With the rapid advancement of information and communication technologies (ICT), technology has become an indispensable part of everyone’s life, changing the way people interact and learn. In the past few decades, educators have witnessed increasing interest and emphasis on technology integration in teaching and learning (Liu et al., 2023). Technology has been deemed by many educators to be an enabling factor for constructivist-oriented teaching and learning (Pleasants & Radloff, 2024), since there is increasing evidence that technology can generate a positive effect on student learning (Petko et al.,2018). Undoubtedly, teachers play a central role in the successful implementation of technology-enhanced instruction and are increasingly expected to adapt to the latest trends of ICT-informed instruction and pedagogy (Zhou, 2023).
However, studies caution that the frequency of technology use does not necessarily reflect the quality or meaningfulness of its integration (Backfisch et al., 2021; Lu et al., 2022). High-quality integration requires deliberate alignment with pedagogical intent and instructional content (Backfisch et al., 2021). Technology integration approaches that do not reflect disciplinary knowledge, the corresponding pedagogical approaches for developing such knowledge, and the critical role of context are ultimately of limited utility and significance, as they ignore the complexity of the dynamics of effective technology integration (Mishra & Koehler, 2006).
Many teacher education programs struggle to integrate training in pedagogical and technological beliefs alongside content knowledge, despite the growing evidence that teachers’ attitudes and perceptions significantly influence the implementation and sustainability of TPACK-based teaching (Luik et al., 2023; Wu et al., 2022). Teacher preparation and professional development programs need to move away from technocentricity and be guided by a strong framework with a more interconnected knowledge network that equips the teachers to assess the value of different technologies and to decide how or when to integrate these technologies in their curriculum to maximize student learning (Chai et al., 2013).
Guided by this need, the present study drew on a federally funded, professional development program informed by technological pedagogical content knowledge (also known as technology, pedagogy and content knowledge, or TPACK): the STARTALK Chinese Teacher Institute, a blended residential summer program offered at a Midwestern university over 4 consecutive years. We examined changes in participants’ beliefs, practices, and TPACK following TPACK-informed professional learning. In doing so, we sought through this study to gain insights into how sustained, framework-guided professional learning can support language teachers’ technology integration and instructional development in Chinese as a foreign language (CFL) context.
Conceptual Framework
To address the complexity of teacher’ technology integration, researchers and teacher educators have increasingly embraced the TPACK conceptual framework (Mishra & Koehler, 2006). TPACK highlights the connections among technologies, content knowledge, and pedagogical developments, demonstrating how teachers’ knowledge of technology, pedagogy, and content can interact with one another to produce effective teaching. Teaching is a complex activity that requires teachers to draw upon their subject content with appropriate pedagogical approaches and specific technologies (Mishra et al., 2009). The TPACK framework suggests that technology, pedagogy, and content knowledge are highly interconnected, and they exist in a relationship that is both dynamic and transactional (see Figure 1).
Figure 1
TPACK Framework
To integrate technology into the classroom meaningfully, teachers should develop a sound and systematic understanding of the technology, subject matter, pedagogy, and the ways these aspects work together (Mishra & Koehler, 2006). The TPACK framework encompasses seven different knowledge domains that teachers need to become proficient in to integrate technologies in their classroom successfully: pedagogical knowledge (PK), content knowledge (CK), technological knowledge (TK), pedagogical content knowledge (PCK), technological pedagogical knowledge (TPK), technological content knowledge (TCK), and TPACK. The interaction and interplay of these bodies of knowledge lead to the type of knowledge teachers need to develop to integrate technology successfully in the classroom.
In the most recent update to the TPACK model, Mishra (2019) has suggested paying more attention to contextual knowledge. In the context of this study contextual knowledge is embedded in the realities of teaching Chinese as a foreign language in the US, and this knowledge is captured in the adapted instruments.
Literature Review
Though Mishra and Koehler (2006) argued that developing TPACK requires thoughtful interweaving of all three key sources of knowledge (technology, content, and pedagogy), research reveals a palpable gap between the emphasis on the integrative relationships between all three forms of knowledge for teachers and its actual enactment in classrooms (Rienties & Townsend, 2012). The literature indicates that many teachers consider themselves to have a good level of content, pedagogy, and specific technological knowledge; however, they often struggle to combine these effectively in meaningful classroom practices (Ottenbreit-Leftwich et al., 2018). This gap is partly attributed to the misalignment between the knowledge gained through teacher preparation or professional development programs and the realities of classrooms.
Researchers assert that the generic TPACK framework provides limited guidance to teachers on integrating technology effectively within subject areas (Willermark, 2018). Therefore, increasing attention has been directed toward subject-specific strategies that bridge the divide between teachers’ existing knowledge and their capacity to integrate technology in disciplinary contexts, aiming to foster the development of robust TPACK (Macrides & Angeli, 2018).
