during a practicum. Nilsson and Driel (2010) indicated that teacher knowledge can be enhanced and developed via the interaction between preservice teachers and mentors during a practicum.
Moreover, teacher pedagogical beliefs are important when exploring technology integration. These beliefs play a critical role in successful technology integration (Ertmer, 2005; Hermans, et al., 2008; Tondeur, van Keer, van Braak, & Valcke, 2008). Beliefs about teaching can be called "preferred ways of teaching" (Teo, Chai, Hung, & Lee, 2008). Technology integration is the implementation of technology during teaching. Therefore, beliefs of preservice teachers about technology integration potentially influence their teaching methods when using technology.
Actual technology use by preservice teachers during a practicum may be related to their training, school-based field experiences with mentors (EWMs), and beliefs about technology integration because preparation courses and participating in field practice foster professional abilities and shape pedagogical beliefs of preservice teachers. Many studies have explored teacher education programs (e.g., Sandholtz & Reilly, 2004), preservice teacher beliefs (e.g., Ertmer, 2005), technology access (e.g., Dexter & Reidel, 2003), and self-efficacy (e.g., Chen, 2010), while few studies documented the combined effects of two major teacher education processes, teacher education courses and school-based field practice courses, which further shape teacher beliefs about integrating technology and teaching, even though these processes are necessary to equip preservice teachers with the required professional skills. This study investigates the significance of, and relationships between, process factors and their direct and indirect effects on technology integration. This study also tests a multivariate hypothesized model. Study results reveal correlation effects of process factors influencing technology use by preservice teachers during practice teaching.
Many factors consisting of internal and external aspects influence technology integration by preservice teachers during practice teaching. Determining the relationships between these factors as direct and indirect effects on technology integration contributes to the development of a multivariate hypothesized model.
Teacher education courses, beliefs, and technology integration
Many researchers have indicated that most teacher education courses worldwide did not provide meaningful contexts for applying technology to improve teaching and learning. Additionally, these courses did not prepare preservice teachers to use technology in instructional settings, even though over half of these countries acknowledged that technology has become compulsory when training teachers as primary or secondary school educators (Balcon, 2003; Moursund & Bielefeldt, 1999). A few countries have addressed the pedagogical application of technology for teaching and learning during teacher training (Usun, 2009).
The teaching philosophy of administrators at teacher education institutes regarding technology integration has been identified as an obstacle preventing successful implementation of technology in classrooms (Dexter et al., 2003; Doering, Hughes, & Huffman, 2003). Leu and Kinzer (2000) argued that many teacher education programs did not prepare teachers for integrating technology into instruction because the teacher education courses were isolated courses worth only two or three credits. Isolated courses have difficulty generating students as masters of the technical skills needed for meaningful application of these skills for student learning. According to Singer and Maher (2007), preservice teachers felt that many experiences and resources in courses in teacher education programs were not helpful for technology integration. Brown and Warschauer (2006) advocated that technology should be integrated into method courses to give preservice teachers effective strategies for integrating technology into classroom instruction, rather than focusing predominantly on technical skills or knowledge.
However, some studies noted that information literacy courses in teacher education programs altered the information-seeking behaviors of these students (Branch, 2003), and increased their confidence in using technology, while course training did not generate actual teaching practice (Branch, 2003; Swain, 2006).
Although these studies identified insufficient outcomes of teacher education courses, these courses remain critical for equipping preservice teachers with the ability to use technology in their teaching careers (Chen & Ferneding, 2003; Franklin, 2007). Empirical evidence indicates that preservice teachers have been equipped with technology skills, not the ability to integrate technology (Maddux & Cummings, 2004; Moursund & Bielefeldt, 1999; Selinger, 2001).
