Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education

British Journal of Educational Technology doi:10.1111/bjet.12748

Vol 0 No 0 2019

1–21

Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education Jo Tondeur, Ronny Scherer, Evrim Baran, Fazilat Siddiq, Teemu Valtonen and Erkko Sointu Jo Tondeur is a professor at Vrije Universiteit Brussel and he is also affiliated as a guest professor at Ghent University, Belgium. His research is situated within the field of educational innovations. Most of his research focuses on the integrated use of ICT in preservice teacher training and compulsory education. Ronny Scherer is a full professor of educational assessment and data analysis at the Department of Teacher Education and School Research (University of Oslo). In his research, he focuses on issues related to the computer-based assessment of twenty-first-century skills, the analysis of log-file data, and the synthesis of research by metaanalytic structural equation modeling. Evrim Baran is an associate professor in the School of Education at Iowa State University. She conducts research at the intersection of technology in teacher education, human– computer interaction, and learning sciences. Fazilat Siddiq works as an associate professor at the University of South-Eastern Norway. Her research focuses on the studies of technology in education, including the implementation and evaluation of educational technology, teaching and assessment of the twenty-first-century skills (ie, Digital literacy and collaborative problem-solving). Teemu Valtonen (PhD, Education) is an associate professor in the University of Eastern Finland (UEF). His research interests lie in the use of information and communication technology in education, targeting especially preservice teachers’ skills and readiness to use ICT in education. Erkko Sointu (PhD, education) is a postdoctoral researcher (tenure track) of learning and novel teaching methods at the University of Eastern Finland. His research interests lie in the utilization and students’ perceptions of flipped classroom/learning (FC/L), technology in education, and strength-based approaches. Address for correspondence: Jo Tondeur, Interfaculty Department of Teacher Education, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium. Email: [email protected]

Abstract This study aims at investigating the profiles of teacher educators in order to explore their ability to prepare preservice teachers for technology integration in education. Specifically, the current study examines whether teacher educators can be grouped on the basis of their attitudes toward ICT (in education), their ICT self-efficacy to design ICT-rich learning environments, their competencies to use ICT in their teaching practice and the strategies they use to prepare preservice teachers for technology integration. These strategies are included in the SQD (Synthesis of Qualitative Data) model and comprise: (1) teacher educators as role models, (2) reflecting on the role of technology in education, (3) learning how to use technology by design, (4) collaboration with peers, (5) scaffolding authentic technology experiences and (6) providing continuous feedback. Data were collected from a sample of 284 teacher educators in Flanders, the Dutchspeaking part of Belgium, and submitted to latent profile analysis. The added value of the current study lies in the account of how SQD strategies and a typical set of determinants of ICT integration can be associated within teacher educators’ profile. Based on the profiles emerging from this study, teacher training institutions should consider their teacher educators to be gatekeepers when preparing future generations of teachers for the learning environments of the twenty-first century. In the discussion section, the implications for practice and future research are discussed.

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Practitioner Notes What is already known about this topic • Teacher educators can play a key role in enhancing preservice teachers’ ICT competencies. • Preparing future teachers to integrate technology in their educational practice is a challenge for teacher educators. • Many researchers have focused on preservice teachers’ characteristics but less is known about the ICT profile of teacher educators. What this paper adds • A person-centered analysis approach that makes visible homogeneous groups of teacher educator ICT profiles. • Two profiles are identified based on teacher educators’ ICT attitudes, their ICT selfefficacy, ICT competencies and SQD strategies. • Teacher educators in Profile 1 reflect lower scores on all the different scales compared to their colleagues in Profile 2. Implications for practice and/or policy • A better insight into the ICT profiles is needed to prepare teacher educators for their task as gatekeepers. • There is a need for a nuanced approach as opposed to a “one-size-fits all” style to professionalize teacher educators. • Design teams can contribute to the development of teacher educators’ ICT competencies.

Introduction Teacher educators can be considered important stakeholders who prepare and motivate a new generation of teachers for teaching in today’s classrooms. They can also play a key role in enhancing preservice teachers’ technology-enhanced educational practices. Consequently, preparing future teachers to integrate technology in their educational practice is a challenge that teacher educators are increasingly confronted with (Liu, 2016; Ping, Schellings, & Beijaard, 2018). To train preservice teachers, the teacher educators need to help them bridge the gap between technology, pedagogy and content knowledge (TPACK; Koehler & Mishra, 2009). According to Koehler and Mishra (2009), TPACK emphasizes the importance of preparing preservice teachers to make sensible choices in their uses of technology when teaching particular content to a specific target group. Yet preparing preservice teachers for educational technology use is a complex process (Aslan & Zhu, 2016; Ottenbreit-Leftwich, Ertmer, & Tondeur, 2015; Uerz, Volman, & Kral, 2018). Many researchers have focused on preservice teachers’ characteristics but less is known about the role of teacher educators. The current study aims to determine which teacher educator characteristics are connected to the support they provide to prepare preservice teachers for educational ICT use. Specifically, the study examines whether teacher educators can be clustered on the base of a typical set of (1) individual characteristics such as their attitudes toward ICT in education, their ICT self-efficacy to design an ICT-rich learning environment, their competencies to use ICT in their teaching practice, and (2) the support they provide to their students with respect to technology integration in education, based on the six strategies of the SQD (Synthesis of Qualitative Data) model. Before presenting the results of the empirical study, we first describe the strategies © 2019 British Educational Research Association

Teacher educators as gatekeepers 3 teacher educators can adopt to prepare preservice teachers for technology integration in education and how they are related to the role of teacher educators. In a concluding paragraph, the implications for practice and future research are discussed. Background of the study Preparing preservice teachers for technology integration in education There are different strategies to prepare preservice teachers for technology use (eg, Kay, 2006; Mouza, Karchmer-Klein, Nandakumar, Ozden, & Hu, 2014). Still, the question remains how teacher training institutions (TTIs) can get a comprehensive overview of effective strategies. In this respect, Tondeur et al. (2012) reviewed 19 qualitative studies in order to develop an SQD model (Synthesis of Qualitative Evidence) on which strategies are depicted to best prepare preservice teachers to integrate technology into their future classrooms (see Figure 1). According to the findings of this review, 12 key themes need to be in place in the teachers’ education programs to prepare future teachers for technology integration. The two outward circles in the SQD model include the conditions necessary at the institutional level: technology planning

Figure 1: SQD model to prepare preservice teachers for technology use (Tondeur et al. , 2012) © 2019 British Educational Research Association


