Skip to main content
NCBI home page
F1000Research logoLink to F1000Research
. 2025 Oct 10;14:709. Originally published 2025 Jul 18. [Version 2] doi: 10.12688/f1000research.165290.2

Exploring the relationship between student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom

Lu Zhang 1,a, Rawin Vongurai 2
PMCID: PMC12640484  PMID: 41287834

Version Changes

Revised. Amendments from Version 1

In the "Literature Review" section, expand the scope of the most recent related studies and explain the reasons why this research is conducted using the relevant variables. These changes include a new Figure.

Abstract

Background

With the growing mismatch between traditional academic training and the demands of the modern workforce, student-centered education has emerged as a key reform in higher education. This study examines the relationships among student-content interaction, student-student interaction, self-efficacy, and learning achievement in student-centered classrooms at Chinese application-oriented universities.

Methods

Data were collected from 524 undergraduate students via online questionnaires and analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM).

Results

The results indicate that both student-content and student-student interactions significantly enhance learning achievement, with self-efficacy playing a critical mediating role. Higher levels of self-efficacy were also directly associated with improved academic achievement, underscoring its central importance in student success.

Conclusions

These findings highlight the value of fostering interactive learning environments and promoting the development of self-efficacy to improve educational outcomes. Educators are encouraged to implement instructional strategies that facilitate meaningful engagement, thereby supporting both the cognitive and motivational dimensions of learning. This study provides empirical evidence to inform the design of effective student-centered teaching practices in higher education.

Keywords: Student-content Interaction, Student-student Interaction, Self-efficacy, Learning Achievement

1. Introduction

With the development of society, the traditional academic skills fostered by universities are no longer aligned with the demands of the modern world, as evidenced by the employment challenges encountered by Chinese university graduates and the growing societal demand for highly skilled individuals and college students ( Zhao et al., 2018). Simultaneously, higher education transitioned from a teacher-centered educational model to a student-centered approach. While the former emphasizes the delivery of content, the latter focuses on what students can learn and apply ( Manzoor et al., 2017). This shift has prompted educational reforms worldwide, and countries such as Finland, the United Kingdom, Germany, and Spain are increasingly embracing non-zoning, open, and flexible learning environments to foster student autonomy, self-regulated learning, collaboration, and digital literacy ( Hartikainen et al., 2021).

Meanwhile, there is an increasing number of application-oriented universities in China that aim to update the instructional content, teaching methods, and learning strategies to comprehensively enhance the standard of teaching and nurture high-quality, application-oriented talents with strong competitiveness and social adaptability to embody the concept of application within the educational framework ( Zhao et al., 2018). According to Coates (2005), students engaged in various activities may achieve high-quality learning. Good interactive relationships, such as group work or instructive feedback, are important for students’ engagement in learning success ( Martin & Bolliger, 2018) and high student-student interaction in courses revealed the most favorable perceptions of engagement and learning achievements ( Tsai et al., 2021). In addition, compared to students with higher self-efficacy, those with lower initial self-efficacy also had lower emotional engagement ( Martin & Rimm-Kaufman, 2015) and positive interactive teaching could promote learners’ self-efficacy ( Li & Yang, 2021). Autonomous motivation, controlled motivation, perceived self-efficacy, and perceived teaching quality are the key determinants of university students’ achievements ( Lai et al., 2021).

Therefore, this study aims to explore the relationships among student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom within the Faculty of Education at five application-oriented universities in Southwest China, most of whom will become teachers in the future. By investigating the mediating role of self-efficacy in the connection between interactive teaching strategies and students’ learning achievements, this study will assist educational practitioners in designing effective teaching methods that meet the developmental needs of students and enhance their achievements.

2. Literature review and research framework

2.1 Literature review

To survey the current research landscape, the author conducted a search in the Web of Science Core Collection in October 2025, using “interaction”, “self-efficacy”, and “learning achievement” as Topic terms. This study retrieved 465 articles and extracted five research topic and 80 items with a word frequency of over ten time (see Figure 1) after the keyword items visualization analysis in VOSviewer ( Van Eck & Waltman, 2010). The research topics included (1) Factors Influencing Student Engagement and Satisfaction in Online Learning within Higher Education, (2) The Impact Mechanism of Motivation and Self-Regulation on Learning Achievement, (3) Factors Affecting Learning Achievement in Technology-Enhanced Learning Environments, (4) Psychological Drivers and Assessment Feedback Mechanisms in University Students’ Learning Achievement, (5) The Influence of Motivation and Social Support on Student Learning Engagement.

Figure 1. The keyword items visualization analysis of studies on Interaction, Self-efficacy and Learning Achievement.

Figure 1.

Open in a new tab

Within the specific context of China’s application-oriented universities, which are actively promoting student-centered educational reform, this study specifically focuses on the relationships between student-content interaction, student-student interaction, self-efficacy, and learning achievement, aiming to provide insights into the effectiveness of interactive learning environments in fostering students’ learning achievements.

2.1.1 Student-content interaction

Tuovinen (2000) points out that student-content interaction means that students engage with instructional materials and planned activities. The materials include textbooks, PowerPoint presentations, web pages, discussion forums, case studies, reports, and videos so on ( Su et al., 2005). Without it, there could be no education, because it is crucial progress through which the student can understand the subject of the study, interact with the content, gain opinions, and integrate cognitive structures ( Kennedy, 2020). In addition, strengthening student-content interaction has a significant impact on students’ achievements ( Bernard et al., 2009) and student achievement can be improved by providing customized learning materials ( Ipinnaiye & Risquez, 2024). Students with varying levels of self-efficacy showed different learning achievements, and those with high self-efficacy were more autonomous and engaged significantly more with the content than those with low self-efficacy ( Tseng et al., 2023).

2.1.2 Student-student interaction

According to Moore (1989), student-student interaction means that one interacts with another student or group members through peer cooperation, discussion, and group presentation to finish the teaching task, through which they could enhance their self-management level and encourage the development of their expertise. Elizondo and Gallardo (2020) suggested that student-student interaction referred to group activities, and peer feedback improved by incorporating factors such as social involvement, level of expertise, anonymity, training, and scoring the feedback. In addition, student-student interaction can have a direct and positive impact on learning outcomes, which subsequently affects academic achievement ( Cardoso, et al., 2011) and student-student interaction is correlated with self-efficacy and achievement ( Miller, 2015). Self-efficacy mediates the relationship between student satisfaction and student-student interaction ( Ahoto et al., 2022).