Moreover, teacher pedagogical beliefs likely influence teaching practices (Kane, Sandretto, & Heath, 2002; Pajares, 1992). Many researchers have demonstrated that teacher pedagogical beliefs are critical in successful technology integration (Ertmer 1999, 2005; Hermans et al., 2008; Tondeur et al., 2008) and are a significant determinant in interpreting why teachers utilized computers in classrooms (Hermans et al., 2008). The beliefs of preservice teachers are also markedly influenced their subsequent instructional decisions and classroom practice (Pajares, 1992; Richardson, 2003). Consequently, teaching preservice teachers how to critically analyze their beliefs about technology use in classrooms influences their technology integration practices (Valcke, Sang, Rots & Hermans, 2010); that is, the pedagogical beliefs of preservice teacher directly predict their technology integration during practice teaching (Sang, Valcke, van Braak, & Tondeur, 2010).
However, some researchers demonstrated the teacher beliefs about the value of educational technology were not good predictors of their instructional practices (Ertmer, Gopalakrishnan, & Ross, 2001; Judson, 2006). Ertmer (2005) also noted that teacher beliefs did not always inform practice.
Lambert, Gong and Cuper (2008) showed that preservice teachers had positive pedagogical beliefs about the importance of computers in education after taking educational technology courses, and indicated that educational technology courses enhance the beliefs of preservice teachers about the benefits of technology use and prepared them to use technology effectively. According to Pajares (1992), teacher beliefs are established by experiences and influenced by professional contexts. When students enter teacher education programs, their beliefs have already been shaped by their personal experiences as students (Keys, 2007; Pajares, 1992; Raths, 2001). That is, preservice teachers' beliefs may be shaped by teacher education programs and are relatively stable and resistant to change.
Based on this assertion, literature reveals that teacher education courses may shape pedagogical beliefs of preservice teachers and enhance their technology skills, rather than the ability to integrate technology while teaching. As a result, teacher education courses shape preservice teacher beliefs and, further, beliefs are predictive of technology integration and worthy of exploration.
Experiences with mentors, beliefs, and technology integration practices
School-based field practice courses in teacher education programs provide preservice teachers with opportunities to observe and interact with mentors, thereby acquiring practical teaching experience. Typically, an experienced teacher writes a mentoring proposal for preservice teacher development to help preservice teachers learn important teaching skills such as designing effective learning activities and assessing student prior knowledge of certain topics (Carroll, 2005). This school-based internship offers both opportunities and challenges for mentors and preservice teachers.
In Taiwan teacher education system, each teacher education student has to participate in school-based field practice after completing all university courses and is assigned at least one mentor for practice teaching. Preservice teachers cannot implement technology integration during practice without permission from their mentors because the responsibility to educate students resides with mentors, not the preservice teachers. The perspectives of, and guidance from, mentors markedly influence technology integration by preservice teachers during practice teaching.
Grove, Strudler, and Odell (2004) applied qualitative methods to collect various perspectives on technology integration during mentoring and student teaching experiences. Grove et al. determined that preservice teachers were aware of mentors' models and student-centered learning activities with technology. Additionally, mentors provided opportunities for preservice teachers to examine their teaching practices and discuss technology integration strategies. Judge and O'Bannon (2007), who examined preservice teachers and mentors participating in a technology teaching model, indicated that preservice teachers incorporated technology into their teaching activities during field experiences while guided by their mentors. Furthermore, in addition to determining that field experiences were effective for preservice teachers in developing the ability to integrate technology into instruction, Judge et al. demonstrated that mentor teachers should use technology and be able to model, mentor, and guide preservice teachers as they learn to use technology to enhance curricula and improve student learning. Haydn and Barton (2007) explored the beliefs of preservice teachers and their mentors and determined that preservice teachers, discussing technology issues with their mentors and either observing or utilizing something with technology positively impacted instructional quality and subject learning. Based on these studies, participating in school-based field experiences with mentors positively affects technology integration during practice teaching for preservice teachers.