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and leadership, training staff, access to resources, cooperation within and between the institutions. The two inner circles include micro-level strategies such as using teacher educators as role models, and scaffolding authentic technology experiences. These six strategies at the micro-level were examined in this study because they specifically focus on the role of the teacher educator. The first strategy involves teacher educators acting as role models (Strategy 1). Although this has proven to be an important motivator for future technology integration in the classroom (Kaufman, 2015), having preservice teachers watch examples of technological applications is helpful, but not sufficient. Because preservice teachers should also be able to interpret these examples in a specific educational context, teacher educators should provide scaffolds to discuss and reflect upon the successful uses of technology (Strategy 2). This might help them to see the utility, value and feasibility of using a particular technology and/or teaching strategy (cf. Mouza et al. , 2014), hence furthering their ability to differentiate between action and purpose and enabling deeper and more critical thinking about technology integration. Research also suggests that providing the opportunity to learn about technology integration by (re-)designing curriculum materials (Strategy 3) can also be a promising strategy (Lee & Lee, 2014). In several studies, preservice teachers stated that technology integration required additional planning and preparation because they had no prior knowledge about or experience with the design of ICT-supported learning activities (eg, Polly, Mims, Shepherd, & Inan, 2010). Many studies have demonstrated that group work (Strategy 4) might mitigate these feelings of insecurity when teachers need to design technology-related curriculum materials (Tearle & Golder, 2008). As a fifth strategy, preservice teachers may also apply their knowledge of educational technology in authentic settings (Sang, Valcke, Van Braak, & Tondeur, 2010; Valtonen et al. , 2015). Finally, the sixth strategy involves ongoing and process-oriented feedback, which has been proven to be beneficial for preservice teachers’ abilities to use technology in the classroom (Banas & York, 2014). In the current study, we aim at delineating profiles on the basis of teacher educators’ attitudes, their self-efficacy and ICT competencies, and the support they provide to their student through the use of six SQD strategies mentioned above. The role of teacher educators Teacher education programs play critical roles in preparing tomorrow’s teachers for effective technology integration in their classrooms (Cochran-Smith, 2003; Foulger, Graziano, SchmidtCrawford, & Slykhuis, 2017). They also play a key role in the implementation of the SQD strategies as mentioned above (Baran, Canbazoglu Bilici, Albayrak Sari, & Tondeur, 2017). To illustrate, teacher educators should act as role models and provide scaffolds to discuss and reflect upon the successful uses of technology. Yet their professional learning and practice regarding the implementation of innovation in their classrooms received limited attention in the literature compared to the much work on the investigation of preservice and in-service teachers’ learning (Ping et al. , 2018). Recognizing the need for establishing new roles for teacher educators, Foulger et al. (2013) identified 12 Teacher Educator Technology Competencies (TETCs) and defined the knowledge, skills and attitudes teacher educators need in preparing preservice teachers for teaching within technology-enhanced learning environments. Some examples of the competency areas included: aligning content with pedagogical approaches and technologies, modeling the use of online and blended learning methods and strategies, guiding the ethical and responsible use of technologies and engaging in leadership for using technology (Foulger et al. , 2013, p. 432). In another review, Uerz et al. (2018) identified four domains of teacher educator competencies in preparing preservice teachers for effective technology use in their future classrooms: “technology competences, competences in pedagogical and educational use of technology, beliefs about teaching and learning, and competences in innovation and professional learning” (p. 21). © 2019 British Educational Research Association

Teacher educators as gatekeepers 5 Clearly, several studies and projects stress the need for teacher educators with a strong ICT profile but at the same time research on teacher educators’ use of educational technologies frequently noted their lack of technological competency as one of the barriers toward integrating technology into teacher education programs (Uerz et al. , 2018). However, technological skills do not necessarily translate to the meaningful applications of educational technologies in teacher education courses. Teacher educators need to integrate their knowledge of content, pedagogy and technology into their preservice teachers’ learning environments, and lead innovation in their programs (Foulger et al. , 2013, p. 432). Moreover, they need to help preservice teachers develop their ability to integrate ICT in teaching and learning processes, as illustrated in Figure 1. This brings us to the need to explore the ICT profiles of teacher educators as gatekeepers to prepare the future generation of teachers to teach innovatively and in line with today’s learners’ needs (OECD, 2015). ICT profiles of teacher educators Several researchers argue that the ICT profile of teachers or teacher educators is highly personal (eg, Niess, 2015). Clearly, some are intrinsically motivated to use ICT in educational practice, while others do not share this affinity (Tondeur, Aesaert, Prestridge, & Consuegra, 2018). Many researchers have centered on critical individual characteristics associated with educational ICT use, such as teachers’ ICT attitudes (eg, Holland & Piper, 2016), their ICT self-efficacy (eg, Tondeur, Pareja Roblin, van Braak, Voogt, & Prestridge, 2016) and ICT competencies. To illustrate, attitudes that teacher educators show toward ICT for instance can be considered as a crucial factor when designing technology-enhanced curriculum materials (Mirzajani, Mahmud, Ayub, & Luan, 2015). Attitudes toward ICT use may be defined as specific feelings that indicate whether a person likes or dislikes using computers (Simpson, Koballa, Oliver, & Crawley, 1994). Consequently, measuring computer attitudes can be seen as an evaluation whereby individuals respond favorably or unfavorably to ICT use. According to these authors, it seems that many teacher educators still have some resistance to integrate ICT in their own practices. In this respect, teacher educators’ attitudes toward ICT can influence whether they integrate ICT into their classrooms. Research of Tondeur et al. (2016) supported that educational ICT use was strongly affected by specific attitudes, such as attitudes toward ICT in education. Therefore, a scale is used in this study that includes a broad spectrum of dimensions such as “usefulness,” “ease of use,” “interest” and “pleasure.” Several studies also show that teachers’ ICT self-efficacy is also positively correlated with an individual’s willingness to choose and participate in ICT-related activities (Scherer & Siddiq, 2015). Self-efficacy regarding technology refers to a person’s perceptions of and capabilities to apply ICT (Compeau & Higgins, 1995). Teachers with higher levels of ICT self-efficacy use ICT more often (Siddiq, Scherer, & Tondeur, 2016). Moreover, studies on the ICT profile of teachers have focused on teachers’ ICT competencies, which most often have been measured using self-reporting. Research has shown that teachers’ attitudes toward ICT and their ICT self-efficacy are closely related to their ICT competencies (Tondeur et al. , 2018; Sang et al. , 2012). Finally, research on the ICT profile of teachers abounds with conflicting findings. Teo (2014) showed that male teachers perceive using ICT in classrooms easier than their female colleagues. Other studies found gender differences in constructs such as teachers’ ICT self-efficacy, perceived usefulness, attitudes, anxiety and ICT use (Ottenbreit-Leftwich et al. , 2015; Oosterwegel, Littleton, & Light, 2004; Sink, Sink, Stob, & Taniguchi, 2008; Tondeur, Van de Velde, Vermeersch, & Van Houtte, 2016; Volman & van Eck, 2001). On the other hand, several studies could not identify any significant gender differences in such measures (eg, Sang et al. , 2010). In their meta-analysis, Cai, Fan, and Du (2017) confirmed this heterogeneity, yet established that men hold more favorable attitudes toward technology (Hedges’ g = 0.17). © 2019 British Educational Research Association


