Exploring relationships among TPACK constructs and

Exploring relationships among TPACK constructs and ICT achievement among trainee teachers Myint Swe Khine, Nagla Ali & Ernest Afari

Education and Information Technologies The Official Journal of the IFIP Technical Committee on Education ISSN 1360-2357 Educ Inf Technol DOI 10.1007/s10639-016-9507-8

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Author's personal copy Educ Inf Technol DOI 10.1007/s10639-016-9507-8

Exploring relationships among TPACK constructs and ICT achievement among trainee teachers Myint Swe Khine 1 & Nagla Ali 1 & Ernest Afari 2

# Springer Science+Business Media New York 2016

Abstract Teaching in the classroom today can no longer sustain the interest of students and be effective if the process involves traditional approach - teachers as sole provider of content information. In recent years technology has played a significant role in transforming education to more progressive and interactive activities. However the use of technology itself does not produce positive results in quality of learning and students’ achievement. Teachers must be competent in subject knowledge, pedagogical skills and technological know-how. The Technological Pedagogical Content Knowledge or TPACK as a conceptual framework can guide teachers to understand the complex relations between the six components of the model. There has been numerous studies on TPACK in international contexts beyond cultural and language boundaries. This paper examined recent studies on TPACK in various countries and reports findings from a study conducted with student teachers in the UAE. Keywords Technological Pedagogical Content Knowledge . Student teachers . TPACK . UAE . Instrument validation . Structural equation modeling

1 Introduction Teachers are encouraged to use technology in the classroom with the premise that technology can help raise motivation, increase interest among students and enhance learning. It is a logical argument to use various types of technology in teaching as students today are ‘tech savvy’ and gadgets are omnipresent among the young generation. The use of technology in education has come a long way - from the days of audio-visual equipment to multimedia computers and internet and the web-based information. While

* Myint Swe Khine [email protected]

1

Emirates College for Advanced Education, Murror Road, Abu Dhabi, United Arab Emirates

2

Petroleum Institute, Abu Dhabi, United Arab Emirates

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a range of devices are available at teachers’ disposal, the effectiveness of teaching with technology has never been guaranteed. Teachers with expert knowledge of subject matters and sound pedagogy must also know how technology can be harnessed to make instructions successful. The confluence of content knowledge, pedagogy knowledge and technological knowledge are described as Technological Pedagogical Content Knowledge (TPACK) (Mishra and Koehler 2006). Since its introduction, TPACK has been used as a conceptual framework to understand the complex relationships between technology, pedagogy and content knowledge. Wide-ranging research on effective integration of technology in teaching have been conducted by many researchers and educators at every level of schooling, from primary to middle and secondary education with the hope that affordances of technology might bring advancement in learning (Khine 2015). Voogt et al. (2013) conducted a comprehensive review of literature on TPACK covering 55 peer-reviewed journals and one book chapter published between 2005 and 2011 with the purpose to investigate the theoretical basis and practical use of the framework. The authors classified different approaches in using TPACK in the studies. It was noted that some of the studies focussed on the development of TPACK in subject domain, some on TPACK and teacher beliefs, and measuring student-teachers’ TPACK. Other research proposed strategies for students-teachers development of TPACK. The authors suggested promising strategies to enhance effective use of technology in instruction.

2 Purpose of the study There are no studies to date that have examined the validity and reliability of Arabic version of the TPACK self-report measure adapted from Schmidt et al. (2009). This study, therefore, aimed at examining the validity and reliability of the TPACK survey among a sample of pre-service teachers, attending a public teacher training institution in the UAE. The TPACK was also examined to find out whether it predicts students’ academic achievement or not. The study was specifically framed by the following research questions as follows: 1. Is the Arabic version of the TPACK valid and reliable when used with a sample of preservice teachers in the UAE? 2. Is there a relationship between preservice teachers’ TPACK and ICT achievement? A structural model specifying the relationship of TPACK and its subscales (TK, PK, CK, TPK and PCK) are presented in Fig. 1.

