Abstract
Faced with the impact of COVID-19 in mainland China in 2022, many universities adopted a hybrid approach, combining online and offline teaching. However, limited research has been conducted on hybrid education. Our study primarily investigated college students in mainland China, examining the effectiveness of hybrid teaching through a questionnaire survey on their learning performance. Due to the recurring outbreaks, many universities continued to implement blended teaching. Our research aimed to develop new models to explore the relationships between students’ interactive learning, learning motivation, immersion learning, cognitive learning, and overall learning performance. We sought to enhance students’ learning outcomes through mixed learning approaches. Our study surveyed N = 387 students, and three hypotheses were found to be positively correlated. Using statistical analysis, we estimated the overall learning rate and examined the relationships among key causal variables. The results indicated that learning motivation and immersion learning are positively correlated with cognitive learning, which, in turn, cognitive learning is positively correlated with learning performance. A significant finding of our research is that students prefer a hybrid learning model that integrates online and offline learning methods.
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Introduction
The main purpose of our research is to observe whether blended learning style will affect students’ learning performance. Our research is mainly a questionnaire study on student performance of blended teaching. Affected by COVID-19, we mainly investigated students’ learning performance in online and offline teaching situations. In this study, we combined the cross-field research of education, psychology, information technology and so on. The most important thing is to study the investigation of blended learning on students’ learning performance. Krueger and Sifuentes (2014) mentioned that the relationship between students’ learning performance is investigated and analyzed through data. Mainly in view of the gender of the students to do a survey of learning performance. Student learning performance has a very important key factor for student outcomes, which is a variable worth studying. Yang et al. (2011) indicated that some students improve their learning performance through data analysis.
Conner (1997) mentioned that learning knowledge and skills comes from one’s own learning motivation. Through such learning motivation, we further enable students to enhance their interest in blended learning. Yang (2011) mentioned that the high-tech approach changes students’ learning style and motivation. As we know, we have been affected by COVID-19. Many primary schools, secondary schools and universities have been affected by COVID-19, which has sometimes led to a mix of online and offline teaching. This is an interesting study to investigate whether this approach can improve their motivation to learn. Argyris and Schon (1996) mentioned that teaching through media can increase learning motivation and is a valuable way for learners. Learning motivation is of great importance to students, including the teacher can learn through tablet tests in class, or through online computer prior to class. Yang et al. (2011) mentioned that prior reading and processing of knowledge is effective. We know that a lot of learning is based on new technological platforms to learn knowledge, and this process is the application of new learning patterns in our lives. Due to the impact of COVID-19, the mixed learning mode of online and offline has become a new type of learning, breaking the traditional offline learning mode. Many students can learn what knowledge is insufficient in the process of learning, through such online and offline learning mode to further achieve their learning performance. Huang (2017) mentioned that learners need to enjoy learning English by themselves to further achieve their learning performance. Our research is mainly to observe the learning performance of the whole college students through the combination of online and offline, hoping to improve the learning performance through such an automatic way of learning. Students’ learning performance mainly depends on their own spontaneity to have effect and significance.
Rotter (1954) mentioned that learning can obtain a new behavior through new experience and become a behavior. John et al. (2006) pointed out that learning experience through virtual means can enable learners to immerse themselves in learning. Pfarr-Harfst (2016) mentioned that VR technology experience can increase students’ learning effect in learning. Students can further achieve the effect of immersion through such technology. Immersion learning is an important learning factor for students, who need to learn through such a mix of online and offline. Especially with the repeated impact of COVID-19 in mainland China, such a way allows students to avoid being affected by the epidemic and interrupt their entire study. Immersion learning is a very important concept for a learning environment, learning platform and resources. This process includes the observation and investigation of the whole situation and learning environment. Kolb (1984) mentioned that the process of immersive experience of learning can transform knowledge into its own memory. Learning depends on the environment and the learner’s own experience, which is gradually transformed into a process of immersion in the learning.
