Talent Cultivation in Electronic Technology under the Interaction Effect in Construction of Teacher Team and Student Engagement

In situations with independent variables ξ₁ and ξ₂, dependent variables η, if there is an interaction between two independent variables, the interaction effect term ξ₁ξ₂ is represented as . When the latent variable effects are not considered, it forms an observable variable interaction effect model as shown in Figure 1. A regression model with an interaction effect term (i.e., the product term) is typically established as follows: (1)η= ξ1γ1+ξ2γ2+ξ1ξ2γ3+ε

Where ε is the error term; γ₁, γ₂, γ₃ is Interaction effect coefficient of significant variable.

Figure 1.

Observable Variable Interaction Effect Model

In the context of latent variables, to ensure clarity, assume that the endogenous latent variable of the dependent variable consists of three indicators y₁, y₂ and y₃. At the same time, independent variables ξ₁ and ξ₂ each consist of three indicators, specifically the indicators of ξ₂ being x₁, x₂ and x₂, while the indicators of ξ₂ are x₄, x₅ and x₆, as shown in Figure 2. The equation of observable variables follows the same form as (1). Specifically, the relationship between the latent variables of each independent variable and the independent variables can be expressed by the following formula: (2){ x1=λ1ξ1+δ1 x2=λ2ξ1+δ2 x3=λ3ξ1+δ3 x4=λ4ξ2+δ4 x5=λ5ξ2+δ5 x6=λ6ξ2+δ6

Where λ₁ to λ₆ represents the effect term with its latent variable; δ₁ to δ₆ is the error term.

Figure 2.

Schematic Diagram of the Latent Variable Interaction Effect Model

The relationship between the latent variable of the dependent variable and the independent variables can be represented as: (3){ y1=−λy1γ3cov(ξ1,ξ2)+λy1η+ε1 y2=−λy2γ3cov(ξ1,ξ2)+λy2η+ε2 y3=−λy3γ3cov(ξ1,ξ2)+λy3η+ε3

Where cov(ξ1,ξ2) represents the covariance between the two independent variables, λ_y1 to λ_y3 is the effect term between the dependent variable and its latent variable, and ε₁ to ε₃ is the error term.

The covariance calculation formula for independent variables is as follows: (4)cov(ξ1,ξ2)=∑i=1n(ξ1i−ξ¯1)(ξ2i−ξ¯2)n−1

Where ξ1i and ξ2i are the individual sample points of the two variables, ξ¯ and ξ¯2 . are their respective means, and the summations extend over all the pairs of data points.

The aforementioned interaction model is a classical model with means, which is complex in modeling. This paper adopts a mean-centered latent variable interaction effect model without mean structure, where the model in the LISREL program does not require the use of KA, TY, and TX for modeling. When the model does not have a mean structure, the estimation will not utilize the mean information of the indicators but only use the covariance matrix information between the indicators, setting the means of the two independent variables to zero. The overall model computation is as follows: (5)η=γ1ξ1+γ2ξ2+γ3[ξ1ξ2−E(ξ1ξ2)]+ζ

Where E(ξ1ξ2) represents the mean of the interaction effect term, treating the overall model ξ1ξ2−E(ξ1ξ2) as an interaction term, ζ is the error term.

To improve the comparability and interpretability of the results, a standardized estimation of the latent variable interaction effect model was conducted. Standardized estimation eliminates the dimensional effects between different variables, allowing the influence of each variable to be compared on the same scale, thereby facilitating a more intuitive understanding of their relative importance. For interaction effects in complex models, standardization can provide clear effect sizes and directions, making it easier to interpret and compare results across different studies. Furthermore, standardization can enhance the robustness and consistency of the model, reducing the adverse effects of extreme values and dimensional differences on model estimation. Below, Z-score standardization is used, with the standardized form of the latent variable interaction effect model defined as follows: (6)η′=γ1″ξ1′+γ2″ξ2′+γ3″[ξ1′ξ2′−E(ξ1′ξ2′)]+ζ*

Where ξ1′ and ξ2′ are the standardized variables of independent variables ξ₁ and ξ₂, respectively, ξ1′ξ2′ is the product term of ξ1′ and ξ1′ , ξ1′ξ2′−E(ξ1′ξ2′) representing the “standardized” interaction structure.

