Evaluation of nursing instructors’ readiness for blended teaching from the perspectives of instructors and students: a cross-sectional survey

Abstract

Background

Blended teaching is becoming the new normal of future education, and the sudden epidemic has accelerated this process. Instructors’ readiness for blended teaching has an important impact on the effectiveness of blended teaching and learning. This study aims to investigate nursing instructors’ readiness for blended teaching from the perspectives of instructors and students, providing implications and suggestions for blended teaching.

Methods

A cross-sectional survey was conducted among 70 nursing instructors and 414 nursing students from Henan University of Traditional Chinese Medicine. Data were collected using the translated Chinese version of the Blended Teaching Readiness Instrument. Descriptive statistics, group comparison tests, effect sizes, confirmatory factor analysis, and multiple linear regression were used for data analysis. Data were collected from March 2025 to May 2025.

Results

The statistical analysis (IBM SPSS 28) reported that the nursing instructors were ready for blended teaching, supported by nursing students’ evaluations. In addition, in the dimensions of dispositions (4.52 vs. 4.74), online integration (4.50 vs. 4.68), and data practices (4.63 vs. 4.83), students’ evaluation is lower than the instructors’ self-evaluation; whereas students’ evaluation is higher than the instructors’ evaluation in the dimension of online interaction (4.68 vs. 4.27).

Conclusions

This study indicated that nursing teachers were ready for blended teaching from both the students’ and instructors’ perspectives. However, there is room for improvement in terms of nursing instructors’ dispositions, online integration, and data practices from the perspective of students.

Clinical trial number

Not applicable.

Background

Blended teaching has increasingly become an important instructional approach in higher education as universities move from a predominantly face-to-face model to a more flexible integration of online and offline learning [1, 2]. Traditionally, classroom teaching relied mainly on direct in-person instruction, immediate teacher supervision, and scheduled classroom activities. However, the rapid development of digital technologies and the widespread use of online learning platforms have changed the ways in which teaching resources are delivered, learning activities are organized, and student engagement is monitored [3, 4]. Blended teaching combines the advantages of face-to-face instruction with the flexibility of online learning, allowing students to complete self-paced learning tasks before class and to participate in discussion, practice, and problem-solving activities during classroom sessions [5, 6]. Nevertheless, the implementation of blended teaching is complex. Designing effective blended learning experiences requires attention to learning quality, instructional design, learning technologies, and pedagogical strategies [7]. Instructors are also expected to design e-learning materials, manage online learning platforms, provide guidance for students’ autonomous learning, and align online and offline activities with course objectives [4, 8].

Previous studies have shown that instructors’ readiness is a key factor influencing the successful implementation of blended teaching and technology integration in education [9, 10]. Instructors need positive attitudes toward blended teaching, sufficient digital competence, the ability to integrate online and face-to-face learning activities, and skills in using learning data to support students’ learning progress [11, 12]. However, existing studies have reported inconsistent findings regarding teachers’ readiness for blended teaching. Some studies found that teachers or instructors demonstrated sufficient readiness for blended teaching [13–15], whereas others reported moderate or low readiness levels, especially in relation to digital pedagogy, online interaction, and the use of learning analytics [16–18]. In China, blended teaching has been widely implemented in higher education, particularly in the post-pandemic era [19]. However, studies among Chinese university teachers have shown that blended teaching readiness may differ across disciplines and teacher groups. For example, Zhang et al. (2022) reported low readiness among Chinese undergraduate teachers [17], Li et al. (2024) found insufficient readiness among foreign language teachers [18], and Guo (2023) reported that physical education teachers in colleges and universities were not fully ready for blended teaching [20].

Despite the growing body of research on blended teaching, several gaps remain. First, most existing studies have focused mainly on instructors’ self-perceptions, while relatively few have examined instructors’ readiness from the perspectives of both instructors and students. Instructor self-evaluation may reflect teaching preparation, professional identity, and perceived competence, whereas students’ evaluations may more directly reflect their actual learning experiences, including whether online resources are useful, whether online and offline activities are clearly connected, and whether feedback and interaction effectively support learning [21, 22]. Second, limited attention has been paid to nursing education, although nursing courses require the integration of theoretical knowledge, clinical reasoning, practical skills, and professional communication. These characteristics make blended teaching particularly relevant but also more demanding for nursing instructors [23, 24]. Third, few studies have examined whether instructors’ demographic and professional characteristics, such as professional title, teaching experience, age, and academic degree, are associated with blended teaching readiness in nursing education. Therefore, further research is needed to evaluate nursing instructors’ readiness for blended teaching from multiple perspectives.

The present study was conducted in a School of Nursing at a university of Traditional Chinese Medicine in Zhengzhou, China. As illustrated in Fig. 1, this school has implemented blended teaching for more than six years and has developed several nationally and provincially recognized blended courses. A MOOC-centered online teaching platform system has been established, supported by Xuexitong, Yuketang, and Ketangpai. In this context, blended teaching is organized across pre-class, in-class, and post-class stages, allowing instructors to upload resources, assign preview tasks, conduct classroom interaction, manage attendance, provide online Q&A, and support post-class review. Although blended teaching has been widely implemented in this nursing school, nursing instructors’ readiness for blended teaching has not yet been systematically evaluated. More importantly, it remains unclear whether instructors’ self-evaluations are consistent with students’ evaluations of instructors’ readiness.

Fig. 1.

Three-stage blended teaching model and MOOC-centerd online platform system

Therefore, this study aimed to evaluate nursing instructors’ readiness for blended teaching from both instructor and student perspectives and to identify factors associated with readiness. Specifically, this study examined the overall level of nursing instructors’ blended teaching readiness, explored whether instructors’ demographic and professional characteristics were associated with their self-evaluated readiness, assessed whether students’ demographic and learning-related characteristics were associated with their evaluations of instructors’ readiness, and compared differences between instructors’ self-evaluations and students’ evaluations across the dimensions of blended teaching readiness.

Methods

Study design

The objective of this study was to evaluate nursing instructors’ readiness for blended teaching from both instructor and student perspectives and to generate recommendations for improving blended teaching practice. A cross-sectional survey design was adopted because the study aimed to assess nursing instructors’ blended teaching readiness and students’ evaluations at a specific point in time. This design was appropriate for describing readiness levels, comparing instructor and student perspectives, and exploring associations between readiness scores and demographic or learning-related characteristics.