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There is little research specifically targeted on teacher educators’ ICT profiles. Yet we anticipate that the two groups of teachers, in-service teachers and teacher educators, may share similar patterns. Consequently, in the current study, we focus on teacher educators’ attitudes toward ICT, their self-efficacy to manage ICT for their own teaching purposes, their ICT competencies and gender. Moreover, we also aim at contributing to the research literature by including empirical evidence on teacher educators’ SQD strategy which has been identified as crucial for preparing future teacher for using technology in their practice. Thus, this brings us to the goals of the study. Purpose of the study One of the main goals of this special issue is to identify the various actors that are moderating the extent and nature of educational engagement with new technologies. Teacher educators can be considered important “gatekeepers.” To our best knowledge, no study included both ICT-related teacher educator characteristics and the support they provide to prepare preservice teachers for technology integration in the profile of teacher educators. In line with the scope of this special issue, we took a person-centered approach that can be rooted in the holistic-interactionist research paradigm (Tondeur et al. , 2018). In this paradigm, variables dynamically interact with person characteristics and are therefore considered to be an integral part of the overall functioning of an individual, in this case, the teacher educator. In contrast, the variable-centered approach—an approach that is most commonly employed in psychological research—typically addresses variables and their relationship to and/or predictive ability for the outcome of a study. The focus is on the structure of the variables across persons rather than the response patterns of homogeneous groups of the “gatekeepers for technology integration in education.” The person-centered approach is based on population heterogeneity and warrants examination of the patterns and relationships among the variables at the level of the individual, allowing for a more holistic view of the persons under study. Taking such an approach, we aim at delineating the profiles of teacher educators in order to explore their influence on how we can prepare student teachers for technology integration in education. These profiles are based on key variables in the context of technology integration and include teacher educators’ ICT attitudes, their ICT self-efficacy and ICT competencies and their ability to support preservice teachers for educational technology use. Better insights into their professional ecology are needed to guide future teachers in the development of educational technology use. Specifically, we take two steps in order to identify and examine teacher educators’ ICT profiles. First, we extract the profiles for the total sample by performing latent profile analysis—a person-centered analysis approach that makes visible homogeneous groups of teacher educators—and describe them descriptively. Second, we validate the latent profiles by testing their comparability across subgroups of teachers (Morin, Meyer, Creusier, & Biétry, 2016); given that the ICT-related variables chosen to identify the profiles may be prone to gender differences (eg, Authors), we chose gender as a grouping variable in this step. These two steps mainly serve the purpose of obtaining robust evidence for the existence of the unobserved (latent) profiles of teacher educators. Method Sample A survey was developed to assess teacher educators’ characteristics and the support they provide to preservice teachers to use and implement educational technology in their professional practice (ie, the strategies included in the SQD model). The survey was conducted among 329 teacher educators (72.3% female, age: M = 41.5, SD = 9.4 years) from 18 TTIs in Flanders, the Dutch-speaking part of Belgium. All respondents received an email invitation to complete the © 2019 British Educational Research Association

Teacher educators as gatekeepers 7 survey; participation was completely voluntary and resulted in a final sample of N = 284 teacher educators who responded to at least one item for each relevant variable. All analyses were based on this sample of 284 teacher educators, although some of their responses to entire scales or selected items within a scale might be missing. Across all scales, between 0% (Attitudes toward ICT in education) and 15.5% (SQD scale) of the responses were missing. These missing data points were handled by the full information maximum likelihood procedure, a procedure that uses all available information in the data (eg, respondents’ available responses and background characteristics) to estimate the relevant model parameters (Enders, 2010). Instruments All the survey instruments that contained multiple items were evaluated psychometrically to ensure that the resultant scores represented adequate measures. The results of these analyses are described in detail in the Appendix. Overall, we performed confirmatory factor analyses, evaluated the model fit and extracted factor scores as representatives of all scales and subscales. Factor scores can be interpreted similar to sum or mean scores: the higher the score, the higher a person’s trait on the scale. Attitudes toward ICT in education Researchers developed and validated a considerable number of computer attitude scales between 1980 and the beginning of 2000. In recent years, ICT has become more accessible, and computer use is almost universal in Western countries. As a result, attitude scales are often not specific enough to differentiate between individuals. Hence, an attempt is made in this study to address the context-specific nature of ICT attitudes and to look for specific types of attitudes (cf. Goode, 2010). In the current study, a scale is used that includes a broad spectrum of four important dimensions: “usefulness,” “ease of use,” “interest,” and “pleasure.” To assess teacher educators’ attitudes toward technology, we administered items that were derived from the “General Attitudes toward ICT Scale,” a scale developed by Evers, Sinnaeve, Clarebout, van Braak, and Elen (2009). It includes items related to “interest” (eg, “I want to know more about ICT”), “pleasure” (eg, “I like to talk about ICT to others”) and “usefulness” (eg, “The use of ICT is useful to me”). The “Attitudes toward ICT in Education Scale” (Evers et al. , 2009) measures students’ attitudes toward the effects of adopting computers in education including the same spectrum of dimensions: “interest,” “ease of use,” “pleasure,” and “usefulness” on a six 6-point scale ranging from 1 (totally disagree ) to 6 (totally degree ). After excluding one item (ATT8), the overall internal consistency was high, Cronbach’s α = 0.91. Self-efficacy to design an ICT-rich learning environment Teacher educators’ self-efficacy, that is, the beliefs about their competencies of using ICT as a supportive tool that may assist them in designing ICT-rich learning environments, was assessed by six items (Tondeur et al. , 2016). Participants had to rate the extent to which they feel competent to engage in several practices, such as, “Select ICT-applications in view of a specific educational setting” and “Design a learning environment with the available infrastructure” on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). After excluding one item (“Employ ICT to differentiate between student teachers”), the scale showed an acceptable, internal consistency, Cronbach’s α = 0.84. Competencies for educational ICT use Besides teacher educators’ self-efficacy in designing ICT-rich learning environments, we administered a scale that assessed their competence beliefs with regard to (a) Fostering the use of ICT to acquire higher order thinking skills such as creative and critical thinking (eg, “Stimulate student teachers to approach ICT critically,” 5 items); (b) information-based problem-solving © 2019 British Educational Research Association


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skills (eg, “Support student teachers to search information by using ICT,” 5 items); and (c) Supporting student teachers to learn independently (eg, “Make student teachers practice with ICT independently,” 2 items). All item responses were based on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). Cronbach’s α was 0.90 for the “higher-order thinking” subscale, 0.94 for the “information-based problem-solving” subscale and 0.92 for the “independent learning” subscale. SQD scale The SQD scale used in this study was constructed around the six significant domains of the inner circle (eg, the micro-level) of the SQD model (Tondeur et al. , 2016), a model based on the synthesis of qualitative evidence (Tondeur et al. , 2012; see Figure 1): (1) using teacher educators as role models, (2) reflecting on the role of technology in education, (3) learning how to use technology by design, (4) collaboration with peers, (5) scaffolding authentic technology experiences and (6) providing continuous feedback. Teacher educators were asked to indicate their agreement with 13 statements that represented these 6 domains (eg, “I give my preservice teachers sufficient feedback on their use of ICT”) on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). The internal consistency was high, Cronbach’s α = 0.96. Latent profile analysis To identify teacher educators’ latent (unobserved) profiles on the basis of the ICT-related variables, we performed latent profile analysis (LPA) on the factor scores (Morin, Morizot, Boudrias, & Madore, 2011). This person-centered approach specified a categorical, latent variable that indicated teacher educators’ likelihood to be assigned to a latent profile. LPA is therefore considered a probabilistic approach that groups individuals in homogeneous profiles (Marsh, Lüdtke, Trautwein, & Morin, 2009). We employed the LPA as a series of models with varying number of profiles that ranged from one to six (Note : Six was chosen as the maximum given that six factor scores were available). We compared these with respect to the differences in relative fit statistics: LPA models with lower information criteria (ie, Akaike’s Information Criterion [AIC], Bayesian Information Criterion [BIC] and the sample size-adjusted BIC [aBIC]) are generally preferred over those with higher information criteria (Marsh et al. , 2009). Besides, we also compared different LPA models via likelihood ratio tests (LRTs). These LRTs, such as the Vuong–Lo–Mendell–Rubin (VLMR) and Lo–Mendell–Rubin (LMR) likelihood ratio tests, compare the differences in deviances on the basis of a chi-square test. A significant outcome (p < .05) of LRTs indicates that the model with k profiles is preferred over the model with k − 1 profiles (Lo, Mendell, & Rubin, 2001). The LRTs and the information criteria were used to determine the number of profiles in the data. We also examined the uncertainty with which individuals were assigned to latent profiles, the so-called “entropy,” in each model. In the software Mplus 7.3 (Muthén & Muthén, 1998–2014), the entropy represents the certainty of classification, and hence high entropies indicate high certainty of classification (Morin et al. , 2011). Entropies larger than 0.70 can be considered sufficient (Reinecke, 2006). LPA was based on robust maximum likelihood estimation (MLR), an estimation procedure that corrects for possible deviations from the normality of variables and handles missing data efficiently. As a final step in the evaluation of latent profiles, we tested whether they could be identified not only for the total sample but also for subsamples of teacher educators. Specifically, we performed multi-group LPA to test the existence of the latent profiles in the data of men and women along with their similarity across gender. Morin et al. ’s (2016) framework of profile similarity was chosen to conduct these analyses in Mplus . © 2019 British Educational Research Association

