Optimism in action: Future confidence fuels digital literacy development in China’s rural students.

Optimism in action: Future confidence fuels digital literacy development in China’s rural students. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimism in action: Future confidence fuels digital literacy development in China’s rural students. Ling Li, Qiuyan Wang, Yu-Leung Ng, Bu Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6122977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rural students often experience lower levels of future confidence compared to their urban peers due to limited access to resources, which can hinder their learning motivation, active learning, and ultimately, their computer and information literacy (CIL). Addressing a critical gap in the literature, this study ( N = 2,393) draws on Dispositional Optimism Theory and educational learning research to examine the chain mediating effects of learning motivation and active learning on the relationship between future confidence and CIL among rural 8th graders in China. The findings revealed three key insights: (1) future confidence is positively associated with CIL; (2) learning motivation and active learning independently mediate the relationship between future confidence and CIL; and (3) learning motivation and active learning together form a chain mediation effect, further strengthening the future confidence–CIL link. These results provide a novel theoretical model to understand how students’ psychological attributes and learning behaviors influence their digital literacy. Practically, the study offers actionable strategies for educators in rural China, such as fostering students’ future confidence, promoting intrinsic learning motivation, and encouraging active participation in computer-based learning activities. The findings provide a pathway to improving rural students’ CIL and bridging the digital divide between rural and urban students. Social science/Education Social science/Psychology Future confidence Computer and information literacy Learning motivation Active learning Dispositional optimism theory Figures Figure 1 1. Introduction Rural students tend to face a series of overlooked educational disparities compared to their urban peer (Kryst et al. 2015 ), particularly in developing computer and information literacy (CIL) (Li et al. 2023 ). These challenges arise from a range of socioeconomic factors, limited access to technology, and systemic inequalities in educational resources (Ren et al. 2022 ; Scherer and Siddiq 2019 ). In China, rural students often contend with resource disparities that impede their ability to cultivate CIL – skills increasingly essential for academic and professional success (Shi et al. 2024 ). Computer and Information Literacy (CIL) is a prerequisite for students to effectively participate in the digital world. The compounded effects of poverty stigma and minimal exposure to technology further diminish their confidence in succeeding in a technology-driven world (Shi et al. 2024 ), making it crucial to address these underlying issues. Future confidence, defined as the belief in one's ability to achieve desired outcomes, is a critical psychological factor influencing students’ motivation and engagement in learning (Karakose et al. 2015; Soner 2019 ; Wang et al. 2020 ). Research has consistently shown that students possessing higher confidence in the future are more likely to actively engage in their education and persist despite challenges (Allen and Baughman 2016 ; Wang et al. 2013 ). However, rural students frequently demonstrate lower levels of future confidence perhaps due to their restricted access to educational resources (China Institute for Educational Finance 2023 ), fewer opportunities for academic and career advancement, or systemic barriers such as geographic isolation (Bonilla-Mejía et al. 2024 ). This lack of confidence can adversely affect their motivation to learn and their ability to develop essential skills like CIL, further perpetuating the cycle of disadvantage. Despite ample research on resource disparities and their impact on educational outcomes (Li and Su 2025 ; Peng et al. 2020 ; Zhao et al. 2022 ), there remains a notable gap in understanding how future confidence specifically affects CIL development among rural students. Most studies focus primarily on external factors, such as access to technology and infrastructure (Campos and Scherer 2024 ; Qin 2015 ; Zhong 2011 ), while neglecting the internal psychological factors that shape students’ willingness and ability to engage with technology. Notably, the interaction between resource disparities and future confidence, as well as the long-term consequences of low future confidence on rural students’ ability to acquire and apply CIL skills, are insufficiently explored. To address the gaps, our research investigates the relationships between future confidence, learning motivation, active learning, and CIL among 8th graders ( N = 2,393) in rural China, exploring the mediating effects of learning motivation and active engagement in computer knowledge on the future confidence–CIL relationship. The findings are essential for gaining valuable insights into the interplay between psychological and contextual factors, ultimately informing targeted interventions aimed at promoting equitable access to technology and education for rural students. 