Introduction

The digital transformation of higher education has become a global phenomenon that has revolutionized traditional teaching methods and laid a foundation for the development of innovative approaches to teaching and learning (Jamison et al., 2014; Li et al., 2021). One significant development in this transformation is the rise of hybrid learning, a form of blended learning that represents a significant advancement in educational practices (Tight, 2007). Hybrid learning, which is defined as a form of learning that blurs the line between physical and virtual environments and facilitates interaction between both online and offline learners and instructors (Akkoyunlu & Soylu, 2006), combines traditional face-to-face instruction with online learning components. This versatile approach caters to diverse learning needs (Bashir et al., 2021).

In educational technology research, blended learning and hybrid learning have frequently been discussed together (Wahyu Ningsih & Yuliana, 2024), but they have distinct focuses (Zhang & Wang, 2014). Blended learning emphasizes students and aims to promote optimal learning outcomes (Dziuban et al., 2018). In contrast, hybrid teaching emphasizes teachers’ leading role in this process (Ahlgren et al., 2020; Linder, 2017) and how teachers can achieve optimal teaching effectiveness for student learning (Li et al., 2021). To promote pedagogical reform, this study focuses on hybrid teaching and modelling in the context of engineering education and defines it as an approach in which educators, at the appropriate time, utilize suitable information technologies to design and deliver tailored learning resources, environments, and activities. This teaching method emphasizes interactions between educators and students, peer-to-peer engagement, and student–content interaction. By fostering dynamic interactions between online resources, students, and teachers, this method facilitates knowledge acquisition and skill enhancement to an optimal extent.

In the postpandemic era, researchers have increasingly focused on hybrid learning approaches in educational settings (Al-Fodeh et al., 2021; Karabulut-Ilgu et al., 2018; Lazorenko, 2021; Li et al., 2021; Osaili et al., 2023). As a result of the integration of online and offline teaching modalities, the interaction networks among participants in the educational process have become more complex and challenging than is the case when traditional models are employed (Boelens et al., 2017). This complexity is particularly evident in engineering education, where the practical and collaborative nature of the subject matter demands a nuanced approach to teaching and learning (Li & Li, 2021). Engineering education often involves hands-on laboratory work, collaborative projects, and real-time problem solving, all of which require effective communication and seamless coordination between students and educators (Jamison et al., 2014; Karabulut-Ilgu et al., 2018). In a hybrid teaching and learning environment, managing these activities requires sophisticated technological infrastructure and innovative pedagogical strategies (Nimunkar et al., 2014). However, few previous studies have focused specifically on hybrid learning in engineering education (Low et al., 2021), especially concerning the factors that influence perceptions of this format of education (Al-Fodeh et al., 2021; Essa, 2023). Hence, the core aim of this study was to explore the comprehensive and dynamic factors that influence teachers’ and students’ perceptions of hybrid learning in the context of engineering education.

To enhance the understanding of the factors that influence teachers’ and students’ perceptions of hybrid teaching, it is necessary to construct a comprehensive framework that includes multiple domains, such as the outer and inner settings (Reed et al., 2021). Based on learning satisfaction theory, Wang constructed a structural equation model that can be used to evaluate the factors that influence student satisfaction in the context of hybrid learning. This model incorporates both internal student factors and external factors (including teacher support, platform support, and peer support; Wang et al., 2023). However, to understand the factors that influence hybrid learning, other stakeholders, such as teachers and staff, should also be considered (Li et al., 2023). Additionally, Katz and Main suggested that elements from political science and economics should be incorporated into engineering education assessment (Katz & Main, 2022), as doing so can provide a systematic perspective on relevant processes within educational systems. Therefore, the first purpose of this study was to establish a framework that integrates various dimensions of engineering education into a hybrid teaching model.

In the work of Bourdieu, the concepts of “field” and “capital” are employed to elucidate the differentiated nature of social spaces in advanced societies and the practical actions that are taken within these spaces (Bourdieu, 1985, 2011). Thus, hybrid teaching modes—which integrate both online and offline interactions—embody various fields in which different forms of capital are at play. The online field, which is enhanced by digital technology, requires not only cognitive capital and educational capital but also technological fluency. Similarly, the offline field maintains its value in terms of social and cultural capital, which are derived from face-to-face interactions and traditional classroom settings.

Consequently, hybrid teaching models require a careful balance and interaction between these fields, reflecting the dynamic relationships outlined in Bourdieu’s theory. This theory demonstrates significant efficacy in establishing a novel framework for analysing hybrid learning and teaching environments, as it offers a nuanced understanding of the complex interactions that occur and the various forms of capital that are exchanged among instructors, students, and support staff. Therefore, the second purpose of this study was to investigate whether the factors included in this newly established framework influence students’ and teachers’ perceptions of the hybrid teaching model in the context of engineering education.

X University in China, renowned for its excellence in engineering education, serves as a prime example of effective hybrid teaching implementation in the engineering field. Therefore, in this research, X University was selected as a case study to investigate various forms of hybrid teaching from a pedagogical perspective using a mixed-methods approach. The third purpose of this study was to analyse the advantages and disadvantages offered by hybrid teaching models in the context of engineering education based on this case study.

In conclusion, this exploratory research employed a Bourdieu-inspired framework to preliminarily identify and elucidate the factors that influence participants’ perceptions of different modes of hybrid teaching in the context of engineering education. This study aimed to identify effective pathways for the seamless integration of online and offline teaching methods and produce a comprehensive understanding of hybrid teaching and learning practices in higher engineering education.

Literature review

Various studies that have investigated hybrid learning across different disciplines have shown that the shift to hybrid teaching has been particularly instrumental in engineering education, in which practical applications and theoretical knowledge are seamlessly integrated to prepare students to meet the demands associated with industry (Dym et al., 2005). In light of the limited amount of classroom time, the hybrid teaching model enables students to engage in foundational prestudy before class, participate in creative design and team discussions during class, and address technical details, case studies, and hands-on exercises through online self-study and practice after class (Al Mamun et al., 2022; Nimunkar et al., 2014). This comprehensive approach equips students to become well-rounded engineers more effectively and enables them to develop strong design thinking skills, effective communication skills, and a solid technical foundation (Crawley et al., 2014). Moreover, this innovative approach allows educators to integrate conceptual understanding, hands-up teaching methods, and interactive simulations that can enhance the learning experience among students in engineering and related disciplines (Dym et al., 2005; Low et al., 2021; Mäkelä et al., 2022).

