The COVID-19 pandemic introduced considerable challenges for adolescents as they adapted to the demands of remote learning, necessitating effective strategies to sustain their academic engagement and performance. This study investigates the relationship between adolescents’ self-perceived innovativeness and their academic resilience, confidence in managing distance learning, and overall academic performance. Using data from a sample of 906 Chinese middle and high school students, the study identifies three primary findings: adolescents with higher levels of self-perceived innovativeness tend to exhibit stronger academic resilience, greater confidence in handling distance learning, and better academic performance; distinct gender differences emerge, with male students reporting higher levels of self-perceived innovativeness than their female counterparts; and differences across educational stages are also evident, as high school students generally rate their innovativeness higher than middle school students. These findings point to the significance of fostering innovative thinking skills in adolescents to strengthen their capacity for adapting to dynamic educational environments and achieving improved academic outcomes. For educators and policymakers, the focus should be on encouraging creativity and adaptability among students while providing them with the necessary tools and support to develop personalized approaches for success in both blended and remote learning settings.

Adapting to online education poses significant challenges for adolescents, especially in adjusting to new learning environments. Research indicates that adolescents often face difficulties in remote learning, frequently due to limited familiarity with technology and insufficient independent learning skills1. Disparities in resources further exacerbate the learning gap, disproportionately impacting adolescents from low-income families. These students frequently encounter obstacles, such as limited access to devices and unreliable internet, which significantly hinder their academic progress2.

Beyond logistical and technological challenges, psychological issues in distance learning present significant concerns. Adnan and Anwar noted that online education restricts adolescents’ social interactions, often resulting in feelings of fear and isolation3. These emotional difficulties subsequently reduce students’ motivation and impede their academic performance.

To meet these challenges, academic resilience and distance learning self-efficacy are recognized as important factors affecting students’ adaptation and performance. Cassidy indicated that students with higher levels of academic resilience are better positioned to manage academic stress and cultivate effective coping strategies, which allows them to sustain strong academic performance4. Furthermore, Amoadu et al. found that academic resilience differs notably across genders and educational levels, highlighting the necessity for further investigation into these differences5.

Distance learning self-efficacy is closely linked to enhanced student confidence, motivation, and academic achievement6,7. Stan et al. noted that students with higher self-efficacy demonstrated greater engagement and adaptability in remote learning settings, while those with lower self-efficacy tended to experience more anxiety and learning challenges8. These challenges subsequently had a negative effect on their academic performance. This evidence indicates that enhancing self-efficacy in students can better prepare them to overcome the challenges of distance learning and improve their academic outcomes.

Although the significance of academic resilience and self-efficacy is widely acknowledged, a notable gap persists regarding the influence of adolescents’ self-perceived innovativeness on their academic resilience, self-efficacy, and academic achievement. Furthermore, previous research has primarily overlooked the moderating effects of factors such as gender and educational level on these relationships. Consequently, additional scientific inquiry is necessary to clarify the mechanisms and pathways linking these variables and to investigate potential moderating influences.

Innovativeness, a key concept in academic research, was first defined by Schumpeter as the integration of novel ideas, technologies, or methods into societal production and practices, highlighting its essential role in promoting social progress9. Building on this foundation, Rogers expanded the discussion through his diffusion of innovations theory, which emphasizes the varied responses of individuals toward the adoption of innovations. This perspective greatly contributed to establishing a systematic understanding of behaviors related to innovation10.

Although early innovation research primarily concentrated on adults—considering individual and organizational contexts—the innovative potential of adolescents and their developmental pathways garnered insufficient scholarly attention until the early 21st century. The emergence of a global innovation-driven strategy11 the impact of service-learning programshas led education systems worldwide to prioritize the integration of innovativeness into adolescents’ skill development. This shift indicates a rising acknowledgment of the necessity to cultivate future-ready innovative thinkers through comprehensive approaches that develop cognitive, technological, and social skills. By promoting such multidimensional growth, educators seek to prepare adolescents to effectively address intricate and rapidly changing global challenges with creativity and resilience.

Adolescent innovativeness is defined in existing research as the ability to engage in creative thinking, problem-solving, and social responsibility within the contexts of cognitive development, technological application, and social adaptation12. This multifaceted concept consists of three primary dimensions: cognitive, technological, and social innovation.

Cognitive innovation forms the basis of adolescent innovativeness and includes abilities such as creative thinking, critical analysis, and cognitive flexibility. These skills empower adolescents to engage in learning and problem-solving with creativity and flexibility. Manning et al. highlighted that early cognitive development, fostered by a stimulating educational environment, is crucial for nurturing these abilities13. Specifically, challenging learning tasks combined with effective teacher guidance have been demonstrated to promote innovative thinking among adolescents. Additionally, research illustrates a strong link between cognitive innovation and academic resilience. Adolescents with advanced cognitive innovation skills typically exhibit increased resilience and effective problem-solving abilities, particularly in academic contexts14.

Technological innovation signifies adolescents’ capacity to comprehend and apply emerging technologies effectively, a skill set that has become increasingly vital amid swift advancements in educational technology. Maas and Hughes discovered that immersive tools, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), greatly enhance adolescents’ engagement in learning and creativity through interactive and experiential environments15. For example, VR technologies allow students to engage with complex, dynamic scenarios directly, thereby enhancing their problem-solving skills. Similarly, AR merges digital information with real-world contexts, improving adolescents’ grasp of abstract concepts and boosting their self-efficacy in distance learning settings. These technology-enhanced skills promote self-directed learning and prepare adolescents for future educational and career challenges by providing them with essential technological competencies.