3 Conceptual framework The Technological Pedagogical Content Knowledge (TPACK) model was introduced by Mishra and Koehler (2006) based on the Pedagogical Content Knowledge (PCK) framework proposed by Shulman (1986). Since then, researchers have been attempting to define the TPACK framework and its purpose. Voogt et al. (2013) referred to TPACK as a guide to build up the knowledge and skills that teachers must gain to be effective teachers in the 21st century (Srisawasdi 2012). Kopcha et al. (2014) specified

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Fig. 1 The TPACK framework and its components

that TPACK has been developed to improve teachers’ technology integration to enhance teaching and students’ learning. Koh et al. (2014) also considered TPACK as one of the technology integration frameworks that focus on effective technology integration with regard to pre-service and in-service teachers’ knowledge, skills, abilities, and competencies (Koehler and Mishra 2008; Mouza et al. 2014; Yurdakul et al. 2012). TPACK identifies not only teachers’ technology knowledge but also other competencies for effective technology integration. These competencies are technology, pedagogy, and content as well as the combinations and interactions between them. These skills, knowledge, abilities and competencies form the seven interrelated domains in the TPACK framework (see Fig. 2). Three fundamental domains; Technological Knowledge (TK), which refers to basic and advanced knowledge of technology; Pedagogical Knowledge (PK), which refers to teaching strategies; and Content Knowledge (CK), which refers to the knowledge of the subject matter (Schmidt et al. 2009).

Fig. 2 Research model

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The interactions between those three domains form another four domains; Technological Content Knowledge (TCK), which refers to knowledge of subject matter as it relates to the use of technology in representing it; Pedagogical Content Knowledge (PCK), which refers to the teaching strategies knowledge with respect to the content of the subject matter; Technological Pedagogical Knowledge (TPK), which refers to the use of technology in implementing teaching strategies; and Technological Pedagogical Content Knowledge (TPACK), which refers to the integration of technology in teaching strategies to teach different subjects (Koh et al. 2014; Tomte et al. 2015; Saengbanchong, et al. 2014; Graham 2011). 3.1 International investigations TPACK has been used as a theoretical lens in examining the complex relationships among content, pedagogy and technology. Many studies have been conducted in the international contexts in recent years. Bate et al. (2013) reported that TPACK inspired initiative in Australia known as Teaching Teachers for the Future to change pre-service teachers’ knowledge of how to best facilitate learning. Their study discussed the outcomes of the initiative and reported stories about changes teachers noticed in their thinking and practice. In another study Sheffield et al. (2015) presented a case study examining how teacher education students learn science unit with TPACK model. The authors concluded that the model provided curriculum designers how to successfully blend technology, pedagogy and content knowledge. In another study, Celik et al. (2015) explored the actual usage of technology among teachers observed by students with the TPACK framework in Turkey. The results from the observations were presented in the form of case studies. Their research involved 15 cases covering mathematics education, language and literature education, biology education and other subjects. The analysis of each case showed how and to what extent teachers are using technology and reasons for limited use of technology. The study by Yurdakul and Coklar (2014) in the same context built a model that predicts the relationships between the TPACK competencies and information and communication technology usages. The study found that increased levels of ICT usage influenced the TPACK competencies at varying degree. In China and Taiwan, Chang et al. (2015) assessed university students’ perceptions of their Physics instructors’ TPACK development. The study used pretest and posttest TPACK survey, instructor interviews, in-class observations and students’ feedback as multiple sources of data. The results found that in one context the instructor emphasized life examples and in another the instructor emphasized students’ knowledge and evaluation. A study in Korea by Kwon (2013) investigated the preservice teachers’ knowledge about technology integration into teaching based on TPACK model as research framework. The instrument was translated into Korean and administered to 128 students who were enrolled in a teacher training institute. It was found that preservice teachers’ knowledge about technology integration into teaching could not be developed effectively and thus recommended to focus on strategies on technology integration. In Malaysia, Lye (2013) examined the opportunities and challenges faced at higher education institutions in Malaysia in implementing the TPACK model in teaching and learning processes. A survey questionnaire was designed and administered to identify the

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aspects of technology knowledge, pedagogical knowledge and the content knowledge among academic staff in these institutions. The study found several factors of hindrance in technology integration. Among those are time consuming to design new instructional materials, lack of support in software and hardware usage and heavy teaching load. Marcelo and Yot (2015) lamented that despite attempts by the government to incorporate technology in the classroom as a learning resource, reports showed that there are difficulties in achieving this aim. The authors reiterated the importance of focusing on implementing technologies in the learning activities. They identified seven types of activities: assimilative, informative management, applicative, communicative, productive, experiential, and evaluation. They believed that using TPACK as a model, teachers can organize technology-based learning activities. Like many countries, schools in Tanzania are equipped with computers meant to be used in preparing teachers to integrate technology in teaching. Kafyulilo et al. (2015) reported that despite efforts, teachers are slow in embracing technology. His team studied perceived knowledge and skills of integrating technology with the use of TPACK framework and found that more staff development programs are needed to develop technological pedagogical knowledge. The above mentioned studies illustrate that TPACK as a theoretical framework have been used in various context and able to identify the strengths and weaknesses in technology integration.