Teacher educators as gatekeepers 9 Results Descriptive statistics and correlations The sample of teacher educators showed a tendency toward positive responses in the rating scales of all constructs. The means of the scales ranged between 4.04 (SQD) and 4.77 (Attitudes toward ICT; Table 1), and the intercorrelations between the scales were positive and significant and ranged between ρ = .38 and ρ = .89. The highest correlations occurred between teacher educators’ competencies, self-efficacy and SQD; the lowest correlations occurred between the attitudes toward ICT and all other scales. Latent profile analysis Table 2 provides an overview of the information criteria, entropy values and the results of the likelihood ratio tests between adjacent LPA models. Both the values of the log-likelihood and the information criteria indicated a major drop between the one- and two-profile models; for the models with more profiles, these values dropped further, yet to a smaller extent. All entropies were sufficiently high and suggested an acceptable classification accuracy of teacher educators in the latent profiles. Between the model with two and the model with three profiles, the entropy dropped; subsequently, the entropy increased and dropped again (see Table 2). The LPA model with five profiles exhibited the largest entropy, yet contained a profile with only seven teacher educators. Concerning the likelihood ratio tests, the corresponding p values did not indicate any improvement in model fit when comparing the model with two profiles with the model with three profiles, the models with three vs. four profiles and the models with five vs. six profiles. Only the comparison between the models with four and five profiles was statistically significant, suggesting that the LPA model with five profiles was preferred; however, these models contained small groups of teacher educators which were hardly interpretable. Overall, we identified the two-profile model as the preferred model for several reasons: First, concerning the model comparisons, neither adding one more profile (ie, three-profile model) nor adding even more profiles improved the model fit Table 1: Descriptive statistics and correlations among the ICT-related variables Correlations ρ

Scale 1. Attitudes toward ICT 2. Self-efficacy in designing ICT-rich learning environments 3. Higher order thinking skills 4. Information-based problem-solving skills 5. Independent learning 6. SQD

M

SD

2.

3.

4.

5.

6.

4.77

0.79

.47*

.47*

.38*

.57*

.40*

4.35

0.87

.72*

.69*

.65*

.60*

4.39

0.96

.89*

.75*

.87*

4.56

0.91

.68*

.75*

4.55

1.04

4.04

0.98

Note. The means and standard deviations of scales are reported. .001.

*p <


.64*

−1191.406

−1148.207 −1116.228

Four profiles

Five profiles Six profiles

40 47

33

12 19 26

Npar

1.2056 1.2445

1.2725

1.0045 1.2425 1.3469

SCF

2376.414 2326.456

2448.812

3419.256 2806.131 2600.062

AIC

2522.373 2496.958

2569.228

3463.044 2875.462 2694.935

BIC

2395.532 2348.919

2464.584

3424.992 2815.212 1612.488

aBIC

0.826 0.801

0.805

1.000 0.783 0.767

Entropy

0.0431 0.1224

0.0640

– 0.0000 0.0791

0.0462 0.1295

0.0669

– 0.0001 0.0827

p(VLMR-LRT) p(LMR-LRT)

Baseline Suggested model No difference between 2 and 3 profiles No difference between 3 and 4 profiles Smallest group: n = 7 No difference between 5 and 6 profiles

Description

Note. LL = Log-likelihood value, Npar = number of parameters, SCF = scale correction factor, AIC = Akaike’s Information Criterion, BIC = Bayesian Information Criterion, aBIC = sample size-adjusted BIC, p (VLMR-LRT) = p value of the Vuong–Lo–Mendell–Rubin (VLMR) likelihood ratio test, p (LMR-LRT) = p value of the Lo–Mendell–Rubin (LMR) likelihood ratio test. The suggested number of profiles is highlighted in gray.

−1697.628 −1384.066 −1274.031

LL

One profile Two profiles Three profiles

Model

Table 2: Information criteria, entropies and results of the likelihood ratio tests for the LPA models with one to six profiles

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Teacher educators as gatekeepers 11 significantly. Second, the more profiles were requested, the smaller the sample sizes of the resultant profiles—this ultimately yields more profiles that are hardly interpretable. Third, the entropy of the three-profile model was sufficiently high—a key criterion that would have otherwise suggested rejecting the model. Striving for parsimony in our modeling approach, we consequently decided for the two-profile model as the model to represent our data. The distributions of the two profiles are shown in Figure 2. Profile 1 contained 134 teacher educators (47.2% of the total sample); profile 2 contained 150 teacher educators (52.8% of the total sample). Teacher educators in Profile 2 reflect higher scores on all the different scales compared to their colleagues in Profile 1. Profile 1 is therefore labeled as “Low (Teacher Educator) ICT Profile.” In contrast, Profile 2 is labeled as High ICT Profile. The overall, multivariate test of the mean differences between the two profiles was statistically significant and explained 68.2% of the variance in the factor scores, Wilk’s Λ = 0.32, F (6, 233) = 83.2, p < .001, partial-𝜂 2 = 0.682. Further tests of between-subject effects within the MANOVA revealed significant profile differences in all variables (F s > 55.8, p s < .001) explaining between 19.0% (Attitudes toward ICT) and 64.6% (Competence beliefs: higher order thinking skills) of variance in the data. These findings support that the two profiles are sufficiently distinct. Multi-group latent profile analysis As noted earlier, the ICT-related variables and, ultimately, the latent profiles may be prone to gender differences, especially because their measures used self-reporting of competence beliefs (eg, Hatlevik & Hatlevik, 2018; Scherer & Siddiq, 2015). To explore these possible differences, we performed multi-group LPA with gender as the grouping variable. Similar to multi-group confirmatory factor analysis, we first established whether the two-profile model was the best representation of the data for both women and men. Comparing LPA models with up to three profiles for 0,8

Means of factors cores

0,6 0,4 0,2 0 -0,2 -0,4 -0,6 -0,8 -1

Attitudes

SQD

Self-efficacy Profile 1

HOT

IPS

INL

Profile 2

Figure 2: Description of the two latent profiles Note . HOT = Higher order thinking skills, IPS = Information-based problem-solving skills, INL = Independent learning © 2019 British Educational Research Association


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both subsamples, we found support for the existence of two profiles for both gender groups (see Appendix). Specifically, only the comparisons between the one- and two-profile models indicated significant improvements in model fit; adding another profile did not improve the model fit significantly. Hence, we could establish that two profiles existed for women and men. These profiles are shown in Figure 3. In the female sample of teacher educators, 46.4% were assigned to profile 1 (n = 97) and 53.6% to profile 2 (n = 112); in the sample of male teacher educators, 41.3% (a) Latent profiles for women

