a r t i c l e i n f o

Article history:

Accepted 5 January 2014

Keywords:Teachers self-efcacy beliefs for technology

a b s t r a c t

The purpose of this study was to identify how pre-service teachers self-efcacy beliefs for technology

ld of teacher edu-evant hardware orchool culture (e.g.,

mer, 1999), technological pedagogicalAlghazo, 2012); and teachers value

nology integration (SETI) is one of thee.g. Abbitt, 2011; Al-awidi & Alghazo,

2012; Anderson, Groulx, &Maninger, 2011; Ertmer & Ottenbreit-Leftwich, 2010; Sang, van Braak, & Tondeur, 2010; Teo, 2009;Wang, Ertmer,& Newby, 2004). Pyschologist, Bandura (2006) has dened self-efcacy belifs as a judgment of capability to execute a given type of per-formances (p. 309). Generally, people with a strong sense of self-efcacy exert a high degree of effort to complete a task, and is likely topersist in challenging difcult situations (Bandura,1977). SETI does not focus on the knowledge or skills that teachers have about technologyintegration, but on their beliefs or condence that they have about things they can do for technology integration (Al-awidi & Alghazo, 2012).The higher SETI a teacher has, the more enthusiastically he/she is likely to use technology for student-centered teaching (Anderson et al.,

* Corresponding author. Tel.: 82 10 3861 3400.

Contents lists available at ScienceDirect

Computers & Education

Computers & Education 73 (2014) 121128E-mail addresses: agnes@knue.ac.kr (Y. Lee), leeji@cau.ac.kr, leeji@post.harvard.edu (J. Lee).software (e.g., Goktas, Yildirim, & Yildirim, 2008); environmental supports such as time, technical supErtmer & Ottenbreit-Leftwich, 2010); teachers skills or knowledge such as computer skills (e.g., Ertand content knowledge (e.g., Koehler, Mishra, Yahya, 2007); teaching experiences (e.g., Al-awidi &beliefs or attitudes such as self-efcacy (e.g., Anderson & Maninger, 2007).

In particular, research provides strong evidence that teachers self-efcacy beliefs toward (for) techmost signicant and determining factors of teachers actual use of technology in their classrooms (Teachers successful integration of technology in their classrooms has been the center of vigorous debates in the ecation. Researchers have investigated various factors that could affect teachers use of technology: resources such as rel

port, training, and s1. IntroductionintegrationLesson planningTechnology integration course designTeachers attitude toward computer0360-1315/$ see front matter 2014 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.compedu.2014.01.001and indirect inuences between SETI and other non-course variables (computer use, teachers attitudetowards computers (TAC), changes in TAC). A total of 136 undergraduate students at a teacher educationuniversity in Korea participated in the study. Our data analyses illustrated signicant increase of pro-spective teachers SETI after their completion of education technology course resulting mostly fromlesson planning practice. The hierarchical multiple regression revealed that the pre-service teachers withhigher positive attitudes toward computers and greater ability for lesson planning showed higher in-crease in their levels of SETI. The path analysis indicated that these two factors inuenced the SETIdirectly, rather than indirectly. Lesson planning practice did not affect pre-service teachers attitudinalgrowth. Implications on effectiveness of the lesson planning and attitudinal factors on SETI, and sug-gestions for teacher education course design are discussed.

2014 Elsevier Ltd. All rights reserved.Received in revised form2 December 2013(instructional media development skills, knowledge on technology, and lesson planning practice) has thehighest impact on the SETI. This research also attempted to explore a more inclusive path of the directReceived 1 August 2013 integration (SETI) can be improved during the coursework intervention, and which of the course factorsEnhancing pre-service teachers self-efcacy beliefs fortechnology integration through lesson planning practice

Youngju Lee a, Jihyun Lee b,*aDepartment of Education, Korea National University of Education, San 7, Darak-ri, Gangnae-myeon, Cheongwon-gun, Chungbuk 363-791,Republic of KoreabDepartment of Education, Chung-Ang University, 211 Heuksuk-dong, Dongjak-gu, Seoul 156-756, Republic of Korea

journal homepage: www.elsevier .com/locate/compedull rights reserved.

2011). Especially for the pre-service teachers or novice teachers, SETI is known to have more direct impact on their actual practices(Anderson & Maninger, 2007; Sang et al., 2010; Teo, 2009).

From the beginning phase of the ICT (Information and Communication Technology) integration initiative, teachers technical skills havebeen considered as the key factor for the effective implementation of technology, and amajority of the related projects such as PT3 (PreparingTomorrows Teachers to use Technology) focused on developing the technological skills of pre-service teachers. Most researchers thus as-sume that teachers SETI is signicantly inuenced by their technological prociency because their frustration at the lack of such skillsobviously drop their competence in using technology integrated instructions (e.g., Abbitt & Klett, 2007; Anderson & Maninger, 2007).Furthermore, teachers are expected to gain more and more advanced technology skills since pre-service teachers who graduate are digitalnatives, and must move beyond being computer literate to technology competent (Smarkola, 2008, p. 1197). However, some contradictoryndings have been raised: technology skills do not signicantly predict actual practice of using technology of both pre-service (Negishi, Elder,Hamil, &Mzoughi, gishi, 2003) and in-service teachers (Becker, 2000) and technology skills indirectly affect the practice via SETI (Anderson &Maninger, 2007). In all cases, although knowledge of technology is necessary, it is not enough (Ertmer & Ottenbreit-Leftwich, 2010, p. 261).