4 Methods and procedures 4.1 Sample This study was conducted in the context of a Bachelor of Education program in Abu Dhabi, UAE. The program focuses in the teaching of Science, mathematics and English for Kindergarten to Grade 5. The participants in this study were Year 3 Emirati students who were enrolled in one of the teacher education colleges in United Arab Emirates. All Year 3 students from all sections were invited to participate in the study. The sample was based on convenience and a willingness to be involved. Only 67 students out of 82 agreed to participate in the study and signed the consent forms. Participants were predominantly female, with 63 versus 4 males, and were between the ages of 18 to 30 years old. 4.2 Data collection instrument The Technology Pedagogical Content Knowledge (TPACK: Mishra and Koehler 2006) is a 36-item self-report questionnaire that measures preservice teachers’ self-assessment of their Technology Pedagogical Content Knowledge. It has six components, namely, Technology Knowledge (TK), Content Knowledge (CK), Pedagogical Knowledge (PK), Pedagogical Content Knowledge PCK), Technological Pedagogical Knowledge (TPK), Technology Pedagogical Content Knowledge (TPACK). The TPACK self-reported survey, originally developed in English was translated into Arabic using the research methodology of translation, back translation, verification, and modification. Each item of the TPACK was translated into Arabic by a professional translator. The next step involved an independent back translation of the

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Arabic version into English by another professional translator, who was not involved in the original translation. Items in the original English version and the back-translated version were then compared to ensure that the Arabic version maintained the meanings and concepts of the original. 4.3 Factor analyses In order to explore the underlying structure of the TPACK, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed. EFA was performed using SPSS version 22. The 36 items of the TPACK were subjected to principal components analysis (PCA), which was used to extract the factors followed by oblique rotation of factors using Oblimin rotation. The number of factors retained was determined by inspection of the scree plot (Catell 1966), and by the use of Horn’s parallel analysis (Horn 1965). The Catell’s scree involves plotting each of the eigenvalues of the factors and inspecting the plot to find a point at which the shape of the curve changes direction and becomes horizontal. The factors above the break in the plot are retained as these contribute the most to the explanation of the variance in the data set (Pallant 2013). The reliability of the resulting factor scales were checked using Cronbach’s alpha. The data were checked for sampling adequacy and sphericity using Kaiser-MeyerOlkin (KMO) and Bartlett tests. The KMO in this study was .81, exceeding the recommended value of .6 (Kaiser 1974) and Bartlett’s Test of Sphericity (Bartlett 1954) indicated that χ2 = 1960.53 and was statistically significant (p < .001). This indicated that correlations between items were sufficiently large and therefore factor analysis was appropriate (Tabachnick and Fidell 2007). The descriptive statistics of the measurement items are shown in Appendix A. All the items for the TPACK subscales had a mean score above the midpoint of 3.00, ranging from 3.16 to 3.94. This indicates that the participants exhibited a strong response for the TPACK subscales. All the standard deviations (SD) were above 1.00, with the exception of seven items, indicating a large spread of item scores around the mean. Univariate normality was examined by inspecting absolute skewness and kurtosis values of the data. All of the items of the TPACK subscales showed skewness and kurtosis values less than the cut-offs of absolute value of 3 or 8 respectively, recommended by Kline (2010), and this supported the univariate normality for individual items. Additionally, multivariate normality was measured using Mardia’s coefficient of multivariate kurtosis. The value of the Mardia’s coefficient obtained in this study, using AMOS 22, was 44.79, which is less than the recommended value (Raykov and Marcoulides 2008). Hence, the requirement of multivariate normality was satisfied. Structural Equation Modeling (SEM) was used to examine the convergent and discriminant validity of the measurements used in the research model. Hair et al. (2010) suggested that the standardised factor loadings should be greater than .70; however, a loading of .50 or .60 may still be acceptable in exploratory research (Chin 1998). In our study, discriminant validity was checked using Fornell and Larcker’s (1981) criteria that the square root of the average variance extracted (AVE) should be greater than the correlation shared between the construct in question and other constructs. Confirmatory factor analysis (CFA) with maximum-likelihoods estimation method was conducted on the sample to evaluate the model fit. Good model fit can be indicated by a non-significant chi-square statistic and several other fit statistics, including the

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following: Goodness of fit index (GFI); the Tucker-Lewis index (TLI); the comparative fit index (CFI); root mean square error of approximation (RMSEA); and the Standardized root mean square residual (SRMR). As proposed by Hu and Bentler (1995), GFI, TLI and CFI, values less than .90 indicate lack of fit, values between .90 and .95 indicate reasonable fit and values between .95 and 1.00 indicate good fit. RMSEA and SRMR values less than .06 and .08 respectively, indicate good fit and values between .08 and .10 indicate lack of fit (Kline 2010). Path coefficients and t-values for the hypothesised relationships of the subscales of the TPACK (See Fig. 2) were calculated to evaluate the magnitude and statistical significance of the relationships. Finally, correlations were conducted between the subscales of the TPACK and students’ achievement (ICT scores).