“Interactive learning” teaching is a kind of teaching mode. We apply interactive learning to hybrid teaching mode, which mainly regards educational activities as a kind of communication, communication and learning between teachers and students in mind and knowledge, breaking the traditional situation and process of less communication between teachers and students. Adler and Stringer (2018) mentioned that interactive learning is of great help to students in learning. Wachtler and Ebner (2017) mentioned that students can actively participate in and interact with the whole learning state through video. Rose et al. (2016) used video teaching to interact with students in class. We know that interactive learning has a great impact on overall student learning. In addition to the interaction between teachers and students, interactive learning can also be seen from the students’ practice and study after class. Wood (2005) mentioned how teachers interact with teachers and students to learn, which becomes a new way of learning. Prince (2004) mentioned that discussion and interaction in learning can increase students’ learning effect. We know that through blended learning, students can enhance their learning with teachers, and directly solve and interact with the knowledge difficulties encountered in learning.
Cognitive learning theory has one of the most important factors for learners. Learners’ active learning, perception and memory have a learning effect for learners. Cognitive learning theory is a combination of neuroscience, psychology, education and other related fields to do the combination. In the process of learning, learners need to combine memory with cognitive learning knowledge to further achieve cognitive learning. We know that in blended learning, teachers and learners will constantly reflect on the overall learning effect through such a process. Glaser (1976) mentioned that human learning may be continuously learned through thought and memory, and further transformed into cognitive learning. Brophy (2001) mentioned the teaching environment and learner group learning, and knowledge memory can be generated in cognitive learning through discussion and learning. Brown et al. (1989) proposed that the process of learning and cognition would be influenced by context and background. We know that cognitive learning is a very important factor for learners, and learners can further achieve the effect of cognitive learning through this way of learning.
We know blended learning, which breaks the traditional model of teaching and learning. Mixed mode is mainly in response to the impact of the novel coronavirus pandemic, many learners will change the way they learn. We know about online and offline learning patterns and allow learners to apply new methods to further their learning. Our research is mainly aimed at blended learning methods, through interactive learning, learning motivation, immersion learning, cognitive learning theory, learning performance and other related factors, to observe the causal relationship.
Literature review
Interactive learning
Interactive learning is a collaborative approach where students and teachers engage together through communication and discussion, contrasting with traditional teacher-centered methods. In interactive learning, both teachers and students actively participate in the classroom process, creating a more enjoyable and engaging learning experience for students. In 2022, the COVID-19 pandemic prompted many universities to adopt blended learning, a model that combines both online and offline teaching. This shift is significant for students’ learning performance, as the pandemic led to a mix of online classes during emergencies and in-person classes when the situation improved. Laine (2003) highlighted that interactive learning is a key factor in student success. AlKhaibary et al. (2020) suggested that interactive learning boosts students’ knowledge and interest, without negatively affecting performance in a blended learning environment. Similarly, Some scholars found that students enjoy the opportunity to interact with teachers in blended learning, enabling them to address problems in real-time. Muller and Wulf (2021) emphasized that teacher-student interaction is essential for fostering students’ passion for learning. Rose et al. (2016) noted that video can be used to enhance the interactivity and enjoyment of learning. Moss and Crowley (2011) proposed that interactive learning platforms offer learners an engaging and immediate learning experience. Choi et al. (2007) pointed out that interactive teaching methods can increase students’ motivation, sparking their interest in the subject matter. In Fuzhou, during the October 2022 outbreak of the BA.5.2 variant of COVID-19, universities transitioned to online teaching, making the study of blended learning models particularly relevant. This mixed approach allows for continuous tracking of student progress, offering insights into the overall effectiveness of teacher-student interaction. O’Neill et al. (2005) suggested that interactive learning methods improve the overall learning outcome. King (2001) highlighted the role of online discussions and interactions, while recent studies by Alias and Razak (2025) and Jing et al. (2025) have examined the impact of interactive learning in digital environments. Ma et al. (2025) explored how interactive guidance in collaborative argumentation enhances learning outcomes. Through these studies, we understand the significant role that online interaction plays in modern education.
Motivation to learn
Learning motivation is a goal-driven tendency that influences students’ learning behaviors and their persistence. Scheiter and Gerjets (2007) noted that blended learning can enhance students’ learning motivation. Giannakos et al. (2017) highlighted that students engage in learning through online videos, which have become a common method of instruction. Bergmann and Sams (2012) emphasized that blended learning, which includes flipped learning, can further improve students’ academic performance. Davies et al. (2013) pointed out that online video teaching often provides students with pre-class materials for practice and reading. This suggests that students’ learning motivation can be better understood and potentially increased through both online and offline learning methods.