Typically, teacher education institutes offer educational technology courses to help pre-service teachers learn how to employ technologyin their teaching. However, the design of the courses varies considerably. Some courses primarily focus on training pre-service teacherstechnical skills, and other courses address general theories on teaching with technology. For instance, Abbitt and Klett (2007) ran fourdifferent courses for technology integration. The courses were different by the instructional time (one credit vs. two credits) and the focus ofthe activities (application of specic software vs. broad focus with regard to educational technology). The results of the study indicated thatthe amount of instructional time did not inuence teachers SETI. Rather, the focus of the courses made a signicant difference in teachersSETI; pre-service teachers in the more general approach showed higher scores in the rating of SETI than those enrolled in the course with afocus on the use of specic software. However, this study was limited in identifying specic characteristics of the courses. Despite thevarious design of the educational technology courses, studies on its effectiveness rarely discovered how different activities and designs ofthe courses contributed to enhancing teachers SETI. Therefore, the previous research ndings cannot fully respond to the questions of howto prepare prospective teachers in the specic design of teacher education courses (Ertmer, Conklin, & Lewandowski, 2003; Goktas, Yildirim,& Yildirim, 2008; Wang et al., 2004).

The implications for designing educational technology courses for pre-service teachers can be gained from a research base called theTPACK (Technological Pedagogical and Content Knowledge) framework (Koehler & Mishra, 2005; Koehler, Mishra, Yahya, 2007). Thisframework proposes that the desirable use of technology in the classrooms requires a complex form of teacher knowledge that integratescontents, pedagogy, and technology. Koehler and Mishra (2005) argued that the teacher educators can support teachers development oftheir TPACK by providing a curricular system where an instructor considers all three types of knowledgedcontents, pedagogy, and tech-nologyd in an integrated manner rather than collecting isolated courses with a focus of only one knowledge among the three. Expertssuggest that technology courses should not be isolated from curriculum and contents and they should be contextually situated in the school-based learning environment (Choy, Wong, & Gao, 2008; Hughes, 2005; Smarkola, 2008; Wentworth, Waddoups, Earle, 2004). Many re-searchers emphasize the importance of content-specic practice of using technology. Hughes (2005) writes the more content-specic theexample, the more likely the teacher will see value and learn it (p. 295). In response to this need, Koehler and Mishra (2005) proposed aconstructivist approach called, Learning technology by design where teachers learn effective technology integration as they participate inauthentic and situated pedagogical tasks. The design-based approach assumes that teachers take amore active role as instructional designerswho value technology as an effective instructional tool, rather than staying as a passive technology recipient (Koehler, Mishra, Hershey, &Peruski, 2004). In this study, we believe that teachers lesson planning activities would provide prospective teachers with experiences ofpedagogical problem-solving where they need to connect their technology skills to their content knowledge and pedagogical knowledge.

Lastly, other than the coursework, one of the most signicant and long-lasting factors that could inuence SETI is teachers attitudetoward computer (TAC). Brinkerhoff (2006) proposes that fear or anxiety for the novice teachers and low value beliefs for expert teacherscan be serious detriments for teachers attempts to integrate technology into curricula. Pre-service teachers beliefs about the value ofclassroom technology integration found to be the best predictor of teachers intentions to use technology in their future classrooms(Anderson et al., 2011). There are a great number of research studies on the relationship between teachers attitude toward computer andtheir intentions or actual behaviors of technology integration (e.g., Anderson et al., 2011; Wu, Chang, & Guo, 2008), it is rare to identifyprevious studies investigating direct relationship between teachers attitude toward computer and their self-efcacy for technology inte-gration. Abbit and Klett(2007)s research is noteworthy in this regard. Their research nding suggests that perceived comfort with computertechnology accounted for approximately 41% of the variance in teachers self-efcacy beliefs toward (for) technology integration.

1.1. Research Questions

Despite the abundant literature, factors that inuence pre-service teachers SETI remain largely unclear, especially when designing aneducational technology course for teacher preparation programs (Abbitt & Klett, 2007). This study tries to identify how pre-service teachersSETI can be improved during the coursework intervention, and which of the course activitiesd1) instructional media development, 2)lecture on technology integration, and 3) lesson planning practicedhas the highest impact on the SETI. In addition, this research attempts toprovide a more complete path of the direct and indirect inuences between the SETI and the other strong factors in order to nd impli-cations for teacher education programs. The research questions that guide this study are the following:

1. Was there a change in teachers SETI after the course completion? If so, which course intervention factor for teachers (instructionalmedia development skills, knowledge on technology integration, and lesson planning skills) was important?

2. Considering teachers existing characteristics prior to the course (computer use, teachers attitude toward computer), which factorinuences the improvement in the level of teachers SETI? What is the unique contribution of the coursework intervention variables tothe enhancement in the level of teachers SETI?