Numerous studies have provided compelling arguments in support of the advantages of hybrid learning in light of its flexibility, accessibility, interpretability, cost efficiency, and superior learning effect (Alcalá et al., 2006; Almusaed et al., 2023; Bathmaker, 2015; Ferreira, 2020; Linder, 2017). Despite its advantages, the implementation of hybrid learning in engineering education remains challenging (Anggaraini Purwaningtyas et al., 2020; Boelens et al., 2017; Castedo et al., 2019; Li et al., 2021). The hybrid learning approach can be especially costly and complex in engineering education because of the extensive collaboration it requires across various fields. Teachers must adapt their instructional strategies and develop new materials, students must manage a more dynamic learning schedule, and staff must maintain the necessary technological infrastructure and support systems (Chai et al., 2022; Karabulut-Ilgu et al., 2018). To address these challenges, most of the literature on engineering education has focused on identifying factors that currently impact hybrid learning, such as students’ motivation, engagement, digital literacy, and attitudes (Chen, 2016; Lin, 2021; Xiao et al., 2020), as well as teachers’ competencies and the technological tools used in this context (Anggaraini Purwaningtyas et al., 2020; Karabulut-Ilgu et al., 2018; Li & Chen, 2022; Li et al., 2021). However, a noticeable gap exists in the extant research on this topic, as few studies have considered all related factors—including learning, teaching, and auxiliary support—that significantly impact the perceptions of hybrid learning (Mäkelä et al., 2022). Consequently, this study aimed to fill this void by exploring the key factors that influence the perception of hybrid teaching in the context of the dynamic interactive processes associated with higher engineering education.

Additionally, Raes et al. (2020) conducted a meta-analysis of 47 studies on hybrid learning, revealing that most previous research on this topic has failed to capture the nuanced attitudes of students and teachers towards hybrid learning. Moreover, the engineering education scholar Woolcock (Woolcock, 2013) called for mixed methods approaches to focus on evaluating a given context. Detailed case studies and mixed methods research conducted based on systematic frameworks are urgently needed to explore the practical perspectives of teachers, students, and support staff, as well as various real-world applications of hybrid teaching in engineering contexts (Karabulut-Ilgu et al., 2018). Such comprehensive approaches can provide a deeper understanding of students’ and educators’ experiences and perceptions in hybrid learning environments.

Research hypothesis

In terms of theoretical frameworks in hybrid learning, researchers have often relied on theories such as transactional theory, the technology acceptance model, and socioconstructivist theory (Karabulut-Ilgu et al., 2018). These researchers have typically conducted case studies of individual classrooms or analysed quantitative data concerning students’ learning experiences. However, hybrid learning and teaching involve complex interactions between educators and students, peer-to-peer engagement, and student–content interaction (Li & Li, 2021). Research that explores interactive network relationships from the combined perspectives of educational technology and sociology remains relatively scarce (Li et al., 2021). Therefore, a comprehensive framework is needed for the systematic adoption of hybrid learning in engineering education (Karabulut-Ilgu et al., 2018), particularly concerning the interactions among teaching practices, teaching support for assistants, and the technical learning environment.

This study focused on Bourdieu’s theory of capital and field (Bourdieu, 1998) and explored the intersections among these conceptualizations to understand the factors that influence the model. Specifically, the research addressed the following questions within the context of a case study of an engineering university:

  • (1) What different fields are created by hybrid teaching in higher education? How do they interact with each other?

  • (2) In the hybrid teaching environment of engineering education, which factors influence the positive perceptions of different participants?

  • (3) What are the opportunities and challenges of hybrid teaching in engineering education?

By examining these questions, this study aimed to provide a comprehensive understanding of the dynamics that underlie hybrid learning environments and the various elements that affect their positive perceptions and effectiveness. Detailed case studies and mixed-method approaches were used to explore the practical perspectives of teachers, students, and assistant staff based on systematic frameworks, thus ensuring a holistic analysis (Fig. 1).

Fig. 1
figure 1

Conceptual framework for this study

Full size image

Based on Bourdieu’s field theory, we propose a basic hypothesis, namely, that the online and offline blended teaching model involves three fields: the teaching, learning, and assistant teaching fields. These three fields influence students’ positive perceptions of hybrid teaching. Each field is associated with different influencing factors, which are detailed as follows:

Instructors’ teaching field

The instructors’ teaching field refers to the networks and learning environments that instructors design and create in both online and offline settings. Previous studies have reported that instructors’ attitudes, interactions, and teaching quality significantly impact students’ positive perceptions and learning outcomes in digital learning environments (Elison-Bowers et al., 2011; Li, 2021; Taghizadeh & Hajhosseini, 2021). In particular, Taghizadeh and Hajhosseini (2021) conducted a multiple regression analysis and revealed that the quality of teaching, including instructors' knowledge, teaching style, and goals, has a more substantial impact on positive student perceptions than do interactions or attitudes.

Bourdieu noted that capital is “accumulated labour” (1986, 241), which includes both material values and invisible cultural attributes. The way in which capital is amassed and distributed affects an individual's particular social space. For teachers, different types of capital can impact the quality of teaching and students’ teaching satisfaction. Typically, capital can take three forms: economic, cultural, and social (Bourdieu, 1986). However, in educational settings, the economic strength of teachers does not influence teaching. Instead, their symbolic capital, i.e., their charisma, often impacts teaching. Cultural capital includes instructors’ professional knowledge, social capital pertains to their industry connections and networks, and symbolic capital concerns their teaching style and personal charisma.

In the hybrid learning context, instructors are crucial influencers of teaching satisfaction. Instructors are not only disseminators of knowledge but also resource coordinators in both online and offline education settings (Zeng et al., 2016). On the one hand, they must use their cultural, social, and symbolic capital to establish their authority and centrality. On the other hand, unlike instructors in traditional classrooms, instructors in hybrid learning environments must help students develop new learning models that use modern information technology tools. They should effectively guide students in the learning and cognitive fields, thereby encouraging interaction within the teaching and social fields.