Sample

Following approval by the Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (Approval No.: 2024HL-503), a total of 70 nursing instructors and 414 nursing students from a university of Traditional Chinese Medicine in Zhengzhou, Henan Province, China, participated in this study. A convenience sampling method was used. Eligible nursing instructors and nursing students from the School of Nursing were invited to participate through WeChat groups. The inclusion criteria for nursing instructors were having taught more than one blended teaching course and having more than three years of teaching experience. The inclusion criteria for nursing students were having participated in two to three blended teaching courses, being second- or third-year students in 2025, and having engaged in blended teaching for at least six months. Participants who submitted incomplete questionnaires or failed the attention-check items were excluded from the final analysis. Data collection was conducted from March 2025 to May 2025.

Sample size adequacy was assessed for both instructors and students. For the instructor group, based on a two-independent-group comparison with a two-sided significance level of 0.05, statistical power of 0.80, and a medium effect size, at least 63 participants were required per group. The final instructor sample included 70 participants, which met this requirement. For the student group, using Cochran’s formula for survey studies with a 95% confidence level, 5% margin of error, and the most conservative proportion of 0.50, the minimum required sample size was approximately 385. The final student sample included 414 participants, which exceeded this requirement. Therefore, the sample size was considered adequate for the main analyses, although subgroup analyses among instructors should be interpreted cautiously because some subgroups were small.

Blended teaching design and alignment with the BTRI dimensions

The blended teaching in this course is built on the integration of a “point–line–plane–space” progressive model for cultivating higher-order thinking with a “three-ring six-level” teaching pathway, as illustrated in Fig. 2. At the “point” level, micro-lectures, interactive animations, and AI-based tutoring agents are used to focus on single knowledge units and core skills. At the “line” level, typical cases, mind maps, and virtual simulation systems are employed to connect discrete skills into complete clinical operation procedures. At the “plane” level, high-fidelity simulators, standardized patients, and multi-station OSCEs are combined to create comprehensive scenarios that train students’ clinical decision-making and teamwork in complex situations. At the “space” level, innovation workshops, small research projects, and practice competitions support students in transferring what they have learned to real or complex clinical problems and in generating new knowledge.

Fig. 2.

Integrated blended teaching model and BTRI alignment

In parallel, the school has established a comprehensive online platform system with MOOCs as the core, supported by Xuexitong (Superstar Learning), Yuketang (Rain Classroom), and Ketangpai (ClassIn), and has structured the teaching process into three stages: pre-class, in-class, and post-class. In the pre-class stage, teachers upload teaching resources, lesson plans, and preview tasks to MOOCs or Xuexitong to guide students’ autonomous learning. During in-class sessions, Ketangpai or Yuketang is used to conduct quick quizzes and interactive Q&A, meanwhile Ketangpai is used exclusively for attendance management. In the post-class stage, teachers use these platforms to assign hierarchical homework, distribute review materials, and provide online Q&A, thereby promoting consolidation and reflection on the learned content. Figure 2 simultaneously illustrates how this blended teaching design maps onto the five core dimensions of the Blended Teaching Readiness Instrument (BTRI): Dispositions, Online integration and management, Data practices, Personalization, and Online interaction.Screenshots of the MOOCs and platform-based online teaching interfaces are presented in Fig. 3 to visually demonstrate the specific functions and content of the software used.

Fig. 3.

Screenshots of the MOOCs and platform-based online teaching interfaces

This blended teaching design is highly consistent with the five core dimensions of the Blended Teaching Readiness Instrument (BTRI). Training in higher-order thinking and innovation strengthens instructors’ and students’ positive attitudes and beliefs toward blended teaching, corresponding to Dispositions. The integrated, multi-platform organization of pre-class, in-class, and post-class activities reflects a high level of Online integration & management. Learning analytics, quiz scores, and process data from the platforms are used to diagnose weaknesses and refine instruction, aligning with Data practices. Hierarchical assignments, differentiated resource recommendations, and one-to-one tutoring operationalize Personalization. Online discussion, real-time quizzes, virtual simulation, and group collaboration create rich Online interaction. Thus, in this study context, BTRI scores reflect not only subjective self-evaluation, but are grounded in a concrete, visually demonstrable blended teaching model and its supporting technologies.

Instruments

Data were collected using the Blended Teaching Readiness Instrument (BTRI), originally developed by Graham et al. (2019) and validated by Archibald et al. (2021) [11, 12]. The BTRI was designed to measure instructors’ readiness for blended teaching. It consists of 43 items across five dimensions: Dispositions, Online Integration and Management, Data Practices, Personalization, and Online Interaction. The Dispositions dimension assesses attitudes and beliefs toward blended teaching; Online Integration and Management assesses the ability to organize and manage online and face-to-face learning activities; Data Practices assesses the use of learning data to monitor and support students; Personalization assesses the ability to provide differentiated learning support; and Online Interaction assesses the ability to promote communication and interaction in online learning environments.

As no Chinese version of the BTRI was available, the instrument was translated and culturally adapted for use in the present study. First, the original English version was independently translated into Chinese by bilingual researchers with experience in higher education and English-language teaching. The translated versions were then compared and synthesized into a preliminary Chinese version through discussion among the research team. Second, the preliminary Chinese version was back-translated into English by an independent bilingual translator who was not involved in the initial translation. The back-translated version was compared with the original English version to identify discrepancies in meaning, wording, and conceptual equivalence. Third, an expert review was conducted to assess semantic equivalence, cultural relevance, clarity, and appropriateness for the Chinese nursing education context. Minor wording adjustments were made to ensure that the items were understandable and suitable for both nursing instructors and nursing students.