Tannenbaum and Yukl (1992) proposed innovative approaches to boost learning motivation. Noe (1986) suggested that motivation could be enhanced by utilizing specific motivational techniques. The hybrid model of online and offline teaching not only allows students to earn additional points but also provides verbal encouragement to improve their learning motivation. Bell and Kozlowski (2008) argued that the most significant motivator for students is their intrinsic motivation. This type of hybrid education can foster students’ autonomous learning before class and encourage self-testing and review after class. Driscoll (2002) mentioned that remote video-based teaching can enhance students’ learning motivation. Some People emphasized that blended learning plays a significant role in improving students’ motivation, particularly through the integration of instructional materials and interactive methods, which increase engagement and enjoyment.
Kinzie and Sullivan (1989) suggested that distance learning materials and content can boost students’ enthusiasm and motivation for learning. Bakacsi (2010) defined learning motivation as the goal achieved through sustained effort. Lehmann et al. (2014) discussed how self-directed learning and cognitive engagement can influence learning outcomes. Zhao (2023) highlighted that students’ learning motivation stems from both their learning behaviors and psychological factors. Filgona (2020) noted that teachers’ interest and motivation during online courses also play a critical role in fostering students’ learning motivation.
Immersion in learning
Immersion learning refers to the further improvement of learning results through the use of focused learning. Chamorro-Koc et al. (2021) mentioned that wearable devices can be used for learning across borders and further promote students’ immersive learning. James (2019) mentioned that immersion learning application in higher education is a creative way. Oddou et al. (2000) mentioned the concept of the importance of immersion learning for students’ learning. As we know, due to the impact of COVID-19, blended teaching is advocated in many colleges and universities in mainland China. The main purpose is to avoid the problems and interruptions in learning caused by the epidemic. We know that blended online and offline learning can guide students to learn in an immersive way. Immersion learning is very important for college students. Students can learn and discuss with learners through online and offline learning methods. Avila and University (2008) mentioned that students can actively experience and immerse themselves in learning through students. In the process of learning, as long as students can learn to feel happy and immersed in the effect, students can experience in the whole process of learning. The impact of immersion learning on the COVID-19 pandemic is one of the important research and investigation factors for students.
Cognitive learning
Cognitive learning theory helps teachers to learn how to expand students’ cognitive structure in education to promote their initiative. Kay and Kibble (2016) mentioned that students can achieve cognitive learning through reflection and learning. Bieg and Dresel (2018) Students’ cognition and teachers’ discussion on learning. Siau et al. (2006) mentioned that learning cognition belongs to the state of learning effect of students. Ashby et al. (1999) mentioned that if learners are passive or have no interest in learning, they will destroy the cognitive activities of learning. Caputo (2003) mentioned that cognitive learning can be further learned through pictures, images and videos. Rambe and Mlambo (2014) pointed out that learners can improve their cognitive learning through visual memory. Moezzi et al. (2017) mentioned that learners can learn and interpret knowledge content through the content of stories.
Performance of learning
Learning performance refers to a kind of learning behavior produced by students through the process of learning. Blasco-Arcas et al. (2013) mentioned that online interactive learning can increase students’ learning performance. Hsiao et al. (2021) proposed that flipped education can improve students’ learning performance. Bandura (1991) pointed out that flipped education can provide students with a good way of learning performance. Choi et al. (2007) proposed that learners take the initiative to discuss learning issues with teachers to further improve learning performance. Kay and LeSage (2009) proposed that improving the classroom environment can increase its learning performance. We know that for students, the learning environment can improve learning performance.
We know that in the face of COVID-19’s complex environment, students cannot afford to be interrupted in their learning. In addition to paying attention to these online and offline platforms, the most important thing for students is to attach importance to the effectiveness and application of these hybrid learning methods. Mustafa (2010) mentioned the concept of interdisciplinary learning, which can further arouse students’ interest and learning performance through face-to-face interaction and communication. Webb et al. (2005) Mixed learning affects students’ learning performance. Edmondson (2002) mentioned the achievement of learning performance through remote group learning. Chong et al. (2012) mentioned that distance teaching provides students with a new way of learning, which enables students to choose different diversified learning methods. Many modern E-generation college students like this way of learning, which can further achieve the performance and effect of learning. Green (2023) refer to how to make student learning more interesting, sustained and satisfying.
Online collaborative learning theory
Collaborative learning is an interactive process of group knowledge construction that enables learners to work together to achieve common goals. Harasim (2012) proposed the theory of Online Collaborative Learning (OCL), which emphasizes that teachers should create a conducive learning environment on online platforms to promote students’ collaborative learning and knowledge construction. The goal is to facilitate group or team collaboration that connects students and enhances their learning experiences.