0,8

Means of factor scores

0,6 0,4 0,2 0 -0,2 -0,4 -0,6 -0,8 -1

Attitudes

SQD


HOT

IPS

INL

IPS

INL

Profile 2

(b) Latent profiles for men

0,8

Means of factor scores

0,6 0,4 0,2 0 -0,2 -0,4 -0,6 -0,8

Attitudes

SQD


HOT

Profile 2

Figure 3: Description of the two latent profiles for (a) women (n = 209) and (b) men (n = 75) Note . HOT = Higher order thinking skills, IPS = Information-based problem-solving skills, INL = Independent learning © 2019 British Educational Research Association

Teacher educators as gatekeepers 13 were most likely to follow profile 1 (n = 31) and 58.7% followed profile 2 (n = 44). Both male and female teacher educators in Profile 2 reflect higher scores on all the different scales compared to teacher educators in Profile 1. After establishing that two profiles were present in both the subsamples of teacher educators, we tested the similarity of these profiles by comparing several multi-group LPA models with different assumptions (Morin et al. , 2016). These assumptions concerned the equality of the following parameters across gender: (1) Number of profiles (configural model), (2) Factor means of all ICT variables (structural model), (3) Factor means and variances (dispersion model) and (4) Factor means, variances and profile membership probabilities (distributional model). We specified these four models in Mplus —the resultant model fit indices and descriptions are presented in the Appendix. Overall, the multi-group LPA revealed that configural, structural and distributional similarities existed between the two latent profiles across gender, yet not dispersion similarity. Hence, the variability within profiles across gender differed significantly, while the shape and membership probabilities were statistically the same. These observations provide some evidence for the robustness of the two latent profiles we identified for the total sample across teacher educators’ gender. Discussion and implications The goal of this study was to investigate the ICT profiles of teacher educators. This is an important step in order to explore their influence on how we can prepare preservice teachers for technology integration in education. Two profiles are identified based on teacher educators’ ICT attitudes, their ICT self-efficacy, different types of ICT competencies and their ability to support preservice teachers for educational technology use. These variables are positively correlated in both profiles. Specifically, teacher educators in Profile 1 reflect lower scores on all the different scales compared to their colleagues in Profile 2. Profile 1 is therefore labeled as “Low (Teacher Educator) ICT Profile.” In contrast, Profile 2 is labeled as High ICT Profile. The two profiles warrant that the teacher educators’ ICT attitudes, self-efficacy and competencies are connected (cf. Agyei & Voogt, 2015; Sang et al. , 2010). In this respect, the findings of the Sang et al. (2010) underpin the importance of an integrated approach for successful ICT integration. Also in the current study, the high correlations between the constructs indicate that it would be useful to focus on developing several facets of the ICT profile and not only one of these. Consequently, a more comprehensive approach to measure and develop teacher educators’ ICT related attitudes, competence and knowledge (TPACK) might be beneficial. Hence, we recommend practitioners and researchers to apply such a holistic approach in the professional development of teacher educators, and to address the different aspects of their personal ICT-related characteristics. Also, the reviewed articles in the study of Uerz et al. (2018) emphasize that developing technological skills in itself is no guarantee for technology integration by teacher educators. They argue that teacher educators need to be aware of their entire ICT profile, to be willing and able to articulate, discuss and change their personal view on educational ICT use (see also Tondeur et al. , 2018). In this respect, the ability to be an innovative, collaborative and researching professional is seen as important for teacher educators to teach and learn with technology. As illustrated in the inner circle of the SDQ model, competencies for ICT integration entail the ability to collaborate, the ability to reflect and having a research-oriented attitude. Interestingly, teacher educators in a profile with high scores on these variables also report high scores on SQD, or the support they provide to preservice teachers to integrate ICT into their educational practice (Tondeur et al. , 2018). A teacher educator with relatively positive ICT attitudes, selfefficacy or competencies also reports higher scores on the support provided to his or her students. © 2019 British Educational Research Association


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Similarly, the results of the Drent and Meelissen (2008) study highlight that teacher educators who use ICT more often in their learning process are characterized by a specific combination of knowledge, skills, attitudes, or competencies that are advantageous for the innovative use of ICT. Uerz et al. (2018) point at the importance of stimulating personal entrepreneurship by: (1) the development of cooperative communities between teachers; (2) the stimulation of reflective behavior with teacher educators toward their own activities; and (3) the freeing-up of time and creation of facilities to experiment with innovations. Teacher educators are important stakeholders who prepare and motivate future teachers in implementing innovation in classrooms (Foulger et al. , 2017). At the same time, our findings show that not all teacher educators feel ready to take up this role. Hence, additional support is needed to specifically provide teacher educators in the Low (Teacher Educator) ICT Profile with training to become digitally literate. To strengthen the support teacher educators provide to their students (with regard to integrating technology in their practice), their own TPACK needs to be supported and further developed. Consequently, it can also be argued that there is a need for a more nuanced approach as opposed to a “one-size-fits all” style to professionalize teacher educators (Baran et al. , 2017; Prestridge, 2012). The question remains—how to professionalize teacher educators with different ICT profiles? Courses or workshops to provide teacher educators with ICT skills have been shown to be insufficient (Wentworth, Graham, & Monroe, 2009). In this respect, design teams have been identified to contribute to the development of teacher educators’ competencies which are necessary to integrate technology in education (eg, Agyei & Voogt, 2015; Alayyar, Fisser, & Voogt, 2012; Tondeur et al. , 2018; Polly, 2011). A teacher (educator) design team (TDT) can be defined as a group of two or more teacher educators who (re-)design curriculum materials together (Handelzalts, 2009). Mishra and Koehler (2006) call this approach “learning technology by design” (p. 1020). They stress that ICT integration in teacher education is facilitated when teacher educators understand how knowledge of technology, pedagogical knowledge and content knowledge is interrelated and how they interact with each other. Based on the ICT profiles of the teacher educators, the design teams can be purposely composed so that members of the strong ICT profile can help their colleagues with a low ICT profile. Furthermore, once the design teams are set up, they reflect and design in groups on how educational technology can support the content and pedagogical aspects of their practice in order to prepare their preservice teachers for educational technology use (Becuwe et al. , 2017). Specifically, they design technology-enhanced materials together, experiment with them and reflect on the results. All these strategies are also identified in the inner circle of the SQD model (Tondeur et al. , 2012): Role models, Reflection, Instructional design, Collaboration, Authentic experiences and Feedback. However, if team members struggle or do not feel responsible themselves for the outcomes to a rather great extent, they may need additional support (Handelzalts, 2009). Such support can be provided by a coach, specifically adapted to team members with different ICT profiles. The discussion about how to implement and sustain programs to better prepare teacher educators with different ICT profiles to integrate technology in educational practice should also be seen as part of the entire teacher education program (cf. Polly et al. , 2010). The institutional level factors are expressed in the outward circles of the SQD model (Figure 1), including for instance “technology planning and leadership,” “access to resources” and “co-operation within and between institutions.” These strategies should be acknowledged when preparing teacher educators for the twenty-first-century learning environments with new technologies. As a consequence, these characteristics of the TTIs should be included in future research. Although the present study provided an in-depth exploration of the ICT profiles of teacher educators, it also reflects some © 2019 British Educational Research Association