A pilot survey was subsequently conducted with 80 nursing students and 15 nursing instructors from Henan University of Traditional Chinese Medicine. Participants were asked to comment on the readability, clarity, comprehensibility, and ease of completion of the Chinese BTRI. Feedback from the pilot survey and on-site interviews was used to further refine the wording of several items. The instrument’s components were not removed. BTRI contained 5 dimensions with a total of 43 entries. The KMO value was 0.880, and Bartlett’s test of sphericity was statistically significant (p < 0.001). In addition, Cronbach’s alpha was 0.865, indicating good internal consistency. Additionally, the BTRI’s Cronbach’s alpha value was 0.865, indicating high internal consistency. The scale is a Likert-type survey in which the options range from 1 to 6. This means that the lowest possible mean value for the items is 1 (less ready), and the highest possible mean value is 6 (more ready). To further examine the structural validity of the translated Chinese version of the BTRI, confirmatory factor analysis was performed using the student sample. The student sample was used for CFA because its sample size was relatively adequate for preliminary structural validation, whereas the instructor sample was small and therefore not suitable for conducting a separate CFA of the 43-item five-factor instrument. The five-factor model was specified based on the original BTRI framework, including Dispositions, Online Integration and Management, Data Practices, Personalization, and Online Interaction. Model fit was evaluated using multiple indices, including the chi-square to degrees of freedom ratio, root mean square error of approximation, standardized root mean square residual, comparative fit index, and Tucker–Lewis index. Standardized factor loadings were also examined to evaluate the association between each item and its corresponding latent factor.

Data collection

The study’s questionnaire was posted to https://www.wjx.cn, an online survey service. To enhance data collection quality, we implemented measures to prevent duplicate responses from the same ID and incorporated attention-check items in the survey. The researcher shared the link with the nursing students in the class WeChat group and the instructors of the blended teaching professional instructors group at the School of Nursing. The survey should only take twenty minutes to complete. The researcher went over the study’s objectives, methodology, confidentiality protocols, and permissions process in the WeChat groups. The poll asked for no personal information and also made it clear that not participating would have no negative impact on grades for students.

Data analysis

Data were analyzed using IBM SPSS Statistics version 28.0. Descriptive statistics were used to summarize demographic characteristics and blended teaching readiness scores, including frequencies, percentages, means, standard deviations, and 95% confidence intervals where appropriate. Before group comparisons, the normality of continuous readiness scores and homogeneity of variance were assessed. For data that met the assumptions of normality and equal variance, independent-samples t-tests and one-way ANOVA were used for two-group and multi-group comparisons, respectively, with Scheffé post-hoc tests conducted when significant differences were found. For data that did not meet these assumptions, non-parametric tests were applied, including the Wilcoxon rank-sum test for two-group comparisons and the Kruskal–Wallis H test for comparisons involving three or more groups. The Wilcoxon rank-sum test was also used to compare instructors’ self-evaluations with students’ evaluations, and the Jonckheere–Terpstra test was used to examine ordered trends across MOOC engagement duration groups. Multiple linear regression analysis was conducted to identify independent factors associated with instructors’ blended teaching readiness, with the total BTRI mean score as the dependent variable and gender, age, years of teaching experience, highest degree, and professional title as independent variables. Effect sizes were reported together with p-values to indicate the magnitude of group differences, and all statistical tests were two-tailed with the significance level set at p < 0.05.

Results

Preliminary psychometric evaluation of the Chinese BTRI

Confirmatory factor analysis was conducted using the student sample to examine the five-factor structure of the Chinese version of the BTRI, and the results are presented in Table 1. The hypothesized five-factor model was specified according to the original structure of the instrument, including Dispositions, Online Integration and Management, Data Practices, Personalization, and Online Interaction. The model converged successfully and showed generally acceptable fit to the data: χ²/df = 3.392, RMSEA = 0.076, and SRMR = 0.048. All standardized factor loadings were positive and ranged from 0.588 to 0.849, indicating that the items loaded adequately on their corresponding latent factors. The CFI and TLI values were 0.860 and 0.852, respectively, suggesting that further validation with larger and more diverse samples is still needed. Overall, these findings provided preliminary support for the five-factor structure of the Chinese version of the BTRI in the present nursing education context.Because the instructor sample was relatively small, CFA was not conducted separately in the instructor sample.

Table 1.

Confirmatory factor analysis fit indices of the Chinese BTRI

Fit index	Value
χ²	2883.491
df	850
χ²/df	3.392
CFI	0.860
TLI	0.852
RMSEA	0.076
SRMR	0.048
Standardized factor loadings	0.588–0.849

Note. CFA was conducted using the student sample. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual

Instructors participants’ demographic variables and group comparisons

Among the 70 instructor participants, the majority were female (88.6%), while male instructors accounted for 11.4%. Regarding age, more than half of the instructors were aged 31–40 years (54.3%), followed by those aged 41–50 years (20.0%), 22–30 years (12.9%), and 51–60 years (12.9%). In terms of teaching experience, 37.1% of the instructors had 11–20 years of teaching experience, 34.3% had 5–10 years, 21.4% had 3–5 years, and 7.1% had more than 20 years of teaching experience.

Regarding highest degree, more than half of the instructors held a master’s degree (58.6%), followed by a doctoral degree (32.9%) and a bachelor’s degree (8.6%). In terms of professional title, most instructors were lecturers (57.1%), followed by associate professors (25.7%). Assistant teachers, professors, and instructors with other professional titles each accounted for 5.7%.

As shown in Table 2, no statistically significant differences in instructors’ self-evaluated blended teaching readiness were found by gender, age, years of teaching experience, or highest degree. However, a statistically significant difference was observed by professional title (F = 2.45, p = 0.04, η² = 0.131). The mean readiness score was lowest among assistant teachers (3.84 ± 0.68) and highest among professors (4.92 ± 0.56), followed by associate professors (4.87 ± 0.80), lecturers (4.66 ± 0.57), and instructors with other professional titles (4.43 ± 0.19). Post-hoc comparisons indicated that assistant teachers reported significantly lower readiness scores than lecturers, associate professors, and professors. No statistically significant differences were found among the remaining professional title groups.

Table 2.