3. How does the strongest course factor affect SETI in relation to teachers attitude toward computer (TAC)? Does it inuence SETI directly

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128122or indirectly via TAC? What are the relative inuences when comparing pre-course factors?

2. Methods

2.1. Participants

A total of 136 undergraduate students at a teacher education university in South Korea participated in the study. The participants wereenrolled in a technology integration course as a part of teacher certicate program. The university offered 6 classes of the course and thecomputer lab could accommodate no more than 25 students per class since the course required to use computers. The rst author of thepaper taught all 6 classes using the same textbook and materials in the classes.

Two thirds of the students were female (64%) and the rest 36%weremale students. Their age ranged from 19 to 27 (M 20.34, SD 1.07).The majority of the participants were freshmen (n 119, 88%), and 40% of the participants weremajoring in elementary education. The nextlarge number of students (32%) was science educationmajors. The rest of the students were social studies educationmajors (12%), education(7.6%), language education (4.6%), early childhood education (2.3%), and etc (art education, music education, and physical education, 1.5%).

2.2. Course intervention description

The rst author of the paper served as a lecturer. During the semester, the technology integration course provided several activities whichwere intended to improve teachers self-efcacy in technology integration. First, the course lecture addressed theories in instructionalmediadhistory in instructional media, the ASSURE model (Heinich, Molenda, Russell, & Smaldino, 1996), principles of effective multimediadesign, and teaching strategies in the use of technology. The ASSURE model is a systematic approach for creating lessons that effectivelyintegrate the use of technology and media. The acronym ASSURE stands for 6 procedural components that assure successful technologyintegration; 1) Analyze Learners, 2) State Standards and Objectives, 3) Select Strategies, Technology, Media, and Materials, 4) Utilize Tech-nology, Media, andMaterials, 5) Require Learner Participation, and 6) Evaluate and Revise. Second, the pre-service teachers were able to learnhow to use computer software for multimedia development (i.e., Photoshops, Audacity, andWindowMovie Maker). The lecturer walked thepre-service teachers through the major functions of the software. The pre-service teachers had an opportunity to practice developinginstructional media by themselves during the course. Lastly, the pre-service teachers were asked to create a lesson plan individually fortechnology integration using the ASSUREmodel. They chose to teach the subjects theymajored in (i.e., mathematics, science, and etc.) and setthe target audience for the classes they designed. While lesson planning, they referred to the national curriculum and teachers guidebooksdistributed by the educational districts for more specic contents. As they nished the rst draft of their lesson plans, the lecturer gave themcommentsand feedback. Basedon the feedback they received,pre-service teachers revised their lessonplans. The revision tookplaceonlyonce.

2.3. Measures

2.3.1. Teachers self-efcacy beliefs for technology integration (SETI)To assess participants condence in technology use in their teaching, we used the self-efcacy beliefs for technology integration scale

developed by Wang et al. (2004). The survey contained 16 statements rated on a ve-point Likert scale (5 strongly agree, 4 agree,3 neither agree nor disagree, 2 disagree, 1 strongly disagree). Example items are as follows: I feel condent that I can successfullyteach relevant subject content with appropriate use of technology., I feel condent about selecting appropriate technology for instructionbased on curriculum standards., and I feel condent that I will be comfortable using technology in my teaching. The reliability of theinstrument was .86 and .89 for pre-survey and post-survey respectively.

2.3.2. Teachers attitudes toward computers (TAC)We adopted a scale of teachers attitudes toward computers (TAC) developed by Agyei and Voogt (2011). We used 18 items composed of

four sub-scales, 1) anxiety (fear to use computers, 4 items), 2) enjoyment (the pleasure someone experiences when using computers, 4items), 3) benets for student learning (perceived advantages of using computers in the class, 4 items), 4) instructional productivity (in-uence of computer use on the instructional productivity, 6 items). All 18 items on the survey were written in statements on a ve-pointLikert scale (1 strongly disagree, 5 strongly agree). Four items for the anxiety sub-scale were reversely coded. The internal consistencyreliabilities were .76, and .88 for pre-survey and post-survey respectively.

2.3.3. Computer usageStudents were asked about how much time they usually spend on using computers a day and the purposes for the computer usage.

2.3.4. Lesson planning skillsWe developed a rubric to evaluate the quality of the lesson plans for technology integrated classes that the students created using the

ASSUREmodel. Based on the 6 stages of the ASSURE model, we generated 8 evaluation items: 1) learner analysis, 2) objective statements, 3)teaching strategies, 4) technology selection, 5) teacher activities prior to class implementation, 6) teacher activities during the class, 7)student activities, and 8) student evaluation plans. The rubric also provided descriptions of quality for the 8 items in three levels; excellent,fair, and poor. Each evaluation item was rated from 1 to 7. The maximum possible score for a lesson plan was 56 (7 points maximum 8evaluation items).

2.3.5. Knowledge for effective technology integrationStudents were evaluated on their conceptual understandings about successful technology integration as their mid-term exam during the

course. The exam reected the contents of the lecture of the coursedhistory of instructional media, the ASSURE model (Heinich, Molenda,Russell, & Smaldino, 1996), principles of effective multimedia design, and teaching strategies in the use of technology. The exam contained10 questions and some specic examples are as follows: Explain instructional affordances of video media, Explain the six steps of the

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128 123ASSURE model, and Give an example of using web 2.0 tools for instruction. The total score of the exam was 30.