However, Singer and Maher (2007) explored the interventions of two pairs of preservice teachers and their mentors in a technology-rich curriculum and determined that the beliefs of preservice teachers about student learning with technology were far from those of their mentors. That is, pedagogical beliefs of preservice teachers may be influenced by other aspects, such as teacher education courses. Moreover, Sahin (2008) demonstrated that many preservice teachers were not exposed to extensive use of technology during their internships because not all mentors used technology while teaching; this may have affected preservice teacher beliefs about technology use. In contrast to previous studies of effective mentoring, the study by Sahin indicated that mentors do not provide preservice teachers with the skills needed for technology integration. Grove et al. (2004) argued that mentors must develop knowledge about how to teach innovatively with technology, have access to technology to practice and develop lessons, and learn how to mentor preservice teachers in teaching in ways that are consistent with existing standards.
Moreover, in terms of preservice teacher beliefs, beliefs of teacher educators at universities can affect preservice teacher beliefs (Bai & Ertmer, 2008), and mentor beliefs influence the beliefs of preservice teachers. Freese (1999) designed a framework that guides preservice teachers in systematically analyzing their lesson plans before, during, and after teaching. Freese suggested that modeling, interaction, and lived experiences while co-constructing and coreflecting on teaching with mentors was valuable and impacted the beliefs and practices of preservice teachers. Kajder (2005) demonstrated that mentor pedagogical beliefs, as perceived by preservice teachers, markedly influenced preservice teacher beliefs about technology use during practice teaching. Therefore, school-based field experiences with mentors directly or indirectly through teaching beliefs influence preservice teacher technology integration.
Brief summary and hypothesized influence model
Several studies analyzed teacher education courses that affect technology integration by preservice teachers (Brown & Warschauer, 2006; Leu & Kinzer, 2000; Singer & Maher, 2007). Notably, school-based field experiences with mentors influence preservice teacher technology use during practice teaching (Grove et al., 2004; Haydn & Barton, 2007; Judge & O'Bannon, 2007; Singer & Maher, 2007). Additionally, some studies identified the shape (Keys, 2007; Lambert et al., 2008) and the effects (Sang et al., 2010; Valcke et al., 2010) of preservice teacher beliefs about the integration of technology and instruction. However, exploring each factor influencing technology integration individually is difficult, especially when trying to interpret the perspectives of preservice teachers on technology use when teaching students in a practicum context. Moreover, teacher education courses and school-based field practice are two important processes in which diversity trainings provide preservice teachers with professional skills. Consequently, when exploring preservice teacher technology use during practice teaching, one must examine teacher education courses in colleges and field experiences with mentors in the practicum context, while also examining teacher beliefs about technology integration. By identifying direct and indirect relationships among factors, this study develops a multivariate hypothesized model (Figure 1).
[FIGURE 1 OMITTED]
This study evaluates a multivariate hypothesized model that predicts the significance of, and relationships among, process factors and their direct and mediated effects on technology integration for preservice teachers during practice teaching in school-based field practicum in Taiwan. Structural equation modeling (SEM) was applied to model the relationships in a set of the following four latent variables: perceived usefulness of teacher education courses; school-based field EWMs; beliefs of preservice teachers about technology integration; and, technology integration during practice teaching. A survey was conducted to collect data. Analyses of the measurement model and path model were included in SEM. The measurement model is a conventional confirmatory factor model, representing a set of observed variables that are multiple indicators of a set of latent variables, which was used to assess data validity. During path analysis, the effect of one latent variable on another is decomposed into direct, mediated, and total effects. A direct effect is the influence of one variable on another that is not mediated by another variable, whereas a mediated effect is the influence of one variable on another that is mediated by at least one other variable. The sum of direct and mediated effects is the total effect (Bollen, 1989).
The initial sample comprised 202 preservice secondary school teachers who participated in school-based field practice between August 2010 and January 2011. These teachers were randomly chosen from a university of teacher education in Taiwan. Each sampled preservice teacher and the other preservice teachers within the same secondary school, who were from other universities of teacher education, were invited to fill out the study questionnaire during December 2010 to January 2011. In total, 466 questionnaires were returned, of which 401 questionnaires were valid.
The sample consisted of 133 male (33.2%) and 268 female (66.8%) graduated from 18 universities, who majored in mathematics (10.2%), literacy and language arts (24.0%), sciences (9.5%), social studies (8.7%), arts and physical education (11.7%), vocational professions (11.5%), integrative activities (12.2%), and special education (12.2%).