Univariate analysis of (BTRI) among instructors (n = 70)

Item	N (%)	± s	95% CI	t/F	P	Effect size
Gender
Male	8(11.4%)	4.78 ± 0.84	4.08 to 5.48	0.94	0.62	d = 0.188
Female	62(88.6%)	4.65 ± 0.64	4.49 to 4.81
Age
22–30 years old	9(12.9%)	4.64 ± 0.75	4.06 to 5.22	0.94	0.43	η² = 0.041
31–40 years old	38(54.3%)	4.64 ± 0.62	4.43 to 4.84
41–50 years old	14(20%)	4.90 ± 0.62	4.54 to 5.26
51–60 years old	9(12.9%)	4.45 ± 0.77	3.85 to 5.04
Years of teaching experience
3–5 years	15(21.4%)	4.53 ± 0.82	4.08 to 4.99	1.31	0.28	η² = 0.056
5–10 years	24(34.3%)	4.54 ± 0.57	4.31 to 4.78
11–20 years	26(37.1%)	4.78 ± 0.64	4.52 to 5.04
> 20 years	5(7.1%)	5.05 ± 0.58	4.33 to 5.76
Highest degree
No degree	0(0%)	0		0.77	0.47	η² = 0.022
Undergraduate	6(8.6%)	4.64 ± 0.49	4.12 to 5.15
Master	41(58.6%)	4.59 ± 0.68	4.38 to 4.81
PhD	23(32.9%)	4.80 ± 0.65	4.52 to 5.09
Professional title
Assistant teacher	4(5.7%)	3.84 ± 0.68	2.76 to 4.93	2.45	0.04*	η² = 0.131
Lecturer	40(57.1%)	4.66 ± 0.57	4.47 to 4.84
Associate Professor	18(25.7%)	4.87 ± 0.80	4.47 to 5.27
Professor	4(5.7%)	4.92 ± 0.56	4.02 to 5.82
Other	4(5.7%)	4.43 ± 0.19	4.13 to 4.73

* indicates P < 0.05

Note. The dependent variable was instructors’ blended teaching readiness mean score. 95% CIs refer to group means. d = Cohen’s d; η² = eta squared

Factors associated with instructors’ blended teaching readiness

A multiple linear regression analysis was performed to further identify the independent factors associated with instructors’ blended teaching readiness, and the results are presented in Table 3. The mean score of blended teaching readiness was used as the dependent variable. Gender, professional title, highest degree, age group, and years of teaching experience were entered simultaneously as independent variables. The model was statistically significant, F(11,58) = 2.960, p = 0.004, indicating that the included variables jointly explained a significant proportion of the variance in instructors’ blended teaching readiness. The model explained 36.0% of the variance in readiness scores, with an adjusted R² of 0.238.

Table 3.

Multiple linear regression analysis of factors associated with instructors’ blended teaching readiness

Variable	Category	B	SE	95% CI	t	p
Gender	Male	Reference
	Female	-0.230	0.252	-0.734 to 0.275	-0.911	0.366
Professional title	Assistant teacher	Reference
	Lecturer	1.076	0.386	0.304 to 1.848	2.791	0.007*
	Associate professor	1.408	0.465	0.478 to 2.339	3.031	0.004*
	Professor	1.892	0.631	0.629 to 3.155	2.999	0.004*
	Other	1.147	0.457	0.231 to 2.062	2.507	0.015*
Highest degree	Bachelor’s degree	Reference
	Master’s degree	-0.614	0.348	-1.310 to 0.083	-1.764	0.083
	Doctoral degree	-0.444	0.422	-1.289 to 0.401	-1.051	0.298
Age	22–30 years	Reference
	31–40 years	-0.262	0.231	-0.724 to 0.201	-1.132	0.262
	41–50 years	-0.419	0.307	-1.035 to 0.196	-1.364	0.178
	51–60 years	-1.564	0.402	-2.369 to -0.759	-3.889	< 0.001*
Years of teaching experience	Continuous, years	0.028	0.016	-0.003 to 0.059	1.790	0.079

* indicates P< 0.05

Note The dependent variable was the mean score of blended teaching readiness among instructors. B represents the unstandardized regression coefficient. CI = confidence interval; SE = standard error. Reference categories were male, assistant teacher, bachelor’s degree, and 22–30 years. Model fit: R² = 0.360, adjusted R² = 0.238, F(11,58) = 2.960, p = 0.004

After controlling for gender, highest degree, age, and years of teaching experience, professional title remained significantly associated with instructors’ blended teaching readiness. Compared with assistant teachers, lecturers had significantly higher readiness scores (p = 0.007). Similarly, associate professors (p = 0.004), professors (p = 0.004), and instructors with other professional titles (p = 0.015) also demonstrated significantly higher blended teaching readiness. These results suggest that instructors with higher professional titles tended to report stronger readiness for blended teaching.

Age was also identified as a significant factor in the multivariable model. Compared with instructors aged 22–30 years, those aged 51–60 years had significantly lower blended teaching readiness scores (p < 0.001). However, no statistically significant differences were observed among instructors aged 31–40 years or 41–50 years when compared with the 22–30-year group. In addition, gender and highest degree were not significantly associated with blended teaching readiness after adjustment for other variables. Years of teaching experience showed a positive but non-significant association with readiness scores (p = 0.079), suggesting that longer teaching experience may be related to higher readiness, although this association did not reach statistical significance.

Overall, the multivariable findings confirmed that professional title and age were independently associated with nursing instructors’ blended teaching readiness. In particular, instructors with higher professional titles showed greater readiness, while instructors aged 51–60 years reported lower readiness than the youngest age group.

Student participants’ demographic variables and group comparisons

Among the 414 student participants, most were female (82.9%), while male students accounted for 17.1%. The proportions of second-year and third-year students were relatively balanced, with second-year students accounting for 51.0% and third-year students accounting for 49.0%.

Regarding preferred teaching methods, the majority of students preferred blended teaching (72.7%), followed by completely traditional teaching (20.5%) and completely online teaching (6.8%). In terms of electronic equipment used for learning, most students used mobile phones (59.2%), followed by both mobile phones and computers (29.2%), laptops (10.9%), and desktop computers (0.7%). Regarding MOOC engagement duration, nearly half of the students had engaged with MOOCs for 1–2 years (49.0%), and 49.5% had engaged with MOOCs for two years or longer. Only a small proportion of students reported MOOC engagement duration of 6–12 months (1.0%) or ≤ 6 months (0.5%).

As shown in Table 4, students’ evaluations of instructors’ blended teaching readiness did not differ significantly by gender, year level, preferred teaching method, or electronic equipment used. However, a statistically significant ordered trend was observed across MOOC engagement duration groups using the Jonckheere–Terpstra test (ZJT = 2.26, p = 0.02, r = 0.111). The mean ranks increased from students with ≤ 6 months of MOOC engagement to those with ≥ 2 years of MOOC engagement, suggesting that students with longer MOOC engagement duration tended to give higher evaluations of instructors’ blended teaching readiness. However, the effect size was small.

