The Impact of Non-Cognitive Factors on TPACK Self-efficacy for Foreign Language Educators in the U.S. and China

• In Event: Teacher education and the teaching profession

Proposal

The study aimed to examine the relationship between Technological Pedagogical Content Knowledge (TPACK) self-efficacy, teaching experience, resistance to technology, and technology grit of language educators in China and in the U.S.

Studies of learning with technology have shown that student achievement increases when technology is integrated into instruction. Technology-assisted L2 instruction contributes to language acquisition with its unique affordances (e.g., customization, portability, choice). Realizing the impact of technology on learning requires instructors to integrate technology in skillful ways. The framework that best describes such an application is TPACK (Mishra & Koehler, 2006). Teachers’ TPACK Self-efficacy impacts teacher decision-making (e.g. Wim, & Jochems, 2010).
There have been contradictory findings concerning the role teaching experience plays in technology integration. Gorder (2008) reported that teaching experience was not significantly correlated with technology integration, while Hernández-Ramos (2005) found out that teaching experience was significantly linked to educators’ frequency of technology integration. These contradictory findings led us to examine the impact of teaching experience.
Grit refers to sustained perseverance and passion for challenging long-term goals (Robertson-Kraft & Duckworth, 2014). Duckworth and her colleagues found that grit was positively related to retention and successful performance in different contexts. Technology integration was reported as challenging in multiple studies (Howard, 2013), which indicated that technology integration might be a potential context where grit could differentiate educators’ practices.
Resistance is defined as an opposition to change (Wachtel, 1982), which describes people’s reluctance to deal with change. Psychological resistance is a construct that comprises behavioral, cognitive, and affective components that generate opposition (Oreg, 2003). Technology integration is a change process for most educators. As expected, resistance to such changes has emerged among educators and negatively influenced technology integration ( Howard, 2013).
Based on the review of the literature the research questions included:
What are the non-cognitive precursors to TPACK self-efficacy for language educators?
How do college educators in China and Chinese educators in the U.S. differ in Resistance to technology integration, Technology Grit, and TPACK self-efficacy?
What role do resistance and grit play in predicting TPACK self-efficacy?
What role does teaching experience play in TPACK self-efficacy for language educators?


Figure 1 Proposed conceptual model based on the literature

Method
Participants
This study included 265 teachers, including 138 Chinese language teachers (92% females) working in U.S. classrooms, and 127 English language teachers (77% females) working in mainland China.
Instruments
This study used three scales:
TPACK Self-Efficacy scale was adapted from Wang and her colleagues (2004) technology self-efficacy scale. The Cronbach alpha for the four-item scale was .726.
Resistance to Technology Integration scale was adapted from Oreg’s (2006) Resistance to Change Scale. It included five items with the Cronbach alpha .702.
Technology Grit scale was adapted from Duckworth’s short Grit Scale (Duckworth & Quinn, 2009). There were six items with a Cronbach alpha of .840.
Results
Structural Equation Modeling (SEM)
Measurement model had an adequate fit. Measurement invariance was examined with confirmatory factor analysis to justify that the two samples could be compared within the structural equation model. In order to test the fit of the theoretical model, we conducted a structural equation model with multiple linear regression estimation. The model explained 84.6% of the variance in TPACK self-efficacy. Neither teaching Experience nor Resistance to technology integration contributed significantly to TPACK self-efficacy. Resistance to technology integration was significantly co-varied with Technology Grit. Technology Grit significantly contributed to TPACK self-efficacy. Model fit indices indicated that this was a good model, X2(87) = 93.648, RMSEA = .017, CFI/ TLI =.994/.993.

Figure 2 Model results for the proposed conceptual model

Group Comparison
The between-group invariance test showed that Chinese language educators in the U.S. context reported higher scores in TPACK Self-Efficacy and Technology Grit and lower scores in Resistance to technology integration compared to college English educators in China. The group comparison result is displayed in Figure 4. For both groups, Resistance to technology integration negatively contributed to Technology Grit, and it did not directly contribute to TPACK self-efficacy. Technology Grit positively predicted TPACK self-efficacy for both groups. However, Technology Grit positively predicted TPACK self-efficacy for college English educators in China, while Experience did not. On the contrary, Experience significantly contributed to TPACK self-efficacy for Chinese language educators in the U.S.


College English Instructors in China


Chinese Language Educators in U.S.
Figure 4 Group Comparison Model
Discussion
Experience
Teachers’ experience did not significantly contribute to TPACK self-efficacy when the sample included both teacher groups. When examining group differences, we found that teaching experience contributed significantly to the TPACK self-efficacy for Chinese language educators in the United States but not for college English educators in China. This aligns with previous literature showing that the relationship between teaching experience and technology integration varies across contexts (e.g. Gorder, 2008).
Technology Grit and Resistance
Technology grit was found to be a strong predictor of TPACK self-efficacy. This result aligns well with findings from previous studies that grit contributes to successful performance (Duckworth et al., 2007). Resistance did not contribute directly to TPACK self-efficacy. This result supports our hypothesis and is also analogous to previous research findings of the prevailing negative influence of resistance on technology integration (e.g. Howard, 2013).
Group Differences
The results indicated a significant difference in TPACK self-efficacy, technology grit, and resistance to technology integration between the Chinese language educators in the United States and college English educators in China. Chinese language educators in the U.S. context had significantly higher TPACK self-efficacy, higher technology grit, and lower resistance to technology integration. This suggests that the environment has a great influence on technology use for educators.
Implications for Professional Development
This study has several implications addressing technology integration for teacher education programs. First, it is important to create a positive environment. Second, it is crucial to use overt strategies to foster technology grit for Chinese language educators, including goal-setting, fostering a growth mindset, providing adequate follow-up support, and encouraging self-reflection (Duckworth, 2016). Provide more opportunities for collaborations across countries in professional development programs and educator preparation programs.

Author

• Guy Trainin, University of Nebraska Lincoln TLTE