Altowairiki (2021) discussed online collaborative learning, noting that analyzing the process through lived experiences provides valuable insights into how collaboration works in practice. This suggests that when teachers and students collaborate, it can significantly improve students’ learning outcomes. Wilcox and Lock (2014) mentioned that online learning creates an effective environment for students to learn, where both teachers and students can grow together and learn from one another. Du et al. (2017) emphasized that teachers should provide students with a comprehensive set of online learning experiences to enrich the learning process.
Research hypothesis architecture and development
Research design
Figure 1 illustrates our research model, presenting the complete framework and the relationships between key factors. The model visually represents the logical connections between the dependent variables, mediating variables, and outcome variables within the architecture. In our study, we proposed four research hypotheses based on learners in the hybrid learning mode. The research framework primarily focuses on interactive learning, learning motivation, immersive learning, cognitive learning, learning performance, and other related variables.
Research model.
Interactive learning
Dewey (1938) mentioned interactive learning that learning knowledge mainly comes from flexible learning. Rupert and Falk (2018) mentioned that students can further achieve knowledge sharing through discussion in online learning. Chiu (2008) mentioned that in order to improve students’ learning, traditional learning methods are transformed into continuous interactive modes. Krajewski and Ritzman (2005) mentioned knowledge learning, which is further achieved through interaction and dialogue.
H1:Interactive learning has a positive correlation with cognitive learning.
Motivation to learn
Brown and Ford (2002) mentioned that learning motivation will affect the overall learning effect of learners. Noe (1986) pointed out that learning motivation comes from learners’ participation and attitude, and they continue to adhere to a series of learning activities. Kinzie and Sullivan (1989) pointed out that distance video education can increase learners’ learning motivation. Learner’s motivation for learning is one of the key factors in the overall learning situation.
H2: Learning motivation is positively related to cognitive learning
Immersion in learning
Auster (2006) mentioned the gradual transition from learning experience to immersion learning. Kolb and David (2005) pointed out the concept of the combination of experiential and immersive learning, through which learners can further achieve the effect of immersive learning. Our research uses both online and offline teachers and learners to experience and learn together, and creates a good learning environment. Our main goal is to enable learners to participate in such a hybrid mode. The value of knowledge comes from learners’ immersion learning, which can produce a good cognition of learning for learners’ cognitive learning. Cheung et al. (2017) proposed that immersion learning can be applied to different kinds of knowledge.
H3: Immersion learning has a positive correlation with cognitive learning.
Cognitive learning
Cognitive psychologists view learning as an individual’s process of acquiring mental knowledge by recognizing, discriminating, and understanding things. What the individual learns in this process are patterns of thought, what cognitive psychologists call cognitive structures. Gorham and Christophel (1990) students will promote students’ cognitive learning through teachers’ humor. Mayer (2001) mentioned the process of investigating students’ cognitive learning in multimedia learning. Cognitive learning theory has an important concept for students, students through cognitive learning to further achieve the effect of learning. Hayes and Allinson (1994) mentioned that learners’ perception, learning and problem solving are helpful to learning cognition. We know that teachers can learn whether learners can accept such a teaching method and mode from their expressions in class or learning process.
We can clearly understand the learners’ emotions and expressions, and we can see their learning effects in learning. We know that learners sometimes face difficult lessons and express them in the learning process. Ross et al. (2005) mentioned the decision model of cognitively oriented learning. Bednarek et al. (2017) mentioned that learners improve their learning performance through interaction and discussion. Brkovic and Chiles (2016) pointed out that learning is not just a process, but the teacher imparts knowledge to learners, and the performance evaluation of learners becomes an important indicator.
H4: Cognitive learning has a positive correlation with learning performance.
Research method
We developed a new model architecture for blended learning and designed a reliable questionnaire to measure key variables. The questionnaire survey was carefully crafted to ensure reliability and consistency. We then assessed the reliability and consistency of the SEM tools by distributing the questionnaires, followed by conducting a correlation analysis to evaluate the relationships between the models in the research tool.