2.3.6. Instructional media development skillsAs part of the course requirement, students were asked to develop instructional media. During the course they had a chance to learn how

to use software to edit image, audio, video media and practice producing instructional media. Finally, students were asked to submit twoinstructional media products (one in an image le format and the other in a video le format) for their course assignments. The scores forthe media product tasks were used to estimate students skills for instructional media development.

2.4. Data collection

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128124In the beginning of the semester, we collected data for SETI (Self-Efcacy beliefs for Technology Integration) pre-test, TAC pre-test, andcomputer usage. During the semester, as the course progressed, students submitted their lesson plans for technology integration,instructional media products, and took a test intended to evaluate their conceptual knowledge for effective technology integration. At theend of the semester, data for SETI post-test and TAC post-test were collected to examine changes in students attitude toward computer andtheir self-efcacy beliefs for technology integration as a result of the course intervention.

2.5. Data analyses

We used a paired-sample t-test in order to investigate the effect of the course on pre-service teachers improvement in their self-efcacybeliefs for technology integration. We also conducted a multiple regression to identify the critical factor of the three course interventionactivities (lesson planning, instructional media development, and conceptual knowledge for effective technology integration). Using ahierarchical multiple regression, we inspected the unique contribution of the course intervention while considering students existingcharacteristics (computer use and TAC) prior to their exposure to the course. Before conducting a regression analysis, we assessed un-derlying assumptions. First, the assumption of multicollinearity was checked by Variance Ination Factor (VIF) and Tolerance values. For allve independent variables, the VIF values were below 10 and the tolerance statistics were above .2, indicating collinearity was not aproblem. Second, the scatter plot of the values of the residuals against the values of the outcomes predicted showed a random pattern,indicating the assumption of linearity and homoscedasticity was met. Third, we examined the histogram and normal probability plot toassess the assumption of normality. The histogram was symmetrical and approximately bell-shaped. The PP plots suggested that theresiduals are normally distributed.

In order to explore the complex relationship of the direct and indirect effects among the strongly inuencing variables (initial TAC, TACgrowth, Computer Usage, Lesson Planning and SETI), we set up a research model. The proposed research model hypothesizes that LessonPlanning affects SETI directly and indirectly via TAC growth, and the pre-course factors (initial TAC and Computer Usage) directly affect SETI.A path analysis was conducted using AMOS 18.0 after the missing data (1 for SETI, 7 for pre-TAC, 1 for post-TAC, and 1 for technology skills,.7%, 5.1%, .7%, and .7% respectively) were replaced by regression imputation provided by the software. Path estimates was calculated bymaximum likelihood estimation and bootstrap analysis considering the sample size and measurement errors of the variables.

3. Results

3.1. Effects of the course intervention

Research Question 1. Was there a change in teachers SETI after the course completion? If so, which course intervention factor for teachers(instructional media development skills, knowledge on technology integration, and lesson planning skills) was important?

We conducted a paired-sample t-test to identify if there was a change in teachers self-efcacy levels for technology integration after thepre-service teachers nished the course. We found a signicant increase in the mean score of teachers self-efcacy beliefs for technologyintegration after the course (fromM 47.27, SD 8.91, before the course toM 58.41, SD 7.19, after the course); t (130)13.69, p< .001.

By running a regression analysis, we further investigated which course-relevant factors contributed the most to the increase in teachersself-efcacy levels for technology integration. First, we examined the relationship between three course-related variables and thedependent variables. As Table 1 shows, instructional media development skills and lesson planning skills variables were positively andsignicantly correlated with the teachers self-efcacy variable. The multiple regression model with three predictors produced R2 .12, F (3,131) 6.11, p .001. However, as Table 2 indicates, lesson planning skills was the only signicant predictor for teachers self-efcacy fortechnology integration. Its positive regression weights indicate that teachers with higher scores in lesson planning tasks are expected tohave higher levels of self-efcacy for technology integration, after controlling for the other variables in the model.

3.2. Effects of the course intervention with the consideration of pre-course factors

Research Question 2. Considering teachers existing characteristics prior to the course (computer use, teachers attitude toward computer),which factor inuences the improvement in the level of teachers SETI? What is the unique contribution of the coursework intervention variables tothe enhancement in the level of teachers SETI?

Table 1Descriptive statistics for course variables.

Variables M SD Correlation with SETI

Instructional media development skills 20.31 3.82 .10Knowledge on technology integration 20.96 6.57 .29*

Lesson planning skills 28.66 5.64 .33**p < .05, **p < .01.

We conducted a multiple regression analysis hierarchically entering computer use, teachers attitudes toward computers (TAC pre-test)in the rst step (forced entry) and the remaining course intervention variables(lesson planning skills, Instructional media developmentskills, knowledge on technology integration) in the second step (stepwise). As we included two independent variables onlydteachers use ofcomputer and attitudes toward computers in our rst regression model, 28% (R2 .28) of the variance in teachers self-efcacy for tech-nology integration was explained by exposure to teachers existing characteristics prior to the start of the coursedtheir computer use and

Table 2Effects of course variables on SETI.