Before participating in school-based field practice courses, preservice teachers in Taiwan must major in a teaching field, complete at least 26 credits in education courses, and be enrolled in at least three courses related to instructional methods and technology, including Instructional Principles, Classroom Management, Theory and Practice of Counseling, Instructional Multimedia and Utilization, Curriculum Development and Design, and Educational Testing and Assessment. Additionally, the Subject/Field-Specific Teaching Method course is a required course.
A questionnaire was developed to identify factors influencing technology integration by preservice teachers in the practicum context. Four sections associated with four variables were included in the survey. Individual items were developed based on literature (Table 1).
The first section, perceived course usefulness (PCU) has 2 items. Perceived usefulness is defined as the degree to which a person believes that using a particular system will enhance his/her job performance (Davis, 1989). The PCU section, addressing the perceived usefulness of technology integration training in teacher education courses, measures the knowledge and ability of preservice teachers to enhance instruction with technology. The second section, beliefs about teaching (BAT) has 4 items for willingness, acceptance, supposition, and confidence in technology integration, based on the belief definition by Teo, Lee, Chai and Wong (2009), who defined belief as an individual's estimated probability that performing a given behavior will result in a consequence. The third section, preservice teachers' experiences with mentors (EWMs) has 3 items--observing mentors (Haydn & Barton, 2007), receiving guidance (Judge & O'Bannon, 2007), and perceived teaching characteristics of mentors (Grove et al., 2004). The final section, technology integration implementation (TII), has 4 items for four general teaching processes--lesson design, material design, teaching activities, and assessments using technology.
Responses to each item were on a four-point Likert scale with 1 for "strongly disagree" to 4 for "strongly agree." All items were repeatedly revised by five professors with relevant expertise. Cronbach's alpha was initially calculated to assess the internal consistency and reliability of the four variables. The alpha value for PCU was 0.76, that for BAT was 0.93, that for EWMs was 0.75, that for TII was 0.94, and that for the total questionnaire was 0.86.
After collecting data, evaluating homogeneity in teaching fields, and confirming internal consistency reliabilities, multivariate normality, validity through factor loadings for the measurement model, the entire model, and model fit were analyzed.
A one-way analysis of variance (ANOVA) was applied to determine whether teaching fields which preservice teachers majored in differ in four variables. The analytic results reveal insignificant differences in teaching fields (PCU, F=2.795, p>.01, BAT, F=.747, p>.01, EWMs, F=2.681, p>.01, TII, F=.685, p>.01), which eliminates the potential effect of the teaching fields on four latent variables.
Assessment of multivariate normality
Structural equation modeling programs utilize maximum likelihood (ML) estimation, which is robust for normality violations and provides remedies for non-normal variables. Parameters were examined to evaluate data nonnormality. According to Bollen and Long (1993), for univariate normality, when both skewness coefficients and that of kurtosis have absolute values < 2.0, normality is reached. This study generated coefficients of -0.469 - -0.078 for skewness and -0.817 - 1.435 for kurtosis (Table 1). That is, data did not violate the univariate normality assumption for each observed variable.
According to Bollen (1989), when Mardia's coefficient is less than p (p + 2), where p is the number of observed variables, multivariate normality exists. In this study, Mardia's coefficient was 101.844, p= 13, 101.844 < 13 (13 + 2) = 165; thus, multivariate normality existed.
To assess validity, standardized regression loading of each observed indictor, and average variance extracted (AVE) compared with correlation coefficients for latent variables were calculated. Standardized regression loading was evaluated for construct validity, and AVE was used for discriminant validity.
Table 2 shows the standardized factor loading of each observed variable. The factor loadings were 0.646-0.927 (t-values, 2.17 - 12.62, p<. 05), exceeding the recommended minimum of 0.50 (Hair, Anderson, Tatham, & Black, 1998). According to Bagozzi and Yi (1998), when construct validity of latent variables exceeds 0.60, measurement instruments have construct validity. The construct validity of each latent variable was 0.750 - 0.898; that is, > 0.60 (Table 2).