Transforming equation (6) using the Z-score method yields: (7)η′=η−E(η)sd(η)=γ1ξ1+γ2ξ2+γ3[ξ1ξ2−E(ξ1ξ2)]+ζsd(η)==γ1sd(ξ1)sd(η)ξ1sd(ξ1)+γ2sd(ξ2)sd(η)ξ2sd(ξ2)+γ3sd(ξ1)sd(ξ2)sd(η){ξ1sd(ξ1)ξ2sd(ξ2)−E[ξ1sd(ξ1)ξ2sd(ξ2)]}+ζsd(η)

Comparing equations (6) and (7) leads to the standardized estimation form of the effect term as follows: (8)γ1″=γ1sd(ξ1)sd(η) (9)γ2″=γ2sd(ξ2)sd(η) (10)γ3″=γ3sd(ξ1)sd(ξ2)sd(η)

Where sd refers to the calculation of the “Standard Deviation.” In statistics, the standard deviation is a measure of the dispersion of data, reflecting the degree of deviation of data points from their mean. The calculation of standard deviation is as follows: (11)sd=1n−1∑i=1n(xi−x¯)2

The construction of teacher teams is a resource assurance for the development of higher education institutions. To comprehensively assess the teacher team in aspects such as teacher ethics, educational teaching, social service, and professional development, nine indicators were selected, including the number of professors, associate professors, and lecturers, as well as representative teacher teams like the national model party branch for teacher party building work and the Huang Danian-style teacher team in higher education. The entropy weight method was used to calculate the comprehensive level of teacher team construction in various universities [19],with the calculation method as follows::

First, standardize each indicator, noting that all evaluation indicators for teacher team construction are positive indicators. For the j-th indicator of the i-th sample x_ij, the standardized value xij′ is calculated using the following formula: (12)xij′=xij−min(xj)max(xj)−min(xj)

Calculate the proportion p_ij for each indicator: (13)pij=xij′∑i=1nxij′(i=1,2,…,n)

Calculate the entropy value E_j for each indicator: (14)Ej=−k∑i=1npijln(pij)

Where k=1ln(n) is the normalization constant, n is the number of samples.

Based on the entropy values, calculate the weights w_j: (15)wj=Ej′∑j=1mEj′

Where m represents the number of indicators.

The weighted sum of the indicators based on the weights yields the comprehensive evaluation result S_i: (16)Si=∑j=1mwjxij′

Student engagement was evaluated using the Student Engagement Scale proposed by Tadesse, which was modified based on the Australian Student Engagement Survey questionnaire. The scale consists of nine factors, namely: Integrated and Collaborative Learning (6 items), Academic Challenge (4 items), Student teacher interaction (3 items), Class interactions (3 items), Assessment tasks (2 items), Supportive Campus Environment (3 items), Interpersonal relationships (2 items) Enriching educational experience (4 items), Reading and writing (4 items). The actual data shows that the scores of all items in the scale are between 2.35 and 2.92, indicating that the importance of each part of the scale is similar. The Cronbach’s coefficient of the scale is 0.91, and the factor convergence validity is 0.21 to 0.56. The scale has good reliability and validity[20].

Academic achievement is assessed using the College Student Academic Achievement Scale proposed by Li Xianyin. This scale consists of five factors: Learning Cognitive Ability Subscale (4 items), Communication Ability Subscale (5 items), Self-Management Ability Subscale (5 items), and Interpersonal Facilitation Subscale (5 items). It uses a Likert 5-point rating scale for quantitative scoring. The scale’s Cronbach’s alpha coefficient is 0.846, the KMO value is 0.839, the Bartlett’s chi-square value is 2536.938, and the significance (sig.) value is 0.0001, indicating that the scale has good reliability and validity[21].

This study employs electronic questionnaires to collect relevant data from students. After obtaining consent from the school management, researchers distributed the questionnaire link to teachers at various universities through an online platform, who then forwarded it to students. Questionnaires completed in less than 3 minutes or more than 20 minutes, as well as those inconsistent or missing more than three items, were excluded. Data related to teacher team construction came from the official websites of various universities.

First, the Pearson correlation coefficient r among the three observable variables was calculated in pairs. The calculation formula is as follows: (17)r=∑i=1n(Xi−X¯)(Yi−Y¯)∑i=1n(Xi−X¯)2∑i=1n(Yi−Y¯)2

As shown in Table 2, the correlations among the three observable variables in the interaction effect model—construction of teacher teams, student engagement, and academic achievement—are significant. Specifically, the correlation between construction of teacher teams and student engagement is 0.372 (p < 0.01), indicating a moderate positive relationship. Additionally, the correlation between academic achievement and construction of teacher teams is stronger at 0.521 (p < 0.01), suggesting that effective teacher collaboration enhances academic outcomes. Furthermore, the correlation between student engagement and academic achievement is 0.536 (p < 0.01), highlighting that higher engagement is associated with improved academic performance. In contrast, the correlation of the moderating variable self-efficacy with other factors is relatively low, indicating its limited impact in this model.