2.1.3 Self-efficacy

Self-efficacy is defined as a set of specific beliefs that influence a person’s ability to execute action plans in future situations, including efficacy and outcome expectations ( Bandura, 1977). According to Wang et al. (2022), self-efficacy and academic emotions mediate the relationship between interaction (learner-content and learner-learner) and learning engagement. University students can enhance their self-efficacy in designing active learning environments, which may lead to higher expectations and improved academic performance ( Latorre-Cosculluela et al., 2022). Additionally, the combined influence of learning motivation, self-efficacy, and blended learning has a substantial impact on students’ academic achievement ( Rafiola et al., 2020). Active teaching and academic self-efficacy are positive predictors of course grades, and academic self-efficacy positively influences course persistence and boosts students’ expectations of success ( Andres, 2020).

H1:

Student- Content Interaction has a significant impact on Self-Efficacy.

H2:

Student-Student Interaction has a significant impact on Self-Efficacy.

2.1.4 Learning achievements

Learning achievement refers to the skills and abilities acquired as a result of the learning process. They play a crucial role in education, particularly by providing teachers with insights into students’ progress in meeting learning objectives through their engagement in educational activities ( Liliana et al., 2020). Learning achievements encompass three key areas: skills and habits (psychomotor), knowledge and understanding (cognitive), and attitude (affective). Each of these can be addressed and developed based on the content provided in the school curriculum ( Rahayu et al., 2018). According to Agwu and Nmadu (2023), interactive teaching methods enhance students’ academic achievement and self-concept more effectively than traditional methods, and learner-centered activity-based strategies foster cooperative and collaborative learning, which supports students’ academic achievement and self-confidence. In addition, there is a significant positive relationship between academic self-efficacy and academic achievement among Chinese college students ( Luo et al., 2023), and self-efficacy has a positive influence on students’ academic achievement in Egypt and the Kingdom of Saudi Arabia ( Rafiola et al., 2020).

H3:

Student-Content Interaction has a significant impact on Learning Achievement.

H4:

Student-Student Interaction has a significant impact on Learning Achievement.

H5:

Self-Efficacy has a significant impact on Learning Achievements.

2.2 Research framework

The theoretical and conceptual frameworks outline the study’s direction, establish foundational theory, and define variables. The main objective was to enhance the relevance of the study’s findings, link them to theoretical constructs in research, and support generalization ( Adom et al., 2018). The conceptual framework shown in Figure 2 was developed by studying the theoretical framework of this research. It displays all causal relationships among variables, including student-content interaction, student-student interaction, self-efficacy, and learning achievement.

Figure 2. Conceptual framework.

Figure 2.

Open in a new tab

3. Methodology

The researcher adapted a quantitative method to conduct this study. The questionnaire was distributed using WJX, an online questionnaire tool, to students from the Faculty of Education at five application-oriented universities in China. Written informed consent was obtained from all participants via an online form embedded on the first page of the questionnaire. The consent statement clearly explained the study’s objectives, voluntary participation, anonymity, data confidentiality, and the right to withdraw at any time. Only participants who provided consent were able to proceed with the questionnaire. The questionnaire consists of three parts. The first part included screening questions to identify the respondents. The second part included demographic factors based on respondents’ gender, age, internship duration, ethnic group, and birth order. The third part consists of 5-point Likert scales to measure four different variables, ranging from extreme disagreement (1) to strong agreement (5) for the analysis of all hypotheses. In the pilot test, 30 questionnaires were distributed to assess their reliability. The researchers used Cronbach’s alpha to verify the reliability of the test. Hair et al. (2010) suggest that the alpha value should be 0.07 threshold. As a result, the questionnaire was considered reliable, as the alpha results were all above 0.07. After the reliability test, the questionnaire was distributed to 550 target respondents, and 524 responses were considered. The measurement models were designed to evaluate the validity of the variables and explore their relationships. Confirmatory Factor Analysis (CFA) was performed to assess the convergent validity within the measurement model. Finally, a Structural Equation Model (SEM) was employed to examine the overall model and analyze the effects of the variables.

3.1 Population and sample size

The study population consisted of university students from the Faculty of Education at five application-oriented universities in China. Most of these students are future teachers; therefore, it is meaningful to examine the relationships among interactive teaching strategies, self-efficacy, and learning achievement. On one hand, the findings can provide valuable suggestions for university instructors; on the other hand, the students themselves can apply interactive teaching strategies in their future classrooms.

These students had been studying theoretical knowledge and practical curricula for at least two semesters and had already completed internships. The researcher used the a priori sample size calculator for Structural Equation Model (SEM) from Danielsoper’s website to refer to the recommended minimum sample size ( Soper, n.d.). There were 4 latent variables and 21 observed variables, with a probability level of 0.05. The minimum recommended sample size was 342 respondents. In this study, the questionnaire was distributed among students from three different categories at five universities, with respondents evenly allocated across each category. A total of 550 participants were randomly selected, of which 524 were ultimately considered.

3.2 Sampling technique

In this study, the questionnaire was distributed using two methods: stratified random sampling, a probability method, and purposive sampling, a non-probability method. First, students in the Faculty of Education at five application-oriented universities fall into three categories: those from junior college (a three-year program), those who have upgraded from junior college to undergraduates (a 3+2-year program), and undergraduates (a four-year program). To select samples that allowed for the representation of the population, a proportional stratified sampling technique was applied to calculate the number of targeted respondents in each group (as shown in Table 1). This technique enables the representation of the sample for a group with a vast population ( Singh and Mangat, 1996). Purposive sampling was employed to select the respondents based on the proportionate representation of each group. The questionnaire was distributed online and included screening questions to ensure that respondents were from the Faculty of Education, had studied theoretical and practical courses for at least two semesters, and had already completed internships.

Table 1. Research population and sample size.

The students in the faculty of education The target students Research sample size
Junior college students (a three-year program) 2407 183
Students upgraded from junior college to undergraduates (a 3+2-year program) 2083 158
Undergraduates (a four-year program) 2743 209
Total 7233 550
Open in a new tab

Note. Data from Student Affairs Department.

4. Results and discussion

4.1 Demographic factors

As shown in Table 2, the sample consisted of 524 valid respondents, with the data presented in terms of frequency and percentage, as detailed below:

Table 2. Demographic profile.