Therefore, the first hypothesis proposed in this research is as follows:

H1: The teaching field (TF) can positively predict the perceptions of a hybrid teaching model.

Students’ learning field

The students’ learning field refers to the learning network constructed by students based on teaching activities, including shallow learning, deep learning, and hands-on learning. This field is influenced by the interactive impacts of relevant platforms and tools as well as various classroom interactions.

In contemporary higher education classrooms, professors play an important role but are not the centre of the learning process. Instead, this process should focus on establishing a student-centred learning environment (Martínez-Jiménez & Ruiz-Jiménez, 2020). Numerous studies have reported that students’ learning attitudes and learning styles significantly influence the effectiveness of hybrid learning (Chen & Chiou, 2014; Pinto & Anderson, 2013; Taghizadeh & Hajhosseini, 2021; Xiao et al., 2020). Kintu & Zhu (2017) identified learner interaction as an important factor among the design characteristics of blended learning. Additionally, Xiao et al. (2020) reported that cognitive engagement is a critical indicator of hybrid learners’ experiences and satisfaction.

In a hybrid teaching model, the learning field is divided into shallow, deep and hands-on learning layers. Shallow learning refers to a relatively passive and mechanical learning state characterized by simple repetition, passive acceptance, and rote memorization. This type of learning often requires students to engage in self-study by using digital materials online before class (Smith & Hill, 2019). Conversely, deep learning involves the critical acceptance of new ideas and facts based on an understanding of the relevant knowledge as well as the integration of these ideas and facts into the existing cognitive structure to make decisions or solve problems (Bennet & Bennet, 2008). Hands-on learning in engineering education involves highly interactive and experiential activities that allow students to apply theoretical concepts in real-world settings, thereby deepening their understanding through direct experience (Sianez et al., 2010).

In the hybrid teaching model context, the shallow learning field is supported primarily by physical carriers such as learning platforms, videos, tools, and technologies and thus represents a form of situational learning (Yu, 2020). Conversely, the deep learning field requires students to deepen their understanding and application of the knowledge they have acquired through interactions with their teachers and peers and with the teaching content. In addition, hands-on learning can be combined with online resources and digital tools to create highly interactive and dynamic learning experiences that cater to diverse learning styles and preferences (Charitopoulos et al., 2022).

Hence, the second hypothesis proposed in this research is as follows:

H2: The learning field (LF) can positively predict the positive perception of a hybrid teaching model.

Assistant teaching field of auxiliary instructional personnel

Auxiliary instructional personnel play a broad range of roles that support core teaching activities in educational environments. These roles can include but are not limited to those of teaching assistants, guest lecturers, laboratory assistants, and technicians. Teaching assistant staff are often easily overlooked in teaching and learning fields (Groom, 2006). This group includes teaching assistants who provide tutoring and answer questions, guest lecturers who deliver external talks and lessons, and administrative and technical staff who offer support. In traditional classroom teaching, these individuals primarily augment the core instructional content delivered by faculty through their involvement in course design, management, and instruction and by answering queries. However, in a hybrid teaching model, these personnel play an indispensable role in ensuring the proper utilization and operation of educational technology tools (Wadams & Schick-Makaroff, 2022).

Additionally, guest lecturers contribute valuable academic and job-market insights. They can meaningfully shape students’ learning experiences and overall course quality when they possess sufficient digital literacy and can access stable internet connections. Although teaching assistant staff may not have the most direct impact on the creation of the learning environment, they provide invaluable support for the assistant teaching field, as they enhance instructional content, deepen students’ learning experiences, and improve the quality and distinctiveness of the courses.

Based on the preceding explanation, the third hypothesis proposed in this research can be formulated as follows:

H3: The assistant teaching field (AF) can positively predict positive perceptions of the hybrid teaching model.

In summary, in the teaching field, the three relevant influencing factors are cultural capital (tf1), social capital (tf2), and symbolic capital (tf3); in the learning field, the three relevant influencing factors are shallow learning (lf1), deep learning (lf2), and hands-on learning (lf3); and in the assistant teaching field, the three relevant influencing factors are teaching assistants (af1), invited guest lecturers (af2), and administrative and technical teachers (af3).

Research design

The case study methodology is commonly used in the social and life sciences to investigate and characterize an individual, group, or organization thoroughly (Heale & Twycross, 2018). This study used this methodology to provide an in-depth, context-specific exploration that could identify nuanced relationships and mechanisms in the context of hybrid teaching in engineering education, thus providing a thorough and practical answer to the research questions. X University was selected as the case study investigated in this research for three main reasons. First, it is one of the top universities in China and was one of the country’s earliest adopters of hybrid online and offline teaching methods. Second, it features campuses in two distinct cities, i.e., Beijing and Shenzhen, which offer diverse forms of hybrid teaching models. For example, the teaching methods are divided into recorded, live broadcast, and offline courses, which are combined with online and offline learning or double-teacher teaching. Third, as this university focuses primarily on science and engineering, it represents an ideal context for exploring the impact and potential of hybrid teaching methods in engineering education. This study employed a mixed-methods approach that combined quantitative and qualitative research to better understand the research questions.

Quantitative participants

First, building on the established theoretical framework, the quantitative research conducted for this study focused primarily on analysing the three main fields of hybrid teaching and the factors that influence teaching satisfaction. The specific quantitative research method employed in this context was the questionnaire survey. Questionnaire surveys are among the most important quantitative research methods used to describe and explain key characteristics and elements (Watson, 2015). A total of 550 online questionnaires were randomly distributed to students, and 489 questionnaires were returned, for a response rate of 89%. A total of 0.2% of these students majored in art design, and the remaining students were recruited from STEM disciplines. These students represented 21 majors, including ocean technology engineering, big data engineering, functional materials and devices, computer technology, electronics and communication engineering, biomedical engineering, intelligent manufacturing, environmental engineering, and pharmaceutical engineering.