In spite of the large body of research work already conducted using the TPACK framework, world language education, particularly CFL instruction, remains underexplored in TPACK research (Tseng et al., 2020; Wang et al., 2019). This lack of research presents a major gap in the TPACK literature, as most current research focuses on English as a second language, or English as a foreign language and the disciplines of science, technology, engineering, and mathematics (STEM).
Many teacher educators and computer-assisted language learning (CALL) researchers believe that technology is effective only when its attributes and affordances align with the subject content and associated theories of learning and teaching practices (Golonka et al., 2014). Accordingly, teacher training should extend beyond technical skills to include the selection and adaptation of technologies for specific subject-area contexts. Effective professional development should equip teachers with the knowledge and experience to integrate content, pedagogy, and technology in ways that support authentic learning (McNeil, 2013). Given the limited empirical research on TPACK in CFL contexts, there is a pressing need for studies that investigate how the framework can be contextualized to support Chinese language teaching.
TPACK and Professional Development
Professional development remains central to teachers’ efforts to integrate technology meaningfully into classroom instruction, particularly when learning opportunities are sustained, context-specific, and connected to subject matter and pedagogy (OECD, 2020; Ye et al., 2024). Recent work suggests that effective professional learning is most likely to support technology integration when it is embedded in teachers’ instructional contexts and oriented toward practical application rather than isolated technical training (see also Frontiers in Education, 2025).
Within the TPACK literature, scholarship continues to emphasize that teachers develop stronger technology integration when professional learning helps them connect content, pedagogy, and technology as an integrated knowledge base rather than as separate competencies (Ye et al., 2024). Previous studies indicated that TPACK could support the construction of teachers’ digital competence, including their pedagogical principles for teaching with technology in classrooms (Baran et al., 2011). With robust feedback and ongoing assessments for teachers in training, TPACK-based professional learning can support teachers in optimizing ICTs in their technology integration practices (Bustamante, 2019).
At the same time, the literature remains limited in its attention to CFL-specific professional development. Although TPACK has been widely studied across STEM and general teacher education contexts, fewer studies have examined how the framework supports Chinese language teachers’ professional learning or how it shapes technology use in CFL classrooms (Tseng et al., 2020; Wang et al., 2019). This gap is especially notable because language teaching requires more than general technology training. It also requires the adaptation of tools to language-specific instructional purposes, such as feedback, interaction, communication, and learner engagement (Golonka et al., 2014; McNeil, 2013).
For CFL teachers, the challenge is not simply whether to use technology, but how to integrate it in ways that align with Chinese language pedagogy and the realities of classroom practice. Accordingly, the present study addressed this gap by examining TPACK development in a CFL professional learning context.
Teacher Beliefs and TPACK Development
Recent research has drawn increasing attention to the role of teacher beliefs, both pedagogical and technological, in shaping how TPACK is acquired and applied. Pedagogical beliefs are described as a teacher’s preferred way of teaching and typically involve teacher-centered orientations and learner-centered orientations (Chai et al., 2013). Research has shown that pedagogical beliefs affect willingness to adopt technology-enhanced pedagogical approaches (Petko, 2012), irrespective of the teacher’s level of technical proficiency. Teachers with more constructivist-oriented beliefs are more likely to integrate technology effectively into their instruction and develop higher levels of TPACK (Chai et al., 2013; Wu et al., 2022).
In addition, teachers’ technological beliefs, particularly their value beliefs, have an effect on their TPACK and technology use. For example, Xie and Hawk (2017) showed that teachers’ value beliefs are an important predictor of technology use in classroom instruction. Evidence indicates that teachers who value technology and believe that technology is manageable tend to be more motivated to develop the skills to integrate technology into classroom settings and to achieve higher levels of TPACK (Hsu et al., 2019; Lu et al., 2022).
While both pedagogical and technological beliefs have been individually linked to TPACK, few studies have explored their interaction, as well as the influence of this interactive relationship on the development of TPACK (Luik & Taimalu, 2023). Moreover, teacher beliefs and TPACK growth are closely tied to the teachers’ teaching practices. Teachers who use technology more frequently have more opportunities to develop knowledge of its classroom applications, which in turn supports higher levels of TPACK (Cheng & Xie, 2018). Yet, what is less explored is how teacher beliefs and their teaching practices interact and influence TPACK.
The current study sought to address this knowledge gap by investigating these relationships in an underinvestigated context of CFL. In this study, beliefs and knowledge were conceptualized as related but distinct constructs. Beliefs refer to teachers’ perceptions, orientations, values, and assumptions about teaching, learning, and technology integration (Ertmer, 2005; Pajares, 1992). These beliefs influence how teachers interpret instructional situations and make pedagogical decisions. In contrast, knowledge refers to teachers’ professional understanding and competencies, particularly their TPACK, which encompasses the integrated knowledge required to effectively combine technology, pedagogy, and content in instructional practice (Koehler & Mishra, 2009; Mishra & Koehler, 2006).