Demographic factors (N=524) Frequency Percent
Gender Male 211 40.27%
Female 313 59.73%
Age 18-19 10 1.91%
19-20 134 25.57%
20-21 301 57.44%
21-22 71 13.55%
Above 22 8 1.53%
Internship Duration Less than 1 month 61 11.64%
2 months 33 6.30%
3 months 309 58.97%
More than 4 months 121 23.09%
Ethnic group Han 336 64.12%
Minority Groups 188 35.88%
Birth order 1 263 50.19%
2 159 30.34%
3 53 10.11%
4 49 9.35%
Others 0 0.00%
Open in a new tab

According to Table 2, in terms of gender distribution, the majority of participants were female, comprising 59.73%, while males accounted for 40.27%. Most participants fell within the 19-21 age range, with 25.57% aged 19-20 and 57.44% aged 20-21. Smaller proportions were observed for those aged 18-19 (1.91%), 21-22 (13.55%), and above 22 (1.53%). Regarding the internship duration, the majority had completed a 3-month internship, representing 58.97% of the sample. This was followed by 23.09% who had internships lasting more than 4 months, 11.64% with internships of less than 1 month, and 6.30% with 2-month internships. The sample’s ethnic composition was primarily Han, making up 64.12%, while minority groups constituted 35.88%. In terms of birth order, participants were most commonly first-born, accounting for 50.19%, followed by second-born at 30.34%, third-born at 10.11%, and fourth-born at 9.35%.

4.2 Confirmatory Factor Analysis (CFA)

Confirmatory Factor Analysis (CFA), a statistical method used to validate data consistent with the conceptual models employed in this study, was initially employed to evaluate the convergent and discriminant validity of the measurement model ( Jöreskog, 1969), using factor loading, Composite Reliability (CR), and Average Variance Extracted (AVE) as the determining criteria. The guidelines suggested by Hair et al. (2006) were used to determine the significance of each item’s factor loading and acceptable thresholds for evaluating goodness of fit. The factor loadings exceeded 0.50, with p-values less than 0.05. Additionally, in accordance with Fornell and Larcker’s (1981) recommendations, the CR surpasses the 0.7 threshold, and the AVE exceeds the 0.5 benchmark, as shown in Table 3.

Table 3. Confirmatory factor analysis result, Composite Reliability (CR) and Average Variance Extracted (AVE).

Variables Source of questionnaire (Measurement Indicator) No. of item Cronbach’s Alpha Factors loading CR AVE
Student-Content Interaction (SCI) Abou-Khalil et al. (2021) 4 0.863 0.734-0.823 0.863 0.612
Student-Student Interaction (SSI) Abou-Khalil et al. (2021) 6 0.858 0.666-0.751 0.861 0.508
Self-Efficacy (SE) Xiang (2020) 5 0.932 0.801-0.890 0.934 0.739
Learning Achievement (LA) Xiang (2020) 6 0.938 0.753-0.882 0.939 0.719
Open in a new tab

Note. CR = Composite Reliability, AVE = Average Variance Extracted.

The square root of the average variance extracted in Table 4 shows that all the correlation values exceed the corresponding correlations for each variable. Additionally, GFI, AGFI, CFI, NFI, and RMSEA were employed as indicators of good model fit in the CFA testing. Convergent and discriminant validity are confirmed, as the values reported in Table 5 surpass the acceptable thresholds. Consequently, this study’s convergent and discriminant validity was established. Furthermore, these measurement results provide evidence of discriminant validity and support the validity of subsequent structural model estimation.

Table 4. Discriminant validity.

Factor correlations
Variables SCI SSI SE LA
SCI 0.782
SSI 0.652 0.713
SE 0.529 0.523 0.859
LA 0.592 0.578 0.750 0.848
Open in a new tab

Note. The diagonally listed value is the AVE square roots of the variables.

Table 5. Goodness of fit.

Index Acceptable values Values
CMIN/DF < 3.00 ( Hair et al., 2006) 2.270
GFI ≥ 0.90 ( Hair et al., 2006) 0.938
AGFI > 0.90 ( Hooper et al., 2008) 0.914
NFI ≥ 0.90 ( Arbuckle, 1995) 0.958
CFI ≥ 0.90 ( Hair et al., 2006) 0.976
TLI ≥ 0.90 ( Hair et al., 2006) 0.969
RMSEA < 0.05 ( Browne & Cudeck, 1993) 0.049
RMR < 0.05 ( Hair et al., 2006) 0.027
Open in a new tab

Note. CMIN/DF = ratio of the chi-square value to degrees of freedom, GFI = goodness-of-fit index, AGFI = adjusted goodness-of-fit index, NFI = normalized fit index, TLI = Tucker-Lewis index, CFI = comparative fit index, RMSEA = root mean square error of approximation, and RMR = root mean square residual.

4.3 Structural Equation Model (SEM)

Jöreskog and Sörbom (1993) described Structural Equation Modeling (SEM) as a method that employs parameters from both observed and latent variable analyses. The results for the overall model fit indices are presented in Table 5. The model fit measurement should not exceed a chi-square/degrees-of-freedom (CMIN/DF) ratio of 3, and the GFI and CFI should be higher than 0.9, as recommended by Hair et al. (2006). After running the SEMs and modifying the model using SPSS AMOS version 26, the goodness-of-fit indices were as follows: CMIN/DF = 2.270, GFI = 0.938, AGFI = 0.914, NFI = 0.958, CFI = 0.976, TLI = 0.969, RMSEA = 0.049, and RMR = 0.027 ( Table 5).

4.4 Research hypothesis testing results

The significance of each variable in the research model was evaluated based on the regression coefficients and R 2 variances. The results presented in Table 6 indicate that all hypotheses were confirmed, with significance at p = 0.05.

Table 6. Hypotheses testing result of the structural model.

Hypotheses Paths Standardized path coefficients (β) S.E. T-value Tests result
H1 SE<---SCI 0.462 0.117 5.363 *** Supported
H2 SE<---SSI 0.197 0.099 2.344 * Supported
H3 LA<---SCI 0.139 0.056 2.183 * Supported
H4 LA<---SSI 0.164 0.046 2.705 ** Supported
H5 LA<---SE 0.647 0.032 12.935 *** Supported
Open in a new tab

Note.

*

=p-value<0.05.

**

= p-value<0.01.

***

= p-value<0.001.

H1: Student–content interaction has a significant impact on self-efficacy (β=0.462, SE=0.117, p<0.001). This indicates that when students actively engage with learning materials, they develop stronger beliefs about their abilities, aligning with prior research by Bernard et al. (2009) and Tseng et al. (2023).

H2: Student–student interaction has a significant impact on self-efficacy (β=0.197, SE=0.099, p<0.05). This finding supports the theories of Miller (2015) and Ahoto et al. (2022), who demonstrated that collaborative learning environments boost students’ confidence in their capabilities.

H3: Student–content interaction has a significant impact on learning achievement (β=0.139, SE=0.056, p<0.05). Engaging with content-rich materials has facilitated improved academic performance, consistent with research by Ipinnaiye and Risquez (2024).