Table 4.

Univariate analysis of (BTRI) among students (n = 414)

Item	N (%)	Mean rank	95% CI	χ²/ Z/JT	P	Effect size
Gender
Male	71(17.1%)	199.54	4.39 to 4.73	-0.616	0.54	r = 0.030
Female	343(82.9%)	209.15	4.52 to 4.66
Year Level
Second year	211(51%)	197.88	4.45 to 4.63	-1.669	0.095	r = 0.082
Third year	203(49%)	217.1	4.54 to 4.72
Favorite teaching methods
Completely traditional teaching	85(20.5%)	189.74	4.31 to 4.62	4.21	0.12	ε² = 0.005
Blended teaching	301(72.7%)	209.35	4.53 to 4.68
Completely online teaching	28(6.8%)	241.48	4.50 to 4.98
Electronic equipment used
Mobile phone	245(59.2%)	197.11	4.46 to 4.62	5.82	0.12	ε² = 0.007
Desktop computer	3(0.7%)	261.67	3.77 to 5.98
Laptop	45(10.9%)	206.89	4.37 to 4.79
Cell phones and computers	121(29.2%)	227.42	4.55 to 4.79
MOOC engagement duration
≤ 6 months	2(0.5%)	44.5	2.47 to 5.13	2.26	0.02*	r = 0.111
6–12 months	4(1.0%)	164.88	2.31 to 5.94
1–2 years	203(49.0%)	197.41	4.45 to 4.63
≥ 2 years	205(49.5%)	219.91	4.56 to 4.73

* indicates P < 0.05

Note. 95% CIs refer to group means. Mean ranks and test statistics were based on non-parametric analyses. r = rank-based effect size; ε² = epsilon squared; ZJT = standardized Jonckheere-Terpstra statistic

The blended teaching readiness levels of nursing instructors

Table 5; Fig. 4 present nursing instructors’ self-evaluations and students’ evaluations of instructors’ blended teaching readiness. The overall BTRI mean score was 4.67 ± 0.66 for instructors and 4.58 ± 0.65 for students, both corresponding to the “Ready” level. No statistically significant difference was observed in the overall readiness score between instructors and students (mean difference = 0.081, Z = 0.852, p = 0.394, r = 0.039).

Table 5.

Descriptive analyses of blended teaching readiness levels of nursing instructors as rated by the nursing instructors themselves and nursing students

Dimension	Students Mean ± SD	Students 95% CI	Instructors Mean ± SD	Instructors 95% CI	Mean difference	95% CI of difference	Z	p	Effect size
Dispositions	4.52 ± 0.73	4.45 to 4.59	4.74 ± 0.83	4.54 to 4.94	0.213	0.004 to 0.422	2.369	0.018*	r = 0.108
Online Integration & Management	4.55 ± 0.70	4.48 to 4.62	4.74 ± 0.76	4.56 to 4.92	0.193	0.001 to 0.385	2.216	0.027*	r = 0.101
Integration	4.50 ± 0.76	4.42 to 4.57	4.68 ± 0.83	4.48 to 4.88	0.184	-0.027 to 0.395	2.006	0.050	r = 0.091
Management	4.59 ± 0.73	4.52 to 4.66	4.79 ± 0.80	4.60 to 4.98	0.199	-0.003 to 0.402	1.902	0.057	r = 0.086
Data Practices	4.63 ± 0.72	4.56 to 4.70	4.83 ± 0.77	4.65 to 5.02	0.208	0.012 to 0.404	2.060	0.039*	r = 0.094
Personalization	4.56 ± 0.77	4.49 to 4.64	4.71 ± 0.78	4.53 to 4.90	0.152	-0.047 to 0.352	1.443	0.149	r = 0.066
Online Interaction	4.68 ± 0.72	4.61 to 4.75	4.27 ± 0.67	4.11 to 4.43	-0.403	-0.576 to -0.229	-3.972	< 0.001*	r = 0.181
Blended Teaching Readiness	4.58 ± 0.65	4.52 to 4.65	4.67 ± 0.66	4.51 to 4.82	0.081	-0.087 to 0.250	0.852	0.394	r = 0.039

* indicates P < 0.05. Legend: 4.76-6.00 = More Ready; 3.51–4.75 = Ready; 2.26–3.50 = Slightly Ready; 1.00- 2.25 = Less Ready

Note. Mean difference = instructors minus students. 95% CI of difference was calculated using Welch’s method. Z and r were derived from rank-sum tests. Effect size r is presented as an absolute value

Fig. 4.

Comparison of blended teaching readiness levels of nursing instructors as rated by the nursing instructors and students

At the dimension level, instructors rated themselves significantly higher than students rated them in Dispositions (4.74 ± 0.83 vs. 4.52 ± 0.73; mean difference = 0.213, Z = 2.369, p = 0.018, r = 0.108), Online Integration & Management (4.74 ± 0.76 vs. 4.55 ± 0.70; mean difference = 0.193, Z = 2.216, p = 0.027, r = 0.101), and Data Practices (4.83 ± 0.77 vs. 4.63 ± 0.72; mean difference = 0.208, Z = 2.060, p = 0.039, r = 0.094). Within Online Integration & Management, neither the Integration subdimension (p = 0.050, r = 0.091) nor the Management subdimension (p = 0.057, r = 0.086) reached statistical significance.

In contrast, students rated instructors significantly higher than instructors rated themselves in Online Interaction (4.68 ± 0.72 vs. 4.27 ± 0.67; mean difference = -0.403, Z = -3.972, p < 0.001, r = 0.181). No statistically significant difference was found in Personalization (p = 0.149, r = 0.066). Among instructors’ self-evaluations, Data Practices received the highest score and was interpreted as “More Ready,” whereas Online Interaction received the lowest score and was interpreted as “Ready.” Among students’ evaluations, Online Interaction received the highest score, while the Integration subdimension received the lowest score; all student-rated scores were interpreted as “Ready.”