While teachers’ beliefs may shape the development and enactment of their TPACK (Abbitt, 2011; Ertmer & Ottenbreit-Leftwich, 2010), beliefs themselves do not constitute knowledge. Thus, the study examined beliefs as potential influences on teachers’ instructional practices and TPACK development rather than as components of TPACK itself and was guided by the following questions:
- What are the changes in participants’ beliefs, practices, and TPACK as a result of TPACK-informed professional learning?
- How are participants’ pedagogical beliefs, technological beliefs, and practices associated with their TPACK?
- Are participants’ pedagogical beliefs, technological beliefs, and practices predictive of their TPACK?
Methods
Professional Development Design
This study was based on a federally funded, TPACK-informed professional development program, the STARTALK Chinese Teacher Institute, a blended residential summer program offered at a Midwestern university over 4 consecutive years.
The program consisted of a 2-week pre-institute online component followed by a 10-day intensive on-campus session. During the online phase, participants engaged in asynchronous Blackboard discussions centered on the book Entertaining an Elephant (McBride, 1997), using guided prompts to reflect on teaching beliefs, learner roles, and instructional practices.
The on-campus component included 10 consecutive days of intensive training (approximately 7 hours daily), structured into four 90-minute instructional blocks integrating Chinese language content, pedagogy, and instructional technologies through hands-on activities, lesson design, and instructional modeling. Daily journaling supported reflection and synthesis.
The STARTALK Chinese Teacher Institute’s curriculum foundation was deeply engrained in the TPACK framework (Mishra & Koehler, 2006), which consisted of content, pedagogy, and technology explorations (see Table 1). A focus on the potentials of technological tools for promoting Chinese language teaching and learning and the pedagogical benefits that a hands-on experience would bring to the participants guided the development of the program. Content (Chinese language and culture), pedagogy (foreign language pedagogy), and technology (diverse technology tools) interplayed, reflecting the connections from the TPACK framework. The participants became active explorers presented with concepts, principles, skills, and integration opportunities. They were encouraged to reflect on their explorations daily in their blog reflections.
Table 1
Content, Pedagogy, and Technology Explored During the STARTALK Chinese Teacher Institute
| Content Explorations | Pedagogy Explorations | Technology Explorations |
|---|---|---|
| Teaching and Learning Chinese | Teaching and Learning Beliefs | Creating Community with PicCollage |
| Literacy: Pinyin and Characters | Backward Design / Instructional Planning | Linguafolio |
| Intercultural Communicative Competence | Goal Setting | E-Portfolio |
| Cultural Activities | Can-Do Statements | iPad Apps for Chinese Teaching and Learning |
| Tea Tasting | Adapting Lessons for Elementary Learners | High Leverage Strategies |
| Chinese Knot | Comprehensible Input | Creating Animal Stories with Narrated PPT |
| Lion Dance | Learner Centered Classroom | Google Forms |
| Creating Learner Centered Activities | Resource Wiki | |
| Learning Stations | Sock Puppet | |
| Inside/outside Circle Interviews | ||
| Keeping it in the Target Language | ||
| Classroom Management | ||
| Intro to Lesson Plan Template | ||
| Swap Shop |
Participants
Participants in this study were drawn from 4 consecutive years of the STARTALK Chinese Teacher Institute. Each year, 15 participants were selected nationwide, resulting in a total of 60 Chinese language teachers from 24 US states, most of whom were teaching at the K–12 or university level at the time of participation. All 60 participants completed the presurvey, and 57 completed the postsurvey, resulting in a 95% response rate. Table 2 presents the demographic information of the participants.
Table 2
Demographic Information of Participants
| Category | Subgroup | Frequency | Percentage |
|---|---|---|---|
| Gender | Female | 53 | 88.3% |
| Male | 7 | 11.7% | |
| Age | 20–29 | 20 | 33.3% |
| 30–39 | 11 | 18.3% | |
| 40–49 | 17 | 28.3% | |
| 50+ | 12 | 20.0% | |
| Teaching Experience | < 1 Year | 11 | 18.3% |
| 1–2 Years | 14 | 23.3% | |
| 3–5 Years | 13 | 21.7% | |
| 6–10 Years | 15 | 25.0% | |
| > 10 Years | 7 | 11.7% |
Research Design
Since this research study aimed to explore the TPACK development of Chinese language teachers through the STARTALK Chinese Teacher Institute, the study adopted a pre-post design. The dependent variables (participants’ pedagogical beliefs, technology beliefs, teaching practices, and TPACK) were measured before and after the treatment (professional development program) to assess whether those changed as a result of the treatment.