LISREL software was utilized to construct a structural equation model to examine the interaction effects of the construction of teacher teams and student engagement on academic achievement. The specific steps are as follows: First, the scores of the construction of teacher teams and student engagement were standardized, and the standardized scores were multiplied to obtain the product term, which was not standardized; then, the model was built with the construction of teacher teams, student engagement, and the product term as independent variables, with academic achievement as the dependent variable and gender and student age as control variables, using maximum likelihood estimation. The resulting interaction effect model, as shown in the Figure 3, has fitting indices such as χ²/df=2.18, GFI=0.96, AGFI=0.95, CFI=0.93, and RMSEA=0.072, demonstrating that the interaction effect model is reasonable.

Figure 3.

Observable Variable Interaction Effect

In Figure 3, the relationships among teacher team building, student engagement, and the interaction term on academic performance are thoroughly examined. The path coefficient between teacher team building and academic performance is notably significant at 0.517. This substantial coefficient indicates that the effectiveness of the teacher team is a crucial factor positively influencing students’ academic achievements. A well-structured and closely collaborating teacher team can significantly enhance students’ learning outcomes and overall academic performance. This finding highlights the importance of fostering collaborative environments among educators, which can lead to improved instructional strategies and better student engagement.

In addition, the impact of student engagement on academic performance is even more pronounced, as evidenced by a path coefficient of 0.632. This value suggests that active participation of students in classroom and learning activities is critical for enhancing their academic performance. High levels of student engagement are associated with increased motivation, which, in turn, facilitates a deeper understanding of the subject matter and enhances the application of knowledge. This relationship underscores the necessity of creating interactive and stimulating learning environments that encourage student involvement.

The path coefficient of 0.192 represents the mutual influence between teacher team building and student engagement. Although this coefficient is relatively small compared to the others, it still indicates that effective construction of the teacher team can promote students’ active participation in learning activities to some extent. This mutual influence suggests that when teachers work cohesively, they are more likely to implement practices that engage students, thus fostering a more dynamic learning atmosphere.

Furthermore, the mutual influence coefficient between student engagement and academic performance is 0.206. While this value is lower than the direct impact of student engagement on academic performance (0.632), it remains an important factor. It highlights that improvements in student engagement can further drive academic performance, and this effect is mediated by the strength of the teacher team. In essence, a robust teacher team can create conditions that enhance student engagement, which subsequently leads to better academic outcomes.

Additionally, the interaction term exhibits a relatively small effect on academic performance, with a path coefficient of 0.073. This value is notably less than the mutual influence coefficient between student engagement and academic performance, indicating that the influence of teacher team building is more pronounced than that of the interaction term itself. However, the overall influence coefficient of the interaction term on academic performance stands at 0.591, which is quite significant. This result confirms the existence of an interaction effect, suggesting that the combined influences of teacher team building and student engagement synergistically contribute to enhancing academic performance.

Figure 4 presents an intricate framework of latent variables and their respective impacts on academic achievement, quantitatively assessed through various path coefficients. Each coefficient serves as a metric reflecting the strength and significance of the relationships among these variables, contributing to a nuanced understanding of the educational dynamics at play.

Figure 4.

Result of Latent Variable Interaction Effect

Beginning with the “Number of Professors” and “Number of Lecturers,” their respective path coefficients of 0.753 and 0.482 indicate a strong positive correlation with the construction of the teacher team. A higher number of professors not only suggests a richer pool of mentorship and subject-matter expertise but also fosters an enhanced academic environment conducive to effective teaching practices. This robust relationship implies that institutions which employ a greater number of professors may be better positioned to cultivate a more cohesive and effective teaching team, ultimately benefiting student engagement and enhancing overall learning outcomes.

Moving to the variable of “Student Engagement”, it is revealed to be significantly influenced by multiple factors within the educational setting. Notably, “Student-Teacher Interaction” exhibits the highest path coefficient at 0.812, underscoring the critical role that effective communication and meaningful interactions between students and teachers play in fostering student involvement in learning processes. The quality of these interactions is paramount, as they can lead to increased motivation, deeper comprehension of the material, and a more enriching educational experience overall.