Additionally, AVE of each latent factor exceeded 0.5, and all square roots of AVE for the four factors exceeded the correlation coefficients among factors, demonstrating good discriminant validity (Anderson, & Gerbing, 1988).The AVE range was 0.501-0.714 (> 0.5), and the square root of AVE for each factor was 0.708-0.845, greater than the coefficients for correlations among factors (Table 2). Therefore, measurement instruments had discriminant validity.
Evaluation of the entire model
Notably, SEM can determine the significance of variance in the entire model. The estimated result had a high significance level ([chi square] = 112.345, df = 60, p=.000<.001). The appropriateness of model data was significant.
Model fit analysis
According to Kline (2005), the suggested [chi square]/df value is < 3 for large samples. For this model, [chi square]/df= 112.345 / 60 = 1.872 (< 3), the value was adequate. Additionally, according to suggested guidelines from Bollen (1989), Kline (2005), and Pedhazur (1997), all other values related to model fit indices were favorable; that is, the research model had a good fit. Table 3 lists model fit results.
Figure 2 shows the path coefficients among each latent variable. Two of the five path estimates were insignificant, and the rest reached significant. All path coefficients were positive. According to Cohen's recommendation, interpretations of effect size of correlations are based on standardized path coefficients with absolute values--small effect ([less than or equal to] 0.1), medium effect (> 0.1, < 0.5), and large effect ([greater than or equal to] 0.5) (Kline, 2005). The total effects, direct effects, and indirect effects among the five latent variables were calculated.
The standardized total effect of BAT on TII was 0.52. Even though the effect of PCU on TII was insignificant, the effect of PCU on BAT was 0.11, and the total effect of PCU on TII was 0.06, a very small effect. That is, when preservice teachers' PCU increase 1SD, TII increases weakly by 0.06SD. The standardized effect of EWMs on TII was 0.27, the effect of EWMs on BAT was 0.32, and the total effect of PCU on TII was 0.44 (0.27 + 0.32 x 0.52), a moderate effect. That is, when preservice teachers' EWMs increases 1SD, TII increased by 0.44SD.
In terms of preservice teacher PCU, the total effect on TII was very small, while the effect of EWMs on TII was moderate.
[FIGURE 2 OMITTED]
Conclusions and discussion
Analytical results reveal that perceived usefulness of teacher education courses, in terms of total effects, has a small influence on technology use. That is, teacher education courses do not generate sufficient technology integration knowledge, thereby failing to facilitate preservice teachers' technology use when teaching students in a practicum context.
Moreover, the preservice teacher EWMs in the practicum context has a moderate effect on TII and BAT. Thus, this study demonstrates that participating in school-based field EWMs help preservice teachers use technology while practicing teaching.
Additionally, the resulting model had an adequate fit to observed relationships among factors influencing teacher technology use during practice teaching.
Effects of teacher education courses
Teacher education courses were perceived by preservice teachers as ineffective in providing sufficient technology integration experiences during practice teaching. These analytical results are corroborated by research findings in literature, indicating that course training was useless in terms of technology integration by preservice teachers (Singer & Maher, 2007). This analytical result which is also supported by previous research (Branch, 2003; Swain, 2006) does not predict actual technology use during practice teaching.
However, teacher education courses have many benefits. Similar to findings in previous research (Keys, 2007; Lambert et al., 2008; Pajares, 1992; Raths, 2001), analytical results in this study confirm that teacher courses have positive effects on the pedagogical beliefs of preservice teachers; that is, pedagogical beliefs can be shaped by experiences as teacher education students.
Furthermore, the pedagogical beliefs of preservice teachers, similar to findings in previous research (Pajares, 1992; Richardson, 2003), markedly influence their subsequent instructional decisions about technology integration and classroom practice based on the analytical finding that pedagogical beliefs have a significant effect on technology use during practice teaching.