2. Literature review The development of students' abilities to utilize digital technologies has gained international recognition as a critical educational goal (Fraillon and Duckworth 2024). For instance, the European Union has set a target to ensure that the percentage of 8 th graders with low computer and information literacy (CIL) remains below 15% by 2030 (European Commission 2020). Similarly, countries like China are committed to enhancing digital literacy among primary and secondary school students (Guangming Online 2024). Despite these efforts, many Chinese rural students continue to struggle with digital skills, significantly lagging their urban peers in areas such as digital content creation, digital learning, and information literacy (Li et al. 2023; Zhang et al. 2024). This disparity poses a risk of hindering rural students from effectively participating in the digital economy and sharing digital resources in the future (Zhao and Xie 2020), underscoring the urgent need to explore strategies for promoting CIL among rural students. 2.1. The current study Considering these challenges, our study centers on rural 8 th graders living in Southwest China’s Guangxi Zhuang Autonomous Region, a region characterized by a diverse population of ethnic minorities and areas that are economically underdeveloped (Yang and Jiang 2021). Guangxi serves as a representative model of rural conditions in China, where many rural students lack stable internet access and sufficient digital resources, such as desktops, laptops, and tablets, due to low family income and inadequate technological infrastructure in schools (China Institute for Educational Finance 2023). Thus, it is likely that this limited exposure to digital technology may result in a lack of future confidence among these students, a psychological factor that could significantly impact students’ CIL development (Hatlevik et al. 2018; Levine and Donitsa-Schmidt 1998). Given the challenges in accessing rural student samples for research, previous studies on the interplay between confidence and CIL have predominantly focused on urban students, potentially exacerbating existing disparities in digital skills between rural and urban student groups. While existing literature has examined various factors affecting CIL, including school, family, and individual psychological influences (Campos and Scherer 2024; Fraillon et al. 2014; Qin 2015; San et al. 2018), psychological factors directly related to students' CIL development warrant further exploration. Factors such as computer anxiety (Gao and Gao 2011), computer self-efficacy (Lee and Huang 2014), and poverty stigma (Shi et al. 2024) have all been found to influence CIL significantly. However, future confidence, a crucial psychological variable, remains understudied. Drawing on dispositional optimism theory (Scheier and Carver 1985), which posits that future confidence positively correlates with academic performance (Meisha and Al-Dabbagh 2021; Stankov et al. 2012), innovative activity (Herz et al. 2014), and mental health (Wang et al. 2022), this study posits that future confidence can enhance students' learning motivation and engagement in information technology education, thereby improving their CIL performance. 2.2 Theoretical lens: Dispositional optimism Dispositional optimism theory posits that people often demonstrate a general tendency to expect positive outcomes in their lives. As a result, optimistic individuals are more likely to believe that good things will happen to them, which can lead to increased motivation and effort in pursuing goals. The theory suggests that this optimistic outlook not only influences emotional well-being but also correlates with better coping strategies in the face of adversity, ultimately resulting in more favorable life outcomes. According to the dispositional optimism theory (Scheier and Carver 1985), future confidence is positively associated with academic success, physical and mental health etc. Likewise, considering that students with future confidence may show higher learning motivation (Chang et al. 2022), and invest more effort in information technology education to enhance their competency in computer knowledge learning (Riswanti Rini et al. 2022), potentially leading to improved CIL. Prior studies had identified the positive impact of dispositional optimism on academic success (Chemers et al. 2001; Tetzner and Becker 2015). Dispositional optimism belongs to a type of optimism, also known as grand optimism (Scheier and Carver 1985). Dispositional optimism is described as the “extent to which people hold generalized favorable expectancies for their future” (Carver et al. 2010, p. 879). This positive outlook appears to help optimistic people to develop more positively in several areas of life, including work, health, social relations, and education (Ramli et al. 2023). It is because dispositional optimism helps guide human thoughts and behavior across multiple contexts, including educational context (Carver and Scheier 2001). Students’ future confidence is an important indicator of whether they are disproportionately optimistic (Joutsenniemi et al. 2013; Stankevicius et al. 2014). Future confidence could be both motivated and motivating (Peterson 2000), it