Quantitative data analysis methods

The quantitative analysis methods used in this research included correlation analysis, multiple linear regression, and structural equation modelling. Preliminary analysis was conducted to examine the appropriateness of our data with respect to Structural Equation Modelling (SEM). In light of the variance inflation factors (VIFs, which ranged from 3.12–4.96) and tolerance values (which ranged from 0.20–0.32), we concluded that this research was not impacted by any multicollinearity problems (Miles, 2005). The assumptions of multivariate normality were subsequently tested. The Shapiro‒Wilk normality test was conducted with the assistance of the minor test package in R software (version 4.2.2); the results indicated the nonnormality of our data (Jarek, 2012). We employed the maximum likelihood with robust standard errors (MLR) method to perform SEM, allowing us to correct biases such as those resulting from nonnormal data distributions (Finney & DiStefano, 2013). The semPower package in R was used to perform a special power analysis for SEM (Moshagen & Bader, 2024). When we set the parameters appropriately for this study (df = 64, N = 489, α = 0.05, RMSEA = 0.05), the results revealed excellent post hoc power (0.99). This dataset contained no missing data; thus, we skipped the step used to address missing values here.

To test our research questions, we performed SEM using a two-step approach with the assistance of Mplus 8.3 software. First, we conducted a confirmatory factor analysis (CFA) to examine the fit of all three latent variables included in this study (i.e., the teaching, learning, and assistant teaching fields) to the measurement model to identify the scales that fit the observed data. Second, the hypothesized structural model was examined, including the paths leading from the three latent variables to the perception of the hybrid teaching model. To evaluate the model fit, multiple fit indices were used, including the comparative fit index (CFI), the Tucker‒Lewis index (TLI), and the standardized root-mean-square residual (SRMR); the cut-off criteria suggested by Hooper et al. (2007) were used (CFI ≥ 0.90, TLI ≥ 0.90, and SRMR ≤ 0.08). Students’ major, grade, gender, and age were included as covariates.

Qualitative data collection

Second, the qualitative research conducted for this study aimed to supplement the findings of the quantitative study. For example, this approach aimed to explore the reasons underlying the quantitative results and identify any additional influencing factors not captured in the data. Qualitative research helps elucidate the subjective thoughts, behaviours, beliefs, decisions, and social backgrounds of research subjects (Fossey et al., 2002). It depends on the suitability of its research methods and theories to its research objects, as these methods and theories can help emphasize and analyse different perspectives and enable the researcher to reflect on the research (Hignett & McDermott, 2015). The specific qualitative research method involved semistructured interviews with stakeholders—including teachers, students, and administrative staff—conducted within this case study’s framework.

Semistructured interviews are the most commonly used data collection method in qualitative research because of their versatility and flexibility (DiCicco-Bloom & Crabtree, 2006). We followed the five-step guide of Kallio et al. (2016) for semistructured interviews to develop different interview question protocols that were tailored to teachers, students, and administrative staff. For example, the teacher interviews focused on the goals, design, and specific cases of hybrid teaching, thereby allowing teachers to share their experiences and thoughts concerning the process of teaching these courses. Students, who took the perspective of audiences, discussed the most impactful online and offline courses, the relevant influencing factors, and the corresponding pros and cons. Staff members provided examples of how they had addressed unexpected situations in the context of hybrid teaching and the relevant influencing factors.

To achieve the objectives of this research, we employed a purposive sampling method to select six teachers, five students, and two technical support staff members to participate in semistructured interviews (Table 1). All these interviewees had experience with hybrid online and offline teaching and learning. Within this group, the six teachers had extensive teaching experience (i.e., more than ten years), and their blended courses received high praise from their students. For example, T5 won the 2022 "X University Annual Teaching Excellence Award." The five students were randomly selected from the majors of Materials Science and Engineering, Environmental Science and New Energy Technology, and Computer Technology and were first- or second-year PhD or master’s students. Each interview with each participant lasted approximately 1 h.

Table 1 Interviewees’ demographic information
Full size table

Qualitative data analysis methods

Qualitative data were processed and analysed primarily by thematic analysis. This method can help researchers systematically identify, interpret, and organize data, thus enabling researchers to summarize shared experiences and outcomes (Braun & Clarke, 2006). This process was supported by NVivo 12 software. Initially, the researchers repeatedly reviewed the textual materials derived from the audio recordings and conducted multiple rounds of thematic integration, completing three coding levels. We used a deductive coding approach based on the theoretical framework for this research to identify the first-level codes as teaching field, learning field, and assistant teaching field (Table 2). These contexts were examined in further detail within the dual settings of online and offline environments. The content was then refined into third-level codes, particularly by highlighting aspects that were not evident in the quantitative data. To ensure accuracy and consistency, the coauthors participated in multiple rounds of coding and discussions, thereby enhancing the reliability and validity of the findings of this research.

Table 2 Coding levels and process
Full size table

Results

Factors influencing the three fields

We hypothesized that the hybrid teaching model, which integrates online and offline elements, generates the teaching, learning, and assistant fields. Table 3 presents the bivariate correlation coefficients among the main variables associated with these three fields. These findings demonstrated that the three fields were significantly correlated with each other; the influencing factors associated with different fields were also significantly correlated.

Table 3 Bivariate correlation coefficients
Full size table

The results of the multiple linear regression analysis (see Table 4) revealed that the regression equation was significant, F (13, 475) = 36.74, p < 0.001. After we controlled for the effects of the covariates, social capital (β = 0.18, p < 0.001), symbolic capital (β = 0.29, p < 0.001), and administrative and technical assistants (β = 0.20, p = 0.004) were identified as significantly positive predictors of perceptions of the hybrid teaching model; together, these variables explained 50.1% of the variation in the overall perceptions of the hybrid teaching model. These results supported H1b, H1c, and H3c.

Table 4 Results of the multiple linear regression
Full size table

After each latent variable was established via confirmatory factor analysis (CFA), the measurement model was revealed to consist of three latent variables, including the teaching field, which was inferred from three items; the learning field, which was inferred from three items; and the assistant teaching field, which was inferred from three items. The measurement model indicated that the data exhibited an adequate fit: CFI = 0.94, TLI = 0.91, SRMR = 0.03, χ2 = 91.57, df = 54. All standardized factor loadings of the observed variables were statistically significant (p < 0.001) and highly correlated, falling within an acceptable range (i.e., from 0.76–0.91; see Table 5).