5 Results 5.1 Exploratory factor analysis Principal Components Analysis (PCA) revealed the presence of seven factors with eigenvalues exceeding 1 (14.53, 4.45, 2.13, 1.94, 1.45, 1.23 and 1.14). These seven components explained a total of 74.64 % of the variance. When the screeplot (Catell 1966) was inspected, it was decided to retain six factors for further investigation. This was further supported by the results of parallel analysis, using the Monte Carlo PCA for parallel analysis (computer software) developed by Watkins (2000). The results showed that only the first six factors exceeded the criterion value obtained from parallel analysis. Inspection of the pattern matrix (Appendix B) showed a relatively clear six-factor solution of TK, CK, PK, PCK, TPK, and TPACK, which replicated Schmidt et al. (2009) TPACK factors. When the structure matrix was analysed, there was an indication of good discrimination between all the factors. The communalities, also presented in Appendix B gives information about how much of the variance in each item is explained, with low values (less than 0.3), indicating that the item does not fit well with the other items in the component (Pallant 2013). All the items of the TPACK had communalities ranging from .60 to 80. This showed a clear six-factor solution in line with the developers of TPACK (Schmidt et al. 2009), with TK (7 items), CK (4 items), PK (7 items), PCK (8 items), TPK (5 items), and TPACK (5 items) factors. 5.2 Confirmatory factor analysis Confirmatory factor analysis (CFA) was used to assess the measurement model. This was conducted with Amos 22 using maximum likelihood estimation (MLE) procedure. The CFA results are shown in Table 1. All parameter estimates were significant at p < .001 level. This is indicated by the t-value greater than 1.96. The standardized estimates ranged from .53 to .93, and were considered acceptable according to Hair et al. (2010). The reliability of the scales of the TPACK was assessed using Cronbach alpha coefficients. The overall Cronbach alpha value for the 36-item TPACK was .95. The Cronbach alpha value for the TK subscale (7 items) was .91, CK subscale (4 items) was .79, PK subscale (7 items) was .89, PCK subscale (8 items) was .90, TPK subscale (5 items) was .89, and TPACK subscale (5 items) was .90. All the Cronbach alpha values

Author's personal copy Educ Inf Technol Table 1 Measurement model results Item

UE

t-value

SE

Technological Knowledge (TK) TK1

1.36

5.64

.79

TK2

1.56

6.05

.86

TK3

1.35

5.90

.82

TK4

1.04

5.73

.65

TK5

1.34

5.92

.84

TK6

1.34

5.81

.83

TK7

1.00

—

.67

Content Knowledge (CK) CK1

.88

4.61

.53

CK2

1.36

4.74

.77

CK3

1.60

3.57

.93

CK4

1.00

—

.57

Pedagogical Knowledge (PK) PK1

1.28

4.56

.69

PK2

1.31

4.27

.80

PK3

1.28

4.10

.75

PK4

1.45

4.31

.77

PK5

1.50

4.45

.85

PK6

1.01

4.55

.64

PK7

1.00

—

.57

Pedagogical Content Knowledge (PCK) PCK1

.73

5.20

.60

PCK2

.95

6.59

.73

PCK3

.89

7.25

.75

PCK4

.73

5.46

.68

PCK5

1.16

8.07

.83

PCK6

.91

6.21

.75

PCK7

.88

5.93

.69

PCK8

1.00

—

.81

Technological Pedagogical Knowledge (TPK) TPK1

.98

7.27

Average variance extracted (AVE)

Composite reliability (CR)

.61

.89

.52

.80

.53

.89

.54

.90

.62

.89

.67

.91

.82

TPK2

1.07

7.19

.77

TPK3

.79

6.02

.66

TPK4

1.07

9.58

.82

TPK5

1.00

—

.84

Technological Pedagogical Content Knowledge (TPACK) TPACK1

.97

6.36

.80

TPACK2

.89

8.04

.85

TPACK3

.99

6.88

.80

TPACK4

1.02

9.21

.83

Author's personal copy Educ Inf Technol Table 1 (continued) Item

UE

t-value

SE

TPACK5

1.00

—

.82

Average variance extracted (AVE)