can influence students’ behavior through the regulation of cognitive, motivational, physical and mental response processes (Che 2002). More specifically, students with sufficient future confidence usually hold a positive attitude toward the world. They tend to make positive predictions about future events. In many cases, they can generate more vivid mental images of positive events, leading to having a stronger sense of pre-experiencing positive events. This positive mindset significantly impacts students’ motivation and learning behaviors in the academic setting. Students who are confident about their future demonstrate higher academic engagement and greater persistence in achieving their academic goals (Carver and Scheier 2001). They actively participate in the learning process and exert sustained effort (Segerstrom 2007). Consequently, students with high future confidence are more likely to attain academic, competitive, and employment success (Peterson 2000). Dispositional optimism theory is equally applicable in CIL education. A study of middle school students found that their positive perceptions of computer abilities are significantly related to computer performance, and this positive relation was mediated by computer learning motivation (Christoph et al. 2015). Another study investigated 7 th through 12 th grades and verified the positive relationship between computer confidence, computer attitudes, and computer knowledge. Specifically, computer confidence and computer attitudes were positively related to each other, and that higher scores on both were associated with higher computer knowledge (Levine and Donitsa-Schmidt 1998). However, in the CIL scholarship, one theoretical perspective that is surprisingly absent is the dispositional optimism theory. Prior research had identified that future confidence can positively relate to students’ academic performance (Kleitman and Moscrop 2010; Ramli et al. 2023) through learning motivation (Lens et al. 2001; Fernández and Brenlla 2023) and active learning (Yusuf 2011). High future confidence increases motivation and effort in pursuing goals in learning process, ultimately resulting in more successful learning outcomes. This is owing to future confidence is the quintessential expression of optimism, a kind of optimistic mindset can often help produce positive results (Williams 2014). However, to the best of our knowledge, few studies have attempted to examine rural secondary school students’ future confidence and their CIL, and the chain mediating roles played by learning motivation and active learning. To address the literature gap, employing dispositional optimism theory and considering literature on educational learning, we test whether future confidence may relate to enhanced CIL indirectly via learning motivation and active learning in the process of CIL acquisition. 2.3 Future confidence and CIL Information and communication technology (ICT) are having an increasing impact on our daily lives, work, and social interactions. Developing CIL is essential for actively participating in today’s globalized and increasingly digitalized societies (Voogt &and Roblin 2012). The development of CIL in middle school students is an important part in basic education. Referring to the International Computer and Information Literacy Study (ICILS) definition of CIL, this study defined CIL as “an individual’s ability to utilize computers for investigative, creative, and communicative purposes in order to participate effectively in family, school, workplace, and community activities” (Fraillon et al. 2013, pp. 17). Drawing on previous studies (Shi et al. 2024), this study selected The Basic ICT Skills Scale, the Advanced ICT Skills Scale, and the ICT Self-Efficacy Scale to measure CIL of 8 th grade middle school students in Guangxi Zhuang Autonomous Region. In the educational context, future confidence, as an essential psychological state, is especially vital to CIL performance. Building on dispositional optimism theory (Scheier and Carver 1985), middle school students with sufficient future confidence usually keep positive prediction about future and trust they can be successful in the future. This confidence is a valuable psychological and emotional resource, which is helpful to develop middle school students’ ICT self-efficacy. Shi et al. (2024) found students’ self-efficacy is positively related to their ICT self-efficacy. In addition, this positive outlook also brings about constructive behaviors. Positive students put more effort into their studies (Tan and Tan 2014), including learning ICT skills. Existing studies have explored various factors similar to future confidence contributing to CIL. For example, a study of 8 th graders found that students’ self-efficacy, covering self-confidence and expectations for the future, was positively correlated with their CIL (Hatlevik et al. 2018). Hence, this study poses the first hypothesis: H1 : Chinese rural students’ future confidence is positively related to their CIL. 2.4 Learning motivation and active learning Learning motivation is a key factor contributing to students’ academic performance (Fraser and Killen 2005) and CIL development (Mortimore and Wall 2009). Learning motivation has always been the focus of educational psychology research (Filgona et al. 2020), referring