The total effect of PCU on TII was small. Consistent with previous literature, teacher education courses did not provide sufficient instruction in how to combine technology and instruction (Leu & Kinzer, 2000). One possible explanation is that isolated training course existed. Technology integration in a classroom must be combined with subject content and instructional strategies. Thus, isolated courses, such as Instructional Principles and Instructional Multimedia and Utilization, cannot provide complete concepts and actual practice in integrating technology and instruction. Although the Subject/Field-Specific Teaching Method course combines subject content and instructional strategies, whether content about integrating technology into instruction is taught depends on educator beliefs about technology use in classrooms. Consequently, although teacher education courses can equip preservice teachers with technology skills or promote instructional strategies, those courses remain insufficient for teaching practice using technology according to evaluation results for the total effect of PCU on TII.
Effects of experiences with mentors
Another study finding is clearly consistent with those in previous research (Grove et al., 2004; Judge & O'Bannon, 2007; Haydn et al., 2007), indicating that preservice teacher EWMs positively affected TII during practice teaching. Additionally, analytical results also confirm that preservice teacher EWMs facilitate technology integration (Freese, 1999; Valcke et al., 2010).
Participating in school-based field practice is important in teacher education programs in Taiwan. For practice teaching, preservice teachers are assigned at least one mentor, who guides preservice teachers in designing appropriate learning activities and assessing student prior knowledge of, and experiences with, a subject. Thus, EWMs, consisting of observing mentors teaching, and receiving guidance from mentors, perceived mentor characteristics, positively affected technology use by preservice teachers during practice teaching.
Another analytical result indicates that EWMs positively affect the pedagogical beliefs of preservice teachers. Similar to findings in previous research (Freese, 1999; Kajder, 2005), interaction and lived experiences in co-constructing and co-reflecting on teaching practices with mentors influence the pedagogical beliefs of preservice teachers. According to Raths (2001), teacher pedagogical beliefs can be shaped by experience. Generally, preservice teachers in school-based field practice spend most of their time interacting with mentors and being guided by mentors. The beliefs of mentors about educating students and teaching methods may be adopted by preservice teachers who have no practical experience or are aware of the uselessness of teacher education courses about technology use during practice teaching. When preservice teachers have successful experiences or make few teaching mistakes when using a mentor's methods, they would likely adopt the mentor's model and structure their educational perspectives and teaching strategies such that they are similar to those of their mentor. This is why the effect of preservice teacher EWMs rather than PCU on BAT was significant.
Moreover, the pedagogical beliefs of preservice teachers greatly influence their subsequent instructional decisions on technology integration and classroom practice. These analytical results confirm that school-based field EWMs are critical, and are mediated through pedagogical beliefs, influencing preservice teachers' TII.
In Taiwan, similar to many countries, teacher education courses and school-based field practice are two important processes to equip preservice teachers with professional skills. This study documented the relationship effects of combining teacher education courses usefulness and school-based field EWMs, further shaping pedagogical beliefs about technology integration for preservice teachers during practice teaching. A noteworthy finding is that teacher education courses had a much weaker effect on technology use than EWMs in school-based field practice. As mentioned previously, technology use during teaching benefits students regardless of which subject. Preservice teachers should be equipped with the ability to technology integration to enhance their subsequent students' learning. However, the study concludes teacher education courses fail to facilitate preservice teachers' technology use when teaching students in a practicum context. The lack of the ability to technology integration for preservice teachers may inhibit innovative teaching and affect students' learning. While teacher education institutes at universities are natural places to develop teaching skills, this study finding can be contrasted with recommendations by Brown and Warschauer (2006), namely, single courses should be revised to provide preservice teachers with effective strategies for integrating technology into classroom teaching, rather than focusing on technical skills or knowledge.
As literature mentioned earlier, preservice teachers felt that teacher education courses were not helpful for technology integration (e.g., Singer & Maher, 2007). Additionally, preservice teachers can develop the ability to integrate technology into instruction while guided by their mentors (e.g., Judge & O'Bannon, 2007). This study synthesizes the above two perspectives, and adds teacher belief as a mediate variable, to develop a new model for investigating the effective factor in technology integration for pre-service teachers and to indicate effectiveness of EWMs.