Variables b SE b B t Sig.

Instructional media development skills .04 .17 .02 .24 .81Knowledge on technology integration .15 .12 .14 1.28 .20Lesson planning skills .32 .13 .25 2.46 .02*

R2 .12Adjusted R2 .10F 6.11**

*p < .05, **p < .01.

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128 125attitudes towards computers, F (2, 124) 22.31, p < .001.The second model accounted for 35.7% of the variance in teachers self-efcacy for technology integration and was a signicant t of the

data, F (3, 123) 22.31, p < .001. This variance is generally considered as a large effect size in educational research (Cohen, 1988). Theincremental R2 of the second model was .07 after the computer use and attitudes toward computers had already been used. That is, thesecond regression model accounted for an extra 7% of the variance in teachers self-efcacy for technology integration over and beyond therst regression model. This unique contribution of the second model was statistically signicant, F Change (1, 123) 14.06, p < .001.

As shown in Table 3, the results of the regression indicated the two variablesdattitudes toward computers and lesson planning skillssignicantly predicted teachers self-efcacy for technology integration. It was found that teachers attitudes toward computers was thestrongest predictor of teachers self-efcacy for technology integration b 9.04, b .47, t 6.00, p < .001. The standardized beta valueindicates that as teachers attitudes toward computers increase by one standard deviation, teachers self-efcacy in technology integrationincreases by .47 standard deviations when the effects of other variables are held constant. The next important predictor was found to beteachers lesson planning skills, b .34, b .27, t 3.75, p< .001. In short, teachers who possessed positive attitudes toward computers andgreat ability for lesson planning were more likely to show high self-efcacy for technology integration.

However, the contribution of teachers computer use to the improvement in teachers self-efcacy for technology integration was notstatistically signicant, t .74, p > .05. In addition, instructional media development skills factor was not found to predict teachers self-efcacy for technology integration (if entered b .13, t 1.41, p > .05). Teachers knowledge on technology integration did not signi-cantly predict teachers self-efcacy for technology integration, either (if entered b .02, t .24, p > .05).

3.3. The direct or indirect effect of the lesson planning on SETI considering TAC

Research Question 3. How does the strongest course factor affect SETI in relation to teachers attitude toward computer (TAC)? Does it inuenceSETI directly or indirectly via TAC? What are the relative inuences when comparing pre-course factors?

As a preliminary exploration of relationships among the variables, the bivariate correlation analysis was conducted resulting in Table 4.The results suggest that all four variables in the model Lesson Planning skills, TAC_d, TAC_i, and Computer Use are signicantly related toSETI. The pre-course factorsTAC_i and Computer Usehave signicant interrelationship, and the attitudinal factorsthe initial TAC (TAC_i)and the difference between pre-TAC and post-TAC (TAC_d) are negatively correlated. The representative course factor Lesson Planning skillsis not signicantly related to the attitudinal variables, i.e., both TAC_i and TAC_d, and Computer Use.

As shown in Fig. 1, TAC_i and Computer Use are exogenous variables hypothesized to affect SETI directly; Lesson Planning skills is anendogenous variable hypothesized to affect SETI directly or indirectly via TAC_d; and TAC_d is both an endogenous mediating variablehypothesized to be affected by Lesson Planning skills or an exogenous variable hypothesized to directly affect SETI. As indicated in Table 5,

Table 3Summary of the hierarchical multiple regression predicting SETI.

Predictors b SE b B t Sig.Model 1Computer use .36 .65 .05 .56 .57TAC pre-test 9.71 1.57 .51 6.17 .00**

R2 .28Adjusted R2 .27F 23.91**

Model 2Computer use .46 .62 .06 .74 .45TAC pre-test 9.04 1.51 .47 6.00 .00**

Lesson planning skills .34 .09 .27 3.75 .00**

R2 .35Adjusted R2 .34F 22.31**

*p < .05, **p < .01.DR2 .07, p < .05.

most of the direct effectsd i.e. the inuences unmediated by other variablesd of independent variables (Lesson Planning Skills, TAC_i, andTAC_d) on SETIwere signicant. However, the direct effect of Computer Use on SETIwas not statistically signicant. The indirect effectsd i.e.the inuences mediated by other variablesdof Lesson Planning Skills via intervening TAC_d on SETI was not signicant because lesson

Table 4Correlation coefcients for the pairs of variables.

SETI Lesson Planning skills TAC_d TAC_i Computer Use

SETI Lesson Planning Skills .334** TAC_d .274** .035 TAC_i .535** .116 195* Computer Use .212** .016 .099 .370**

*p < .05, **p < .01, two-tailed. TAC_i denotes the scores of TAC pre-test. TAC_d denotes the difference between the TAC pre-test and the TAC post-test.

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128126planning skills did not affect teachers attitudinal growth. These variables explained 49% of the variance of SETI, among which TAC_i is thehighest predictor (b .57) followed by TAC_d (b .38) and Lesson Planning Skills (b .26) with its indirect inuences (b .02). The estimatedstandardized path coefcients are presented in Fig. 1.