Social innovation highlights the roles of adolescents in social practices and their ability to creatively and responsibly address societal challenges. This dimension is often nurtured through civic education and service-learning initiatives. Annette16 observed the impact of service-learning programs on adolescents’ social adaptability, civic awareness, and innovative thinking. By addressing real-world issues, these programs foster a greater sense of responsibility in adolescents and support the use of creative solutions to societal challenges. For example, participation in community-based sustainable development projects has been shown to improve adolescents’ problem-solving skills and social responsibility. These experiences strengthen academic resilience and facilitate long-term personal and academic growth, preparing adolescents to become socially responsible and creative contributors to society.

In the context of globalization and rapid technological change, adolescent innovativeness possesses significant academic and practical importance. The ongoing advancement of emerging technologies has transformed educational frameworks, elevating the role of virtual and distance learning. In this changing landscape, adolescent innovativeness is vital for improving academic self-efficacy and technological adaptability17. Furthermore, challenges associated with globalization—such as environmental protection and resource scarcity—require adolescents to cultivate innovative thinking and social responsibility to effectively confront real-world issues. Participating in social innovation activities prepares adolescents to meet the future demands of societal development, establishing them as significant contributors to social progress18.

Academic resilience refers to an individual’s ability to adjust effectively and maintain consistent performance despite challenges or difficulties in academic settings19,20.This concept focuses on strengths instead of weaknesses, highlighting the ability to manage challenges while encouraging proactive personal development21. The concept of academic resilience is based on Self-Determination Theory (SDT). Liu and Huang22 suggest that meeting core psychological needs—such as autonomy, competence, and relatedness—greatly strengthens adaptability and supports academic resilience.

Key features of academic resilience include:

Persistence and problem-solving Students show strong perseverance when facing adversity, often seeking solutions instead of avoiding difficulties23.

Creativity and self-efficacy Academic resilience is strongly tied to creativity, independent thought, and self-efficacy, helping students overcome complex challenges24.

Positive emotions and interpersonal connections Resilient students often maintain positive emotions and develop meaningful interpersonal connections25.

Academic resilience emerges from interconnected factors that can be grouped into three primary categories:

Individual characteristics Personal characteristics and mental states are crucial in shaping academic resilience. Attributes like perseverance, adaptability, and creativity help individuals tackle challenges successfully26. Positive emotions, along with self-confidence in academic tasks, play a major role in strengthening resilience23.

External support systems External support systems provide important resources that shape academic resilience, including.

Teacher Support: Emotional encouragement and guidance from educators assist students in sustaining positive attitudes during stressful learning experiences27.

Family and Social Support: Emotional encouragement, access to supportive networks, and financial resources (e.g., scholarships) are critical for fostering resilience26,28.

Cultural and social context Academic resilience appears in diverse ways across cultures and social settings, shaped by differing values and available resources. De Oliveira Durso et al. explored how academic resilience is closely tied to cultural norms, social resource allocation, and education systems29. Hapsari et al.26 observed that in modern educational systems, adapting to changes in technology and learning settings requires abilities such as teamwork and flexibility.

Academic resilience emerged as a critical area of focus in overcoming educational challenges during the COVID-19 pandemic. Hapsari et al.26 examined how students from low-income families in Indonesia overcame academic obstacles during the pandemic through personal strategies and institutional aid, highlighting the balance between individual flexibility and external resources. Similarly, Bozkurt et al. conducted narrative reviews and empirical studies to explore how teachers maintained their professional stability and personal health during the pandemic30.

Academic resilience is commonly evaluated through two main approaches:

Definition-Based Approach: This method identifies academic resilience by recognizing individuals who fulfill particular risk and achievement thresholds31.

Process-Based Approach: This approach investigates how risk and protective factors influence academic outcomes, shedding light on the gradual development of resilience31.

The Academic Resilience Scale (AR-S), widely used in research, has shown consistent reliability across various studies32. However, existing tools for measuring academic resilience require further refinement to enhance reliability and precision, which calls for additional research22.

Emotional support from teachers plays a central role in connecting students’ academic resilience with their participation in learning. For example, Romano et al. observed that positive teacher-student relationships increase students’ motivation and involvement, ultimately fostering their academic resilience27.

Academic resilience helps individuals address educational difficulties and achieve sustained growth, particularly in online learning settings. Future studies should prioritize several areas: (1) examining the relationship between academic resilience, self-confidence in distance learning, and educational outcomes to refine theories of digital education; (2) evaluating the long-term effects of resilience on career growth and lifelong learning; and (3) creating and testing research-based strategies, such as cognitive-behavioral approaches, emotional skills programs, and support networks, to enhance resilience and adaptability in difficult educational situations33. Studying academic resilience, its main factors, and practical findings allows educators and policymakers to design supportive learning conditions that help students adapt effectively and make significant progress in their education, while advancing fairness and individual growth.

Distance learning self-efficacy (DL self-efficacy) is an individual’s confidence to effectively participate in and accomplish activities in a distance learning setting34. This idea is vital in online and distance education as it can influence how an individual views, embraces, and utilizes technology for learning35. Studies indicate that self-efficacy influences students’ willingness to embrace e-learning. A study at Malaysian higher education institutions discovered that self-efficacy enhances the connection between performance expectancy and social impact regarding e-learning adoption35. Self-efficacy has a considerable impact on perceived ease of use in distance learning systems, as indicated by Rahmi et al.36. It also impacts students’ academic achievement in remote learning settings. A study in Kenyan public institutions discovered that students’ self-efficacy significantly influenced the connection between institutional characteristics and the academic achievement of distance-learning students37.