H4: Student–student interaction has a significant impact on learning achievement (β=0.164, SE=0.046, p<0.01). This supports the findings of Cardoso et al. (2011), who highlight the critical role of peer collaboration in achieving academic success.

H5: Self-efficacy has a significant impact on learning achievements (β=0.647, SE=0.032, p<0.001), affirming Bandura’s (1977) theoretical frameworks and Luo et al.’s (2023) empirical studies. Students with higher self-efficacy demonstrate greater motivation and academic success.

4.5 Direct, indirect and total effects of relationships

The relationship structure among variables was analyzed using the AMOS software to assess direct, indirect, and total effects. A direct effect represents the link between the two variables, without any mediators involved in the model. An indirect effect includes at least one mediating variable that influences the relationship between two variables ( Raykov & Marcoulides, 2000). In this study, there were four variables: two independent variables, one mediator, and the dependent variable. The results of all relationships are as follows:

As Table 7 shows, student-content interaction has a significant direct positive effect on self-efficacy (β=0.462), meaning that increased interaction with content positively influences students’ self-efficacy. Student-student interaction has a weaker direct effect on students’ self-efficacy (β=0.197), indicating that interaction with peers also positively affects self-efficacy, but to a lesser extent. The R 2 value of 0.396 for self-efficacy shows that 39.6% of its variance is explained by student-content and student-student interactions. Self-efficacy had a strong direct positive effect on learning achievement, indicating that higher self-efficacy significantly contributed to improved learning achievement.

Table 7. Direct, indirect and total effects of relationships.

Variables Self-Efficacy (SE) Learning Achievement (LA)
Direct effect Indirect effect Total effect R 2 Direct effect Indirect effect Total effect R 2
Standardized path coefficients (β) Standardized path coefficients (β)
Student- Content Interaction (SCI) 0.462 - 0.462 0.396 0.139 0.299 0.438 0.732
Student-Student Interaction (SSI) 0.197 - 0.197 0.164 0.127 0.292
Self-Efficacy (SE) - - - 0.647 - 0.647
Open in a new tab

In addition, student-content interaction affects learning achievement indirectly and is mediated by self-efficacy, with an indirect effect coefficient of 0.299 and a direct effect of 0.139, showing that student-content interaction impacts learning achievement through both direct and indirect paths. Student-student interaction also impacts learning achievement indirectly via self-efficacy, with an indirect effect of 0.127 and a direct effect of 0.164, indicating both direct and mediated influences on learning. The R 2 value of 0.732 for learning achievement means that 73.2% of its variance is explained by student-content interaction, student-student interaction, and self-efficacy. This suggests that self-efficacy has a significant mediation effect, and that these factors collectively have a substantial influence on learning achievement.

5. Conclusion, recommendation and limitation

5.1 Conclusion

This study investigates the relationship between student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom. Hypotheses were developed based on the conceptual framework to examine how these interactions influence students’ learning outcomes, with self-efficacy acting as a mediating factor. Confirmatory Factor Analysis (CFA) was used to assess the validity and reliability of the research model, and Structural Equation Modeling (SEM) was employed to analyze the relationships among the variables.

The findings of this study are summarized as follows. First, the student-content interaction had a significant direct impact on self-efficacy (β=0.462, p<0.001) and learning achievement (β=0.139, p<0.05). This indicates that when students actively engage with learning materials, they develop stronger beliefs about their abilities, which in turn enhances their academic performance. These findings align with prior research by Bernard et al. (2009) and Tseng et al. (2023), which emphasize the importance of meaningful content engagement in fostering academic success.

Second, student-student interaction also significantly influenced self-efficacy (β=0.197, p<0.05) and learning achievement (β=0.164, p<0.01). Collaborative learning environments such as group projects and peer discussions boost students’ confidence in their capabilities and contribute to improved academic outcomes. This supports Miller’s (2015) and Ahoto et al.’s (2022) theories, which highlight the critical role of peer collaboration in achieving academic success.

Third, self-efficacy emerged as a crucial mediator between interactive teaching strategies and learning achievement with a strong direct effect on learning outcomes (β=0.647, p<0.001). Students possessing stronger self-efficacy exhibited increased motivation and superior academic performance, supporting Bandura’s (1977) theoretical models and the empirical findings of Luo et al. (2023). The total effects analysis showed that self-efficacy explained 73.2% of the variance in learning achievement, underscoring its significant mediating role.

In conclusion, this study provides valuable insights into the mechanisms through which interactive learning strategies can enhance academic achievement. By fostering self-efficacy and promoting active engagement, educators can create more effective and inclusive learning environments that cater to students’ diverse needs. To maximize these benefits, educators are encouraged to adopt strategies, such as collaborative projects, peer reviews, personalized learning materials, and technology-enhanced tools, to stimulate meaningful interactions ( Rajaram & Rajaram, 2021). Creating a supportive learning atmosphere in which students can share ideas, receive constructive feedback, and reflect on their learning processes further strengthens their self-efficacy and academic performance ( Affuso et al., 2023). Universities should also invest in professional development programs that equip educators with interactive teaching skills to ensure their effective implementation across diverse educational settings. The validated model, supported by Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), provides a robust framework for designing educational interventions that promote academic success through interactive learning practices.

5.2 Recommendation

First, educational institutions should implement interactive teaching strategies such as collaborative projects, problem-based learning, and peer-led discussions. These methods stimulate active participation and deeper cognitive engagement by encouraging students to share their knowledge, apply concepts, and engage in critical thinking. Research supports this approach, demonstrating that interactive learning materials significantly enhance learning achievements by fostering learner autonomy and engagement while balancing self-directed learning with structured guidance ( Tseng, Chen, & Lin, 2023). Professional development programs for educators should focus on equipping educators with the skills required to design and facilitate interactive learning experiences.

Second, as self-efficacy significantly influences academic outcomes, educators should foster supportive environments that build students’ confidence in their abilities. This can be achieved through regular constructive feedback, personalized learning goals, and the recognition of student achievements ( Luo et al., 2023; Wang et al., 2022). Creating a classroom culture in which students feel valued and empowered can enhance both motivation and academic performance, as demonstrated by recent studies on the role of teacher support in fostering self-efficacy and academic success ( Wang et al., 2022).

Third, the use of digital tools, such as learning management systems, online forums, and virtual collaboration platforms, can enhance interactions between students and course content ( Almeida & Simoes, 2023; Khan & Ahmed, 2023). Universities should invest in modern educational technologies and provide training to both students and teachers. Incorporating multimedia content, gamification, and interactive simulations can further enrich the learning experience, as demonstrated by recent studies of the positive impact of these tools on student engagement and academic performance ( Khan & Ahmed, 2023).