Instruments
Two instruments were used to measure the participants’ TPACK, beliefs, and practices (Mishra & Koehler, 2006) in their classrooms: the adapted Chinese Language Teaching Institute survey (ACLTI; Moeller et al., 2011) and the adapted TPACK survey (Chai et al., 2013).
The ACLTI Survey
The first instrument, the ACLTI survey (Moeller et al., 2011), measured teachers’ beliefs and knowledge of pedagogy, technology, and content and is grounded in established language teaching frameworks (American Council on the Teaching of Foreign Languages, 2015). It has also been used in prior research with language teachers (Bustamante, 2014), supporting its applicability in foreign language contexts.
We changed the original survey to fit within the TPACK framework and adapted some of the survey items. We, therefore, conducted factor analyses to validate the new subscales. The ACLTI survey (Moeller et al., 2011) consists of five scales: Instructional Practices (14 items), Assessment Practices (5 items), Pedagogical Beliefs (11 items), Technological Beliefs (8 items), and Pedagogical Knowledge (13 items) with a total of 51 items. The range of internal consistency measures for the different scales was .69-.91 in the pretest and .71-.94 in the posttest. These met the standard for research purposes.
Student-Centered Instructional Practices. Chinese teacher participants’ instructional practices were assessed using the Instructional Practices scale ranked from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating instructional practices more in line with the state-of-the-art pedagogical practices in the field of world language education. Fourteen items covered vocabulary and grammar teaching, individual and collaborative learning, choice of teaching materials, use of storytelling, differentiation, classroom management, and use of technology.
We conducted an exploratory factor analysis using principal component analysis with promax rotation. The results indicated that only the instructional practices scale consisted of two distinct factors (see Table 3), while all other scales were unidimensional. The first factor included 10 items focused on student-centered practices. Sample items include, “I have students work in small groups.” The second factor included four items focused on teacher-centered practices; for example, “I primarily lecture to students.” The factor analysis led to the removal of one item, as it was not related to either factor. We decided to focus on the first factor, student-centered instruction, since it was the focus of the professional development program.
Table 3
Component Loadings for Instructional Practices
| Item | Student-Centered Instruction | Teacher-Centered Instruction |
|---|---|---|
| Peer Assessment | .76 | |
| Group Work | .70 | |
| Standards-Based Instruction | .65 | |
| Individual Products | .64 | |
| Student-Led Reasoning | .64 | |
| Technology-Assisted Vocabulary Instruction | .63 | |
| Authentic Chinese Materials | .62 | |
| Self-Assessment | .59 | |
| Textbook-Driven Instruction | .57 | |
| Interactive Assessment | .53 | |
| Lecture-Based Instruction | .79 | |
| Grammar-Focused Instruction | .76 | |
| Vocabulary Lists | .72 | |
| Teacher-Centered Technology Use | .46 | |
| Learning From Classmates | .39 |
Assessment Practices. Chinese teacher participants’ assessment practices were assessed using the Assessment scale from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating assessment practices more in line with the state-of-the-art assessment practices in world language education. Sample items included, “I continually assess students’ progress,” and “I assess students through collaborative projects.”
Pedagogical Beliefs. Chinese teacher participants’ pedagogical beliefs were assessed using the Pedagogical Beliefs scale from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating pedagogical beliefs more in line with the state-of-the-art pedagogical beliefs in world language education. Eleven items regarding contextualized learning, teacher-centered versus student-centered learning, collaborative learning, grammar instruction, target language use, the relevancy of content to students, and the use of authentic materials were used. The sample items of the scale included, “Content must be relevant to students’ lives in order to maintain interest in language learning,” and “Lesson plans should be shaped based on the needs of the students.”
Technological Beliefs. Chinese teacher participants’ technological beliefs were assessed using the Technological Beliefs scale from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating technological beliefs more in line with the state-of-the-art technological beliefs in world language education. Eight items covered the benefits of technology in language instruction and learning, and the necessity of its adoption by teachers and students. Sample items of the scale included, “Technology helps make Chinese input comprehensible,” and “Technology engages students in language practice.”
Pedagogical Knowledge. Chinese teacher participants’ PK was assessed using the Pedagogical Knowledge scale from 1 (not familiar) to 5 (very familiar), with higher scores indicating more familiarity with the state-of-the-art PK in world language education. Thirteen items covered teaching culture, differentiated learning, task-based instruction, backward design, cooperative learning, student-centered learning, authentic assessment, constructivist learning, standards-based instruction, Total Physical Response Storytelling (TPRS), Intercultural Communicative Competence, and scaffolding.