Self-efficacy factors for distance learning include performance anticipation, social influence, enjoyment perception, and self-efficacy38. Students with high self-efficacy are typically more confident and secure while engaging in specific actions, such as using technology. Confidence significantly influences how individuals perceive, adopt, and utilize online learning resources35. Additional elements linked to self-efficacy in online learning are general proficiency in technology and favored teaching methods. Students with medical disabilities demonstrated greater online learning self-efficacy than students with psychological and learning problems39. Promoting transversal abilities, such as self-efficacy, might enhance teachers’ ability to handle stressful situations like coordinating remote learning, thereby improving distance learning self-efficacy40. Intrinsic learning motivation and computer self-efficacy can enhance students’ engagement and improve learning effectiveness in a web-based learning environment41. Distance learning self-efficacy measurement scales can be created using questionnaires that evaluate self-efficacy related to particular tasks or behaviors. A study on instructors’ self-efficacy in remote learning utilized a scientific questionnaire to assess teachers’ self-efficacy in distance education42. Different research utilized a self-administered online survey to assess intrinsic learning motivation, computer self-efficacy, and learning engagement in university students41.

Self-efficacy in distance learning is also important at all levels of education, from high school to college to adult learning, because it can greatly affect a student’s motivation, engagement, and academic success. In high school education, self-efficacy in online learning is linked to self-directed learning and academic motivation. Among public senior high school pupils, self-directed learning correlates significantly with both self-efficacy in learning and academic motivation, according to a study43. A study discovered that college students might develop academic self-efficacy through observational learning and direct personal experience. The research identified notable variations in academic drive and self-assurance among university students engaged in online distance education, depending on their age, gender, and course44. A different study discovered that factors like time and supervision within an internship program can influence self-efficacy in remote education students45. A study in adult education revealed that character strength and self-efficacy can influence the subjective pleasure of adult learners in distant nursing education46. Adult learners in a different study showed a favorable outlook on distance learning in medical education, valuing its ease regarding flexible scheduling and virtual participation47. These studies indicate that distance learning self-efficacy is influenced by age, gender, course, learning environment, and character strength. Self-efficacy substantially influences students’ academic motivation, performance, and attitudes toward remote learning. Implementing measures to boost self-efficacy might prove advantageous in enhancing results in distance learning at all educational levels.

Various factors impact academic success, including human, environmental, institutional, and technological aspects. Personal aspects encompass the student’s study habits, time management abilities, quality of sleep, and potential internet addiction. A study discovered that time management significantly impacted the academic performance of Bachelor Computer and Mathematical Science students48. Research by Rathakrishnan et al. revealed a connection between smartphone addiction, poor sleep quality, and decreased academic performance49. Environmental influences encompass the student’s home environment and peer group. The culture established within a student group can influence academic performance. A higher peer-group average achievement or a greater number of high-performing peers can positively impact a student’s performance50. Institutional variables encompass the caliber of instruction and the resources available at the school.

All-English instruction typically enhances academic achievement, with a more significant impact observed among advanced students51. According to Mohamed52, academic performance can be influenced by the study habits promoted by the school, the interaction between students and staff, and the activities and services offered by the school. Physical activity levels, psychosocial state, and cognitive skills have been shown to impact academic achievement, particularly during the COVID-19 pandemic53. Socioeconomic factors, including parents’ income and education level, have significantly influenced academic success54. Lastly, the influence of technological elements on academic performance should be considered. Research shows that technology has beneficial and negative effects on academic achievement, making it a complex subject. Technology can greatly improve students’ enjoyment and academic and functional performance. A study discovered that the influence of technology through interactive learning substantially affected students’ pleasure, academic performance, and functional performance. The study also discovered that technology’s influence on self-regulated learning had a beneficial effect on these aspects55. Metaverse technology, encompassing virtual reality (VR) and augmented reality (AR), can improve student engagement and academic achievement. Metaverse technology offers immersive and interactive learning environments that can enhance students’ intrinsic motivation and encourage active participation in their learning. Digital learning aids, such as electronic applications, games, or resources that aid education, have provided benefits like improved learning efficiency, greater feedback, and detailed assessments56.

Nevertheless, there are obstacles linked to the utilization of technology in education. Some students may require a more favorable learning environment, improved internet access, financial assistance, technical support, or dealing with demotivation57. Additionally, the impact of technology on education can be affected by factors like the caliber of online materials, how often they are utilized, and the digital proficiency of students and educators58.

Based on the above review, our research question is: How do adolescents’ innovativeness and academic resilience relate to their self-efficacy for distance learning and academic performance?

The following research hypotheses will be tested:

Adolescents’ distance learning innovation is positively related to adolescents’ academic resilience, distance learning self-efficacy, and academic performance. The higher the adolescents’ distance learning innovation, the better the adolescents’ academic resilience, distance learning self-efficacy, and academic performance.

Adolescents’ academic resilience, distance learning self-efficacy, and academic performance positively affect adolescents’ distance learning innovation. The better the adolescents’ academic resilience, distance learning self-efficacy, and academic performance, the higher the adolescents’ distance learning innovation.

There is a significant difference between adolescents’ gender and grade level regarding self-innovativeness, academic resilience, distance learning efficacy, and academic performance.

A quantitative correlational self-report questionnaire59was used to determine relationships among adolescent self-innovativeness, academic resilience, DL self-efficacy, and academic performance variables.

The data for this study were collected by three research assistants who distributed questionnaires electronically. A total of 1,000 questionnaires were distributed, and 906 valid responses were received, reflecting a 90.6% response rate. The sample primarily comprised participants from central China, all of whom attended public schools characterized by medium to high socioeconomic status (SES). Demographic data showed that 51.2% of the participants were female (n = 464), while 48.8% were male (n = 442). Among the participants, 452 (49.9%) were middle school students, and 454 (50.1%) were high school students. Specifically, the middle school group included 182 freshmen, 150 sophomores, and 140 juniors, while the high school group comprised 193 seniors, 134 sophomores, and 107 juniors. Furthermore, 47.8% of students (n = 433) reported living in urban areas, whereas 52.2% (n = 473) lived in rural areas. Participants ranged in age from 13 to 18 years, with an average age of 15.49 years (SD = 1.683).