Finally, educational strategies should be tailored to fit learners’ cultural and contextual characteristics. Customized learning materials and culturally relevant teaching methods can help bridge cultural gaps and increase student engagement, as culturally responsive pedagogies (CRP) emphasize the importance of leveraging students’ diverse backgrounds to enhance learning outcomes ( Price et al., 2020). Conducting cross-cultural research and fostering international collaboration can further inform the development of context-sensitive educational practices, ensuring that teaching methods are both inclusive and globally informed.

5.3 Limitation and further study

This study has certain limitations that should be explored in future research. First, the findings are based on a specific sample size and population, which limits the generalizability of the results to other educational contexts. Future research could incorporate larger and more diverse samples across different departments and cultural settings to enhance the applicability of these conclusions. Second, the study focused on a limited set of variables, including interactive learning strategies and self-efficacy, while excluding other potentially influential factors, such as motivation, emotional intelligence, and learning environment characteristics. Therefore, future research should examine these additional variables as potential moderators of interactive learning environments. Moreover, longitudinal studies can provide deeper insights into the long-term impact of interactive teaching strategies on academic success. Collaboration among researchers, educators, and policymakers can further support the development of evidence-based best practices in higher education.

The statement of ethical approval and consent

This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Review Working Group of the Academic and Research Department, Yunnan Technology and Business University, China on January 4, 2024 (Reference Number: 2023042). Written informed consent was obtained from all participants via an online form embedded on the first page of the questionnaire. The consent statement clearly explained the study’s objectives, voluntary participation, anonymity, data confidentiality, and the right to withdraw at any time. Only participants who provided consent were able to proceed with the questionnaire.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 2; peer review: 2 approved

Data availability statement

The data supporting this study are openly available in Figshare at http://doi.org/10.6084/m9.figshare.29319401 ( Zhang, L. & Vongurai, R., 2025).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