Composite reliability (CR)

SE standardised estimate, UE unstandardised estimate — This value was fixed at 1.00 for model identification purposes

exceeded the recommended value of .70 (Nunnally and Bernstein 1994), indicating adequate internal consistency. The reliability and validity of the construct were also measured using the composite reliability (CR), and average variance extracted (AVE). As shown in Table 1, the standardised factor loadings, CR and AVE of all constructs met the recommended guideline of .50 and above. Finally, the test of model had an acceptable fit (χ2 = 794.29; df = 545; χ2/df = 1.46; CFI = .92; TLI = .91; RMSEA = .053; SRMR = .048). 5.2.1 Convergent validity Convergent validity was assessed by calculating item reliability of each measure, composite reliability (CR) of each construct, and the average variance extracted (AVE), as suggested by Fornell and Larcker (1981). The standardised factor loadings, composite reliability and the average variance extracted are reported in Table 1. The CR, factor loadings, and AVEs of all the constructs met the recommended minimum value of .5 (Fornell and Larcker 1981). Therefore, the measurement properties satisfied all three necessary criteria of convergent validity. 5.2.2 Discriminant validity The degree to which the constructs are empirically different was assessed by the discriminant validity. As suggested by Barclay et al. (1995), discriminant validity is present when the variance shared between a construct and any other construct in the model is less than the variance that construct shares with its measures. Table 2 reports Table 2 Correlations among TPACK subscales and Square Root of Average Variance Extracted TPACK Subscales

TK

CK

PK

PCK

TPK

Technology Knowledge (TK)

(.78)

Content Knowledge (CK)

.31*

(.72)

Pedagogical Knowledge (PK)

.30*

.71** (.73)

Pedagogical Content Knowledge (PCK)

.19

.59** .68** (.73)

Technological Pedagogical Knowledge (TPK)

.56** .57** .58** .56** (.79)

Technological Pedagogical Content Knowledge (TPACK) .50*

TPACK

.61** .68** .61** .77** (.82)

The elements in bold and parentheses in the main diagonal are the square roots of average variance extracted **p < .01; *p < .05

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the inter-construct correlations and square root of average variance extracted. The results in (Table 2) support the discriminant validity because, for each construct, the square root of the AVE is larger than inter-construct correlation. Hence the discriminant validity was achieved. 5.2.3 Relationship between TPACK subscales The results of the hypotheses testing and path coefficients of the proposed model using SEM analysis (see Fig. 3) are reported in Table 3. Overall, seven out of nine hypotheses were supported by the data. TK was significantly related to TPK (β = .48, p < .001) and TPACK (β = .38, p < .01). Also, PK was significantly related to TPK (β = .73, p < .001), TPACK (β = .40, p < .01), PCK (β = .75, p < .05). Additionally, CK was significantly related to TPACK (β = .71, p < .05) but was not significantly related to PCK (β = .04, p > .05). TPK was also found to be significantly related to TPACK (β = .62, p < .001), but PCK was not significantly to TPACK (β = .01, p > .05). Three endogenous variables (TPK, TPACK and PCK) were tested in the research model (refer to Fig. 2). The results, reported in Table 4, were all higher than the minimum cut-off of .10 (Falk and Miller 1992). Technological pedagogical knowledge The model explained 78 % of the variance in technological pedagogical knowledge. Students’ perceptions of their technological knowledge (β = .48, p < .001) and pedagogical knowledge (β = .73, p < .001) were positively related to their sense of technological pedagogical knowledge. Technological pedagogical content knowledge Students’ perceptions of technological knowledge (β = .38, p < .01), pedagogical knowledge (β = .40, p < .01), and content knowledge (β = .71, p < .05) were related to their sense of technological pedagogical content knowledge. The model explained 81 % of the variance in students’ sense of technological pedagogical content knowledge. Pedagogical content knowledge Of the predictor variables (TPACK subscales), students’ perceptions of pedagogical knowledge (β = .73, p < .05) was positively related to

Fig. 3 Structural equation model for the relationship of TPACK and its subscales

Author's personal copy Educ Inf Technol Table 3 Standardized path coefficients and t-value Hypotheses

Path

Standardized path coefficient

t-value

Result

H1 H2

TK → TPK

.48

4.46***

Supported

TK → TPACK

.38

3.31**

Supported

H3

PK → TPK

.73

3.78***

Supported

H4

PK → TPACK

.40

3.14**

Supported

H5

PK → PCK

.73

2.45*

Supported

H6

CK → TPACK

.71

2.40*

Supported

H7

CK → PCK

.04

.19 ns

Not supported

***p < .001, **p < .01, *p < .05, ns (non-significant)

their sense of pedagogical content knowledge, but their sense of content knowledge was not significantly related to their sense of pedagogical content knowledge. The model explained 62 % of the variance in students’ sense of pedagogical content knowledge.