This study employed the Adolescent Self-Innovation Scale proposed by Ng & Lucianetti60, which includes 9 items divided into three dimensions: idea generation, idea dissemination, and idea implementation, using a 6-point Likert scale (1 = “never,” 6 = “always”). To adapt to the remote learning context, the study made contextual adjustments to some items while maintaining the original structure of the scale. For example, in the idea generation dimension (items 1–3, α = 0.812), “I create new ideas for improvement” was adjusted to “I come up with new learning methods or tools to enhance the effectiveness of remote learning”; in the idea dissemination dimension (items 4–6, α = 0.843), “I mobilize support for innovative ideas” was adjusted to “I share new learning tools or methods with classmates or teachers to gain support”; in the idea implementation dimension (items 7–9, α = 0.863), “I turn innovative ideas into useful applications” was adjusted to “I use new learning platforms or tools to apply innovative ideas to actual learning”. Through content validity review by three educational psychology experts and a pilot test with 30 adolescents, the results indicated that the modified items were easy to understand and suitable for the remote learning context. In the formal administration, the exploratory factor analysis (EFA) of the scale showed that the KMO value was 0.966, and Bartlett’s test of sphericity was significant (χ2 = 5935.176, p < 0.001), suitable for factor analysis. The extracted three factors accounted for a cumulative variance of 78.182%, with factor loadings for each item ranging from 0.65 to 0.85. Further confirmatory factor analysis (CFA) showed that the three-factor structure fit well (χ2/df = 2.85, RMSEA = 0.07, NFI = 0.92, IFI = 0.94, CFI = 0.94). Overall reliability (Cronbach’s α) was 0.941, indicating that the scale has good reliability and structural validity, and can effectively assess adolescents’ innovative abilities in the context of remote learning.

This study used the Adolescent Academic Resilience Scale developed by Cassidy61, which consists of 30 items divided into three dimensions: Perseverance, Reflective and Adaptive Seeking Help, and Negative Impact and Emotional Reaction. Each subscale has high reliability, with the Perseverance dimension (items 1, 2, 3, 4, 5, 8, 9, 10, 11, 13, 15, 16, 17, and 30) having a Cronbach’s α of 0.957, with a sample item being “I do not accept feedback from my tutor”; the Reflective and Adaptive Seeking Help dimension (items 18, 20, 21, 22, 24, 25, 26, 27, and 29) has a Cronbach’s α of 0.938, with a sample item being “I use my past successes to help motivate myself”; the Negative Impact and Emotional Reaction dimension (items 6, 7, 12, 14, 19, 23, and 28) has a Cronbach’s α of 0.916, with a sample item being “I might would get depressed”. The results of the Exploratory Factor Analysis (EFA) indicated that the KMO value was 0.993, and Bartlett’s test of sphericity was significant (χ2 = 22295.956, p < 0.001), suitable for factor analysis. Ultimately, 3 factors were extracted, accounting for 65.8% of the cumulative variance explained, with factor loadings for each item ranging from 0.64 to 0.75. The overall scale reliability was 0.979, indicating that the scale has good internal consistency and structural validity, and can effectively measure adolescent academic resilience.

This study adopted the Remote Learning Self-Efficacy Scale designed by Manwaring et al.62, which includes 4 items to measure adolescents’ self-efficacy in remote learning contexts. Sample items include: “I usually achieve excellent grades in online courses” and “I usually understand the most complex material in online courses”, with students self-assessing on a 6-point Likert scale (1 = “strongly disagree”, 6 = “strongly agree”). The results of the Exploratory Factor Analysis (EFA) indicated that the KMO value was 0.763, and Bartlett’s test of sphericity was significant (χ2 = 688.188, p < 0.001), with a cumulative variance explained of 80.8%, and factor loadings for each item ranging from 0.72 to 0.81. The overall reliability (Cronbach’s α) was 0.761, indicating that the scale has good internal consistency and reliability, and can effectively assess adolescents’ academic self-efficacy in remote learning.

This study adopted the Academic Achievement Scale proposed by Hosen et al.63, which includes 5 items to assess students’ online learning performance. Sample items include: “This online course has enhanced my knowledge” and “This online course has increased my understanding,” rated on a 6-point Likert scale (1 = “strongly disagree,” 6 = “strongly agree”). The results of the Exploratory Factor Analysis (EFA) showed a KMO value of 0.834, with Bartlett’s test of sphericity being significant (χ2 = 1700.643, p < 0.001). The single-factor structure accounted for 69.8% of the variance explained, with factor loadings for each item ranging from 0.68 to 0.84. The overall reliability (Cronbach’s α) of the scale was 0.806, indicating that the scale has good reliability and validity, and can effectively assess adolescents’ online learning performance.

Adolescents were invited to freely fill out an anonymous online questionnaire during their free time. The questionnaire was segmented into four parts: inventiveness, academic resilience, distance learning self-efficacy, and academic performance, along with personal background inquiries. Before the initiation of data collection, all prospective participants were provided with a comprehensive explanation regarding the study’s objectives, potential advantages and disadvantages, and the practice of volunteering. Participants in the study were required to sign an online consent form. The study adhered to the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical guidelines. The questionnaire was completed in twenty minutes.