Implications and limitations
Professional courses at teacher education institutes must be re-examined to promote preservice teacher use of technology while teaching. In terms of Taiwan, current teacher education system has not contained the courses that combine technology, pedagogy, and subject content yet. Several countries are also similar to Taiwan. I recommend that technology should be integrated into core method courses, not limited to isolated courses. Further, this study identifies the factors influencing technology use by preservice teachers during teaching practice, particularly for the two important processes of teacher education, teacher education course training and school-based field experiences. The research model can serve as a base model for future studies.
The study has one significant limitation. Course information in this study is from Taiwan's Ministry of Education. However, Taiwan has 40 universities with teacher education departments that train secondary school teachers (Center for Educational Research and Evaluation, Taiwan, 2010). The sample of this study graduated from 18 universities. Although all preservice teachers must complete at least 26 credits in education courses before participating in school-based field practice, some universities do not provide a sufficient number of elective courses for students, as qualified lecturers are lacking. Thus, a bias may exist due to differences in students enrolling in training courses.
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(Submitted January 04, 2011; Revised March 30, 2011; Accepted June 10, 2011)
Table 1. Descriptive Statistics and Assessment of Normality Observed variables Mean SD. skew kurtosis Teacher education courses equipped 2.95 0.90 -.201 -.817 me with technology integration knowledge. (PCU1) Teacher education courses enhanced 2.76 0.91 -.435 -.693 my technology integration effectiveness. (PCU2) I am willing to use technology 3.28 0.59 -.469 .935 during my teaching career. (BAT1) I accept the fact that technology 3.33 0.55 -.210 .370 helps students learn. (BAT2) Every teacher should develop 3.30 0.56 -.219 .389 technology integration ability. (BAT3) I have strong confidence in my 3.31 0.56 -.248 .346 ability to implement technology integration. (BAT4) Observing my mentor teach was 3.07 0.57 -.317 1.435 helpful for my technology integration. (EWMs1) Receiving mentor guidance was 3.00 0.60 -.207 .456 helpful for my technology integration. (EWMs2) Perceived mentor characteristics 2.88 0.66 -.226 .096 facilitate my technology integration. (EWMs3) I have designed at least a lesson 3.32 0.55 -.078 .580 that uses technology. (TII1) I have designed digital material for 3.27 0.56 -.175 .526 lesson preparation. (TII2) I have employed teaching activities 3.19 0.57 -.190 .461 that utilize technology. (TII3) I have used technology to assess 3.30 0.53 -.179 .413 student performance. (TII4) Multivariate (Mardia's coefficient) 101.844 Table 2. Construct and Discriminant Validity Latent Observed Loading Construct AVE [square root variables variables of (AVE)] PCU PCU1 .790 * .755 .607 .779 PCU2 .768 BAT BAT1 .837 * BAT2 .924 * .898 .714 .845 BAT3 .927 * BAT4 .796 * EWMs EWM1 .680 * EWM2 .790 * .750 .501 .708 EWM3 .646 * TII TII1 .893 * TII2 .911 * .885 .668 .817 TII3 .814 * TII4 .923 * Latent correlation coefficients among variables the factors PCU .192 ** for PCU--BAT .183 ** for PCU--EWMs .209 ** for PCU--TII BAT .300 ** for BAT--EWMs .373 ** for BAT--TII .584 ** for EWMs--TII EWMs TII * p<.05, ** p<.01 Table 3. Results of model fit indices for the model Model fit indices Values Suggested guidelines [chi square]/df 1.872 < 3 CFI .985 [greater than or equal to] .9 GFI .960 [greater than or equal to] .9 AGFI .939 [greater than or equal to] .9 NFI .968 [greater than or equal to] .9 IFI .985 [greater than or equal to] .9 RMR .025 < .05 RMSEA .047 < .05…