5.2.4 Relationship between TPACK subscales and academic achievement The relationship between TPACK subscales and ICT achievement (ICT scores) was investigated using Pearson product–moment correlation coefficient. The correlational results can be found in Table 5 below. To ensure no violation of the assumptions of normality, linearity and homoscedasticity, preliminary analyses were performed. According to the guidelines suggested by Cohen (1988), r = .10 to .29 indicates a small correlation. A moderate correlation is indicated by r = .30 to .49, and r = .50 to 1.0 indicates a large correlation. There was a moderate positive correlation between ICT scores and TK, CK, TPK, and TPACK (p < .01). No correlation was found between ICT scores and PK and PCK (p > .05).

6 Discussion In the literature, the TPACK framework, which builds on Shulman’s (1986) Pedagogical Content Knowledge, has been extensively discussed, validated and utilized. However, no validation of Arabic version of TPACK has been reported. The purpose of this work was to examine the validity and reliability of the Arabic version of the TPACK in Table 4 Coefficient of determination (R2) of the endogenous variables Endogenous variables

Coefficient of determination (R2)

TPACK

.81

PCK

.62

TPK

.78

Author's personal copy Educ Inf Technol Table 5 Correlations matrix for TPACK subscales and ICT Achievement TPACK Subscales

TK

CK

PK

PCK

TPK

TPACK

TK

1

CK

.32*

1

PK

.30*

.71**

PCK

.16

.59**

.68**

1

TPK

.56**

.57**

.58**

.56**

1

TPACK

.50**

.61**

.68**

.61**

.77**

1

ICT-scores

.33**

.32**

.15

.14

.34**

.32**

ICT-scores

1

1

**p < .01; *p < .05

the UAE. Also, the study examined the relationship between preservice teacher’s TPACK and academic achievement. This section discusses the key findings and implications of this study. Also, recommendations for future studies are offered, as well as a discussion of the limitations of the study. 6.1 Key findings Results from EFA revealed a 36-item, six factors similar to those reported by Schmidt et al. (2009). All of the items loaded significantly on their factors. This was further confirmed by CFA using maximum likelihood estimation. From the research model (Fig. 1), nine hypotheses were formulated to depict the relationships among the six factors. Results from the analysis supported the first 7 hypotheses. The findings indicated that TK, PK, CK and TPK were significant predictors of TPK, TPACK and PCK, with the exception of CK which did not significantly relate to PCK and also PCK did not significantly relate to TPACK. These findings are consistent with existing research which provided evidence that pre-service teacher’s basic knowledge about TK and PK were related positively to the TPK and the TPACK (Chai et al. 2011). The relationship between pre-service teachers’ technological and content knowledge (TPACK) and achievement was also analyzed. The findings indicated that there were moderate positive correlation between achievement and TK, CK, TPK and TPACK. These findings were consistent with emerging body of literature that indicates that the TPACK significantly predicts GPA scores and that TPACK plays an important role in student achievement (Erdogan and Sahin 2010). 6.2 Limitations The use of SEM approach to analyze the data provided a renewed rigour and depth to the interpretation of the results. However, findings in this study were obtained using self-reported data which has the potential for flawed self-assessments to confound the results. The study involved a relatively small number of pre-service teachers (67 students from a teachers college in the UAE), therefore generalization of the results to other populations should be made with caution.

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6.3 Implications and recommendations for future research The findings of this study have implications on the professional training of teachers. Pre-service teachers need to develop effective specific level of TPACK in their subject area. The findings also indicated that the TPACK could be used to assess the various knowledge domains of pre-service teachers. As mentioned by Erdogan and Sahin (2010), TPACK is an important knowledge base to increase teacher candidates’ professional competencies. Future works can be considered administering to larger cohort of pre-service teachers as well as to in-service teachers. The research can also be extended to other countries in the region where Arabic is the official language. 6.4 Conclusion This study reported on the construct validity of the Arabic version of the TPACK through exploratory and confirmatory factor analysis. The findings suggest that the Arabic version of the TPACK instrument was reliable and valid in the context of the UAE. The model in Fig. 1, tested with data from pre-service students in the UAE revealed that seven out of nine hypotheses were supported. The relationship between the identified factors provided by the model may help researchers to better understand the contribution of TK, PK, and CK towards the other constructs, namely TPK, TPACK and PCK. Finally, the results of the study indicated that TPACK is a significant predictor of student achievement.