to improving an individual’s learning behavior, directing the purpose of learning, and maintaining this learning activity, thereby adjusting and strengthening the student’s inner journey or inner psychological state (Tollefson 2000). Depending on where the incentive comes from, it might be intrinsic or extrinsic (Murayama 2022). Intrinsic motivation is motivated by personal gratification, interest, or enjoyment in the activity itself. Extrinsic motivation stems from outside influences or rewards including money, recognition, grades, or admiration (Murayama 2022). Learning motivation can be invoked to explain differences in learning behavior (Beckmann and Heckhausen 2018; Sánchenz-Bolívar and Martínez-Martínez 2022). Usually, students who are highly motivated apply extra effort and exhibit more positive emotions in the face of adversity, and achieve better outcomes (Elliot et al. 1999; Feng et al. 2023; Li et al. 2022). In contrast, students who lack learning motivation put in less effort, which subsequently leads to poor academic performance (Fraser and Killen 2005). Significant positive relationship has been found between learning motivation and academic success. A study conducted among university students revealed a positive relationship between learning motivation and academic performance, with effort acting as a mediating variable (Goodman et al. 2011). Ma et al. (2023) observed positive correlations between motivation, English learning strategies, and academic achievement. A study demonstrated that college students’ learning motivation strengthened the positive relationship between learning approaches and academic achievement (Bakhtiarvand et al. 2011). In the field of information technology education, learning motivation, as an important psychological factor characterizing an individual, is also a key factor influencing CIL. A study corroborated a positive relationship between motivational belief strategies and digital literacy competency, which signified the important role of self-motivation in promoting digital literacy as well as preparing students to be a part of the digital future (Lilian 2022). A study conducted among college students indicated that the higher the intrinsic motivation of students, the better their digital literacy (Marth and Bogner 2019). A study found both intrinsic and extrinsic academic motivation were found to be positively related to information literacy self-efficacy (Ross et al. 2016), and information literacy self-efficacy was positively correlated with CIL (Hatlevik et al. 2018). Understanding the relationship between future confidence and learning motivation is critical to comprehending students’ CIL performance. Students with high future confidence are more likely to establish ambitious academic goals and demonstrate a strong commitment to reaching them (Che 2002; Ryan 2017). This confidence in their talents inspires them to participate more thoroughly in learning activities, employ effective learning tactics, and persevere in the face of adversity (Karakose et al. 2023; Soner 2019; Wang et al. 2020). The relationship between confidence and learning motivation has been supported by empirical research. A study reported that academic self-efficacy is a positive predictor of both academic motivation, self-control and self-management of university students (Soner 2019). Chen (2001) has pointed out that students’ confidence in their competence positively predicts their motivation. In summary, both students’ future confidence and learning motivation are correlated with enhanced CIL. Meanwhile, future confidence positively relates to learning motivation. Thus, this study poses the following research hypotheses: H2 : Future confidence is positively related to learning motivation while learning motivation is positively related to CIL. H3 : Learning motivation mediates the positive relationship between future confidence and CIL among rural students. Except for the learning motivation as the mediating variable, active learning is also vital to foster CIL (Anthonysamy et al. 2020; Lee et al. 2015). Different phrases have been used to describe active learning, including self-directed learning, self-planned learning, self-teaching, autonomous learning, and independent study (Ainoda et al. 2005; Brockett and Hiemstra 1991). In this study, active learning is described self-planned learning as a learning activity with the characteristics of being self-initiated and occurring in isolation (Hiemstra 1976) and self-education (Brookfield 1986). Accordingly, active learning of computer knowledge is defined as students’ self-directed and independent learning of computer knowledge, including searching information, communicating with others, and learning via the Internet. It can be viewed as both a competency and a learning activity. Confidence is recognized as a key factor influencing learners’ active learning of knowledge (Panadero 2017; Usher 2012; Xu et al. 2024). Students with high confidence are more likely to see the value of a task and thus be more engaged in learning. Allen and Baughman (2016) found that students with more confidence and knowledge are more willing to learn actively and independently. A study conducted among Chinese and German college students showed positive relationships between self-efficacy, use of self-regulated