Content validity
Under the appropriate research framework and environment, the questionnaire project is designed as the background of blended learning content. We use five variables to design, including interactive learning, learning motivation, immersion learning, cognitive learning theory, learning performance and other related variables, and observe its overall reliability and validity. Pavlou and Fygenson (2006) proposed Likert’s seven-point scale measurement, which ranges from strongly disagree (1) to strongly agree (7). Hair et al. (1998) To test this structural equation model (SEM), we designed a component analysis and a reliability and validity analysis for observation estimation.
Pre-text and Pilot-test
In order to improve the subject content and overall design of the 39-item questionnaire, we conducted a pre-test on a sample composed of six academic experts and six doctoral students. Pasquali (1999) mentioned the minimum sample size, and the questions of the survey and verification questionnaire.
Respondents were asked to complete the questionnaire and provide comments on the expression, comprehensibility and clarity of the items, as well as the overall appearance and content of their questionnaire. Finally, the difficult questions were slightly modified, and the questionnaire was obtained to test our research model. Table 1 lists 39 items, corresponding structures measured and reference sources.
In the process of testing, we will first simulate, observe and analyze. Questionnaires are issued according to the situation and scenario. The design of the whole questionnaire conforms to the consistency and ductility, and the design of the questionnaire conforms to the scientific design.
Data collection
In Table 1, we designed the question of the questionnaire based on the questionnaire variables. Our questionnaire extends the design designed by relevant scholars. We can see from Table 1 the rigor and ductility of the questionnaire design.
Data collection and respondents’ profiles
We used Wechat, QQ group and other related software groups to send questionnaires. Samples were collected from October 5, 2022 to November 10, 2022. We distributed 420 samples and collected 387 of them, with a collection rate of 92%. Gay (1992) refers to Correlation analysis studies should have at least 30 samples. Five variables were designed for structural equation model analysis. Our design is to collect samples by questionnaire survey. The overall demographic data analysis can be seen in Table 2, and its main correlation analysis can be clearly seen.
Common method bias
With the evolution of statistical technology, more and more scholars in the field of statistics focus on Common method variance (CMV). CMV refers to the overlap of variations between two variables that results from the use of similar measurement tools, rather than representing the true relationship between underlying constructs. Although previous studies have shown that CMV does not necessarily lead to bias in research results, it should be taken into account in practical studies.
Two random variables may or may not be independent. The so-called independent, is a random type of independence, the table knows the value of one variable, has no effect on the value of the other variable. If two random variables are not independent, then there is a relationship between them. This relationship may be strong or weak. This section introduces two ways to measure the strength of the relationship between two random variables.
The relationship between random variables is strong and weak. If it is independent, then of course the relationship is the weakest.
Data analysis and findings
To use the structural equation model, we applied Amos 24.0 and analyzed the entire data structure and the significance relationship. SEM mainly observes the correlation between variables and potential variables, and constructs one or more factors for analysis. SEM is divided into two types, mainly analyzing its measurement and model (Anderson and Gerbing, 1988). We designed the analysis of the architecture diagram, mainly observing the causal relationship, and through the data analysis can clearly see through the correlation.
Assessment of the measurement model
Our main research applies structural equation models to design and further estimate the relationship between overall structures and measurements. We first analyzed 65 projects to evaluate the structure and measure if there was a good combination. We did the whole study, we did the whole fit state, the whole structure and the measure to look at the fit state.
Assessment of the structural model
We applied that the overall fit of the structural model was acceptable because all the fit measures reached the acceptable level (χ2 = 2830.344, df = 681, α = 0.001; GFI = 0.760; AGFI = 0.92; CFI = 0.910; NFI = 0.91; RMSEA = 0.08). Our overall measure and structure conform to the fitting degree of the overall structural equation model.
Test of mediating effects
In order to check the existence of the mediating effect of the structural equation model, the existence of the mediating relationship between them can be seen in Tables 5 and 6. It includes Independent Variable (interactive learning, learning motivation and immersion), mediating Variable (cognitive learning theory) and Dependent Variable (learning performance). In Table 4, we can see the overall coefficient of differential validity, which is highly correlated and has a certain validity relationship. The AVE square root value is greater than 0.76, and we can see its correlation through Table 4. In order to test the overall reliability of the structural equation, it can be seen from Table 3 that the overall reliability is greater than 0.7, which is consistent with the overall SEM detection. Mulaik et al. (1989) mentioned that structural equation model is mainly to observe and analyze structure and measure the relationship between variables.