The overall model t was assessed using the t indices of c2, c2/df, RMSEA (Root Mean Square Error of Approximation), NFI (NormedFit Index), CFI (Comparative Fit Index), and TLI (Turker Lewis Index). The Chi-square (c2 3.58, df 3) was not statistically signicant(p .310), which shows a good t between the causal model and the observed data (Kline, 2011). The c2/df is 1. 19, which is a good t (Kline,2011). The Root Mean Square Error of Approximation (RMSEA) was .038, which indicates a good t (Hu & Bentler, 1999). The indicesof Normed Fit Index (NFI), Comparative Fit Index (CFI), and TuckerLevis Index (TLI) are .971, .995, and .983 respectively, meaning a good t(Hu & Bentler, 1999; Kline, 2011). All of the t values suggest an obviously good t, and imply a sound validation of the hypothetical modelTable 6.

4. Discussions

Despite diverse research studies, factors that inuence pre-service teachers self-efcacy for technology integration (SETI) still remainambiguous, especially when designing educational technology courses for teacher preparation programs. This study reports the ndings onthe pre-service teachers SETI while an educational technology course featuring three distinct course activities were offered 1) instruc-tional media development, 2) lecture on technology integration, and 3) lesson planning practice. Ourmain purposewas to identify how pre-service teachers SETI can be improved during the course intervention, andwhich specic course variable has the highest impact on the SETI.We also attempted to explore a more inclusive path of the direct and indirect inuences between the SETI and the other non-course relatedvariables in order to nd implications for designing teacher education courses.

Our data analysis results implied that the course that we offered was effective since the teachers self-efcacy beliefs toward (for)technology integration signicantly increased after their completion of the course mostly due to the lesson planning factor. The hierarchicalmultiple regression also revealed that the initial teachers attitude toward computer as a pre-course factor and the lesson planning skills as acourse factor explained a signicant amount of variance in teachers self-efcacy beliefs toward (for) technology integration (34%). In otherwords, the pre-service teachers with higher positive attitudes toward computers and greater ability in lesson planning showed higher self-efcacy beliefs for technology integration. As we considered the growth of teachers attitude toward computer in themodel, 49% of variancein teachers self-efcacy beliefs toward (for) technology integration was explained. Moreover, teachers attitudes toward computers andtheir lesson planning skills directly inuenced their self-efcacy beliefs for technology integration. The interpretations and implications thatemerged during the study are discussed and the future study recommendations and limitations of the study are presented below.

The ndings obtained from all three research questions of the study consistently showed the positive impact of pre-service teacherslesson planning activities on the increase of their self-efcacy levels for technology integration. Despite the strenuous efforts in teachereducation programs for effective technology implementations in classes, research revealed concerns about unsuccessful practice of teachereducation programs for technology integration. Traditionally, teacher training courses for technology use mainly dealt with masteringtechnical skills of using computer software and neglected how to link these technology skills to curriculum and teaching methodology(Ertmer et al., 2003; Koehler, Mishra, & Yahya, 2007). As a result, teaches still do not feel condent about how to apply technology pro-ciency that they had learned in their technology courses to their own teaching to support students meaningful learning (Ertmer &Ottenbreit-Leftwich, 2010; Goktas et al., 2008).Fig. 1. Standardized path coefcients of the research model (***p < .001).

Table 5Standardized direct, indirect and total effects.

Dependent variable Independent variable Direct effect Indirect effect Total effect R2

SETI Lesson Planning Skills .26** .02 .28 .49TAC_d .38** .38TAC_i .57** .57Computer Use .05 .05

TAC_d Lesson Planning Skills .06 .06 00

*p < .05, **p < .01.

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128 127Koehler et al. (2007) advocated teachers understanding of the complex interplay between technology, content, and pedagogy (i.e.,Technological Pedagogical Content Knowledge, TPACK) for effective teaching with technology. The TPACK framework can illuminate theissues of implementing technology in teacher education programs. In order to enact this integrated approach, researchers have proposedproviding teachers with authentic and classroom-situated experiences in their training that include microteaching, modeling, collaborationwith peers, and instructional designing (Ertmer & Ottenbreit-Leftwich, 2010; Kay, 2006; Koehler et al., 2007; Tondeur et al., 2012). In thisstudy, we offered pre-service teachers chances for learning technology by designing and developing lesson plans using the ASSURE model.Through the lesson planning activities, the pre-service teachers were able to play an active role as designers of technology, relating tech-nology to pedagogy and content.

In addition, we identied the inuential effects of the attitudinal variablesdteachers attitude toward computers (TAC) prior to thecourse, the growth in teachers TAC at the completion of the course) on teachers self-efcacy beliefs, which conrmed previous literature.Teachers attitude toward computers has been documented by many studies to be a crucial factor that inuences teachers intention, andactual practices of technology integration (e.g., Anderson et al., 2011; Ottenbreit-Leftwich, Glazewski, Newby, & Ertmer, 2010; Wu et al.,2008). Teachers with a positive attitude toward computers tend to successfully integrate technology in their classroom, and such attitudinalor affective factors are more critical than the external factors such as resources, administrative support, training and experiences (Ertmeret al., 2003). This fact was also veried in our study by the insignicant path coefcient from time that pre-service teachers usually spend onusing computers (computer use variable) to teachers self-efcacy beliefs toward (for) technology integration (SETI variable). We presumethat the highest predictive power of the teachers attitude toward computers on teachers self-efcacy beliefs may be due to the scope of theinstrument we used in this study. Agyei and Voogt (2011) measure covered a broader scope of attitudes, namely, both the value-relatedattitude such as benets for student learning and inuence on the instructional productivity and affective attitude such as comfort andenjoyment.