Table 5 Descriptive statistics and factor loadings pertaining to the indicators
Full size table

The fit statistics pertaining to the SEM were good (CFI = 0.95, TLI = 0.93, SRMR = 0.04, χ2 = 160.21, df = 64). Figure 2 illustrates the final SEM results (to highlight the relationships among the main variables more clearly, no covariates are displayed). The results indicated that the teaching, learning, and assistant teaching fields were highly correlated with each other (r = 0.82–0.94, p < 0.001). The perceptions of hybrid teaching were significantly better predicted by the teaching field (β = 0.61, p < 0.001) than by the learning field (β = -0.34, p = 0.32) or the assistant teaching field (β = 0.47, p = 0.08). These results supported H1.

Fig. 2
figure 2

Final SEM. Note: R2, determination coefficient; TF, teaching field; LF, learning field; AF, assistant teacher learning field; tf1–tf3, items measuring the teaching field; lf1–lf3, items measuring the learning field; af1–af3, items measuring the assistant teacher learning field; OE, perceptions of hybrid teaching; the dashed line indicates no significant paths; all regression coefficients are standardized. ***p < .001

Full size image

These results indicate that, among the three fields, the teaching field significantly impacted overall positive perceptions of hybrid teaching, particularly with respect to teachers’ social capital and symbolic capital. However, does this finding imply that the influencing factors in the other two fields had no impact? What could be the reasons underlying such findings? These exploratory questions could be addressed using an in-depth qualitative analysis of interviews conducted with teachers, students, and administrative staff.

Teaching field: online knowledge and offline interaction

Hybrid teaching expands the teaching fields of teachers from a single classroom space to a more diverse online knowledge pool. Teachers generally recognize that the most significant advantage of integrating online learning is that it can help overcome the traditional boundaries of classroom teaching, thereby guiding students to diverse online learning spaces. At this point, students are expected to engage in independent learning and exploration, thus allowing them to acquire personalized knowledge based on their individual differences. One important reason for this approach is that traditional didactic knowledge delivery is often unable to meet the needs of different students. For example, one teacher in this study explained, “Some competent students may already be familiar with many concepts before I begin teaching foundational knowledge. Therefore, classroom learning does not meet their needs, and they require access to more advanced online content” (T5). Therefore, all the teachers interviewed as part of this research maintained that knowledge acquisition, especially in the context of STEM education, can take advantage of a variety of online learning resources. Online learning, as an extension of classroom teaching content, helps students expand their learning of specific knowledge and cultivates their ability to engage in independent learning and inquiry (T6). Furthermore, one approach that can be used to balance classroom and online teaching content is to task students with repeatedly studying fragmented knowledge points online, thus allowing multiple knowledge points to be connected for discussion in the classroom (T2).

In the context of learning in an offline classroom, interaction is one of the most crucial influences on teaching effectiveness. Many interactive methods can be used in the classroom, such as traditional group discussions and question–answer sessions, as well as interactive experiences supported by technological tools, such as barrage discussions, whiteboard drawings, quiz competitions, and online discussion forums (T3, T4, T5). Moreover, the ultimate goal of hybrid teaching is to increase teaching effectiveness, which includes not only imparting knowledge but also stimulating students’ enthusiasm and curiosity with respect to learning and research (T3, T6). Students are often eager to listen to teachers’ personal experiences and other stories, which can stimulate their interest in a given subject (T1, T3). A highly popular class is not dependent on the number of PowerPoint slides it employs but rather on the fact that it introduces several vivid cases and interesting stories. One participant said, “You need to understand what questions students care about. I incorporate some content that cannot be found on search engines into my classes, answering students' questions based on my personal experience” (T3). These findings also align with the symbolic capital proposed in our hypothesis about the teacher field. Additionally, sharing technology news (T3), the most up-to-date research findings (T4), technical practices (T2), live assessments (T6), and necessary eye contact (T5) can enhance the interaction between teachers and students in the classroom. In addition, teachers maintained that practical, hands-on exercises are best conducted in offline classroom settings or laboratories.

Online and offline integration is a rather complex process that requires thorough preparation and instructional design (T1, T4). On the one hand, ensuring the presence of basic technical tools such as infrastructure, online tools, and networks is essential. On the other hand, instructional design is paramount. In line with the relevant teaching objectives, teachers must select appropriate teaching tools and anticipate potential issues by considering how to resolve them (T1). The most critical consideration in instructional design is increasing student participation and engagement (T4). Teaching content should be “student-centred,” i.e., tailored to meet students’ learning needs (T1). Another key factor in this context is the instant feedback mechanism, which involves testing students’ mastery of knowledge and adopting more flexible and differentiated optional homework formats (T3, T4). Real-time feedback on learning content, instead of delayed gratification, can help teachers and students understand students’ learning progress, enhancing interest and engagement in learning (T1).

Learning field: making learning more lively

All the students interviewed as part of this research acknowledged that hybrid teaching makes classroom learning more engaging and livelier. One student (S5) described a course that utilized both online and offline teaching methods, which greatly benefited him:

What impressed me the most was the blended teaching course on Scientific Writing and Research Papers. The first reason is that the instructor introduced the Hetang Cloud Classroom for post-class Q&A interactions. Since the class size was quite large, students like me, who are more introverted, were reluctant to engage in class discussions. However, the text-based Q&A interaction on the Cloud Classroom platform made me feel more comfortable expressing my thoughts. The instructor also engaged with our feedback on the platform during class, which was very friendly for students with social anxiety. The second reason is that the instructor used many online tools during class, such as class login attendance and a random selection system for student interactions, which made the format quite fun. The third reason is the clever design and innovative approach of the course’s online teaching methods. The course included an online session with an experienced author, which was incredibly inspiring for us. This direct interaction with the author made the entire class more engaging even though it was online. Compared to the teacher praising how good an article is, interacting with the author is far more captivating and helpful.

Similarly, other students indicated that online tools and learning make learning more convenient and tailored to their individual needs. For example, online recording tools can capture classroom teaching content, thus allowing students who miss key knowledge points to review those points repeatedly after class (S2):

Online recorded courses are very convenient. If the teacher or students are unable to attend the class in person due to unforeseen circumstances, they can still benefit from the high-quality teaching online. Additionally, if any key points or difficult concepts are missed during the classroom, students can revisit the recorded lectures anytime, aiding their understanding and absorption of knowledge. Furthermore, high-quality online recorded courses help disseminate educational resources to less-developed areas, enabling students to learn and absorb knowledge more efficiently. Ultimately, this promotes the sustainable use of high-quality educational resources.