Hypotheses’ testing
In the whole process of SEM data, our data analyzed the relationship between the cause variable and the effect variable of the overall deconstructed equation model. We applied the ML estimated value to predict and analyze. We can see the whole path relationship in Fig. 2. It can be seen that the four hypotheses support the original hypothesis test, and the four hypotheses meet the positive correlation. We can clearly see from Fig. 2 that three hypotheses have significant positive correlation and one hypothesis has negative correlation significance.
Research model Significant relationship.
Structural equation modeling (SEM) is a statistical method used to analyze causal relationship models, which can also be used for path analysis (PA), factor analysis, regression analysis and variance analysis.
In the whole study, the significant relationship between the mean and the factor analysis in Table 3 can be seen. It can be seen from Table 3 that the Cronbach’s α value of each variable is greater than 0.7, which conforms to the value of reliability of confirmatory analysis of structural equation model. We can see the coefficient relationship of the whole matrix statement from Table 4. We can see the relationship between its path analysis in Table 5. The direct and indirect relationships between them can be seen in Table 6.
Multi-group analysis
From Tables 7 to 10, it is clear that blended learning has a certain significant relationship with learners in interactive learning, learning motivation, immersion learning and cognitive learning theory. In the whole framework, we can see from the research model in Table 2 that interactive learning in H1 has a significant negative correlation with cognitive learning theory. This is a significant finding in our research, because we found that the online learning part of blended learning may not be as effective as the offline learning in learners’ interaction.
We made a cluster structure, and made a cluster analysis on the frequency of learning platform on the cluster terminal for/times per week and gender. cluster analysis is a method to simplify data. According to the common properties between samples, relatively similar samples are gathered together to form a cluster. Distance is usually used as the basis for classification. The closer the relative distance is, the higher the degree of similarity will be. After grouping, the differences within groups will be small while the differences between groups will be large.
Discussion
According to our hypothesis, this paper designs five variables, including interactive learning, learning motivation, immersion learning, cognitive learning theory, and learning performance. As shown in Fig. 2, it can be clearly seen that three hypotheses have significant positive correlation, and one H1 has significant side correlation. Such a finding is consistent with previous relevant studies (Cohen and Prusak, 2001). We can see the whole research framework, including the negative correlation significance of interactive learning with cognitive theory of learning, the positive correlation significance of learning motivation with cognitive theory of learning, the positive correlation significance of immersion learning with cognitive theory of learning, and the positive correlation significance of cognitive theory with learning performance. We can clearly see whether the blended learning content has learning performance in the process of learners. And it further becomes the effect of cognitive learning in the process of blended learning. Our findings suggest that cognitive learning theory holds a key element for blended learning. We have broken the traditional offline research and increased the combination of online and offline learning. No scholars do research on such a concept. We have combined psychology, education, information technology and other concepts to do a series of design and research. We cluster the gender in Table 7 and Table 8, and cluster the frequency of use per week in Tables and 10.
Theoretical implications
Our research on blended learning has developed new models, and the research concepts and models are as follows. Our research architecture diagram clearly shows that the three hypotheses belong to the positive relationship, respectively H2, H3 and H4 belong to the positive relationship significance, while H1 belongs to the negative relationship significance. In addition, we use cognitive learning theory to review the theoretical development relationship of the whole article, and finally find that online learning in blended learning will produce some conditions in terms of interaction and cognitive learning. Specifically, the theory of learning cognition is the core part of the whole article, and the development of the whole theory has innovative applications for the article. As we know, there are many online learning platforms in mainland China, including Super Star and Tencent Classroom. The interaction between teachers and learners in online learning is a very interesting topic.
Managerial and practical implications
The main purpose of this study is to provide a survey of the learning performance that blended teaching can bring to learners in learning. This is a very interesting issue design. First of all, cognitive learning theory is the most important factor for learners’ learning performance. Secondly, the combination of online and offline hybrid learning mode has a negative correlation between learners’ interaction. We have observed that some learners cannot interact well with the actual situation in online learning, which will affect their learning performance. Thirdly, under the influence of learning motivation and immersion learning, learners and teachers need to maintain the overall teaching quality and learning effect based on the continuous learning effect of learners in such a hybrid mode.
Firstly, we can see in H1 that interactive learning has negative significant correlation with cognitive learning theory.