Furthermore, “Academic Challenge” with a path coefficient of 0.619 and “Assessment Tasks” at 0.547 emerge as important contributors to student engagement. A challenging academic environment serves to encourage students to invest greater effort and engage more actively in their studies. Similarly, well-designed assessment tasks that are aligned with learning objectives can significantly stimulate student involvement and commitment. This alignment is crucial, as it ensures that assessment tasks not only evaluate student understanding but also drive engagement in the learning process.

The impact of student engagement on academic achievement is notably robust, characterized by a path coefficient of 0.632. Students who are actively engaged in their learning are more likely to achieve better academic outcomes due to their enhanced motivation and increased participation in various learning activities. This finding emphasizes the critical role that student engagement plays as a mediating factor between educational inputs and academic outcomes.

Finally, the coefficients associated with academic abilities—“Learning Cognitive Ability” (0.867), “Communication Ability” (0.493), and “Interpersonal Facilitation” (0.735)—highlight the essential skills necessary for achieving academic success. The dominant influence of learning cognitive ability suggests that cognitive skills are foundational for students to effectively process, apply, and integrate knowledge in various contexts. Communication ability also plays a significant role, as effective interpersonal communication is crucial for collaboration and knowledge sharing. Interpersonal facilitation further underscores the importance of collaborative skills, indicating that the ability to work well with others contributes substantially to a student’s overall academic achievement.

In summary, the relationships depicted in Figure 4 provide valuable insights into the multifaceted factors that influence academic achievement. The interplay between teacher resources, student engagement, and essential academic abilities forms a complex web that educators and institutions can leverage to enhance educational outcomes. This comprehensive analysis underscores the importance of fostering an environment that promotes high-quality student-teacher interactions, academic challenges, and the development of critical cognitive and communication skills.

The following section analyzes the moderating effects of the three components in the interaction effect model through simple slope tests. The moderating effect model is used to study how independent variables (such as student engagement) influence dependent variables (such as academic achievement) under certain conditions, and to explore the role of the moderating variable (such as the construction of teacher teams) in this process. The basic form of the moderating effect is: (18)Y=β0+β1X+β2M+β3(X×M)+ϵ

Where β₀ is the constant term, β₁, β₂, β₃ are the effect coefficients, Y is the dependent variable under moderation,X is the independent variable under moderation, M is the moderating variable, and X × M represents the interaction effect between the independent variable and the moderating variable, while ϵ is the error term.

Figure 5.

Moderation effect model

Simple slope analysis typically selects different levels of the moderating variable to analyze the impact of these levels on the model results, allowing for a deeper understanding of the nature of the moderating effect. The moderating variable is usually categorized into low, medium, and high levels, denoted as low (-1SD), medium (mean), and high (+1SD). The simple slopes at these three states can be calculated using the following formulas: (19)(Y=β0+β1X+β2(Mlow)+β3(X×Mlow) (20)Y=β0+β1X+β2(Mmedium)+β3(X×Mmedium) (21)Y=β0+β1X+β2(Mhigh)+β3(X×Mhigh)

Where M_low, M_medium and M_high represent the moderating variable at low, medium, and high levels, respectively.

When the construction of teacher teams is at a low level, the effect of student engagement on academic achievement is minimal, as evidenced by a relatively modest simple slope value (Bsimple=0.2, t=7.56, p<0.001). Although the relationship is weak, it is highly significant. The underlying implication here may be that when there is less support and collaboration among teachers, students’ sense of participation in the classroom may be suppressed, subsequently affecting their academic performance. This suggests that the effectiveness of the construction of teacher teams may significantly impact students’ motivation and engagement.

As the level of construction of teacher teams increases, the effect of student engagement on academic achievement gradually strengthens. At the medium level, the simple slope (Bsimple=0.6, t=6.37, p<0.001) indicates that cooperation and support from teachers begin to have a positive impact on students’ learning. This change may reflect that better construction of teacher teams can enhance interaction and communication, allowing students to feel a stronger sense of belonging and engagement. In such an environment, students are more likely to actively participate in classroom activities, thereby improving their learning performance and achievement.

When self-efficacy is at a high level, the impact of student engagement on academic achievement is not only more evident but also significantly stronger (Bsimple=1.2, t=7.14, p<0.001). This indicates that under high levels of teacher team support, students’ self-efficacy is significantly enhanced, which in turn boosts their engagement and motivation for learning. Effective communication and teamwork among teachers can stimulate students’ intrinsic motivation, leading to higher enthusiasm and confidence in their learning processes. Such an environment helps students face challenges more bravely, thereby improving academic achievement.