Moreover, online learning encourages students to learn autonomously according to their own needs (S1). As noted by S1, “The teacher’s online recorded videos are not limited to fixed time slots. We can choose the time and frequency freely, which helps us develop good habits of independent learning and scientific time management skills.”

In addition, student feedback validated the importance of teacher symbolic capital and social capital within the teaching field. One student interviewed as part of this research (S4) identified a course titled Literature Retrieval and Thesis Writing as the most impactful course for her. She said:

The instructor of this course is unique and left a deep impression on me. He is a relatively young teacher who has just transitioned from being a graduate to taking on the role of an educator. Therefore, he guides us by sharing his experiences of writing papers and the interesting moments from his academic career, making the class very engaging. Another distinctive feature of this course is the teacher’s personal network—he invited an editor from Nature Communications, a prestigious journal in the eyes of many students, to give us an online lecture. This was a rare opportunity and exactly what we were looking for. Even though the editor was overseas, the online experience was nearly as effective as in-person. Interacting with the editor and even asking questions was a great experience.

In my view, a successful course has three essential elements: first, the content taught by the instructor must be what we genuinely want to learn, and it should be connected to regular feedback; second, the teaching methods employed by the instructor should enhance the learning experience; and third, the instructor should inspire students to explore the rich online teaching resources available independently.

Assistant teaching field: making things happen

Teaching assistant staff primarily support instructors by ensuring the smooth conduct of online teaching. Their main tasks include disseminating information, addressing queries, collecting feedback, and dealing with emergencies (AS2). The most crucial task for administrative and technical assistants involves swiftly addressing any unforeseen issues. According to AS1, the most common scenarios in online teaching include online interactions with guest speakers and students’ remote course participation. These sessions often entail many technical problems. For example, an invited guest lecturer might face issues with transmitting his or her voice due to problems with the offline player, thus making it seem as if the guest is performing a mime routine. In such cases, the technical assistant must respond quite rapidly, troubleshoot the issue, and implement various solutions. Ultimately, AS1 resolved this problem by switching the audio output device. This situation is in line with the data indicating that the ability of administrative and technical assistants to resolve technical bottlenecks effectively significantly influences teaching satisfaction.

Under current technological conditions, hybrid teaching faces many technical and interaction-related challenges. In particular, for “beginner” students, unfamiliar concepts make it difficult for them to perform well in classroom discussions. For teachers, lecturing in front of a screen limits their ability to derive mathematical formulas, write on the board, and gauge student feedback through the screen. Therefore, the most critical factor in hybrid teaching remains the interaction between teachers and students in both online and offline contexts. In the cross-spatial virtual environment, teachers cannot assess students’ learning and psychological states based on their facial expressions and lack of emotional exchanges (AS1). This situation thus imposes greater demands on teachers, requiring them to be proficient in various technological tools and methods and to craft effective teaching designs. Such designs should include clear learning objectives, a variety of learning resources, and immediate student feedback. In this process, technical assistants can offer timely technical support, thereby ensuring that teachers and students can use online learning tools and resources smoothly.

Despite these challenges, from the perspective of technical assistants, the integration of online and offline blended teaching offers great potential for learning in STEM fields. One major reason for such potential is that engineering education combines textbook knowledge with real-world applications (AS1, AS2). Traditional lecture-based teaching methods fail to develop students’ practical skills, design thinking, and problem-solving abilities. However, online learning, interaction, and practice offer more possibilities in this regard. For example, live broadcasts from otherwise inaccessible corporate production lines can safely and conveniently expose students to real-world environments (AS1). High-demand experiments, such as the preparation of solar cells, which require specialized equipment, can also be demonstrated online (SA2). These vivid real-world interactions help STEM students more readily acquire cutting-edge, interdisciplinary, and comprehensive knowledge.

In addition, although the data analysis did not reveal any direct correlation between guest speakers and teaching satisfaction, teachers, students, and technical assistants all highlighted the importance of external guest speakers drawn from the industry. Inviting industry professionals to share real-world stories is a crucial component of teaching design. Although students prefer to meet guest speakers in person, geographical constraints sometimes make online lectures a more viable option (T2). For example, T1 invited guest lecturers from Silicon Valley and Michigan to share their publication experiences, T3 invited senior scholars to discuss research frontiers, T4 invited R&D directors from companies to share their current challenges, and T5 invited technical experts to explain core technologies. These sessions became the most memorable parts of the courses for students, and they promoted students’ interest in their studies (S2, S3, S4, S5). Consequently, this hybrid teaching approach provides both teachers and students with more opportunities to connect with both industry and society.

The quantitative and qualitative findings of this research mutually support and complement each other. The quantitative analysis performed for this research visually demonstrated the correlations, regression results, and structural equation modelling among the three fields. This analysis confirmed the significant influence of teachers, technical assistants, and the teaching field on positive perceptions of hybrid teaching. Furthermore, the qualitative analysis substantiated and explained the influencing factors associated with these fields. The deeper levels of online and offline interactions that occurred within the teaching field were observed to enhance teaching effectiveness. In addition to the two forms of capital, student-centred instructional design and practice were also identified as critical. The learning and assistant teaching fields echoed the importance of the teaching field and provided diverse perspectives on the opportunities and challenges associated with hybrid teaching. These qualitative findings can thus help interpret and supplement the results from the quantitative data.

Discussion and conclusion

This study accomplished three primary research purposes. First, it established a comprehensive framework that can be used to explore the factors that influence the positive perceptions of hybrid teaching models in the context of higher engineering education. Based on Bourdieu’s field theory, we identified and analysed three distinct fields and their corresponding subfactors, thereby providing a theoretical foundation for understanding the complex dynamics underlying hybrid learning and teaching environments. Second, using a mixed-methods approach, this study revealed how different forms of capital and related factors within multiple teaching fields are linked to and influence each other. Third, the in-depth case study analysis conducted as part of this research highlighted the advantages and challenges associated with using hybrid teaching models in engineering education. The following paragraphs elaborate on these three points in further detail.