The data were analyzed using SPSS 26.0, with several statistical methods applied to ensure a structured analysis and reliable findings. The distribution of adolescents across four primary variables—distance learning innovation, academic flexibility, self-efficacy in digital learning, and academic achievement—was analyzed descriptively, with the mean (M) and standard deviation (SD) calculated. Frequency and percentage distributions of control variables, including gender, grade level, and urban/rural location, were analyzed to outline the sample’s demographic profile and provide a basis for further analyses. Pearson correlation analysis assessed the relationships among key variables, including distance learning innovation, academic flexibility, digital learning self-efficacy, and academic achievement. Control variables—gender, grade level, and urban/rural location—were included in the analysis to evaluate their associations with the main variables and support subsequent regression analyses. Multiple linear regression was applied to investigate the predictive effects of distance learning innovation, academic resilience, and digital learning self-efficacy on academic achievement. By comparing R, R2, ΔR2, adjusted R2, F, β, and significance levels (p), the explanatory power and unique contributions of each predictor were assessed, confirming relationships between variables and supporting the hypotheses. Independent samples t-tests evaluated the effects of gender and grade on adolescents’ innovation in online learning, academic resilience, self-efficacy in digital learning, and academic achievement. All statistical tests were two-tailed, with a significance threshold of p < 0.05 and a 95% confidence interval. The Variance Inflation Factor (VIF) was calculated to check for multicollinearity and ensure no significant covariance among independent variables, enhancing the robustness of regression results. Hypotheses were tested for normality, linearity, and chi-square prior to data analysis, confirming the appropriateness of statistical methods and the reliability of results.

Table 1 Descriptive statistics of the study dimensions and sub-dimensions are shown in Table 1. The table details the data for each dimension and sub-dimension, including key statistics such as mean (M), standard deviation (SD), kurtosis, and skewness. Skewness and kurtosis rules (i.e., if the number is more significant than + 1 or less than 1, the distribution is skewed, flat, or peaked) were used64. The data shows that the kurtosis values are predominantly negative, meaning the distribution has narrower tails and flatter peaks compared to a normal distribution. The skewness values further emphasize the asymmetry of the data, with negative skewness indicating a longer left tail for most dimensions.

Table 1 reveals that adolescents’ perceptions of self-initiated innovation, idea generation, dissemination, and implementation are generally low to moderate. For example, the mean of self-initiated innovation is 3.2183 (SD = 1.3143, kurtosis = -1.387, skewness = 0.494). This shows a certain degree of recognition, although there are some differences in some details. The mean of academic resilience and its three sub-dimensions are low, indicating that the surveyed adolescents have low academic resilience. Taking the lowest dimension of tenacity as an example, the mean is 2.7938 (SD = 1.02903, Kurtosis=-1.461, Skewness=-0.506), and the relatively low value may indicate that adolescents need extra attention and support in this aspect. Regarding remote learning efficacy, the respondents showed a moderate to high level of perception. The mean of remote learning efficacy is 3.49 (SD = 0.98, kurtosis = -1.01, skewness = 0.35), showing relative confidence in this domain. Regarding academic performance, the respondents generally expressed a high positive perception. The mean of academic performance is 3.7369 (SD = 1.15365, Kurtosis=-1.20, Skewness=-0.035), reflecting the optimistic view of adolescents on their academic achievements, and the kurtosis is negative, which means that the distribution of answers is more dispersed.

In order to test the research hypotheses, a Pearson correlation analysis was conducted between the research variables (Fig. 1). Figure 1 shows the research variables’ positive, strong, and significant correlations.

Pearson correlations between the main research dimensions. Note S-I: Self-Innovativeness; AR: Academic Resilience; DL: DL Efficacy; AP: Academic Performance; **p < 0.01.

Harman’s single-factor test was applied to evaluate potential common method bias. A single factor accounted for 88% of the variance, exceeding the 40% threshold and indicating substantial common method bias. To address this issue, statistical control methods and hierarchical factor analysis were employed to adjust and validate the results. Social desirability bias and socio-demographic variables were incorporated into the statistical control process to reassess correlations among variables. The adjusted analysis produced results closely aligned with the original data, showing only a minor reduction in correlation coefficients (Fig. 1). These findings suggest that while common method bias was reduced to some extent, the relationships between variables retained their strength and validity.

Hierarchical factor analysis was applied to separate the theoretical construct from the methodological factor using a two-factor model. The analysis showed that the methodological factor’s negative effect was significantly diminished, strengthening the reliability of the results. Correlation coefficients between Self-Innovation (SI) and Academic Resilience (AR), Distance Learning Efficacy (DL), and Academic Performance (AP) were 0.779, 0.153, and 0.859, respectively. The results indicate strong positive relationships between Self-Innovation, Academic Resilience, and Academic Performance, while the association with Distance Learning Efficacy was considerably weaker. A correlation of 0.612 between Academic Resilience (AR) and Distance Learning Efficacy (DL) indicated that adolescents with higher levels of academic resilience were more likely to succeed in distance learning. The correlation of 0.324 between Distance Learning Efficacy (DL) and Academic Performance (AP) suggested a modest positive relationship, though it remained weak.

While substantial common method bias existed, its influence was notably diminished through statistical control techniques and hierarchical factor analysis, improving the credibility and reliability of the findings. The adjusted analyses demonstrated that significant positive relationships among self-innovation, academic resilience, distance learning efficacy, and academic achievement remained consistent, reinforcing the validity of research hypotheses H1 and H2.

Analyzing the correlation coefficients in Fig. 1, it can be seen that all variables are correlated, and all variables are positively correlated. To validate H1 and H2 further, the study conducted regression analyses on adolescents’ distance learning innovation, academic toughness, distance learning self-efficacy, and academic performance.