Discussion

Nursing instructors were ready for blended teaching

The findings from the study suggested that nursing instructors were ready for blended teaching, which was verified from the perspective of nursing students. This result should be interpreted in relation to the institutional context of the study. The nursing school had implemented blended teaching for several years, developed several national and provincial blended courses, and established a multi-platform teaching system based on MOOCs, Xuexitong, Yuketang, and Ketangpai. These institutional conditions may have contributed to instructors’ readiness by providing technical infrastructure, teaching resources, and repeated opportunities for practice. This corresponded to the recent research studies [13, 25–27]. Specifically, nursing instructors were ready on all dimensions. In other words, nursing instructors had a high level of attitudes and beliefs in blended teaching, and they had a high level of ability to effectively implement blended teaching. They demonstrated proficiency in integrating online and offline courses, as well as effectively managing students and course content online. Furthermore, they efficiently leveraged online data to monitor student learning progress in real-time and maintained active online interactions with students. They could timely and appropriately adopt personalized interventions that effectively promote students’ achievement, and increase interactions. Öztürk, et al. (2024) [15]also obtained similar results in the study of pre-service teachers. However, Su, et al. (2020) [16] showed that the overall readiness of college foreign language teachers’ blended teaching was at a medium level. This is related to the experience of nursing instructors who have previously carried out blended teaching and participated in relevant training. In addition, after experiencing online teaching during the pandemic, the instructors believe that online teaching is important.

Despite strong readiness overall, instructors frequently described a gap between willingness and actual execution, noting that blended teaching requires substantial time to design courses, manage multiple platforms, and continuously update digital resources. Many instructors indicated that the heavy workload of teaching, research, and family obligations limits the depth of blended-teaching practice, which may suppress the teaching benefits expected from highly positive attitudes. Similar observations were reported by Creagh et, al. (2023) [28], who emphasized that insufficient instructional design support and digital fluency can constrain practical readiness even when motivation is high.

Nursing instructors’ readiness for blended teaching is affected by professional title: assistant teachers demonstrate the lowest readiness

The results revealed that the nursing instructors who were Assistant teachers were significantly less ready for blended teaching than those who were Lecturers, Associate Professors, or Professors. The reason may be that the research subject’s institution took the lead in building a large number of MOOC courses as early as 2017, and began and continued to carry out blended teaching reform. During this period, the university continuously held various training and lectures on various technologies related to blended teaching and took various measures to encourage instructors to carry out blended teaching. Thus, the blended teaching readiness of all instructors was improved. However, due to the lack of teaching experience, the Assistant teachers’ understanding of the course content and the design of the classroom need to be improved. The lower readiness among assistant teachers does not necessarily indicate resistance to blended teaching; rather, it may reflect limited practical experience and insufficient professional support. Blended teaching requires not only subject knowledge, but also digital pedagogy, platform management, learning analytics, and online interaction skills. Therefore, early-career instructors may need more structured mentoring and practical training. Their competencies in blended teaching are relatively low, and their blended teaching readiness is also low. However, Tulyakul (2020) [23] put forth that senior teachers were less ready for blended teaching due to technological problems. In addition, the results show that the nursing instructors’ readiness for blended teaching did not significantly differ according to their gender, age, years of teaching experience, and highest degree. Gültekin & Mede (2024) [14] also agreed that teachers’ education level did not affect their readiness for Blended teaching. Jenkins and Crawford (2021) [29] stressed that strengthening instructors’ training is extremely important for improving the readiness of blended teaching. This highlights the urgent need for blended teaching training tailored to Assistant teachers compared to senior instructors. The multiple linear regression analysis further strengthened this interpretation by showing that professional title remained significantly associated with blended teaching readiness after controlling for gender, highest degree, age, and years of teaching experience. This finding suggests that the influence of professional title cannot be fully explained by demographic differences alone. Rather, professional title may reflect instructors’ cumulative participation in curriculum reform, online course construction, teaching innovation projects, and institutional training related to blended teaching.

Age-related differences in nursing instructors’ blended teaching readiness

The multiple linear regression analysis also showed that age was independently associated with nursing instructors’ blended teaching readiness. Specifically, instructors aged 51–60 years reported significantly lower readiness scores than those aged 22–30 years. This finding suggests that age-related differences may exist in instructors’ adaptation to blended teaching environments. Although senior instructors often have rich teaching experience and strong professional knowledge, blended teaching requires continuous adaptation to digital platforms, online resources, learning analytics, and technology-supported interaction. Therefore, older instructors may face additional challenges when transforming traditional teaching experience into an integrated online–offline teaching model.

This finding does not indicate that older instructors are less competent in teaching. Rather, it highlights the need to provide more targeted digital teaching support for different age groups. For older instructors, practical training in platform operation, learning data interpretation, online interaction tools, and efficient management of online teaching activities may be particularly useful. At the same time, their accumulated teaching experience should be fully valued and integrated into blended course design. Cross-generational collaboration may help combine the digital familiarity of younger instructors with the pedagogical experience of senior instructors, thereby improving the overall quality of blended teaching.

Nursing instructors’ readiness for blended teaching is affected by students’ duration of MOOC engagement

The findings demonstrated that students with a long contact time with MOOCs had a higher evaluation of instructors’ blended teaching readiness than students with a short contact time with MOOCs. Moreover, students’ evaluation of instructors’ blended teaching readiness did not significantly differ according to their gender, year level, favorite teaching methods, and electronic equipment used. This study also found that the vast majority of students’ favorite teaching method was Blended teaching, which revealed that in the post-epidemic era, most students have gradually accepted and begun to adapt to the new learning method of blended learning. MOOC is an important part of Blended teaching. The longer students contact MOOCs, the deeper their acceptance and experience of Blended teaching, and the higher their recognition of instructors’ blended teaching. This result may reflect the role of learner familiarity in shaping perceptions of blended teaching. Students who have used MOOCs for a longer period may better understand the structure, rhythm, and expectations of blended learning. They may also be more capable of navigating online resources, completing pre-class learning tasks, participating in online discussions, and using digital platforms for review and reflection. In contrast, students with limited MOOC experience may face difficulties adapting to self-directed online learning, managing learning time, and connecting online materials with face-to-face classroom activities. Therefore, students’ evaluations of instructors’ readiness may reflect not only instructors’ teaching competence, but also students’ own readiness for blended learning.

Significant perception gaps between nursing instructors’ self-evaluations and students’ evaluations

One of the most important findings of this study is the difference between nursing instructors’ self-evaluations and students’ evaluations of instructors’ blended teaching readiness. The results showed that nursing instructors’ self-evaluations were higher than students’ evaluations in terms of Dispositions, Online Integration & Management, and Data Practices. However, in the dimension of Online Interaction, students’ evaluations were higher than instructors’ self-evaluations. These perception gaps are pedagogically meaningful because they suggest that instructors and students may evaluate blended teaching readiness from different perspectives and according to different criteria.