A new framework: more suitable and comprehensive

In the context of hybrid learning in engineering, a holistic framework for understanding the unique dynamics at play during hybrid learning in engineering is essential. This study provides a preliminary model for such a framework, revealing specific subfactors contributing to hybrid teaching in engineering education in the digital era.

First, the framework aligns with the digital age because it takes advantage of changes in digital resources. Just as social change involves alterations in the relationships among existing fields or the establishment of new fields, fields of higher education must be expanded in the context of hybrid teaching (Schirone, 2023).

In addition, the subfactors associated with each field reflect the distinctive characteristics of engineering education, which depend to a large extent on practical experience and connections with industry (Al Munifi & Alfawzan, 2023). For example, the learning field contains three components: shallow learning, deep learning, and hands-on learning. These elements reflect the unique learning characteristics of engineering students, who focus on practical application, hands-on skills, and collaboration.

The findings of this study align with and reinforce key conclusions from prior research in engineering education. Specifically, the selected factors resonate with established scholarship, highlighting the critical roles of hands-on work and real-time problem solving in effective engineering education (Jamison et al., 2014). Furthermore, by combining online and offline components, hybrid learning necessitates administrative support for technical issues within the assistant teaching field to ensure smooth integration (Abuhassna et al., 2022). However, the social and symbolic capital of teachers and their ability to invite guest lecturers to deliver online lectures have rarely been mentioned in previous studies of hybrid learning (Abuhassna et al., 2022). By highlighting the influence of social and economic factors on students’ access to resources, the potential connection between teachers and the practical engineering market, and learning opportunities (Dika & Martin, 2018), this framework can guide the development of more inclusive and practical hybrid teaching models in the context of engineering education, thereby allowing this field to adapt to the needs of modern society for engineering talent (Han & Chai, 2024).

New insights: more possibilities for participation in hybrid education

This mixed-methods research upheld the central roles of students and teachers shown in previous studies (Mäkelä et al., 2022; Osaili et al., 2023; Xiao et al., 2020). Furthermore, our research explored new interactions among various roles in hybrid learning, introducing invited guest lecturers and teaching assistants as new participants. These diverse interactions create more possibilities for participation in hybrid education. By incorporating guest speakers and teaching assistants in administration and technology, schools enhance the learning experience, providing students with unique insights and support that enrich their educational journey. This approach fosters a collaborative learning environment and bridges the gap between theoretical knowledge and practical application, ultimately preparing students for success in their future careers (Abuhassna et al., 2022; Han & Chai, 2024). Therefore, it is necessary to refine the understanding of the educational experiences of all participants in the teaching process and to maximize the value that technology adds to education.

The quantitative findings of this research revealed that the teaching field has the most significant positive impact on overall satisfaction with hybrid teaching (β = 0.61, p < 0.001). Within this field, social capital (e.g., teacher‒student interactions) and symbolic capital (e.g., teacher charisma and teaching style) emerged as key predictors of positive perceptions (β = 0.18, p < 0.001 and β = 0.29, p < 0.001, respectively). The assistant teaching field, particularly concerning administrative and technical support, also positively influenced positive perceptions (β = 0.20, p = 0.004). Although the learning field did not directly predict overall perceptions (β = -0.34, p = 0.32), this finding is inconsistent with the results of previous research on this topic (Almusaed et al., 2023), which emphasized the strong impact of students’ learning experience on their perceptions of hybrid learning. Our lack of quantitative findings that reinforce these past research results may be attributed to the context of our participants, who were students from East Asian environments. These students are often accustomed to teacher-centred teaching methods (Matsuyama et al., 2019), leading them to focus more on the teacher’s role rather than their own experiences when evaluating the teaching experience. However, subsequent qualitative analysis complemented this interpretation of the data by providing a more comprehensive understanding of this topic. The learning field offered students engaging and flexible learning opportunities by combining online tools for self-paced learning with offline interactions, thus enabling them to obtain a deeper understanding and hands-on experiences. These findings align with prior research on the characteristics of hybrid learning, which has highlighted the importance of teachers’ efforts to adapt their strategies to enhance students’ learning experiences (Li et al., 2023) and technological accessibility (Mäkelä et al., 2022).

This study revealed that teachers in the hybrid teaching field represent core facilitators. This extends the new direction set out in previous research by Han and Chai (2024), which emphasized the importance of networking outside the classroom. However, the social value provided by teachers in the formal classroom was also not neglected in this study. Overall, the construction of the teaching field has a significant and positive direct effect on perceptions of hybrid teaching (H1). Furthermore, social capital (H1b) and symbolic capital (H1c) significantly positively affect overall perceptions of hybrid teaching. All six teachers who participated in the interviews mentioned that the ideal state of hybrid teaching involves students’ learning knowledge online according to their needs and then interacting offline in the classroom. In particular, in engineering education, fixed knowledge points and diverse learning resources can be accessed more efficiently online. Moreover, offline interactions, including group discussions, Q&A sessions, and various interactive activities, stimulate students’ interest in and enthusiasm for learning. With respect to other factors that influence the hybrid model, teachers highlighted that thorough preparation, student-centred instructional design, diverse technological tools, immediate feedback mechanisms, and flexible communication platforms can facilitate the smooth implementation of hybrid teaching (Abuhassna et al., 2022).

Although the learning field does not have a direct effect on the perceptions of the hybrid teaching model, students can obtain more learning opportunities and experiences from the online and offline hybrid learning model, as has been reported in previous research (Al Munifi & Alfawzan, 2023; Osaili et al., 2023). From the students’ perspective, the experience of online learning and discussions is useful and interesting. On the one hand, students can conveniently access various learning resources according to their needs. For example, they can repeatedly, freely, and selectively review recorded course materials to learn specific knowledge points. On the other hand, particularly with relatively introverted students, the online platform allows them to express their ideas freely without speaking in public. Additionally, online learning tools make teacher‒student interactions more enjoyable, increasing students’ interest in learning. In addition, the student interviews confirmed H1b and H1c, which the questionnaire results had previously supported. The students felt that, in the context of blended teaching, how teachers deliver their lessons, such as by sharing their writing experiences or utilizing their resources, can significantly enhance their learning. Other factors, including regular feedback, appropriate teaching methods, and a rich array of learning resources, can also help increase positive student perceptions of the hybrid teaching context.