Appendix A

Table 6 Descriptive Statistics of the Items in the TPACK Mean SD

Skewness Kurtosis

TK1

I know how to solve my own technical problems.

3.25

1.20 −.18

−.93

TK2

I can learn technology easily.

3.64

1.28 −.51

−.91

TK3

I keep up with important new technologies.

3.16

1.19 −.10

−.82

TK4

I frequently play around with the technology.

3.57

1.12 −.34

−.77

TK5

I know about a lot of different technologies.

3.27

1.08 −.12

−.68

TK6

I have the technical skills I need to use technology.

3.39

1.14 −.19

−.82

TK7

I have had sufficient opportunities to work with different technologies.

3.36

1.08 −.25

−.60

CK1

I have sufficient knowledge about my teaching subjects

3.36

CK2

I can think about the content of my teaching subject like a subject matter expert.

3.45

CK3

I have various ways and strategies of developing my understanding of teaching subjects.

CK4

I am confident about teaching the subject matter.

.91 −.03

−.76

.15

−.42

3.19

.96 −.40

.02

3.49

1.16 −.68

−.21

1.00

Author's personal copy Educ Inf Technol Table 6 (continued) Mean SD

Skewness Kurtosis

PK1

I know how to assess student performance in a classroom

3.69

1.16 −.39

−.74

PK2

I can adapt my teaching based upon what students currently understand or do not understand.

3.54

.97 −.48

.16

PK3

I can adapt my teaching style to different learners.

3.61

1.01 −.05

−.40

PK4

I can assess student learning in multiple ways.

3.36

1.07 −.35

.01

PK5

I can use a wide range of teaching approaches in a classroom 3.34 setting.

1.01 −.49

−.03

PK6

I am familiar with common student understandings and misconceptions.

3.67

.92 −.40

.34

PK7

I know how to organize and maintain classroom management. 3.30

1.11 −.63

PCK1

Without using technology, I can address the common 3.45 misconceptions my students have for my teaching subject.

PCK2

Without using technology, I know how to select effective teaching approaches to guide student thinking about and learning of the subject matter.

3.52

PCK3

Without using technology, I can help my students to understand the content knowledge of my teaching subject through various ways.

3.55

PCK4

Without using technology, I can address the common learning 3.28 difficulties my students have with my teaching subject.

PCK5

Without using technology, I can facilitate meaningful discussion about the content students are learning in my teaching subject.

PCK6

Without using technology, I can engage students in solving real world problems related to my teaching subject.

PCK7

.97

.008

1.04 −.36

.89

.102

.15 −.529 −.06

−.725

.93 −.14

−.11

3.43

1.12 −.33

−.69

3.31

1.03

.10

−.48

Without using technology, I can engage students with hands- 3.61 on activities to learn the content of my teaching subject.

1.06 −.26

−.49

PCK8

Without using technology, I can support students to manage their learning of content for my teaching subject.

3.70

1.00 −.39

−.05

TPK1

I can choose technologies that enhance the teaching approaches for a lesson.

3.82

1.00 −.57

.15

TPK2

I can choose technologies that enhance students’ learning for a 3.60 lesson.

1.10 −.46

−.36

TPK3

My teacher education program has caused me to think more 3.94 deeply about how technology could influence the teaching approaches I use in my classroom.

.95 −.97

1.19

TPK4

I am thinking critically about how to use technology in my classroom.

3.84

1.05 −.95

.65

TPK5

I can adapt the use of the technologies that I am learning about 3.73 to different teaching activities.

.98 −.73

.43

TPACK1 I can teach lessons that appropriately combine subject content, 3.52 technologies, and teaching approaches.

1.16 −.42

−.54

TPACK2 I can select technologies to use in my classroom that enhance 3.69 what I teach, how I teach, and what students learn.

.96 −.61

.42

TPACK3 I can use strategies that combine content, technologies, and 3.75 teaching approaches that I learned about in my coursework in my classroom.

1.16 −.63

−.43

Author's personal copy Educ Inf Technol Table 6 (continued) Mean SD

Skewness Kurtosis

TPACK4 I can provide leadership in helping others to use technology and teaching approaches in school.

3.57

1.14 −.14

−.93

TPACK5 I can choose technologies that enhance the content for a lesson.