First, the study obtained the following results through regression analysis of adolescents’ distance learning innovation, with adolescents’ distance learning innovation as the independent variable and academic resilience (AR), distance learning self-efficacy (DL), and academic performance (AP) as the dependent variables. First, distance learning innovation showed a strong positive correlation with academic resilience, with an R-value of 0.779, indicating a significant association between the two, with the model explaining 60.7% of the variance in academic resilience. Additionally, the effect of distance learning innovation on distance learning self-efficacy was weaker, with an R-value of 0.153, but still significant, with the model explaining 2.3% of the variation in self-efficacy. Most significant was the positive correlation between distance learning innovation and academic performance, with an R-value of 0.859 and the model explaining 73.8% of the variance in academic performance (Table 2). This suggests that adolescents’ distance learning innovations enhance academic resilience and self-efficacy and significantly contribute to overall academic performance. In regression analysis, the β-value reflects how much a unit change in the independent variable affects the dependent variable, indicating how many standard deviations the dependent variable changes for each standard deviation change in the independent variable while other independent variables remain constant. In this study, the results of the regression analysis showed that the β-value of academic toughness was 0.779, indicating that an increase in distance learning innovation per unit showed a strong positive correlation with an increase in academic toughness, emphasizing the significant role of distance learning innovation in promoting academic toughness. The β-value of distance learning self-efficacy is 0.153, which is a relatively small effect despite the positive correlation, indicating that distance learning innovations have a weak effect on enhancing distance learning self-efficacy. In contrast, the β-value of academic performance was 0.859, emphasizing the strong positive effect of distance learning innovations on academic performance, with a significant relationship between an increase in distance learning innovations per unit and an increase in academic performance. Research hypothesis H1 was established.

This study applied a multiple linear regression model to explore how academic performance (AP), distance learning self-efficacy (DL), and academic resilience (AR) influence adolescents’ distance learning innovations (Table 3). The overall fit metrics suggest that the regression model is highly effective. An R2 value of 0.825 indicates that the model explains 82.5% of the variance in adolescent distance learning innovation, confirming that the three independent variables (AP, DL, and AR) serve as strong predictors of the dependent variable. The adjusted R2 value of 0.825 closely matches the R2 value, further supporting the model’s stability and predictive reliability. The significance test yielded an F-value of 361.951 (p < 0.001), validating that the regression model performs significantly better than the null model without independent variables.

The ΔR2 values illustrate the incremental contribution of each variable to the model’s explanatory strength. Specifically, Academic Performance (AP) independently accounted for an R2 value of 0.738, explaining 73.8% of the variance in adolescent distance learning innovations. Incorporating distance learning self-efficacy (DL) raised the R2 value to 0.755, adding 1.7% (ΔR2 = 0.017) to the explained variance. Finally, adding academic resilience (AR) increased the R2 value to 0.825, accounting for an additional 7% (ΔR2 = 0.070) of the variance. These results suggest that academic resilience has the strongest independent predictive influence on distance learning innovations among adolescents.

The standardized regression coefficients (β) were: academic performance (AP) at 0.448 (p < 0.001), distance learning self-efficacy (DL) at 0.385 (p < 0.001), and academic resilience (AR) at 0.604 (p < 0 0.001). This suggests that each unit increase in academic performance and distance learning self-efficacy positively impacts distance learning innovation, with academic performance exerting a slightly stronger influence. Furthermore, the β value for academic resilience (AR) was significantly higher than the other predictors, emphasizing its dominant role in distance learning innovation. The findings demonstrate that academic performance, distance learning self-efficacy, and academic resilience are significant predictors of distance learning innovation among adolescents. Among these variables, academic resilience explained the highest variance (ΔR2 = 0.070) and exhibited the largest standardized regression coefficient (β = 0.604), highlighting its critical importance. These results support the research hypothesis H2.

This study conducted a systematic covariance diagnosis on three linear regression models to evaluate the independence of independent variables, ensure the stability of model parameters, and reduce the impact of multicollinearity on regression outcomes (Table 4). Covariance issues can result in inaccurate regression coefficient estimates, thereby reducing the model’s reliability in educational technology research. The analysis examines the covariance diagnostic results of three models, where the independent variable is self-innovativeness (SI), and the dependent variables are academic resilience (AR), distance learning self-efficacy (DL), and academic performance (AP). The diagnostic results for each model are presented in Table 4.

The table (Table 4) presents a comparison of the performance and statistical results of three models (Model 1, Model 2, and Model 3). Model 1 (AR) demonstrates consistent performance, with strong metric values, statistically significant results, and the best overall effectiveness. Model 2 (AR, DL) exhibits improved statistical outcomes (e.g., 55.120) following the inclusion of deep learning variables; however, some metrics (e.g., 0.625) show fluctuations, and the model’s complexity increases. Model 3 (AR, DL, and additional variables) incorporates more features, but its overall performance declines, marked by significantly lower metric values (e.g., 19.025) and greater error and instability (e.g., 5.854). In summary, Model 1 demonstrates the best performance, Model 2 shows potential for improvement with further parameter optimization, while Model 3 appears ineffective due to overfitting or excessive complexity.

This study employed independent sample t tests to examine how gender and grade level influence adolescents’ performance in four key areas: online learning innovativeness, academic resilience, digital learning self-efficacy, and academic performance. Table 5 summarizes the results, presenting the mean values (M), standard deviations (SD), mean differences (MD), t values, and effect sizes (η2). The significance level was set at p < 0.001. Below is a detailed interpretation of the findings.

The analysis revealed significant gender differences in online learning innovativeness, academic resilience, and academic performance, with males outperforming females in all three areas. In contrast, the difference in digital learning self-efficacy between males and females was smaller. Specifically, males scored significantly higher in online learning innovativeness (M = 3.97, SD = 1.25) compared to females (M = 2.47, SD = 0.88), with a mean difference (MD) of 0.34 (t = 17.46, η2 = 0.33). This suggests that gender plays a substantial role in explaining variations in this area. Similarly, males scored higher in academic resilience (M = 3.57, SD = 0.79) than females (M = 2.47, SD = 1.07), with an MD of 1.10 (t = 17.45, η2 = 0.32).