References

  1. Abou-Khalil V, Helou S, Khalifé E, et al. : Emergency online learning in low-resource settings: Effective student engagement strategies. Educ. Sci. 2021;11(1):24. 10.3390/educsci11010024 [DOI] [Google Scholar]
  2. Adom D, Hussein EK, Agyem JA: Theoretical and conceptual framework: Mandatory ingredients of a quality research. Int. J. Sci. Res. 2018;7(1):438–441. [Google Scholar]
  3. Affuso G, Zannone A, Esposito C, et al. : The effects of teacher support, parental monitoring, motivation and self-efficacy on academic performance over time. Eur. J. Psychol. Educ. 2023;38(1):1–23. 10.1007/s10212-021-00594-6 [DOI] [Google Scholar]
  4. Agwu UD, Nmadu J: Students’ interactive engagement, academic achievement, and self-concept in chemistry: An evaluation of cooperative learning pedagogy. Chem. Educ. Res. Pract. 2023;24(2):688–705. 10.1039/D2RP00204J [DOI] [Google Scholar]
  5. Ahoto AT, Mbaye MB, Anyigbah E: The impacts of learner-instructor interaction, learner-learner, learner-content interaction, internet self-efficacy, and self-regulated learning on satisfaction of online education of African medical students. Open Access Library Journal. 2022;09(9):1–24. 10.4236/oalib.1109149 [DOI] [Google Scholar]
  6. Almeida F, Simoes J: The role of digital tools in enhancing student engagement and learning outcomes in higher education. Comput. Educ. 2023;201:104812. 10.1016/j.compedu.2023.104812 [DOI] [Google Scholar]
  7. Andres H: The role of active teaching, academic self-efficacy, and learning behaviors in student performance. J. Int. Educ. Bus. 2020;13(2):221–238. 10.1108/JIEB-07-2019-0039 [DOI] [Google Scholar]
  8. Arbuckle J: AMOS: Analysis of moment structures user’s guide. Small Waters;1995. [Google Scholar]
  9. Bandura A: Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977;84(2):191–215. 10.1037/0033-295X.84.2.191 [DOI] [PubMed] [Google Scholar]
  10. Bernard RM, Abrami PC, Borokhovski E, et al. : A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 2009;79(3):1243–1289. 10.3102/0034654309333844 [DOI] [Google Scholar]
  11. Browne MW, Cudeck R: Alternative ways of assessing model fit. Bollen KA, Long JS, editors. Testing structural equation models. Sage;1993; pp.136–162. [Google Scholar]
  12. Cardoso AP, Ferreira M, Abrantes JL, et al. : Personal and pedagogical interaction factors as determinants of academic achievement. Procedia Soc. Behav. Sci. 2011;29:1596–1605. 10.1016/j.sbspro.2011.11.403 [DOI] [Google Scholar]
  13. Coates H: The value of student engagement for higher education quality assurance. Qual. High. Educ. 2005;11(1):25–36. 10.1080/13538320500074915 [DOI] [Google Scholar]
  14. Elizondo-Garcia J, Gallardo K: Peer feedback in learner-learner interaction practices: Mixed methods study on an xMOOC. Electron. J. e-Learn. 2020;18(2):122–135. 10.34190/EJEL.20.18.2.002 [DOI] [Google Scholar]
  15. Fornell C, Larcker DF: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981;18(1):39–50. 10.1177/002224378101800104 [DOI] [Google Scholar]
  16. Hair JF, Black WC, Babin BJ, et al. : Multivariate data analysis. 7th ed. Prentice-Hall;2010. [Google Scholar]
  17. Hair J, Black W, Babin B, et al. : Multivariate data analysis. 6th ed. Pearson Education;2006. [Google Scholar]
  18. Hartikainen J, Poikkeus AM, Haapala EA, et al. : Associations of classroom design and classroom-based physical activity with behavioral and emotional engagement among primary school students. Sustainability. 2021;13(14):8116. 10.3390/su13148116 [DOI] [Google Scholar]
  19. Hooper D, Coughlan J, Mullen M: Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods. 2008;6(1):53–60. [Google Scholar]
  20. Ipinnaiye O, Risquez A: Exploring adaptive learning, learner-content interaction, and student performance in undergraduate economics classes. Comput. Educ. 2024;215:105047. 10.1016/j.compedu.2023.105047 [DOI] [Google Scholar]
  21. Jöreskog KG: A general approach to confirmatory maximum likelihood factor analysis. Psychometrika. 1969;34:183–202. [Google Scholar]
  22. Jöreskog KG, Sörbom D: LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International;1993. [Google Scholar]
  23. Kennedy G: What is student engagement in online learning and how do I know when it is there? Melbourne CSHE Discussion Papers. 2020;1–6. [Google Scholar]
  24. Khan A, Ahmed S: The impact of gamification and virtual collaboration platforms on student motivation and academic performance. J. Educ. Technol. Soc. 2023;26(2):112–125. 10.30191/ETS.202304_26(2).0008 [DOI] [Google Scholar]
  25. Lai HM, Hsieh PJ, Uden L, et al. : A multilevel investigation of factors influencing university students’ behavioral engagement in flipped classrooms. Comput. Educ. 2021;175:104318. 10.1016/j.compedu.2021.104318 [DOI] [Google Scholar]
  26. Latorre-Cosculluela C, Suárez C, Quiroga S, et al. : Facilitating self-efficacy in university students: An interactive approach with flipped classroom. High. Educ. Res. Dev. 2022;41(5):1603–1617. 10.1080/07294360.2021.1983457 [DOI] [Google Scholar]
  27. Li L, Yang S: Exploring the influence of teacher-student interaction on university students’ self-efficacy in the flipped classroom. J. Educ. Learn. 2021;10(2):84–90. 10.5539/jel.v10n2p84 [DOI] [Google Scholar]
  28. Liliana RA, Raharjo W, Jauhari I, et al. : Effects of the online interactive learning media on student’s achievement and interest in physics. Univ. J. Educ. Res. 2020;8(3B):59–68. 10.13189/ujer.2020.081507 [DOI] [Google Scholar]
  29. Luo Q, Chen L, Yu D, et al. : The mediating role of learning engagement between self-efficacy and academic achievement among Chinese college students. Psychol. Res. Behav. Manag. 2023;16:1533–1543. 10.2147/PRBM.S412938 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Manzoor A, Aziz H, Jahanzaib M, et al. : Transformational model for engineering education from content-based to outcome-based education. Int. J. Contin. Eng. Educ. and Life-Long Learning. 2017;27(4):266–286. 10.1504/IJCEELL.2017.087136 [DOI] [Google Scholar]
  31. Martin DP, Rimm-Kaufman SE: Do student self-efficacy and teacher-student interaction quality contribute to emotional and social engagement in fifth grade math? J. Sch. Psychol. 2015;53(5):359–373. 10.1016/j.jsp.2015.07.001 [DOI] [PubMed] [Google Scholar]
  32. Martin F, Bolliger DU: Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning. 2018;22(1):205–222. 10.24059/olj.v22i1.1092 [DOI] [Google Scholar]
  33. Miller G: Associations between learner interaction and achievement in an online course: A longitudinal study. NACTA J. 2015;59(3):197–201. [Google Scholar]
  34. Moore MG: Editorial: Three types of interaction. Am. J. Dist. Educ. 1989;3(2):1–7. 10.1080/08923648909526659 [DOI] [Google Scholar]
  35. Price D, Green D, Memon N, et al. : Richness of complexity within diversity: Educational engagement and achievement of diverse learners through culturally responsive pedagogies. Soc. Educ. 2020;38(1):42–53. 10.3316/informit.359349816121963 [DOI] [Google Scholar]
  36. Rafiola RH, Setyosari P, Radjah CL, et al. : The effect of learning motivation, self-efficacy, and blended learning on students’ achievement in the industrial revolution 4.0. Int. J. Emerg. Technol. Learn. 2020;15(8):71–82. 10.3991/ijet.v15i08.12525 [DOI] [Google Scholar]
  37. Rahayu AB, Hadi S, Istyadji M, et al. : Development of guided inquiry-based learning devices to improve student learning outcomes in science materials in middle school. Eur. J. Alt. Educ. Stud. 2018. [Google Scholar]
  38. Rajaram K, Rajaram K: Learning interventions: Collaborative learning, critical thinking and assessing participation real-time. Evidence-based teaching for the 21st century classroom and beyond: Innovation-driven learning strategies. Springer;2021; pp.77–120. [Google Scholar]
  39. Raykov T, Marcoulides GA: A first course in structural equation modeling. Lawrence Erlbaum Associates;2000. [Google Scholar]
  40. Singh R, Mangat NS: Stratified sampling. Elements of survey sampling. Springer;1996; pp.102–144. [Google Scholar]
  41. Soper DS: A-priori sample size calculator for structural equation models [Software]. Free Statistics Calculators. n.d.. October 14, 2024. Reference Source
  42. Su B, Bonk CJ, Magjuka RJ, et al. : The importance of interaction in web-based education: A program-level case study of online MBA courses. J. Interact. Online Learn. 2005;4(1):1–19. [Google Scholar]
  43. Tsai C-L, Ku H-Y, Campbell A: Impacts of course activities on student perceptions of engagement and learning online. Distance Educ. 2021;42(1):106–125. 10.1080/01587919.2020.1869525 [DOI] [Google Scholar]
  44. Tseng Y-C, Chen M-RA, Lin Y-H: An investigation of the effects of EFL students’ self-efficacy in an asynchronous online course with interactive contents. Educ. Technol. Soc. 2023;26(4):1–13. 10.30191/ETS.202310_26(4).0001 [DOI] [Google Scholar]
  45. Tuovinen JE: Multimedia distance education interactions. Educ. Media Int. 2000;37(1):16–24. 10.1080/095239800361473 [DOI] [Google Scholar]
  46. Van Eck NJ, Waltman L: Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–538. 10.1007/s11192-009-0146-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wang Y, Cao Y, Gong S, et al. : Interaction and learning engagement in online learning: The mediating roles of online learning self-efficacy and academic emotions. Learn. Individ. Differ. 2022;94:102128. 10.1016/j.lindif.2022.102128 [DOI] [Google Scholar]
  48. Xiang Q: The construction and empirical research of a student engagement model in a blended learning environment.[Master’s thesis, Yangtze University]. ProQuest Dissertations & Theses Global (if applicable).2020.
  49. Zhao X, Tan F, Liu L, et al. : How to cultivate application-oriented undergraduates – The growth path of an outstanding graduate on e-commerce major. Educ. Q. Rev. 2018;1(2):153–167. 10.31014/aior.1993.01.02.18 [DOI] [Google Scholar]
  50. Zhang L, Vongurai R: Exploring the relationship between student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom.[Data set]. Figshare. 2025. 10.6084/m9.figshare.29319401 [DOI]
F1000Res. 2026 Jan 22. doi: 10.5256/f1000research.189510.r422490

Reviewer response for version 2

Zhi Liu 1

While I appreciate the amendments made regarding the literature scope and the inclusion of the visualization figure, I feel that the depth of the theoretical discussion—particularly regarding the mechanisms of student-student interaction—has not yet been fully optimized to meet the previous reviewer's suggestions. To further elevate the paper's academic contribution, please consider the following points:

Although the structural model confirms the relationships, the discussion on why student-student interaction yielded a weaker effect on self-efficacy compared to student-content interaction remains somewhat surface-level; could you provide a deeper sociocognitive explanation for this specific finding within your discussion section?