Moreover, the moderate mediation index quantitatively reflects this relationship, with a value of 0.0057 and a standard error of 0.002, falling within the 95% confidence interval of [0.0015, 0.009]. This further validates the significant moderating role of the construction of teacher teams on the relationship between student engagement and academic achievement at different levels.

In summary, the construction of teacher teams not only influences students’ sense of engagement but also enhances their motivation for learning by improving their self-efficacy. Therefore, schools, when engaging in the construction of teacher teams, should focus not only on internal collaboration and support but also on how to effectively enhance students’ participation and self-efficacy to promote their overall development and academic achievement.

Figure 6.

Simple slope tests

Teacher team building plays a crucial role in enhancing students’ academic achievement. Research results indicate that the composition of the teaching staff—particularly the number of lecturers and professors—directly impacts students’ academic performance. The path coefficient of 0.517 demonstrates that teacher team building not only provides necessary academic support but also creates a conducive learning environment for students. Lecturers and professors represent different levels of teaching resources, and the combination of both can offer more personalized and in-depth guidance to students. This guidance extends beyond mere knowledge transmission, encompassing the direction of students’ learning methods and thinking styles, thereby improving their learning efficiency and initiative.

In practical applications, teacher team building should emphasize diversity and collaboration. A diverse teaching team can provide students with various perspectives and teaching strategies, greatly benefiting them in the learning process. Therefore, it is recommended that schools focus on recruiting teachers with different academic backgrounds, teaching experiences, and educational philosophies to form an effective team. Additionally, regular teacher training and team-building activities are essential to promote communication and cooperation among teachers, enhancing team cohesion.

Teacher team building not only has a direct impact on academic achievement but can also indirectly affect students’ learning performance by enhancing their self-efficacy. When teachers feel supported by their team, they are more likely to implement positive teaching strategies in the classroom, thereby increasing student engagement and motivation to learn. Consequently, schools and educational administrators should prioritize teacher team building as a vital strategy for improving educational quality, effectively enhancing teachers’ professional development and collaborative abilities.

Student engagement has been identified as a significant factor influencing academic achievement. Research results indicate a path coefficient of 0.632, emphasizing the role of student engagement in academic challenges, assessment tasks, and teacher-student interactions in promoting academic performance. The importance of “academic challenges” (path coefficient of 0.619), “assessment tasks” (path coefficient of 0.547), and “teacher-student interaction” (path coefficient of 0.812) points to specific directions for enhancing student engagement.

First, teachers should focus on creating academically challenging and engaging tasks in their classroom designs to stimulate students’ interest in learning. By designing open-ended questions and project-based learning activities, teachers can encourage critical thinking and innovation, thus enhancing students’ self-efficacy. Additionally, allowing students more autonomy in assessment tasks—such as participating in the formulation of evaluation criteria—can increase their sense of responsibility and involvement.

Second, the role of teacher-student interaction cannot be overlooked. Frequent communication between teachers and students enables better comprehension of course content while allowing teachers to timely assess students’ learning needs. Interactive formats such as group discussions and role-playing can facilitate communication and collaboration among students and between students and teachers, further enhancing student engagement.

Finally, schools should regularly assess the effectiveness of strategies aimed at increasing student engagement and adjust teaching methods accordingly. By implementing these measures, schools can effectively enhance students’ sense of involvement, subsequently fostering their academic achievement in the long run.

The interaction effect in the study highlights the complexity and diversity of how teacher team building and student engagement influence academic achievement. Through simple slope analysis, it was observed that when the level of teacher team building is low, the impact of student engagement on academic achievement is minimal (Bsimple=0.2). This finding emphasizes the importance of teacher team building: in the absence of support, students may feel isolated, thereby limiting their learning potential.

As the level of teacher team building increases, the impact of student engagement on academic achievement gradually strengthens. At a medium level, the simple slope value (Bsimple=0.6) reflects that collaboration and support from teachers begin to play a positive role, suggesting that collective efforts from the teaching team allow students to experience a greater sense of belonging and involvement, enhancing their motivation to learn.

At a high level of teacher support, students’ self-efficacy significantly increases, with a simple slope value reaching 1.2, indicating a stronger influence. This suggests that effective communication and collaboration within the teacher team can stimulate students’ intrinsic motivation, leading them to demonstrate higher levels of engagement and confidence in their learning. This optimized environment empowers students to confront academic challenges with greater courage, thereby improving their academic achievement.

Educational institutions should fully recognize the existence of interaction effects and develop targeted educational policies that promote the coordinated development of teacher team building and student engagement. Through multifaceted efforts, schools can not only enhance students’ motivation to learn but also provide a solid foundation for their overall development.