In academic performance, males (M = 4.29, SD = 1.03) also achieved significantly higher scores than females (M = 3.20, SD = 1.01), with an MD of 1.09 (t = 16.25, η2 = 0.31). These findings highlight the significant influence of gender on these three variables.

In contrast, the difference in digital learning self-efficacy between males and females was much smaller. Males achieved slightly higher scores (M = 3.68, SD = 0.85) than females (M = 3.29, SD = 1.06), with an MD of 0.39 (t = 6.16, η2 = 0.05). While the difference was statistically significant, the small effect size indicates that gender has a relatively minor impact on this variable. These findings support the research hypothesis (H3).

The results also revealed significant grade-level differences in online learning innovativeness, academic resilience, and academic performance, with high school students outperforming middle school students in these areas. However, the grade-level difference in digital learning self-efficacy was relatively modest. High school students scored significantly higher in online learning innovativeness (M = 3.92, SD = 1.26) compared to middle school students (M = 2.52, SD = 0.94), with an MD of 1.40 (t = -18.89, η2 = 0.36), reflecting a strong explanatory power of grade level for this variable. Likewise, high school students outperformed middle school students in academic resilience (M = 3.59, SD = 0.75 vs. M = 2.41, SD = 1.06), with an MD of 1.18 (t = -19.412, η2 = 0.35).A similar pattern was observed in academic performance, where high school students (M = 4.33, SD = 1.00) scored significantly higher than middle school students (M = 3.14, SD = 0.98), with an MD of 1.19 (t = -18.10, η2 = 0.32). These results demonstrate the substantial impact of grade level on these three variables.

On the other hand, the difference in digital learning self-efficacy between high school and middle school students was less pronounced. High school students achieved slightly higher scores (M = 3.73, SD = 0.86) compared to middle school students (M = 3.25, SD = 1.03), with an MD of 0.48 (t = -7.51, η2 = 0.06). While the difference was statistically significant, the small effect size indicates that grade level has a limited impact on this variable. These findings also support the research hypothesis (H3).

This study aimed to investigate the impact of teenagers’ autonomous innovativeness on their academic resilience, distance learning efficacy, and academic achievement while also examining the influence of gender and grade level on these factors. The results indicate a significant association between adolescents’ perceptions of innovativeness and other aspects. The study indicates that adolescents who feel innovative and take more responsibility for their learning are more capable of managing distance learning and enhancing their effectiveness. Previous studies have shown links between teenagers’ autonomous innovativeness and motivation, learning techniques, attitudes toward learning, and confidence in learning65. Prior studies have confirmed a link between independent innovativeness and self-responsibility, recognizing it as a crucial element in an individual’s achievements in academics and the workplace66.

Studies have shown a direct link between independent creativity and academic perseverance in teenagers. Novi and Etikariena discovered a substantial correlation between college students’ resilience and innovative work habits67. A study by Lih et al.68 indicates that remote digital learning might enhance students’ resilience and encourage teenagers to pursue sustainability goals related to innovation and entrepreneurship. The article examines past studies and demonstrates that adolescent autonomous innovativeness impacts all other variables. Adolescents’ independent innovativeness substantially impacted their learning efficacy and outcomes in distant learning. Autonomous creativity boosted teenagers’ ability to bounce back academically, affecting their views on the effectiveness of remote learning and ultimately making them more motivated to enhance their performance in distance education.

Adolescents have shown proficiency in researching and producing ideas but must improve in presenting and applying those ideas when analyzing autonomous innovativeness components. Amorim & Cardoso’s study focused on the relationship between adolescents’ independent creativity and their use of technology69. The study analyzed several aspects of creativity but did not cover the processes of exploring, creating, presenting, and applying ideas. The study findings indicate that adolescents have a sense of creativity in generating ideas but struggle with executing these ideas, particularly in terms of presenting and applying them to teachers or peers in the classroom. Adolescents may possess numerous innovative and effective ideas for educational initiatives or programs that align with their interests and requirements. However, their instructors or peers occasionally do not comprehend or embrace these concepts due to inadequate communication abilities. Transformative, innovative ideas in learning practices will only be used if they go unnoticed by the teacher or classmates.

The study found that adolescents faced challenges in distance learning, particularly managing study time and using different strategies while struggling to maintain engagement and motivation in synchronous or asynchronous learning. Overbaugh & Casiello discovered that adolescents face challenges in distance learning, such as time management, strategy implementation, and staying engaged and motivated in synchronous and asynchronous learning settings70. Recent studies and publications71 confirm that generating student involvement is the primary issue when implementing distance learning during the initial phase of the COVID-19 pandemic. Adolescents’ online learning and engagement are influenced by various factors linked to the instructor, including technology access, peer cooperation, cognitive problem-solving, teacher interaction, and learning management72. These aspects can be particularly intricate and demanding for middle and high school adolescents, as they necessitate elevated degrees of self-management, self-regulation, and self-motivation to sustain high levels of enthusiasm for participation, learning, and creativity in distance learning.

This study reveals the significant effects of gender and grade level on adolescents’ self-directed innovativeness, academic resilience, distance learning efficacy, and academic achievement, which enriches empirical research in the field of adolescent learning and development and provides valuable theoretical underpinnings for educational practice.