The current recommendations for educators (e.g., "implement collaborative projects") are still quite generic; could you refine these into more specific, evidence-based strategies that address the unique constraints of application-oriented universities in China?

Regarding the sampling method, while the stratified approach is described, the rationale for combining it with purposive sampling needs clearer justification to address potential concerns about selection bias and generalizability.

To strengthen the theoretical framework regarding interaction mechanisms, I strongly recommend integrating recent studies that link social engagement to cognitive outcomes. specifically, citing "Students’ Social-Cognitive Engagement in Online Discussions: An Integrated Analysis Perspective" (Educational Technology & Society, 2023)  would provide a crucial theoretical basis for understanding how social interactions influence cognitive engagement, and "Exploring the relationships between students’ network characteristics, discussion topics and learning outcomes in a course discussion forum" (Journal of Computing in Higher Education, 2023) would offer necessary empirical support for connecting interaction patterns directly to learning achievements.

The cultural context of Chinese application-oriented universities is mentioned, but how specifically do local educational habits (such as high-stakes testing culture or teacher-authority reliance) mediate the relationship between self-efficacy and interaction?

In the "Limitations and Future Study" section, it would be better to explicitly mention the lack of behavioral process data (such as log data or discussion analysis) which could corroborate the self-reported survey results.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

learning engagement; educational technology; online learning; blended learning; learning analytics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

References

  • 1. : Exploring the relationships between students’ network characteristics, discussion topics and learning outcomes in a course discussion forum. Journal of Computing in Higher Education .2023;35(3) : 10.1007/s12528-022-09335-0 487-520 10.1007/s12528-022-09335-0 [DOI] [Google Scholar]
  • 2. : Students' Social-Cognitive Engagement in Online Discussions: An Integrated Analysis Perspective. ducational Technology & Society .2023; 10.30191/ETS.202301_26(1).0001 [DOI]
F1000Res. 2026 Jan 6. doi: 10.5256/f1000research.189510.r430043

Reviewer response for version 2

Mireya Mallén-Berdejo 1

This study examines the relationships between student–content interaction, student–student interaction, self-efficacy, and learning achievement in student-centered classrooms within Chinese application-oriented universities. Using survey data from 524 undergraduate students, the authors apply Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to test a mediation model in which self-efficacy plays a central role. The findings indicate that both forms of interaction positively influence learning achievement, directly and indirectly through self-efficacy, which emerges as a strong mediator.

The topic is relevant and timely, particularly in the context of student-centered reforms in higher education and teacher education. The dataset is substantial, the analytical approach is appropriate, and the article demonstrates technical competence in the use of CFA and SEM.

Clarity of presentation and use of literature:

The manuscript is generally well structured and clearly written, with a logical progression from introduction to conclusions. Key constructs are identifiable, and tables reporting CFA and SEM results are clearly presented. The study cites a substantial body of relevant literature, including foundational and recent empirical work.

However, the literature review remains largely descriptive rather than analytical. While many relevant studies are cited, there is limited synthesis of contrasting findings or explicit positioning of the study within current theoretical debates on engagement and interaction. In addition, although some recent references are included, the integration of the most up-to-date literature (2024–2025), particularly on technology-enhanced and AI-supported learning environments, could be strengthened to enhance the manuscript’s contemporary relevance.

Study design and technical soundness:

The quantitative research design is appropriate for the stated aims. The conceptual framework is theoretically grounded, drawing primarily on interaction theory and Bandura’s self-efficacy framework. The sample size is adequate and statistically justified for SEM, and the use of CFA prior to structural modeling is methodologically sound. Overall, the study is technically robust and free from major design flaws.

One aspect that would benefit from further clarification is the combination of stratified random sampling and purposive sampling, as this has implications for generalizability that are not fully addressed in the manuscript.

Methods and replicability:

The methods section provides a clear overview of the participants, instruments, data collection procedures, and analytical techniques. Reliability and validity are reported using appropriate indices (Cronbach’s alpha, CR, AVE), and model fit indices are comprehensively presented.

That said, some methodological details remain insufficiently specified to fully ensure replication. For example, further clarification on instrument adaptation and validation within the Chinese educational context (e.g., translation procedures or cultural adaptation) would strengthen transparency. Minor inaccuracies (such as the reporting of the Cronbach’s alpha threshold) should also be corrected to avoid confusion.

Statistical analysis and interpretation:

The statistical analyses are appropriate and competently conducted. CFA and SEM results are clearly reported, and model fit indices fall within accepted thresholds. Hypotheses are tested systematically, and the mediation role of self-efficacy is supported by both direct and indirect effects.

Interpretations are generally consistent with the statistical results. However, the discussion could benefit from deeper engagement with the magnitude of effects (practical significance), rather than focusing primarily on statistical significance.

Data availability and reproducibility:

All source data underlying the results are openly available via Figshare under a CC-BY license. This ensures transparency and allows for full reproducibility, which is a clear strength of the manuscript.

Conclusions and support from results:

The conclusions are broadly supported by the reported findings and accurately reflect the tested model. The emphasis on self-efficacy as a key mediating mechanism is well justified by the results.

However, the conclusions tend to reiterate the findings rather than critically reflecting on their theoretical contribution, contextual limitations, or broader implications. A clearer distinction between empirical findings, theoretical contributions, and practical recommendations would strengthen the closing section.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Educational research; higher education; student engagement and learning processes;  quantitative research methods in education (SEM); teacher education and student-centered learning.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2026 Jan 6.
lu zhang

Thank you for your supportive suggestions. I will be sure to incorporate the points you've mentioned in my future research.

F1000Res. 2025 Nov 22. doi: 10.5256/f1000research.189510.r430042

Reviewer response for version 2

Muhammad Kamran 1

The paper is clear. All the Psychometrics and SEM are clearly explained. The model fit indices are in excellent ranges.  Confirmatory factor analysis result, Composite Reliability (CR) and Average Variance Extracted (AVE) are clearly explained. Discriminant validity is well documented. Goodness of fit model has been explained well. Structural Equation Model (SEM) has been portrayed well.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Psychometric Properties such EFA, CEA, SEM, Construct Validity, Discriminant Validity, and IRT.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2025 Sep 2. doi: 10.5256/f1000research.181904.r407900

Reviewer response for version 1

KAZI ENAMUL HOQUE 1

1. Is the work clearly and accurately presented and does it cite the current literature?

The manuscript is generally clear, well-structured, and accurately presented. Introduction-conclusion flow is reasonable. Literature review is descriptive in nature instead of analytical, and while there are many relevant references provided (2018–2023), latest research (2024–2025) is not adequately referenced.