The assistant teaching field is a crucial support platform that can facilitate smooth interactions and integration between the teaching and learning fields. Multiple regression analysis revealed that technical support staff and assistants positively affected perceptions of hybrid teaching. This finding aligns with the results of the interviews conducted with technical assistants. Technical support personnel are key facilitators who can help ensure that online teaching and learning proceed smoothly and that online teaching is seamlessly integrated with offline interactions. At present, hybrid teaching faces many technical challenges. Technical support staff must often respond quickly to emergencies, such as network disconnections and audio failures. This finding echoes the conclusions of previous research, such as that of Wadams and Schick-Makaroff (2022), who emphasized that the speed of response to technical issues and the ability to resolve these issues significantly affect students’ learning experiences. With respect to technical issues in hybrid teaching modes, support staff believed that timely technical support, clear teaching objectives, abundant learning resources, and immediate student feedback were essential factors in ensuring the effectiveness of hybrid teaching.

Additionally, guest speakers can enhance positive perceptions in the context of blended online and offline teaching. This aligns with previous research emphasizing the importance of integrating external resources to assist engineering students in improving their practical skills and competitiveness in the engineering job market. When exposed to industry professionals and experts, students gain valuable insights into current trends and expectations, which can significantly enhance their learning experience and prepare them for real-world challenges. This approach enriches the educational environment and fosters a stronger connection between academic learning and professional practice, ultimately benefiting students as they transition into their careers. The teachers, students, and technical staff interviewed in this research generally maintained that incorporating one or more guest speakers—including scholars, corporate executives, technical personnel, researchers, and journal editors—into the hybrid teaching model can enhance the student experience. Although this finding was not statistically significant in the survey data, at least four students identified guest speakers’ lectures as the most impactful component of their course. Online interactions offer more possibilities, especially when guest speakers face constraints related to location, time, and geography. Therefore, to design blended teaching models, selecting “external minds” (Han & Chai, 2024) closely related to the teaching content to provide additional perspectives can also help improve students’ positive perceptions of hybrid teaching.

New challenges: advantages and disadvantages of hybrid teaching

The online and offline hybrid teaching model provides new opportunities for STEM education. One of the main reasons for such opportunities is that the knowledge system associated with engineering education is relatively structured and focuses on integrating textbook knowledge with practical applications. Traditional lecture-based teaching methods focus on knowledge transmission but often fail to effectively develop students’ thinking and problem-solving skills (Karabulut‐Ilgu et al., 2018). The application of technologies, including advanced AI technologies, offers great potential concerning the future of engineering education (Bourne et al., 2019). Combining online learning with offline interactions can offer more opportunities to enhance STEM students’ learning and hands-on abilities. Many studies have proposed linking engineering education with students’ practical abilities and their competitiveness in the real-life job market (Chen et al., 2021; Häfner et al., 2013). This article expands on previous research (Abuhassna et al., 2022b; Chai et al., 2022; Li et al., 2023) by delving deeper into engineering education and integrating it with societal contexts. By leveraging Bourdieu’s theories in engineering education research, we will not only enrich our understanding of the field but also contribute to developing more equitable and effective educational practices (Devine, 2012). Considering the broader social implications of engineering education, this research emphasizes a novel way to transform the engineering education teaching system into a hybrid model. Such transformation should utilize the potential resources of schools and the influence of teachers in the engineering field to help shape a future that prioritizes both technical proficiency and social connections, ultimately preparing students for the complexities of the modern workforce.

Despite technological advancements designed to address the limitations of spatial distance, certain fundamental challenges of online teaching remain. These challenges include a sense of physical isolation, difficulties maintaining focus, the absence of a conducive learning environment, and obstacles to communicating with educators and peers (Osaili et al., 2023). Additionally, the hybrid teaching model does not eliminate the possibility that teachers may encounter problems with instructional content, nor has it fully transformed the traditional lecture format, which is characterized by passive knowledge transfer. With respect to this study, the most prominent limitation of the case study method is the difficulty associated with generalizing and extending the findings of such research to a larger number of universities (Eisenhardt & Graebner, 2007). Although the results of a single case study can offer exploratory insights, further validation with a larger scope is necessary. Future researchers should focus on multisite studies and can conduct comparative studies to analyse the effectiveness of hybrid teaching models (Mäkelä et al., 2022). Nevertheless, as educators navigate this transitional era, the continual refinement of hybrid teaching strategies, which can integrate both online and offline elements, can help reveal new possibilities for the effective dissemination of knowledge.

This exploratory study focused on hybrid learning and teaching in the context of engineering education. Future researchers could consider additional potential factors in this context, such as socioemotional factors (Li et al., 2023), supportive and engaging environments (Xiao et al., 2020), and students’ self-efficacy (Wang et al., 2023).

Moreover, future researchers are highly encouraged to employ Bourdieu’s theories of field and capital (Dika & Martin, 2018). These theories allow for a deeper exploration and elaboration of the unique characteristics of online–offline connections (Bathmaker, 2015; Ignatow & Robinson, 2017). Research on engineering education often emphasizes practical skills, hands-on experience, and technical knowledge (Al Munifi & Alfawzan, 2023; Castedo et al., 2019). However, it sometimes overlooks the broader social context and social connections that influence these final educational outcomes (Dika & Martin, 2018). There is a critical omission, as engineering education is closely linked to the job market, which demands a practical approach and fosters a tighter connection between educational institutions and industry needs (Al Mamun et al., 2022). Bourdieu’s theories provide valuable insights into how social capital factors—such as educational equity, resource accessibility, and the relationship between engineering education and the job market—impact teachers’ and students’ experiences and outcomes (Devine, 2012). By applying Bourdieu’s concepts, researchers can better understand how various forms of capital (social, cultural, and symbolic) interact within the online and offline educational fields. This approach allows researchers to investigate how these environments can either reinforce or challenge existing inequalities in access to resources and opportunities, thereby enhancing student engagement and success and ensuring that all students benefit from the evolving educational landscape.