In H1, we observe a significant negative correlation between interactive learning and cognitive learning. Jennifer et al. (2019) noted that the flipped classroom model can enhance learning outcomes through interactive processes. Conducted a survey on primary and secondary school students in Taiwan, examining how video-based interactions influence learning. Tobai et al. (2016) highlighted the emergence of electronic interactive learning across various disciplines. In this hypothesis, we propose a blended interactive learning model that integrates both online and offline interactions, moving beyond traditional direct interaction-based designs. The findings suggest that interactive learning does not solely rely on teacher-led interactions; rather, students must take initiative to improve their cognitive learning outcomes.
Wyss et al. (2014) mentioned that virtual learning can increase students’ learning motivation. Irida et al. (2022) mentioned that they were affected by COVID-19 and used the means of online class video to further maintain students’ learning motivation. Li et al. (2021) mentioned that online learning can promote learners’ information learning process to further improve their learning motivation. Therefore, we can see from H2 that learning motivation has a significant positive relationship with cognitive learning theory.
Levine (2008) pointed out the importance of immersive learning in the virtual world. Wagner (2019) mentioned that immersive learning experience can increase the learning effect of educational learning. Immersion learning is a key success factor for learners’ cognition, and learners have very important cognitive learning psychological factors for immersion learning. Learner immersion is an interesting study. Angela and Harrison (2017) mentioned that the introduction of information information system can enhance learners’ immersive learning effect in situational learning. Engeser (2012) mentioned that learning immersed in activities is a very important concept. Schiefele and Raabe (2011) pointed out that immersive experience comes from the concept of concentration. Schiefele and Roussakis (2006) proposed that immersion learning and positive participation can experience a kind of learning process. Therefore, we can see from H3 that immersion learning has a significant positive relationship with cognitive learning theory.
Cognitive learning theory belongs to a concept of psychology. Cognitive science mainly aims to further achieve the effect of learning through cognitive theory. Xu et al. (2019) proposed that cognitive learning is an important survey concept for big data analysis of educational learning. Chen et al. (2017) mentioned that cognitive learning can be seen from facial expressions. Odobez and Ba (2007) pointed out that learning cognition comes from whether attention is focused or not. Therefore, we can see in H4 that cognitive learning theory has a positive correlation with learning performance.
Discussion and conclusions
The COVID-19 pandemic has had a significant impact on the outcomes of blended learning. This study examines the relationship between cognitive learning and blended learning by analyzing how various online and offline learning styles affect learners’ performance, using data collected through a questionnaire survey. The primary objective is to explore and address how these learning modes influence academic performance.
Our findings highlight several key insights:
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1.
Cognitive learning plays a positive role in enhancing learning performance.
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2.
Blended interactive learning, motivational learning, and immersive learning contribute to cognitive learning development.
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3.
Online collaborative learning theory provides additional support for overall learning performance.
While our research offers valuable perspectives, further studies could explore additional factors influencing blended learning, such as technological advancements, individual learning preferences, and long-term engagement strategies. By continuing to refine blended learning approaches, educators and institutions can create more effective and inclusive learning environments.
Limit and future research
Although the study has made significant contributions, it still has some shortcomings. We have summarized the following points: (1) The sample is primarily drawn from mainland China. In the future, it can be expanded to other countries or regions for comparative analysis. (2) In selecting variables, we chose cognitive learning as one of the characteristics of personal learning. We suggest that future researchers consider incorporating related variables such as social diffusion and emotion extraction to further explore innovation. (3) Our research focuses on both online and offline learning methods. Future researchers may introduce the concept of AI-assisted learning, which can help learners acquire more diverse knowledge and interact with machines to facilitate knowledge exchange.
We sincerely recommend that future studies adopt the above perspectives to inspire more innovative research and ideas.
Data availability
No datasets were generated or analysed during the current study.
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Li-Wei Lin and SHIH-YUNG WEI: Conceptualization, Methodology, analysis and investigation; Li-Wei Lin: Writing original drafting, Writing-review and editing, Kuo liang Lu, Shuo Wang and TAI-GE YAN: Validation and Resources; Li-Wei Lin, Shuo Wang and TAI-GE YAN: Supervision.
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Lin, LW., Wei, SY., Lu, KL. et al. The influence of interactive learning, learning motivation, immersion learning and cognitive learning on learning performance. Humanit Soc Sci Commun 12, 1165 (2025). https://doi.org/10.1057/s41599-025-05303-y
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DOI: https://doi.org/10.1057/s41599-025-05303-y