3.63

1.09 −.45

−.53

Appendix B

Table 7 Pattern and structure matrix for PCA with oblimin rotation of six factor solution of TPACK items Pattern

Items

1

Structure

2

3

4

5

6

1

2

Comm- Internal unalities Consistency (α) 3

4

5

6

TK1

.77 −.03

TK2

.76

TK3

.88 −.02

TK4

.70

TK5

.89 −.02 −.00

TK6

.80

.09 −.02 −.01 −.02

TK7

.60

.38 −.22 −.03 −.19 −.11 .70

CK1

.04

.62

.23

.31

.11 −.33 .36

.66

.31

.44

CK2

.17

.76

.16

.07 −.09 −.18 .50

.80

.33

.34 −.03

.18 .74

CK3

.01

.68

.13

.24 −.12

.12 .43

.81

.26

.50 −.08

.44 .76

CK4

.17

.72 −.14 −.16

.27 −.01 .33

.73

.01

.10

.33

.24 .65

PK1

−.06

PK2

.08

.21

.05 .79

.36

.30

.19

.22

.17 .71

.14 −.03 −.04

.25

.18 .82

.47

.21

.12

.25

.26 .79

.09 −.10 .87

.30

.09

.07

.10 −.04 .78

.06 .72

.34

.11

.08 −.39

.13 .68

.02 −.12 −.04 .88

.31

.09

.08 −.11

.02 .79

.44

.18

.13 −.02

.24 .74

.06

.04

.26 −.10

.00

.02

.01 −.04 −.04

.17 .84

.48 −.06

.12 −.02 .65

.00 −.03

.42 .29

.12

.70

.27

.01

.61 .63

.66 −.06 −.04

.19 .50

.09

.79

.27

.01

.48 .71

.11 .30

.12

.78

.54 −.05

.41 .72

.05 −.35 .58

.18

.68

.48

.57

.04

.68

.33 −.09

.36 −.02

.49

.18

PK5

.40 −.05

.50 −.01 −.05

.20 .63

.19

.69

.34 −.21

PK6

.16

.09

.65

.14 −.08 −.06 .43

.22

.75

.43

.26 −.05 .63

PK7

−.06

.27

.70

.18 −.02

.33

.76

.46

.22

PCK1

−.01 −.07 −.04

.60

.38 −.11 .26

.00

.27

.68

.52 −.13 .60

PCK2

−.03 −.04

.38

.50

.18

.21 .34

.07

.57

.69

.21

.48 .69

.11

.74

.08 −.02 .41

.21 .72

.10

.18

.39

.81

.07

.25 .70

.04 −.14 −.00

.75 −.32

.04 .25 −.06

.22

.77 −.34

.25 .72

PCK5

.41 −.10

.06

.53 −.17

.14 .62

.13

.41

.72 −.18

.42 .74

PCK6

.30

.07 −.10

.66 −.03

.07 .53

.24

.25

.75 −.06

.30 .67

PCK7

−.05

.03 −.00

.72

.23

.23 .27

.09

.31

.76

.21

.41 .68

.42

.21

.39 .57

.06

.42

.64

.20

.61 .75

.01

.89

.49 .73

PCK4

.37 −.16

.79

.37 −.34 .77

PCK3

PCK8

.06

.20 .32

.91

.06 −.20 −.04 .66

PK4

PK3

−.12

.15

.90

Author's personal copy Educ Inf Technol Table 7 (continued) Pattern

Items

1

Structure

2

3

4

5

6

1

2

Comm- Internal unalities Consistency (α) 3

4

5

6

TPK1

.18

.01

.04 −.12

.72

.05 .46

.34

.32

.02

.82

.30 .72

TPK2

.20

.06

.14 −.14

.62 −.13 .46

.35

.38 −.13

.76

.24 .65

TPK3

−.01

.00

.14

.48

.59

.02 .24

.32

.34

.48

.65

.24 .66

TPK4

.19

.04 −.07

.38

.73

.03 .48

.37

.21

.39

.81

.25 .83

TPK5

.10

.02

.01

.12

.75

.14 .40

.38

.31

.12

.84

.38 .74

TPACK1

.08

.09

.04 −.11

.04

.71 .37

.39

.33 −.10

.29

.79 .66

TPACK2 −.00 −.02

.12

.11 −.04

.86 .34

.35

.39

.11

.24

.88 .80

TPACK3

.01

.09

.08 −.16 −.01

.77 .33

.40

.37 −.15

.28

.83 .73

TPACK4

.18

.33 −.08

.08

.23

.48 .42

.60

.26

.11

.48

.71 .71

.23

.00 −.03

.75 .30

.51

.34

.02

.28

.82 .72

TPACK5 −.02

.02

.89

.90

Major loadings for each item are in bold

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