TPACK Survey
The second instrument was adapted from the TPACK survey (Chai et al., 2013), which was originally created to assess the development of preservice teachers’ knowledge of TPACK. In this study, the instrument was used to assess the development of the teacher participants’ knowledge of TPACK throughout their professional development program and classroom experiences. The TPACK survey was validated using exploratory and confirmatory factor analyses, resulting in a factor structure with strong model fit and high internal reliability (α = .95). As shown in Table 4, the range of internal consistency measures for the different scales was .69-.89 in the pretest and .89-.95 in the posttest. All scales met the internal consistency reliability criteria.
Table 4
Internal Consistency Values for Survey Instruments
| Instrument | Domain | Internal Consistency (alpha) Pre | Internal Consistency (alpha) Post |
|---|---|---|---|
| ACLTI | Student-Centered Instructional practices | .77 | .89 |
| Assessment Practices | .71 | .71 | |
| Pedagogical Beliefs | .69 | .74 | |
| Technological Beliefs | .92 | .93 | |
| Pedagogical Knowledge | .91 | .94 | |
| TPACK | CK | .84 | .91 |
| PCK | .69 | .89 | |
| TPACK | .89 | .90 | |
| TCK | .87 | .95 | |
| TPK | .84 | .93 | |
| TK | .89 | .93 |
This survey collected data using a Likert scale on the seven domains of TPACK. Since the TPACK survey (Chai et al., 2013) was designed for preservice teachers in diverse subject areas, items were modified to adapt to Chinese teachers. The Pedagogical Knowledge scale was removed from the original instrument to avoid duplication with the first instrument, the ACLTI survey. As this TPACK survey was an adapted scale, we used factor analysis to validate the original factor structure. Results of the factor analysis validated the original factor structure.
Data Collection
The ACLTI and the adapted TPACK surveys were administered using Qualtrics at two different points. The presurvey was collected before the institute commenced to determine a baseline for the participants’ general beliefs, practices, and knowledge concerning their TPACK. The postsurvey was completed right after the institute to gain an understanding of the immediate impact of the 2-week institute.
Data Analysis
Data analysis was conducted to examine changes in participants’ TPACK, beliefs, and classroom practices following participation in the professional development program, as well as the relationships among these variables. First, descriptive statistics were calculated for all variables in the study, including means, standard deviations, and reliability coefficients, where appropriate. Paired-samples t-tests were then conducted to compare participants’ pre- and postsurvey scores and to determine whether significant changes occurred following participation in the TPACK-informed professional development program.
To further explore relationships among participants’ pedagogical beliefs, technological beliefs, classroom practices, and TPACK, correlational analyses were conducted using Pearson’s correlation coefficients. In addition, multiple regression analyses were performed to examine the extent to which participants’ beliefs and practices predicted their TPACK development. These analyses allowed for the identification of significant predictors associated with changes in participants’ TPACK. All quantitative analyses were conducted using IBM SPSS Statistics for Windows (Version 27.0), a statistical software package used for data management and statistical analyses (IBM Corp., 2020). Statistical significance was evaluated at the .05 level.
Results
Paired-samples t-test analyses of pretest and posttest scores revealed notable gains across multiple domains of teacher knowledge, beliefs, and practices. Effect sizes were categorized following Cohen’s (1988) guidelines: .2–.4 represents a small effect size, .4–.8 a medium effect size, and .8 and above a large effect size. To correct for experiment-wise errors, we used the Bonferroni correction that set the statistical significance at .004. The findings are detailed in Table 5 and summarized in the following section.
Table 5
Descriptive and Inferential Statistics for ACLTI
| Measure | Item | Pre | Post | Pre-Post t-Test | Effect Size | ||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | t (56) | d | ||
| Student-Centered Instructional Practices | 10 | 3.7 | .4 | 3.9 | .6 | 2.33 | 0.31 |
| Assessment Practices | 5 | 3.2 | .5 | 3.6 | .5 | 4.48**** | 0.59 |
| Pedagogical Beliefs | 10 | 4.1 | .4 | 4.3 | .4 | 3.55**** | 0.47 |
| Technological Beliefs | 8 | 4.2 | .6 | 4.5 | .5 | 3.33*** | 0.44 |
| Pedagogical Knowledge | 13 | 3.5 | .7 | 4.2 | .6 | 8.37**** | 1.11 |
| Note. *** p < .005, ****p < .001 | |||||||
The improvement in student-centered instructional practices from the pretest to the posttest was not significant, t(56) = 2.33, p = .02 (this is not statistically significant because of the Bonferroni correction), and a small effect size (d = 0.31; see Table 5). Assessment practices showed a significant increase, t(56) = 4.48, p < .001, and a medium effect size (d = 0.59). For pedagogical beliefs, the difference was statistically significant, t(56)= 3.55, p < .001, and a medium effect size (d = 0.47). Technological beliefs demonstrated a significant improvement, t(56) = 3.33, p = .002, and a medium effect size (d = 0.44).
