Influencing factors of behavioral intention to use micro-lectures for teaching among pre-service mathematics teachers in China: a modified UTAUT-2 | cinetotal.com.br

Influencing factors of behavioral intention to use micro-lectures for teaching among pre-service mathematics teachers in China: a modified UTAUT-2

Research designAn explanatory quantitative design was adopted to examine the BI of PMTs to use micro-lectures for teaching, including the major factors influencing this intention. The research framework was based on a modified UTAUT2 model, adjusted to properly fit the context of pre-service teachers.A cross-sectional survey method and Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to systematically test the hypothesized relationships within the modified framework. Additionally, the mediating effects within the proposed model was examined and used to explore the indirect pathways through which certain factors influence PMTs’ behavioral intention. This explanatory method allowed for a structured validation of the theoretical model, providing insights into how different factors contributed to PMTs’ adoption of micro-lectures in teaching practices.RespondentsIn China, PMTs typically received educational technology and micro-lectures design training in the second semester of the second or third year. This was followed by school-based teaching practicums, usually in the second semester of the third year for undergraduates. The experiences gained provided first-hand exposure to teaching and the application of educational technology. Regarding this background, the research focused on third-year and higher PMTs, as these individuals possessed the relevant pedagogical knowledge and practical experience to accurately respond to the distributed questionnaire. The survey was conducted at the end of the second semester to ensure participants had completed relevant coursework and practicum experiences, since it investigated PMTs’ willingness to use micro-lectures for teaching. However, first- and second-year PMTs were excluded due to limited exposure to these areas.This study was conducted in Guangxi Zhuang Autonomous Region, a southern Chinese region with limited educational resources, particularly in rural and border areas. As highlighted in the introduction, micro-lectures help bridge educational gaps in under-resourced regions. Examining PMTs’ adoption of micro-lectures in this context offers insights into their role in enhancing instructional quality and equity.The sampling method used was convenience sampling. Specifically, the questionnaire was distributed to faculty members in the mathematics education departments of universities in Guangxi that offer pre-service mathematics teacher education programs. These faculty members then shared the questionnaire with their students via class group chats, allowing voluntary participation.Data analysis toolsPLS-SEM was used to analyze factors influencing PMTs’ willingness to adopt micro-lectures for teaching. The analysis was conducted using Smart-PLS 4.0 software.In line with the description above, PLS-SEM and Covariance-Based SEM (CB-SEM) are two major methods in structural equation modeling. Based on this context, CB-SEM is theory-driven, requiring strong related foundations, large sample sizes, and normally distributed data, while PLS-SEM is prediction-oriented and particularly useful in analyses where theoretical frameworks are evolving or being adapted (Hair et al., 2016). Although SEM was adopted for hypothesis testing and explanation, the modified model incorporated adjustments rather than directly applying a fully established theory. PLS-SEM was selected as it maximized explained variance in endogenous variables, making the approach well-suited for analyzing PMTs’ BI (Hair et al., 2016). This method also accommodated complex models with multiple constructs, indicators, and mediators/moderators, in line with the research objectives (Hair et al., 2016).The analysis included measurement model validation and structural model evaluation(J. F. Hair et al., 2016). Reliability and validity were assessed using Cronbach’s Alpha (CA) and Composite Reliability (CR) for internal consistency (>0.7), Average Variance Extracted (AVE) for convergent validity (>0.5), and the Fornell-Larcker criterion for discriminant validity. The structural model evaluation primarily reports the Coefficient of Determination (R²) for explanatory power, Goodness of Fit (Gof) for overall model fitness, p values for statistical significance, and Path Coefficients (β) for testing hypothesized relationships.Questionnaire designPrior to the development of the questionnaire, scales related to the designed model were collected based on the UTAUT2 (Venkatesh et al., 2012). To avoid contextual errors caused by translation, two language experts with expertise in mathematics education were invited to proofread the questionnaire, particularly designed for the research theme and target population. Finally, PMTs and professors from mathematics