Gender differences were clearly evident across the indicators. The study demonstrated that male adolescents exhibited higher levels of autonomy, innovativeness, academic resilience, and academic success compared to female adolescents. These findings align with Amorim and Cardoso’s69 observation that male adolescents display greater motivation and independence in using technology and exploring novel ideas. This advantage may stem from male adolescents’ stronger creative self-efficacy and a more self-reinforcing evaluative process. Conversely, female adolescents’ lower scores in autonomous innovativeness may indicate a more cautious approach to creative behavior and academic resilience, as well as a greater tendency toward dependence or self-doubt, particularly when facing stress. Regarding distance learning efficacy, although male adolescents scored slightly higher, the gender difference was minimal, suggesting that as technological applications become more widespread, self-regulation and learning confidence are becoming increasingly balanced between genders. These results highlight the role of technology and learning strategies in reducing gender disparities.

Grade level differences had a clear impact on the indicators, showing that autonomous innovativeness and academic performance are closely connected to psychological maturity and academic experience. High school students showed stronger results than middle school students in autonomous innovativeness, academic resilience, distance learning efficacy, and academic performance. This finding differs from Skinner and Saxton’s73 conclusion that motivation is stronger in lower grades. It instead indicates that high school students demonstrate greater autonomy and resilience in academic tasks, aided by higher levels of self-regulation and adaptability as they advance through grades. The increased academic pressure and the pressing demands for future preparation in high school may boost students’ motivation for innovation and academic engagement, contributing to their success.

The study identified potential risks associated with methodological bias. To assess common method bias, a Harman one-factor test was conducted. The results showed that the first factor accounted for 88% of the variance, significantly exceeding the 40% threshold, indicating substantial bias in the data. Particular attention is required regarding the social desirability effect inherent in self-report questionnaires. In gender-differentiated results, male adolescents were more likely to overestimate their abilities, whereas female adolescents tended to be more reserved in their self-assessments. This bias may have influenced the findings. Future studies could improve the reliability of the results by using data collected at multiple time points and from diverse sources, including teacher evaluations, behavioral observations, and objective measures.

This study examined the relationship between adolescents’ creativity and their academic resilience, distance learning confidence, and performance. The findings showed a meaningful positive link between adolescents’ creativity and their academic resilience, confidence, and performance. During the COVID-19 pandemic, distance learning posed significant challenges to adolescents’ adaptability. Those with higher creativity demonstrated greater resilience and confidence by creating and applying new learning approaches, leading to improved academic outcomes. In comparison, less creative adolescents relied more on external help, such as guidance from teachers or parents, while their peers with higher creativity were more likely to approach and solve problems on their own. This finding highlights the role of creativity in adjusting to distance learning and offers evidence for how adolescents manage academic obstacles.

The contributions of this study are reflected in three key areas: theory, empirical analysis, and practical relevance. From a theoretical perspective, this study expands the discussion of creativity to include distance learning, enhancing the framework linking creativity to academic performance. It emphasizes the key roles of academic adaptability and confidence in this process, providing a new angle for studying distance learning. Through empirical analysis, this study confirmed the links among adolescents’ creativity, academic resilience, confidence, and performance using multivariate statistical methods, ensuring the reliability of the results. The analysis included control factors such as gender, grade level, and urban/rural status to limit potential confounding effects and enhance the applicability of the results. In practice, this study demonstrates the value of creativity as a psychological resource in education. It offers practical guidance for educators, curriculum developers, and policymakers, supporting efforts to integrate modern educational tools with creative skill development.

Based on the results of this study, the following recommendations are made:

Educational institutions should implement programs that cultivate creativity through problem-solving and project-based learning methods. Students should be guided to apply innovative strategies to enhance their critical thinking and practical skills.

Adolescents’ academic resilience and confidence in distance learning can be enhanced through consistent support and constructive feedback from teachers. Encouraging self-assessment and peer evaluation helps promote independence and adaptability.

An environment that nurtures creativity can be established through collaboration between schools and families. Adolescents should access varied learning resources, explore personalized learning approaches, and improve their performance in distance education.

As blended learning gains wider adoption, young people should be equipped with skills to connect online and offline learning environments. Educators should guide students in using communication tools to facilitate meaningful interactions with teachers and peers, supporting academic performance and well-being.

This study contains limitations and provides suggestions for future research. The study examined the connections between the four factors using previous research and a literature review. The associations identified in the study may be validated by incorporating additional variables associated with innovativeness.

The current study did not account for many variables influencing adolescents’ views toward distance learning. Adolescents’ innovativeness and distance learning goals may impact their interactions with other variables. Future studies should evaluate adolescents’ perceived innovativeness levels using online surveys to see how varying degrees affect resilience and performance in distance learning.

The adolescent innovativeness study was assessed using self-reports, a subjective metric prone to biases. As self-perception evolves, the findings suggest a potential bias where older teenagers report higher levels of innovativeness. Enhancing self-report measurements with qualitative techniques could provide a more detailed description of relationships between variables. Future research might look into the influence of adolescents’ learning experiences and activities on innovativeness through interviews and the collection of their experiences. Studying adolescents through interviews and observing their engagement with distant learning might provide insights into their self-perceptions and inventive behaviors. Future research might examine the level of innovativeness across teenagers with varied learning backgrounds to gain a deeper insight into the influence of these experiences.

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

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Faculty of Education, Henan Normal University, Xinxiang, China

Chunlin Qi

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ChunLin Qi wrote the main manuscript text . All authors reviewed the manuscript.

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Chunlin Qi.

The authors declare no competing interests.

Consent was not obtained as the data were collected and analyzed anonymously through an online survey. The studies involving human participants were approved by the faculty of education at HeNan Normal University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. The submitted work is original and have not been published else-where or submitted to more than one journal for simultaneous consideration.

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Qi, C. The impact of adolescent innovation on academic resilience, distance learning self-efficacy, and academic performance.
Sci Rep 15, 12396 (2025). https://doi.org/10.1038/s41598-025-91542-7

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Received: 03 November 2024

Accepted: 21 February 2025

Published: 11 April 2025

DOI: https://doi.org/10.1038/s41598-025-91542-7

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