Teacher educators as gatekeepers 15 shortcomings. Apart from the added value of seeking an evaluation of the ICT profile of teacher educators outside Belgium, teacher educators are not only influenced by ICT-related factors. Just as in-service teachers, they interpret innovations like ICT integration according to their beliefs about “good education” (Ertmer, 2005; Prestridge, 2012). It is therefore necessary to understand teacher educators’ pedagogical beliefs. Taken these pedagogical beliefs into account, future studies should also adopt an iterative approach in developing the teacher educators’ profiles over time. It would be interesting to verify how and why profiles change and how they can impact preservice teachers’ adoption of technology in education. Conclusion The added value of the current study lies in the richer account of how a typical set of determinants of ICT integration can be associated to the SQD strategies within teacher educators’ ICT profile. A better insight into their educational ICT profile is needed to prepare them for the task as gatekeepers for future teachers in the development of educational technology use. Teacher educators are expected to become gatekeepers: to model adequate ICT integration and provide future teachers with the necessary ICT entry qualifications. As a consequence, a teacher educators’ integration of ICT in teaching and learning processes also explicitly shows preservice teachers how ICT can be used in their educational practice. Considering the role teacher education institutes are expected to fulfill, it is important to understand the ICT profiles of teacher educators. This study provides insight into these profiles, including the strategies they use to prepare a next generation of teachers for technology integration in education. Statements on open data, ethics and conflict of interest To ensure confidentiality, teachers’ personal identifiers were removed prior to processing the data. The library of the VUB will provide free and open access to the SPSS file of the study as soon as the manuscript is published online. There is no conflict of interest. References Agyei, D. D., & Voogt, J. M. (2015). Pre-service teachers’ TPACK competencies for spreadsheet integration: Insights from a mathematics-specific instructional technology course. Technology, Pedagogy and Education, 24(5), 605–625. Alayyar, G. M., Fisser, P., & Voogt, J. (2012). Developing technological pedagogical content knowledge in pre-service science teachers: Support from blended learning. Australasian journal of educational technology, 28(8). Aslan, A., & Zhu, C. (2016). Influencing factors and integration of ICT into teaching practices of pre-service and starting teachers. International Journal of Research in Education and Science, 2(2), 359–370. Banas, J. R., & York, C. S. (2014). Authentic learning exercises as a means to influence preservice teachers’ technology integration self-efficacy and intentions to integrate technology. Australasian Journal of Educational Technology, 30(6), 728–746. Baran, E., Canbazoglu Bilici, S., Albayrak Sari, A., & Tondeur, J. (2017). Investigating the impact of teacher education strategies on preservice teachers' TPACK. British Journal of Educational Technology. Becuwe, H., Roblin, N. P., Tondeur, J., Thys, J., Castelein, E., & Voogt, J. (2017). Conditions for the successful implementation of teacher educator design teams for ICT integration: A Delphi study. Australasian Journal of Educational Technology, 33(2), 159–172. Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers & Education, 105, 1–13. © 2019 British Educational Research Association


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Cochran-Smith, M. (2003). Learning and unlearning: The education of teacher educators. Teaching and teacher education, 19(1), 5–28. Compeau, D. R., & Higgins, C. A. (1995, June). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. Drent, M., & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers & Education, 51(1), 187–199. Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford Press. Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational technology research and development, 53(4), 25–39. Evers, M., Sinnaeve, I., Clarebout, G., van Braak, J., & Elen, J. (2009). Monitoring ICT in het Vlaamse Onderwijs. OBPWO-Project 06.05. Foulger, T. S., Burke, D., Williams, M. K., Waker, M. L., Hansen, R., & Slykhuis, D. A. (2013). Innovators in teacher education: Diffusing mobile technologies in teacher preparation curriculum. Journal of Digital Learning in Teacher Education, 30(1), 21–29. Foulger, T. S., Graziano, K. J., Schmidt-Crawford, D. A., & Slykhuis, D. A. (2017). Teacher educator technology competencies. Journal of Technology and Teacher Education, 25(4), 413–448. Goode, J. (2010). The digital identity divide: How technology knowledge impacts college students. New media & society, 12(3), 497–513. Handelzalts, A. (2009). Collaborative curriculum development in teacher design teams. Hatlevik, I. K., & Hatlevik, O. E. (2018). Examining the relationship between teachers’ ICT self-efficacy for educational purposes, collegial collaboration, lack of facilitation and the use of ICT in teaching practice. Frontiers in psychology, 9. Holland, D. D., & Piper, R. T. (2016). A technology integration education (tie) model for millennial preservice teachers: Exploring the canonical correlation relationships among attitudes, subjective norms, perceived behavioral controls, motivation, and technological, pedagogical, and content knowledge (TPACK) competencies. Journal of Research on Technology in Education, 48(3), 212–226. Kaufman, K. (2015). Information communication technology: Challenges & some prospects from preservice education to the classroom. Mid-Atlantic Education Review, 2, 1–11. Kay, R. (2006). Evaluating strategies used to incorporate technology into pre-service education: A review of the literature. Journal of Research on Technology in Education, 38, 383–408. Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70. Lee, Y., & Lee, J. (2014). Enhancing pre-service teachers' self-efficacy beliefs for technology integration through lesson planning practice. Computers & Education, 73, 121–128. Liu, P. (2016). Technology integration in elementary classrooms: Teaching practices of student teachers. Australian Journal of Teacher Education, 41(3), 6. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778. Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 191–225. Mirzajani, H., Mahmud, R., Ayub, A. F. M., & Luan, W. S. (2015). A review of research literature on obstacles that prevent use of ICT in pre-service teachers’ educational courses. International Journal of Education and Literacy Studies, 3(2), 25–31. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record, 108(6), 1017. Morin, A. J., Meyer, J. P., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231–254. Morin, A. J. S., Morizot, J., Boudrias, J. S., & Madore, I. (2011). A multifoci person-centered perspective on workplace affective commitment: A latent profile/factor mixture analysis. Organizational Research Methods, 14(1), 58–90.