education departments in teachers education universities were invited to conduct a thorough review of the questionnaire’s context, organization, and expression to ensure its clarity, coherence, precision, and effectiveness. These professors specialized in mathematics education and technology integration, providing deep insights into the role of micro-lectures in mathematics teaching, as well as the characteristics of PMTs. The active participation in the activities organized by micro-lectures society of Guangxi Normal University, including Mathematics Dynamic Geometry Design Competition, Micro-Lectures Design Competition, Micro-Lectures Teaching Seminar, and other programs enabled the comprehensive understanding of the PMTs characteristics, mathematics micro-lectures, and its use in teaching for a solid scale design.After the completion of the design process, the questionnaire was divided into four parts. The first part explained the basic content and purpose of the questionnaire to the participants, clarifying the definition of micro-lectures and its use in teaching. The second part focused on the basic information of the respondents, including age, gender, and other relevant characteristics. Furthermore, the third part was a multiple-choice question that surveyed PMTs based on the biggest barriers encountered when using micro-lectures for teaching. This included relevant training, lack of professional help, and insufficient time for teaching, which helped in the discussion session. The fourth section was the main theme of the research, which focused on investigating factors influencing PMTs’ Behavioral Intention to use micro-lectures for teaching. A 5-point Likert scale was used for scoring, with 5, 4, 3, 2, and 1 representing Strongly Disagree, Disagree, Unsure, Agree, and Strongly Agree. The section consisted of 31 question items for nine variables, with each containing not less than three items. The questionnaire was developed by adapting and refining established scales to fit the research context. The core constructs were drawn from the UTAUT2 model, while ATT, TPACK, and TR were incorporated to reflect the specific needs of PMTs.The original UTAUT2 model was designed to analyze Consumer Acceptance and Use of Information Technology, including PE, EE, and FC with four items each, while SI, HM, and BI contained three items respectively. To conform with this research’s focus on PMTs’ adoption of micro-lectures, item phrasing was revised by replacing information technology with micro-lectures and adjusting the subject to fit teaching scenario. For example, the UTAUT2 item Using mobile Internet increases productivity was modified to Incorporating micro-lectures into teaching can enhance instructional effectiveness. Additionally, the review of prior research that applied the UTAUT2 model in educational contexts, led to the identification of overlapping constructs. To enhance clarity, redundant items were merged, and the UTAUT2 originally included two EE items: Mobile Internet is simple to use, and It is easy to become skillful at using mobile Internet. Since both conveyed similar meanings, it was consolidated into Using micro-lectures in teaching is easy. The final adaptation resulted in Performance (4 items), and EE (3 items), SI (4 items), FC (3 items), Hedonic Motivation (3 items), and BI (3 items) referenced from the original UTAUT2 scale(Venkatesh et al., 2012).Attitudes (3 items) were mainly adapted from the original TAM scale (Davis, 1989), with additional modifications informed by research on technology adoption in educational settings. Since TAM was designed to assess general user acceptance of technology, its original items did not target any specific instructional tool. Therefore, similar to the adaptation of UTAUT2, TAM-based items were revised to fit micro-lecture teaching context for PMTs. Additionally, prior research on students’ attitudes toward micro-lectures (Jiang et al., 2022), which included three ATT items, such as positive feelings about learning mathematics using micro-lectures, was reviewed. Based on this, a corresponding item for PMTs, was formulated namely overall attitude toward using micro-lectures in teaching was positive.TPACK (4 items) was adapted based on the theoretical definition and prior research (Akyuz, 2018; Joo et al., 2018). In Measuring TPACK through Performance Assessment, three related items were particularly developed for PMTs, such as students’ potential technological answers and difficulties encountered were anticipated. Similarly, in Factors Influencing Preservice Teachers’ Intention to Use Technology, TPACK was identified as a main variable influencing PMTs’ technology integration, with items such as lessons could be delivered by appropriately combining contents, technologies, and teaching methods. In this discussion, TPACK was defined as PMTs’ ability to effectively integrate micro-lectures technology into mathematics instruction. Given this focus, the questionnaire was designed to assess how well