In addition to improvements in classroom practices and teacher beliefs, scores demonstrated significant increases across all measured TPACK domains from pretest to posttest as well. PK showed a significant increase, t(56) = 8.37, p < .001, and a large effect size (d = 1.11). For TK, scores increased significantly, t(56) = 3.66, p < .001, and a medium effect size (d = 0.49; see Table 6).
Table 6
Descriptive and Inferential Statistics for TPACK by Scale
| Measure | Item | Pre | Post | Pre-Post t-Test | Effect Size | ||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | t(56) | d | ||
| TK | 6 | 3.6 | .7 | 3.9 | .7 | 3.66**** | 0.49 |
| TPK | 5 | 3.7 | .5 | 4.2 | .6 | 5.55**** | 0.74 |
| TCK | 5 | 3.6 | .7 | 4.2 | .5 | 6.61**** | 0.88 |
| CK | 6 | 4.1 | .5 | 4.3 | .5 | 3.48*** | 0.46 |
| PCK | 5 | 3.8 | .5 | 4.2 | .6 | 4.77**** | 0.63 |
| TPACK | 5 | 3.4 | .7 | 4.1 | .6 | 5.79**** | 0.77 |
| Note.***p < .005, ****p < .001 | |||||||
There was a significant difference in the scores on TPK, t(56) = 5.55, p < .001, and a medium effect size (d = 0.74). For TCK, there was a significant increase in scores, t(56) = 6.61, p < .001, and a large effect size (d = 0.88). Regarding CK, scores improved significantly, t(56) = 3.48, p < .001, and a medium effect size (d = 0.46). There was a significant difference in the scores on PCK, t(56) = 4.77, p < .001, and a medium effect size (d = 0.63). Last, in TPACK, scores rose substantially, t(56) = 5.79, p < .001, and a medium effect size (d = 0.77). This result reflects a robust difference in participants’ ability to synthesize technology, pedagogy, and content.
Regression Predicting TPACK
Pearson’s correlations were conducted to determine the relationships between the participants’ beliefs and practices as well as with the total TPACK score. The results showed that pedagogical beliefs (ACLTI) and assessment practices (ACLTI) were positively correlated in the pretest, r(55) = .42, p =. 001, but not in the posttest, r(55) = .22, p = .109 (see Table 7). Pedagogical beliefs (ACLTI) and technological beliefs (ACLTI) were found to be not correlated in the pretest, r(55) = .26, p = .05, but positively correlated in the posttest, r(55) = .53, p < .001. Similarly, pedagogical beliefs (ACLTI) and TPACK were found to be not correlated in the pretest, r(55) = .18, p = .18, positively correlated in the posttest, r(55) = .37, p < .001, (Table 7). Technological beliefs (ACLTI) and TPACK were positively correlated in both the pretest, r(55) = .39, p =. 002, and the posttest, r(55) = .49, p < .001. TPACK and assessment practices (ACLTI) were also positively correlated both in the pretest, r(55) = .44, p < .001, and in the posttest, r(55) = .57, p < .001.
Table 7
Pearson’s Correlations
| Variable | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1. Student-centered instructional practices | — | -.15 | -.15 | -.16 | -.15 |
| 2. Assessment | -.03 | — | .42** | .20 | .44*** |
| 3. Pedagogical beliefs | .05 | .22 | — | .26 | .18 |
| 4. Technological beliefs | .16 | .20 | .53*** | — | .39** |
| 5. TPACK | -.01 | .57*** | .37** | .49*** | — |
| Note. Correlations above the diagonal were for pretests, and correlations below the diagonal were for posttests. | |||||
A linear regression analysis was conducted to examine the multivariate relationships that explain the extent to which teachers’ pedagogical and technological beliefs and teaching practices predicted their postintervention TPACK scores. Before conducting the analysis, we tested the variables for normality (skewness and kurtosis) as well as multicollinearity. All variables were within accepted ranges for normality, and collinearity diagnostics indicate mild to moderate multicollinearity that does not rise to a level of serious concern. The maximum condition index was 28.014, which falls below the commonly cited threshold of 30 that signals problematic multicollinearity (Belsley et al., 1980).
A hierarchical regression approach was employed to examine factors associated with TPACK at posttest. Hierarchical regression was selected because it aligns with the theoretically driven nature of the analysis, allowing factors to be entered in a sequence determined by prior literature and conceptual logic rather than by statistical criteria alone (Tabachnick & Fidell, 2019). This approach is particularly well-suited to a small sample context because it constrains the model to a theoretically justified set of factors entered in a predetermined order, thereby reducing the risk of overfitting and avoiding the inflated standard errors that accompany undisciplined variable inclusion. By entering factors in blocks that reflect the hypothesized structure of TPACK development, hierarchical regression also allows for the examination of incremental variance explained at each step (ΔR²), making explicit the unique contribution of each factor set above and beyond what was already accounted for (Cohen et al., 2003). As a result, we entered Pre-TPACK first and then Technological and Assessment practices post scores.