From the instructors’ perspective, readiness may be evaluated mainly according to their teaching preparation, platform use, instructional design, and efforts invested in blended teaching. For example, instructors may consider themselves well prepared because they have uploaded online resources, designed pre-class learning tasks, used online quizzes, monitored learning data, and organized online and offline teaching activities. However, from the students’ perspective, readiness may be judged more directly through their actual learning experience, such as whether online and offline activities are clearly connected, whether the learning workload is manageable, whether feedback is timely and useful, and whether online resources help them understand difficult knowledge. Therefore, the lower student evaluations in Dispositions, Online Integration & Management, and Data Practices may indicate that instructors’ teaching intentions and preparations were not always fully perceived or experienced by students.

This finding can be interpreted through the Community of Inquiry framework, which emphasizes the interaction of teaching presence, social presence, and cognitive presence in effective online and blended learning environments [21]. Instructors’ higher self-evaluations may reflect their perception of teaching presence, including course organization, learning task arrangement, platform management, and instructional guidance. However, students’ lower evaluations may suggest that teaching presence does not automatically lead to cognitive presence unless students clearly perceive how online learning activities support deeper understanding, knowledge construction, and clinical application. This is particularly important in nursing education, where blended teaching should not only deliver theoretical knowledge but also support clinical reasoning, practical skill development, problem-solving ability, and reflective learning.

The perception gaps can also be explained by constructive alignment theory. According to constructive alignment, teaching activities, learning tasks, assessment methods, and intended learning outcomes should be consistently aligned [22]. In this study, instructors may have believed that they had achieved online-offline integration by arranging MOOCs, pre-class tasks, online quizzes, classroom interaction, and post-class review activities. However, students may have evaluated readiness based on whether these activities were clearly connected to classroom learning, assessment requirements, and their own learning outcomes. Therefore, the lower student ratings in Online Integration & Management and Data Practices may indicate that the alignment among online resources, face-to-face instruction, learning analytics, and assessment feedback was not always sufficiently visible to students.

Self-determination theory further helps explain why students’ perceptions of readiness may differ from instructors’ self-evaluations. This theory suggests that students’ motivation and engagement are supported when their needs for autonomy, competence, and relatedness are satisfied [30]. In blended teaching, students may feel more supported when they can control their learning pace, receive timely feedback, and maintain meaningful interaction with teachers and peers. If online tasks are perceived as excessive, fragmented, or insufficiently connected with students’ learning needs, students may experience reduced autonomy and competence, even when instructors believe that they have provided sufficient online resources and support. This may partly explain why students’ evaluations were lower than instructors’ self-evaluations in several dimensions.

Another possible explanation for instructors’ relatively higher self-evaluations is social desirability bias. Since blended teaching has been widely promoted in higher education and is regarded as an important direction of teaching reform, instructors may tend to evaluate their own readiness more positively. In other words, higher self-ratings should not be interpreted only as evidence of stronger actual competence; they may also reflect institutional expectations, professional identity, and instructors’ desire to present themselves as competent in blended teaching. This interpretation is consistent with the concern that self-reported data may be influenced by socially desirable responding [31]. This may partly explain why instructors’ self-evaluations were higher than students’ evaluations in Dispositions, Online Integration & Management, and Data Practices.

Interestingly, Online Interaction was the only dimension in which students’ evaluations were higher than instructors’ self-evaluations. This result suggests that students may have perceived frequent online quizzes, platform notifications, online Q&A, discussion activities, and teacher-student communication as evidence of active online interaction. Relevant studies have demonstrated that students face a substantial load in their online learning endeavors [27, 32, 33]. In the present study setting, the research institution typically offered three or four blended courses simultaneously in a single semester, thereby increasing students’ exposure to online learning tasks and online interaction with teachers. For students, the visibility and frequency of online interaction may have increased their sense of teacher support and participation in blended learning.

Online courses have been shown to effectively facilitate the active engagement of conservative students [11, 34]. Additionally, blended teaching and learning approaches can enhance the effectiveness of providing feedback to students [35, 36]. These findings may help explain why students in this study rated instructors relatively highly in Online Interaction. However, certain studies have also highlighted challenges related to interaction in blended teaching [13, 36]. Instructors may have evaluated online interaction more critically, focusing not only on the frequency of interaction but also on its depth, quality, sustainability, and actual contribution to learning outcomes. Therefore, the difference in Online Interaction may reflect different standards of evaluation: students may emphasize whether interaction occurs, whereas instructors may emphasize whether interaction is pedagogically meaningful and effective.

During the blended learning process, it is crucial to design offline and online settings simultaneously [37]. Therefore, online interaction should not simply increase the number of platform-based tasks or discussion activities. Instead, it should be carefully aligned with teaching objectives, course difficulty, students’ workload, and expected learning outcomes. Frequent online interaction does not necessarily guarantee deep learning, and excessive online tasks may even increase students’ learning burden. Nursing instructors should therefore design online interaction activities that promote timely feedback, collaborative learning, reflection, and knowledge application, rather than interaction for its own sake.

Overall, the perception gaps identified in this study indicate that nursing instructors should not rely solely on self-evaluations when assessing their readiness for blended teaching. Regular student feedback, learning analytics, peer observation, and reflective teaching meetings may help instructors better understand whether their blended teaching design is effectively experienced by students. In particular, instructors should pay attention to the alignment between online and offline activities, the clarity of learning tasks, the usefulness of learning data, and the quality of teacher-student interaction. By narrowing the gap between instructors’ teaching intentions and students’ learning experiences, nursing schools can further improve the effectiveness of blended teaching and promote higher-quality nursing education.

Implications and recommendations

Based on the results of the study, several suggestions are put forward for further research. First, the study examined the preparation of nursing instructors for blended teaching from the perspectives of nursing students and instructors. However, it is very necessary to expand the scope of the participants to other majors, so future research can explore the blended teaching readiness of students and instructors in relevant majors to their instructors. In addition, it is crucial for the cultural shift from the existing teaching mindset to Blended teaching, which requires every relevant stakeholder to be aware of the changes in their roles in a blended environment [38]. According to Geng, et al. [39], the findings indicate that students’ technological preparedness is an important factor in blended learning. Therefore, future research should not only include the readiness of instructors, but also the readiness of students, university administration, and blended learning environments. Combining all these data can provide an effective program design for blended learning. Finally, future research could provide more information through a combination of quantitative and qualitative research, and provide new insights into teaching and learning in a blended environment.