Teacher educators as gatekeepers 17 Mouza, C., Karchmer-Klein, R., Nandakumar, R., Ozden, S. Y., & Hu, L. (2014). Investigating the impact of an integrated approach to the development of preservice teachers' technological pedagogical content knowledge (TPACK). Computers & Education, 71, 206–221. Niess, M. L. (2015). Transforming teachers’ knowledge: Learning trajectories for advancing teacher education for teaching with technology. In C. Angeli & N. Valanides (Eds.), Technological pedagogical content knowledge: Exploring, developing, and assessing TPCK (pp. 19–37). Boston, MA: Springer, US. OECD (2015). Students, computers and learning: Making the connection. Pisa, Paris: OECD publishing. Oosterwegel, A., Littleton, K., & Light, P. (2004). Understanding computer-related attitudes through an idiographic analysis of gender-and self-representations. Learning and Instruction, 14(2), 215–233. Ottenbreit-Leftwich, A. T., Ertmer, P. A., & Tondeur, J. (2015). 7.2 interpretation of research on technology integration in teacher education in the USA: Preparation and current practices. In International handbook of interpretation in educational research (pp. 1239–1262). Dordrecht: Springer. Ping, C., Schellings, G., & Beijaard, D. (2018). Teacher educators' professional learning: A literature review. Teaching and Teacher Education, 75, 93–104. Polly, D. (2011). Developing teachers' technological, pedagogical, and content knowledge (TPACK) through mathematics professional development. International Journal for Technology in Mathematics Education, 18(2). Polly, D., Mims, C., Shepherd, C., & Inan, F. (2010). Evidence of impact: Transforming teacher education with preparing tomorrow’s teachers to teach with technology (PT3) grants. Teaching and Teacher Education, 26, 863–870. Prestridge, S. (2012). The beliefs behind the teacher that influences their ICT practices. Computers & education, 58(1), 449–458. Reinecke, J. (2006). Longitudinal analysis of adolescents’ deviant and delinquent behavior. Methodology, 2(3), 100–112. Sang, G., Valcke, M., Van Braak, J., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54(1), 103–112. Sang, G., Valcke, M., van Braak, J., Zhu, C., Tondeur, J., & Yu, K. (2012). Challenging science teachers' beliefs and practices through a video-case-based intervention in China's primary schools. Asia-Pacific Journal of Teacher Education, 40(4), 363–378. Scherer, R., & Siddiq, F. (2015). Revisiting teachers' computer self-efficacy: A differentiated view on gender differences. Computers and Human Behavior, 53, 48–57. Siddiq, F., Scherer, R., & Tondeur, J. (2016). Teachers' emphasis on developing students' digital information and communication skills (TEDDICS): A new construct in 21st century education. Computers & Education, 92, 1–14. Sink, C., Sink, M., Stob, J., & Taniguchi, K. (2008). Further evidence of gender differences in high school– Level computer literacy. Chance, 21(1), 49–53. Simpson, R. D., Koballa, T. R. Jr., Oliver, J. S. Jr., & Crawley, F. E. (1994). Research on theaffective dimensions of science learning. In D. White (Ed.), Handbook of research on science teaching and learning (pp. 211–235). New York, NY: Macmillan. (PDF) Gender differences in the ICT profile of university students: A quantitative analysis. Retrieved from https://www.researchgate.net/publication/290436321_Gender_Differences_in_the_ICT_Profile_ of_University_Students_A_Quantitative_Analysis Tearle, P., & Golder, G. (2008). The use of ICT in the teaching and learning of physical education in compulsory education: How do we prepare the workforce of the future? European Journal of Teacher Education, 31(1), 55–72. Teo, T. (2014). Unpacking teachers' acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127–135. Tondeur, J., Aesaert, K., Prestridge, S., & Consuegra, E. (2018). A multilevel analysis of what matters in the training of pre-service teacher's ICT competencies. Computers & Education, 122, 32–42. Tondeur, J., Pareja Roblin, N., van Braak, J., Voogt, J., & Prestridge, S. (2016). Preparing beginning teachers for technology integration in education: Ready for take-off? Technology, Pedagogy and Education, 26(2), 157–177. © 2019 British Educational Research Association


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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), 134–144. Tondeur, J., Van de Velde, S., Vermeersch, H., & Van Houtte, M. (2016). Gender differences in the ICT profile of University students: A quantitative analysis. Journal of Diversity and Gender Studies, 3, 57–77. Uerz, D., Volman, M., & Kral, M. (2018). Teacher educators' competences in fostering student teachers’ proficiency in teaching and learning with technology: An overview of relevant research literature. Teaching and Teacher Education, 70, 12–23. Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on pre-service teachers' intentions to use ICT for teaching and learning. Computers & Education, 81, 49–58. Volman, M., & van Eck, E. (2001). Gender equity and information technology in education: The second decade. Review of educational research, 71(4), 613–634. Wentworth, N., Graham, C. R., & Monroe, E. E. (2009). TPACK development in a teacher education program. In L. T. W. Hin & R. Subramaniam (Eds.), Handbook of research on new media literacy at the K-12 level: Issues and challenges (pp. 823–838). Hershey, PA: IGI Global.

APPENDIX REFERENCES Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: Guilford. Devlieger, I., & Rosseel, Y. (2017). Factor score path analysis. An alternative for SEM? Methodology, 13, 31–38. https://doi.org/10.1027/1614-2241/a000130 Muthén, L. K., & Muthén, B. O. (1998–2014). Mplus Version 7.3 [Computer software]. Los Angeles, CA: Muthén & Muthén. Satorra, A., & Bentler, P. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248. https://doi.org/10.1007/s11336-009-9135-y Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. https://doi.org/10.1177/0013164413495237

APPENDIX A: Evaluation of instruments All the survey instruments that contained multiple items were evaluated psychometrically to ensure that the resultant scores represented adequate measures. Specifically, we evaluated the factor structure underlying these instruments through confirmatory factor analysis. All analyses were conducted using the statistical software MPlus 7.3 (Muthén & Muthén, 1998–2014); item responses were treated continuously due to the sufficiently large number of response options, the minor deviations from normality and the limited sample size which ultimately constrained the number of model parameters that could be estimated. To allow for possible deviations from normality, we used the robust maximum likelihood estimator and performed a Satorra–Bentler correction of the chi-square fit statistic (SB-χ 2; Satorra & Bentler, 2010). We referred to the common guidelines of model fit to evaluate measurement models and performed chi-square difference testing to compare competing models (ie, Comparative Fit Index [CFI] > 0.90 or 0.95, Root Mean Square Error of Approximation [RMSEA] < 0.08 or 0.05 for a reasonable or acceptable fit; see, for instance, Brown, 2015). The following subsections presents the instruments and their evaluation in greater detail. After evaluating the instruments and examining their factor structure, we extracted the factor scores of all latent variables. These scores can be interpreted similar to sum scores: High factor scores indicate high self-efficacy, competencies, SQD strategies and positive attitudes toward ICT (see Brown, 2015). We chose factor scores instead of latent variables or sum scores as representatives of all scales for two reasons: (1) the relatively small sample size and the considerable number of parameters that would have to be in estimated latent variable models (Wolf, Harrington, Clark, & Miller, 2013); (2) the measurement models deviated from simple, clear-cut factor structures and contained residual correlations. In such situations, sum scores provide biased representations of the scales (Devlieger & Rosseel, 2017). © 2019 British Educational Research Association

Teacher educators as gatekeepers 19 Attitudes toward ICT in education To assess teacher educators’ attitudes toward technology, we administered items that were derived from the “General Attitudes toward ICT Scale,” a scale developed by Evers et al. (2009). It includes items related to “interest” (eg, “I want to know more about ICT”), “pleasure” (eg, “I like to talk about ICT to others”) and “usefulness” (eg, “The use of ICT is useful to me”). The “Attitudes toward ICT in Education Scale” (Evers et al. , 2009) measures students’ attitudes toward the effects of adopting computers in education including the same spectrum of dimensions: “interest,” “ease of use,” “pleasure” and “usefulness” on a 6-point scale ranging from 1 (totally disagree ) to 6 (totally degree ). Submitting the 10 items to confirmatory factor analysis resulted in a reasonable model fit (SB-χ 2 [35] = 99.8, p < .001, CFI = 0.94, RMSEA = 0.08); however, one item (“Developing didactically responsible blended material seems simple”) had a substantially low factor loading (λ = 0.36) and was therefore deleted. The resultant model with nine items had a reasonable fit, SB-χ 2 [27] = 80.0, p < .001, CFI = 0.95, RMSEA = 0.08. However, adding three residual correlations between items of similar wordings resulted in a model with an acceptable fit, SB-χ 2 (24) = 36.8, p = .046, CFI = 0.99, RMSEA = 0.04. In fact, this model showed a significant improvement in the fit to the data, as indicated by the chi-square difference test, ∆SB-χ 2 (3, N = 284) = 36.4, p < .001. The one-factor model with residual correlation was accepted as the measurement model of ICT attitudes. The nine items measuring teacher educators’ attitudes toward ICT and the ICT use in education formed a scale with an acceptable internal consistency, Cronbach’s α = 0.91.