Table 8
ANOVA Predicting TPACK
| Mode 1 | Unstandardized | Standard Error | Standardized | t | p |
|---|---|---|---|---|---|
| M₀ (Intercept) | 4.133 | 0.067 | - | 62.081 | < .001 |
| M₁ (Intercept) | 0.184 | 0.567 | - | 0.324 | 0.747 |
| TPACK_pre | 0.109 | 0.075 | 0.143 | 1.441 | 0.156 |
| TB_post | 0.394 | 0.104 | 0.379 | 3.789 | < . 001 |
| AS_post | 0.505 | 0.107 | 0.476 | 4.742 | < .001 |
| Note. The model includes Technological beliefs and Assessment practices in the posttest. ** p < .001 | |||||
The final model retained all factors, whether they significantly contributed to explaining the variation in the dependent variable or not. Model adequacy was assessed using multiple criteria, including adjusted R², RMSE, and analysis of residuals. As shown in Table 8, the final model (M₁) included controlling for TPACK pretest score, technological beliefs, and assessment practices, which together accounted for approximately 49.5% of the variance in TPACK posttest scores. The model fit was statistically significant, F(3, 53) = 17.30, p < .001. This represents a notable improvement from the null model (M₀) and reduced the RMSE from 0.50 to 0.37.
In terms of individual factors, both technological beliefs (β = 0.38, p < .001) and assessment practices (β = 0.48, p < .001) had significant positive associations with TPACK posttest scores. Specifically, higher scores in technological beliefs and assessment practices were associated with higher levels of TPACK following the intervention. Conversely, pedagogical beliefs and student-centered instructional practices, though initially considered, did not enter the final model, suggesting that these variables did not contribute significantly to the prediction of posttest TPACK outcomes.
These results indicate that while the broader framework of teacher beliefs and practices was conceptually relevant to TPACK development, only technological beliefs and assessment practices demonstrated significant predictive power in the posttest phase. Their inclusion in the final model highlights their predictive importance and the effectiveness of the professional learning experience in enhancing these specific domains.
Discussion
This study investigated the development of Chinese language teachers’ TPACK through their participation in a TPACK-informed professional development program. Drawing on the data from pre- and postprogram surveys, the findings revealed statistically significant gains across almost all dimensions of teachers’ knowledge, beliefs, and classroom practices.
TPACK Domains and Overall Development
Crucially, the study revealed statistically significant improvements across all seven areas of the TPACK framework from the pretest to the posttest. This included standalone areas of TK, CK, and PK, as well as the integrated constructs and the all-encompassing construct of TPACK. Effect sizes ranged from moderate to large, with the largest gains observed in PK (d = 1.11) and TCL (d = 0.88). This pattern of improvement indicates the professional development was effective at providing a holistic experience that best helped teachers understand how to leverage technology in pedagogically meaningful and content-specific ways.
These findings are consistent with studies that advocate for TPACK-based professional learning as a framework for long-term instructional improvement (Voogt et al., 2020). These findings align with prior research suggesting that TPACK-based professional learning can support teachers in developing pedagogically meaningful technology integration practices over time. Consistent with Baran et al. (2011) and Bustamante (2014), the significant gains across TPACK domains observed in this study indicate that sustained feedback, instructional support, and reflective engagement within TPACK-informed professional development can enhance the teachers’ TPACK and strengthen their ability to integrate ICTs effectively in classroom practice.
Teacher Beliefs and TPACK
Results indicated a statistically significant increase in participants’ pedagogical beliefs from the pretest to posttest with a medium effect size (d = .47). This effect size means that the professional development shifted participants’ orientations toward a more learner-centered and communicative approach. In the wider literature, the majority of studies have generally found that constructivist or student-centered pedagogical beliefs are positively related to TPACK (Chai et al., 2013). However, there are also diverging results: Wu et al. (2022) found teacher-centered pedagogical beliefs to be positively related to TPACK, implying that, in certain contexts, more traditional pedagogies may lead teachers to feel confident about technology integration.
Our findings add additional specificity to this conversation. In the pretest, pedagogical beliefs had no significant correlation to technological beliefs or TPACK, but there were significant positive correlations in the posttest. This shift suggests that as teachers’ pedagogical orientations became more student-centered following the professional development, these beliefs began to align with their technological beliefs and their developing TPACK. The change in pedagogical beliefs may have set the stage for significant reciprocal relations between beliefs and knowledge.