PMTs combined technological (micro-lectures integration), pedagogical (teaching strategies), and content knowledge (mathematics concepts). For example, the following modified item micro-lectures could be effectively used to help students overcome significant difficulties in learning mathematics, was included to ensure the scale accurately reflected all three dimensions of TPACK in the teaching context.The TR scale was developed based on its defined role, which refers to PMTs’ reflective behaviors in teaching training and use of micro-lectures to enhance instruction. The scale was mainly adapted from research on technology acceptance, self-assessment in respect to usage, and reflective thinking in learning. For example, the research on smart classroom learning environments reported that reflective thinking was perceived as major factors, based on the following items thinking deeply about how to become a better learner. (MacLeod et al., 2018). Similarly, an analysis on students’ acceptance of online English courses assessed self-evaluation using items such as communicating with teachers to ascertain personal performance with online English learning. (Sijing Zhou et al., 2021). Building on these references, the TR scale consisted of four items that assessed PMTs’ reflection on micro-lectures integration. For example, actively seeking feedback and suggestions from teachers and mentors on the use of micro-lectures in simulated teaching, microteaching, or university classroom presentations. This item was adapted to reflect PMTs’ engagement in self-reflection, peer discussions, and feedback-driven improvement in micro-lectures teaching context. In addition, detailed questionnaire items were provided in the appendix.Questionnaire distribution and collectionThe questionnaire was divided into pre and formal surveys. In the pre-survey stage, the research samples who had studied the course Modern Technologies in Mathematics Education were randomly selected to fill in the questionnaire on teaching methods of micro-lectures. Moreover, a total of 95 questionnaires were distributed, and 90 were recovered. After the screening process, 82 valid responses were obtained, with the reliability and validity of the questionnaire tested using Smart-PLS 4.0 software. The results showed that CA was within the range of 0.805–0.946 and CR within 0.881–0.961, all exceeding 0.7, confirming good reliability. Factors Loading ranged from 0.655 to 0.953, all greater than 0.6, thereby supporting reliability. The AVE values were within the range of 0.714–0.860, with all greater than 0.5, showing convergent validity. The square roots of AVE values for latent variables were greater than the correlation coefficients with other similar variables, confirming discriminant validity. Based on these results, the questionnaire was deemed suitable for use in the formal survey.The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the School of Mathematical Sciences, Beijing Normal University (Approval Code: MS202404038; Approval Date: April 3, 2022). Informed consent was obtained from all subjects involved in the study on June 10, 2022.During the formal survey phase in June 2022, a hybrid approach of online and offline data collection was employed to ensure efficient and high-quality questionnaire retrieval. It is recommended that the ratio of study samples to questionnaire items should ideally be at least 1:10. In this study, with 9 core variables comprising 31 measurement items, the required sample size should exceed 310. Accordingly, 572 questionnaires were collected. After a manual review to remove responses that were not seriously completed or were incomplete, 535 valid questionnaires remained, resulting in a validity rate of 93.53%. The distribution of the sample is detailed in Table 1.Table 1 Basic information about the research sample.Ethics statementThis research was reviewed and approved by the Ethics Committee of Beijing Normal University on June 1, 2022, prior to the commencement of any formal data collection. The ethics approval document included detailed descriptions of the research design and planned data collection procedures to facilitate the review process. All formal data used were collected after the approval date, in June 2022.Informed consent was obtained from all participants prior to their participation. For online respondents, a consent statement was presented on the first page of the questionnaire, clearly outlining the purpose of the study, the voluntary nature of participation, and the right to withdraw at any time. Proceeding to the questionnaire indicated informed consent. For offline respondents, printed questionnaires were distributed only after a verbal explanation was provided, and participation was based on voluntary informed agreement. No identifying personal information was collected. All responses were anonymous, and the data were kept strictly confidential and used solely for academic research purposes.

Publicado: 2025-10-30 00:00:00

fonte: www.nature.com