Initially, we hypothesized that lesson planning practice would exert inuence on teachers self-efcacy beliefs for technology integrationboth directly and indirectly inuencing teachers attitudinal growth on computers. With regard to the indirect effect, we presumed thatteachers lesson planning practice would make an impact on teachers self-efcacy beliefs for technology integration via teachers growth intheir attitudes toward computers. However, unlike our expectations, the lesson planning practice did not make a signicant effect onchanges in teachers attitudes toward computers and the indirect effect was not proven by the data analysis. This result can be interpreted inthe following two ways. First, since the majority of the participants in this study were freshmen (88%), the task of lesson planning whichrequired them to integrate technology, contents, and pedagogy would have been a demanding challenge, which might have hindered themfrom feeling comfortable with computers. They also might have perceived less learning benets or instructional productivity of computers.This interpretation can be supported by previous studies documenting signicant difference in attitudes toward computers among differentgroups of people in their age (Pamuk & Peker, 2009; Taghavi, 2006). Another possibility might be that lesson planning practice might not besufcient enough to have an impact on pre-service teachers attitudinal change on computers. Although lesson planning is a good startingactivity that can bridge the gap between theory and practice, it is more or less distant from the real teaching practices. Micro-teaching, onthe other hand, engages student teachers in an actual teaching practice at least for a short period of time, and eld experiences even allowthem to intimately interact with learners in a real situation.

This study has some limitations that can suggest future research recommendations. As mentioned above, the instrument that measuredteachers attitudes toward computer covers four sub-areasdcomfort, enjoyment, learning benet, and instructional productivity. Amongthe four, the rst two relates to affective attitude toward computers per se whereas the last two concerns with pedagogical value beliefstoward teaching and learning with computers. If the former and the latter can be investigated separately, it could produce different results.Another limitation can be the sample size of this study which was 136 participants. Structural equation modeling or path analysis usuallyrequires a large sample size (n> 200) to avoid unstable estimates in case of small samples (Klein, 2011). The sample size of this study falls inthe medium size (100 < n < 200), which may entail the issue of not having complete stable power.

This study investigated specic effects of three different activities in the educational technology course and explored the course effects inrelation to the pre-course learner variables (computer use, and attitudes towards computers). We believe that the ndings of the study

would contribute to the on-going discussions on how to effectively design teacher preparation courses for technology integration.

Table 6Summary of model t indices.

Fit Index Cutoff criteria Research model

c2 Not signicant at p < .05 (good t) 3.58 (df 3), p .310c2/df .90 (acceptable t) .995TLI >.95 (very good t), >.90 (acceptable t) .983

References

Abbitt, J. T. (2011). An investigation of the relationship between self-efcacy beliefs about technology integration and technological pedagogical content knowledge (TPACK)among preservice teachers. Journal of Digital Learning in Teacher Education, 27(4), 134143.

Abbitt, J. T., & Klett, M. D. (2007). Identifying inuences on attitudes and self-efcacy beliefs towards technology integration among pre-service educators. Electronic Journalfor the Integration of Technology in Education, 6(1), 2842.

Agyei, D. D., & Voogt, J. M. (2011). Exploring the potential of the will, skill, tool model in Ghana: predicting prospective and practicing teachers use of technology. Computers &Education, 56(1), 91100.

Al-awidi, H. M., & Alghazo, I. M. (2012). The effect of student teaching experience on preservice elementary teachers self-efcacy beliefs for technology integration in theUAE. Educational Technology, Research and Development, 60(5), 923941.

Anderson, S. E., Groulx, J. G., & Maninger, R. M. (2011). Relationships among preservice teachers technology-related abilities, beliefs, and intentions to use technology in theirfuture classrooms. Journal of Educational Computing Research, 45(3), 321338.

Anderson, S. E., & Maninger, R. M. (2007). Preservice teachers abilities, beliefs, and intentions regarding technology integration. Journal of Educational Computing Research,37(2), 151172.

Bandura, A. (2006). Guide for constructing self-efcacy scales. In F. Pajares, & T. Urdan (Eds.), Self-efcacy beliefs of adolescents (Vol. 5); (pp. 307337). Greenwich, CT:Information Age Publishing.

Becker, M. R. (2000). Faculty development model: addressing the disparity between pre-service and in-service educators in technology integration of the public schoolclassroom. In D. Willis, et al. (Eds.), Proceedings of society for information technology & teacher education international conference (pp. 16041608). Chesapeake, VA: SocietyFor Information Technology and Teacher Education.

Y. Lee, J. Lee / Computers & Education 73 (2014) 121128128Brinkerhoff, J. (2006). Effects of a long-duration, professional development academy on technology skills, computer self-efcacy, and technology integration beliefs andpractices. Journal of Research on Technology in Education, 39(1), 2243.