This finding reinforces the need for professional development to extend beyond technical training and focus on changing teachers’ pedagogical orientations. As noted in earlier research, pedagogical beliefs often function as mediators of how teachers interpret and implement technology integration (Hsu et al., 2019; Tondeur et al., 2017). Therefore, the importance of TPACK-informed professional development is not only for directly developing teachers’ TPACK but also for developing learner-centered pedagogical beliefs that mediate technological beliefs to assist with more reflective, intentional, and content-responsive uses of technology.
The results also indicated there was a statistically significant increase in technological beliefs from pretest to posttest, exhibiting a medium effect size (d = 0.44). Equally noteworthy, technological beliefs were significantly correlated with posttest TPACK scores in the final regression model, affirming their potential importance in enhancing educators’ ability to purposefully integrate technology into pedagogical, contextualized instruction. The findings of this study are in line with a growing amount of research discussing the importance of teachers’ value beliefs as central to integrating technology.
Prior research has shown that when teachers believe that technology is useful and viable, they are more motivated to learn the capabilities needed to effectively integrate technology into their classroom (Ertmer et al., 2021). For instance, Lehtinen et al. (2016) demonstrated that value beliefs related to the usefulness of simulations had a direct correlation to growth in TK, while Hsu et al. (2019) demonstrated positive correlations between value beliefs associated with game-based instruction with several domains of TPACK, although in the latter study, value beliefs were no longer predictive after controlling for other variables. Such mixed findings suggest that technological beliefs may not always function as direct predictors of TPACK but often interact with other factors such as pedagogical orientations and classroom practices.
The results of the current study expand upon this literature by demonstrating that the technological and pedagogical beliefs became more tightly coupled after the professional development, and that teachers’ technological and pedagogical beliefs were jointly related to TPACK. This interactive relationship relates to arguments that belief structures can function as cognitive enablers: they enable teachers to flexibly navigate the intersections of technology, pedagogy, and content area (Chai et al., 2019). The program’s design, embedding digital tools within pedagogy-and-content‐focused sessions, provided authentic opportunities for participants to notice how technology can support instruction, solidifying their beliefs and knowledge.
Teachers’ practices further illuminate this relationship. Assessment practices were significantly and positively correlated with TPACK at both pretest and posttest. Along with technological beliefs, assessment practices were a significant factor in the regression model. This aligns with prior research stating that technology integration practices have shown consistent positive associations with TPACK (Cheng & Xie, 2018; Tondeur et al., 2017), but suggests that in the short-term aspect of the professional development program, the assessment practices may have changed sooner than instructional practices. Given the short duration of the program, it is likely that the instructional practices were still too short to enact substantial changes, which may require longer durations of implementation and contemplation (Chai et al., 2019).
Limitations
There are a few limitations that need to be acknowledged. First, the relatively small sample size (n = 57) may limit statistical power and reduce the ability to detect more subtle relationships among variables. Although the sample is appropriate for an exploratory study within a specific professional development context, future research with larger samples would allow for more robust analyses and greater generalizability.
Second, the study employed a pre–post design without a control group, which limits the ability to draw causal inferences. While the observed changes in TPACK, beliefs, and practices are encouraging, they cannot be attributed solely to the professional development program, as other factors may have contributed to these changes.
Third, the short duration of the intervention may not have been sufficient to produce measurable changes in more complex domains such as instructional practices, which often require extended time and classroom implementation to fully develop. Longer-term professional development or follow-up studies may provide a clearer picture of sustained changes in teaching practices. Finally, the study relied on self-reported survey data, which may be subject to biases such as social desirability or participants’ perceptions rather than actual classroom practices. Future studies could strengthen the validity of findings by incorporating multiple data sources, such as classroom observations, teaching artifacts, or student outcomes.
Conclusion
The findings of this research indicate that technological beliefs are related to TPACK while being dynamically interacted with assessment practices to support TPACK development. The strong advancements in both pedagogical and technological belief structures, coupled with the predictive value of technological beliefs and assessment practices for TPACK development, suggest the importance of working towards aligned beliefs and practice-oriented opportunities within professional development environments. Aligned development could pave the way for sustained TPACK growth for teachers by treating beliefs and practices as mutually reinforcing drivers of technology integration.
The implications of our findings for professional development designers and classroom teachers are that effective TPACK-focused training should include all three domains. Professional development providers should prioritize integrated session designs. In addition, professional development providers should create opportunities for teachers to examine and revise their beliefs, since the discussions of usefulness and explicit modeling of learner-centered technology integration are central for TPACK development. Finally, this study shows that even relatively short interventions are enough to shift beliefs, and that assessment-focused work appears to be a high-leverage entry point.
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Appendix
Adapted Chinese Language Teaching Institute Survey and TPACK Instrument