Self-efficacy to design an ICT-rich learning environment Teacher educators’ self-efficacy, that is, the beliefs about their competencies of using ICT as a supportive tool that may assist them in designing ICT-rich learning environments was assessed by six items (Tondeur et al. , 2016). Participants had to rate the extent to which they feel competent to engage in several practices, such as, “Select ICT-applications in view of a specific educational setting” and “Design a learning environment with the available infrastructure” on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). Confirmatory factor analyses indicated a poor fit of a one-factor model with all six items to the data, SB-χ 2 (9) = 135.6, p < .001, CFI = 0.75, RMSEA = 0.24. After excluding one item (“Employ ICT to differentiate between student teachers”) and adding two residual correlations between items of similar wording, the model fit was excellent, SB-χ 2 (3) = 5.6, p = .13, CFI = 0.99, RMSEA = 0.06. The resultant five items form a scale with an acceptable internal consistency, Cronbach’s α = 0.84.

Competencies for educational ICT use Besides teacher educators’ self-efficacy in designing ICT-rich learning environments, we administered a scale that assessed their competence beliefs with regard to (a) Fostering the use of ICT to acquire higher order thinking skills such as creative and critical thinking (eg, “Stimulate student teachers to approach ICT critically,” 5 items); (b) Information-based problem-solving skills (eg, “Support student teachers to search information by using ICT,” 5 items); and (c) Supporting student teachers to learn independently (eg, “Make student teachers practice with ICT independently,” 2 items). All item responses were based on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). On the basis of the assumption that the three subscales may describe the factor structure underlying the 12 items, we specified a 3-factor model which showed an acceptable fit to the data, SB-χ 2 (51) = 125.36, p < .001, CFI = 0.95, RMSEA = 0.08. However, adding two residual correlations between items of similar wording provided a better-fitting model, SB-χ 2 (49) = 90.9, p < .01, CFI = 0.97, RMSEA = 0.06. In fact, the model fit improved significantly, ∆SB-χ 2 (2, N = 242) = 25.8, p < .001. This model was accepted as the final measurement model of teacher educators’ competence beliefs. Cronbach’s α was 0.90 for the “higher-order thinking” subscale, 0.94 for the “information-based problem-solving” subscale and 0.92 for the “independent learning” subscale.

SQD scale The SQD scale used in this study was constructed around the six significant domains of the inner circle (eg, the micro-level) of the SQD model (Tondeur et al. , 2016), a model based on the synthesis of qualitative © 2019 British Educational Research Association


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evidence (Tondeur et al. , 2012; see Figure 1): (1) using teacher educators as role models, (2) reflecting on the role of technology in education, (3) learning how to use technology by design, (4) collaboration with peers, (5) scaffolding authentic technology experiences and (6) providing continuous feedback. Teacher educators were asked to indicate their agreement with 13 statements that represented these 6 domains (eg, “I give my preservice teachers sufficient feedback on their use of ICT”) on a 6-point Likert scale (1 = strongly disagree , 6 = strongly agree ). Previous research indicated that the SQD scale forms a unidimensional scale with some dependencies between items (Tondeur et al. , 2016). In light of this observation, we specified a one-factor model first. This model exhibited a poor fit to the data, SB-χ 2 (90) = 459.6, p < .001, CFI = 0.82, RMSEA = 0.13. However, adding four residual correlations resulted in an acceptable fit, SB-χ 2 (61) = 149.8, p < .001, CFI = 0.95, RMSEA = 0.08. The chi-square difference test indicated a significant improvement of the model fit, ∆SB-χ 2 (29, N = 240) = 315.1, p < .001. The internal consistency was high, Cronbach’s α = 0.96.

Multi-group latent profile analysis: Tests of profile similarity After establishing that two profiles were present in both the subsamples of teacher educators, we tested systematically the similarity of these profiles. First, we specified a multi-group LPA model that assumed the same number of profiles across gender and that used the same overarching model—this model is referred to as the “configural model” and forms the basis for subsequent tests of profile similarity (Morin et al. , 2016). The resultant information criteria are shown in Table A1 (Model M1). Second, we specified a model that tests whether the factor means of all ICT variables were the same across gender—this model is referred to as the “structural model” (Model M2, Table A1). Comparing this model with the configural model (ie, Model M1 vs. M2) using likelihood ratio testing resulted in an insignificant chi-square statistic, ∆SB-χ 2 (12, N = 284) = 13.0, p = .37. This finding suggests that structural similarity of the two latent profiles across gender was given; in other words, adding the constraints on factor means did not deteriorate the fit of the configural model. Third, we tested for the similarity of dispersion between the latent profiles, that is, the similarity of factor means and variances. The dispersion similarity model tested whether the variability of the ICT variables was the same across gender profiles. Table A1 shows the information criteria of this model (Model M3). The likelihood ratio test between Models M2 and M3 revealed that significant differences in the variability of profiles across gender existed, ∆SB-χ 2 (6, N = 284) = 13.4, p = .04. Hence, within-profile interindividual differences in profiles existed (Morin et al. , 2016). Fourth, we tested for the distributional similarity of profiles, constraining factor means and variances and the profile membership probabilities across gender (Model M4, Table A1). The model of distributional similarity did not fit the data worse than the configural model, ∆SB-χ 2 (19, N = 284) = 26.3, p = .12. This finding suggests that the relative size of profiles can be assumed to be equal across gender. Overall, the multi-group LPA revealed that configural, structural and distributional similarities existed between the two latent profiles across gender, yet not dispersion similarity. Hence, the variability within profiles across gender differed significantly, while the shape and membership probabilities were statistically the same. These observations provide some evidence for the robustness of the two latent profiles we identified for the total sample across teacher educators’ gender.



12 19 26

12 19 26

39 27

21

20

−421.254 −332.691 −298.279

−1532.843 −1541.147

−1547.216

−1548.015

Npar

−1264.315 −1036.202 −937.475

LL

1.2303

1.2253

1.1928 1.1548

0.8681 1.0209 1.0883

1.0291 1.3754 1.3673

SCF

3136.030

3136.433

3143.686 3136.294

866.508 703.383 648.558

2552.630 2110.405 1926.949

AIC

3209.009

3213.061

3285.996 3234.816

894.318 747.415 708.813

2592.738 2173.909 2013.850

BIC

3145.588

3146.470

3162.325 3149.198

856.497 687.532 626.868

2554.715 2113.707 1931.468

aBIC

0.892

0.891

0.892 0.890

1.000 0.852 0.858

1.000 0.760 0.776

Entropy

–

–

– –

– 0.0001 0.0948

– 0.0145 0.0965

p(VLMR-LRT)

–

–

– –

– 0.0001 0.1024

– 0.0159 0.1004

p(LMR-LRT)

Note. LL = Log-likelihood value, Npar = number of parameters, SCF = scale correction factor, AIC = Akaike’s Information Criterion, BIC = Bayesian Information Criterion, aBIC = sample size-adjusted BIC, p (VLMR-LRT) = p -value of the Vuong–Lo–Mendell–Rubin (VLMR) likelihood ratio test, p (LMRLRT) = p -value of the Lo–Mendell–Rubin (LMR) likelihood ratio test. The suggested number of profiles is highlighted in gray.

Women (n = 209) One profile Two profiles Three profiles Men (n = 75) One profile Two profiles Three profiles Profile similarity M1: Configural M2: Structural (means) M3: Dispersion (means, variances) M4: Distributional (means, variances, probabilities)

Model

Table A1: Information criteria, entropies and results of the likelihood ratio tests for the LPA models across gender

Teacher educators as gatekeepers 21

Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education

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