Recommendation: Update the literature review by synthesizing conflicting findings and incorporating more recent references, especially on digital and AI-assisted learning. Ensure consistency of reference formatting.

2. Is the study design appropriate and does the work have academic merit?

Assessment: Yes. The study has a good quantitative design with SEM and CFA, which is appropriate for the test of the hypothesized relationship. The conceptual framework theory basis is adequate (Bandura's model of self-efficacy, interaction theory). The sample size is sufficient and statistically sufficiently justified (N=524). My recommendation is Clarifying the rationale for combining stratified random sampling and purposive sampling because such a combination could affect generalizability.

3. Are sufficient details of methods and analysis provided to allow replication by others?

Generally, yes. The procedures are clearly set out (sampling process, instrument development, reliability testing, ethical clearance, SEM procedures). This should facilitate replication. Gross typographical mistakes (e.g., Cronbach's alpha criterion misspelled as 0.07 instead of 0.70) reduce readability. I suggest correcting typographical mistakes, and state whether the instruments were validated within the Chinese education environment (translation/back-translation, cultural modification).

4. If applicable, is the statistical analysis and its interpretation appropriate?

Yes. CFA and SEM analyses were conducted correctly with clear reporting of factor loadings, CR, AVE, and model fit indices. Statistical interpretation is satisfactory, and all hypotheses are evidence-based with p-values and coefficients. I suggest displaying mediation effects more graphically (e.g., path diagram with standardized coefficients) to facilitate interpretability. Expand the discussion to include reasons for why student-content interaction had bigger effects than student-student interaction.

5. Are all the source data underlying the results available to ensure full reproducibility?

Yes. The dataset has been made available on Figshare under a CC-BY license to ensure transparency and reproducibility.

 6. Are the conclusions drawn adequately supported by the results?

Yes. The conclusions align with the findings and highlight the mediating role of self-efficacy. The conclusion part, however, in its duplication of some findings rather than identifying unique contributions is to be minimized, focus on theoretical contribution (application-oriented Chinese universities and teacher education), and differentiate practical implications from empirical findings clearly.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Education Management and Leadership

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2025 Sep 2. doi: 10.5256/f1000research.181904.r405561

Reviewer response for version 1

Zhi Liu 1

This study provides a valuable empirical investigation into the relationships between student-content interaction, student-student interaction, self-efficacy, and learning achievement in student-centered classrooms. The research design is methodologically sound, employing CFA and SEM to validate the proposed model. However, the manuscript would benefit from deeper theoretical framing, updated literature reviews, and more nuanced discussions of contextual factors. Below are specific recommendations to enhance the rigor, relevance, and contribution of this work to the related field.

The study can strengthen the theoretical grounding by explicitly linking student-content/student-student interactions to contemporary engagement frameworks (e.g., Community of Inquiry model) by citing recent meta-analyses. 

The authors may explicitly position their study within contemporary social-cognitive and learning engagement theories. The current framing relies heavily on Bandura’s  self-efficacy theory but overlooks recent advancements in social-cognitive engagement frameworks (e.g., Liu et al., 2023,  Educational Technology & Society).

The sample is limited to Chinese application-oriented universities. The authors should address how cultural and institutional factors (e.g., high-stakes testing cultures) may mediate the observed relationships.

The manuscript briefly mentions "technology-enhanced tools" but does not engage with recent research on AI-supported collaborative learning (e.g., Kong et al., 2025,  Internet and Higher Education), the study may discuss how AI tools (e.g., chatbots, virtual labs) might amplify or disrupt the observed relationships, citing Kong et al.’s (2025) findings on AI’s role in scaffolding discussions.

The recommendations for educators are generic (e.g., "use collaborative projects"). Ground them in evidence-based frameworks.

Standardize terms (e.g., "learning achievement" vs. "academic performance") and define key constructs (e.g., "student-content interaction") using recent literature.

The study can report full psychometric properties (factor loadings, CR, AVE) for all survey items in an appendix.

Add a participant recruitment flowchart showing response rates and exclusions per  STROBE guidelines. Specify exact Likert scale wording.

Supplement p-values with Cohen's d or η² for all path coefficients to assess practical significance. Compare effect sizes to benchmarks in recent meta-analyses of educational interventions.

Develop specific, evidence-based teaching strategies (e.g., "structured peer feedback protocols") rather than generic recommendations, such as some ground suggestions in some small teaching approaches.

Highlight the need for temporal analysis given self-efficacy's developmental nature by discussing longitudinal studies on efficacy trajectories.

The manuscript can add a limitations subsection acknowledging the lack of behavioral data (e.g., forum logs).

-read for hyphen consistency (e.g., "student centered" → "student-centered").

There are some other minor suggestions to be considered:

Add a conceptual diagram visualizing the mediation model with standardized coefficients

Include a table comparing findings with prior studies in similar contexts

Discuss potential negative effects of excessive peer interaction (e.g., social loafing)

propose specific faculty development programs for implementing the strategies

Address potential equity issues in access to interactive learning technologies

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

learning engagement; educational technology; online learning; blended learning

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2025 Oct 8.
lu zhang

Dear editor,

Based on the student-centered educational context in Chinese application-oriented universities, this paper investigates how classroom interaction and self-efficacy jointly influence students' learning achievement. Building upon a prior 12-week classroom intervention study that successfully used interactive activities to enhance student engagement (as confirmed by paired-sample t-tests), the current research aims to explore whether and how different forms of interaction and self-efficacy continue to impact learning achievement. The findings will subsequently inform the design of a targeted instructional intervention program to further optimize teaching and learning effectiveness in this specific educational setting.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Zhang L, Vongurai R: Exploring the relationship between student-content interaction, student-student interaction, self-efficacy, and learning achievements in a student-centered classroom.[Data set]. Figshare. 2025. 10.6084/m9.figshare.29319401 [DOI]

    Data Availability Statement

    The data supporting this study are openly available in Figshare at http://doi.org/10.6084/m9.figshare.29319401 ( Zhang, L. & Vongurai, R., 2025).

    Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

    Articles from F1000Research are provided here courtesy of F1000 Research Ltd

    RESOURCES