Limitations

This study has several limitations. First, the cross-sectional design limits the ability to draw causal conclusions. The findings reflect nursing instructors’ and students’ perceptions at one point in time and cannot determine whether instructors’ readiness for blended teaching leads to better teaching quality or improved student learning outcomes. Therefore, future longitudinal studies are needed to examine how nursing instructors’ blended teaching readiness changes over time and how it influences students’ learning experiences and educational outcomes.

Second, the study was conducted in a single nursing school at one university of Traditional Chinese Medicine in Zhengzhou, China. Although this institution has implemented blended teaching for several years and has established a relatively mature MOOC-centered online teaching platform system, the findings may not be generalizable to other universities, disciplines, regions, or institutions with different levels of digital teaching infrastructure and blended teaching experience. The study examined nursing instructors’ readiness for blended teaching from the perspectives of nursing students and instructors. However, it is necessary to expand the scope of participants to other majors and institutions, so that future research can further explore blended teaching readiness among instructors and students in different educational contexts.

In addition, it is crucial to promote the cultural shift from the existing teaching mindset to blended teaching, which requires every relevant stakeholder to be aware of the changes in their roles in a blended environment [38]. According to Geng et al. (2019), students’ technological preparedness is an important factor in blended learning [39]. Therefore, future research should not only examine instructors’ readiness, but also include students’ readiness, university administration, and blended learning environments. Combining data from multiple stakeholders may provide a more comprehensive basis for the design and improvement of blended learning programs.

Third, although 70 nursing instructors participated in the study, the instructor sample was relatively small, particularly for subgroup analyses by professional title, age, degree, and teaching experience. Some subgroups contained only a small number of participants, which may have reduced statistical power and increased the instability of group comparison results. Therefore, the findings regarding differences by professional title should be interpreted cautiously and should be verified in future studies with larger instructor samples.

Fourth, this study relied entirely on self-reported questionnaire data from instructors and students. Although the inclusion of both perspectives provided richer information than using instructor self-reports alone, self-reported data may still be affected by social desirability bias, recall bias, and subjective interpretation. Instructors may have overestimated their readiness because blended teaching is institutionally encouraged and professionally valued, while students’ evaluations may have been influenced by their personal learning preferences, workload, or familiarity with MOOCs.

Fifth, this study did not include objective indicators of blended teaching quality or learning outcomes, such as classroom observation, learning management system log data, teaching portfolios, peer evaluation, student academic performance, or clinical skill assessment. As a result, the findings mainly reflect perceived readiness rather than actual teaching effectiveness. Future research could provide more comprehensive evidence through a combination of quantitative and qualitative methods and offer new insights into teaching and learning in a blended environment.

Finally, although the translated Chinese version of the BTRI showed acceptable internal consistency and preliminary structural validity in this study, its psychometric validation remains limited. Confirmatory factor analysis (CFA) was conducted only in the student sample because the instructor sample was relatively small and not suitable for a separate CFA of the 43-item five-factor instrument. Moreover, the CFA results in the student sample yielded CFI and TLI values below 0.90, indicating that the factor structure of the Chinese BTRI requires further refinement and validation. In addition, measurement invariance between instructors and students was not examined. Therefore, future studies should further validate the Chinese version of the BTRI using larger and more balanced samples of instructors and students, and continue to refine its factor structure before applying it more widely in Chinese nursing education contexts.

Conclusions

This study found that nursing instructors demonstrated an overall readiness for blended teaching from both instructor and student perspectives. Professional title was associated with instructors’ self-evaluated readiness, with assistant teachers reporting lower readiness than lecturers and senior instructors.Age was also independently associated with readiness, with instructors aged 51–60 years reporting lower readiness than the youngest age group.Students’ MOOC engagement duration was associated with their evaluations of instructors’ blended teaching readiness, suggesting that students’ familiarity with online learning platforms may influence their perceptions of blended teaching.

Significant perception gaps were identified between instructors and students. Students rated instructors lower in Dispositions, Online Integration and Management, and Data Practices, whereas they rated instructors higher in Online Interaction. These findings indicate that nursing instructors should pay greater attention to the alignment between online and offline activities, the meaningful use of learning data, students’ online workload, and the quality rather than the frequency of online interaction.Nursing schools should provide targeted training and mentoring for early-career instructors, establish regular mechanisms for collecting student feedback, and optimize the number and design of blended courses offered each semester. These measures may help improve instructors’ blended teaching readiness and enhance the quality of nursing education.

Author contributions

S.X.L: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition.X.Y.: Methodology, Investigation, Data curation, Formal analysis, Writing – review & editing.Z.Z.L.: Investigation, Data curation, Visualization, Writing – review & editing.C.Y.H.: Investigation, Formal analysis, Writing – review & editing.J.D.: Investigation, Data curation, Validation, Writing – review & editing.Q.Y.M: Investigation, Validation, Writing – review & editing.All authors read and approved the final manuscript.

Funding

This study was supported by the 2021 Annual General Project Approval of Humanities and Social Sciences Research for Colleges and Universities in Henan Province[2021-ZDJH-18].

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study titled “Research on Blended Teaching Readiness of Nursing Teachers” has obtained the rapid review approval from the Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (Acceptance/Approval No.: 2024HL-503; Approval Date: December 3, 2024). The research strictly adheres to the ethical guidelines specified in the Declaration of Helsinki and relevant institutional regulations. Prior to data collection, detailed research information was provided to all participating nursing teachers, and their written informed consent was obtained in accordance with the Informed Consent Form for Non-interventional Clinical Research (Teacher Version). Personal information of participants has been anonymized during data processing, and all collected data are only used for academic analysis of this study, with strict compliance with data confidentiality principles. Participants retain the right to voluntarily participate in or withdraw from the study at any time, and such decisions will not cause any adverse impacts on them. The study has obtained informed consent from all participating teachers and students, and informed consent forms have been filled out.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.