Choy, D., Wong, A. F., & Gao, P. (2008). Singapores preservice teachers perspectives in integrating information and communication technology (ICT) during practicum. InChanging climates: education for sustainable futures: Proceedings of the Australian association for research in education conference (pp. 114). Canberra, ACT: AustralianAssociation for Research in Education.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erbaum: Hillsdale, NJ.Ertmer, P. A. (1999). Addressing rst-and second-order barriers to change: strategies for technology integration. Educational Technology Research and Development, 47(4), 4761.Ertmer, P. A., Conklin, D., & Lewandowski, J. (2003). Increasing pre-service teachers capacity for technology integration through the use of electronic models. Teacher Ed-

ucation Quarterly, 30(1), 95112.Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: how knowledge, condence, beliefs, and culture intersect. Journal of Research on Technology in

Education, 42(3), 255284.Goktas, Y., Yildirim, Z., & Yildirim, S. (2008). A review of ICT related courses in pre-service teacher education programs. Asia Pacic Education Review, 9(2), 168179.Heinich, R., Molenda, M., Russel, J., & Smaldino, S. (1996). Instructional media and technologies for learning. Upper Saddle River, NJ: Prentice-Hall.Hu, L., & Bentler, P.M. (1999). Cutoff criteria fort indexes in covariance structure analysis: conventional criteria versus newalternatives. Structural EquationModeling, 6(1),155.Hughes, J. (2005). The role of teacher knowledge and learning experiences in forming technology-integrated pedagogy. Journal of Technology and Teacher Education, 13(2),

277302.Kay, R. H. (2006). Evaluating strategies used to incorporate technology into pre-service education: a review of the literature. Journal of Research on Technology in Education,

38(4), 383.Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.Koehler, M. J., & Mishra, P. (2005). Teachers learning technology by design. Journal of Computing in Teacher Education, 21(3), 94102.Koehler, M. J., Mishra, P., Hershey, K., & Peruski, L. (2004). With a little help from your students: a new model for faculty development and online course design. Journal of

Technology and Teacher Education, 12(1), 2555.Koehler, M. J., Mishra, P., & Yahya, K. (2007). Tracing the development of teacher knowledge in a design seminar: Integrating content, pedagogy and technology. Computers &

Education, 49(3), 740762.Negishi, M., Elder, A. D., Hamil, J. B., & Mzoughi, T. (2003). Predicting elementary education candidates technology integration during their eld placement instruction. In

Proceedings of the annual meeting of the mid-south educational research association (pp. 114). Mississippi, MS: Mid-South Educational Research Association.Ottenbreit-Leftwich, A. T., Glazewski, K. D., Newby, T. J., & Ertmer, P. A. (2010). Teacher value beliefs associated with using technology: addressing professional and student

needs. Computers & Education, 55(3), 13211335.Pamuk, S., & Peker, D. (2009). Turkish pre-service science and mathematics teachers computer related self-efcacies, attitudes, and the relationship between these variables.

Computers & Education, 53(2), 454461.Sang, G., Valcke, M., Braak, J.v., & Tondeur, J. (2010). Student teachers thinking processes and ICT integration: predictors of prospective teaching behaviors with educational

technology. Computers & Education, 54(1), 103112.Smarkola, C. (2008). Efcacy of a planned behavior model: beliefs that contribute to computer usage intentions of student teachers and experienced teachers. Computers in

Human Behavior, 24(3), 11961215.Taghavi, S. E. (2006). The effects of age, access to a computer, and college status on computer attitudes. Journal of Information Technology Impact, 6(1), 18.Teo, T. (2009). Modeling technology acceptance in education: a study of pre-service teachers. Computers & Education, 52(2), 302312.Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012). Preparing pre-service teachers to integrate technology in education: a synthesis of

qualitative evidence. Computers & Education, 59(1), 134144.Wang, L., Ertmer, P., & Newby, T. (2004). Increasing pre-service teachers self-efcacy beliefs for technology integration. Journal of Research on Technology in Education, 36(3),

231252.Wentworth, N., Earle, R., & Connell, M. L. (2004). Technology integration into a teacher education program. In N. Wentworth, G. L. Waddoups, & R. Earle (Eds.), Integrating

information technology into the teacher education curriculum: Process and products of change (pp. 114). Abingdon: Routledge.Wu, W., Chang, H., & Guo, C. (2008). An empirical assessment of science teachers intentions toward technology integration. The Journal of Computers in Mathematics and

Science Teaching, 27(4), 499520.

Enhancing pre-service teachers' self-efficacy beliefs for technology integration through lesson planning practice1 Introduction1.1 Research Questions

2 Methods2.1 Participants2.2 Course intervention description2.3 Measures2.3.1 Teachers' self-efficacy beliefs for technology integration (SETI)2.3.2 Teachers' attitudes toward computers (TAC)2.3.3 Computer usage2.3.4 Lesson planning skills2.3.5 Knowledge for effective technology integration2.3.6 Instructional media development skills

2.4 Data collection2.5 Data analyses

3 Results3.1 Effects of the course intervention3.2 Effects of the course intervention with the consideration of pre-course factors3.3 The direct or indirect effect of the lesson planning on SETI considering TAC

4 DiscussionsReferences