learning strategies, and English language test scores (Wang et al. 2013). In the field of computer information science, the level of students’ active learning of computer knowledge is positively related to the level of their CIL. A study demonstrated that students’ self-directed learning of computer knowledge contributed positively to their digital literacy (Riswanti Rini et al. 2022). Another study on doctoral students revealed that the higher the students’ active learning scores, the higher their level of information literacy, and that information literacy mediates the relationship between active learning and self-efficacy (Hosseinitabaghdehi and Salehi 2018). Massive open online courses can improve students’ active learning and thus strengthen their digital skills (Chatwattana 2021). As if students have sufficient confidence, they are eager to learn computer knowledge and may continue their computer studies actively, so they can effectively acquire digital skills. Accordingly, this study poses the following research hypotheses: H4 : Future confidence is positively related to active learning, which is also positively related to CIL. H5 : Active learning mediates the relationship between future confidence and CIL. Active learning is a key positive predictor of CIL, while its existence presupposes that students have a high degree of confidence in themselves and sufficient motivation to learn. Motivation is expected to be an important basis for judging whether a student is a self-directed learner. Studies have pointed out that motivation is an important predictor of active learning. For example, Pang (2001) found that students’ intrinsic motivation is a prerequisite for active learning. Wang (2014) discovered that learning motivation is a key factor relating to improved English active learning ability. Ryan and Deci (2000) found that both intrinsic and extrinsic motivations were important factors influencing autonomy-driven learning. Lei et al. (2024) found that the two dimensions of learning motivation, namely internal learning motivation and external learning motivation, had a significant positive impact on learning engagement. Based on the theory of dispositional optimism and relevant literature on educational learning, having a high degree of future confidence tends to regulate students’ learning motivation (Fernández and Brenlla 2023; Lens et al. 2001) and active learning of knowledge (Yusuf 2011), ultimately contributing to enhanced CIL (Levine and Donitsa-Schmidt 1998). Combining the above hypotheses, we predict that rural secondary school students’ confidence in the future would relate to enhanced CIL indirectly via their increased learning motivation and active engagement in the process of learning computer knowledge and skills. Thus, we propose the following research hypotheses: H6 : There is a chain mediating effect of learning motivation and active learning on the relationship between future confidence and CIL. 3. Methods 3.1 Sampling and procedure The data came from the project of Education Quality Evaluation of Junior High Schools in the rural school districts of a city in Southwest China’s Guangxi Zhuang Autonomous Region. This project was sponsored and funded by the city’s education authorities. None of the data had been used in any other research projects. For this study, 6,997 students were recruited from 106 rural schools, who completed the Programme for International Student Assessment (PISA) with the help of local school administrators and teachers. After removing incomplete and missing data, 2,393 8 th graders ( N Female = 1,338, 55.9%) were included in the study. Their demographics are reported in Table 1. The study proposal was reviewed and granted approval by the Institutional Review Board of the first author’s university prior to the commencement of data collection. A researcher who had been properly trained provided an overview of the survey to the schools, parents, and legal guardians, and subsequently gave their approval. Students were informed about the survey, and only those who provided their consent participated in the study. Students completed the paper-and-pencil survey as part of a class assignment during regular school hours. Table 1. The sample demographics Variables Category N % Residence Urban areas 206 8.6 Rural areas 2176 90.9 No registration 11 0.5 Gender Male 1055 44.1 Female 1338 55.9 Ethnicity Han nationality 2091 87.4 Zhuang nationality 279 11.7 Others 23 1.0 Computers at home No computers 1374 57.4 One or more computers 1019 42.6 Mother’s education No education 138 5.8 Secondary school 868 36.3 Junior high school 949 39.7 Senior high school 355 14.8 University or above 83 3.5 Father’ s education No education 44 1.8 Secondary school 733 30.6 Junior high school 1055 44.1 Senior high school 441 18.4 University or above 120 5.0 Mother’ s job Unemployed 138 5.8 Farmer 1232 51.5 Others 1023 42.9 Father’ s job Unemployed 85 3.6 Farmer 1218 50.9 Others 1090 45.5 M SD ICT skills 1.97 0.55 ICT efficacy 2.89 0.58 Learning motivation 3.64 0.70 Active learning 2.92 0.56 Future confidence 3.14 0.79 3.2 Measures 3.2.1. Future confidence. Future confidence was measured using a single item: “How confident are you about your future?” Respondents rated their future confidence on a scale from 1 ( not confident at all ) to 4 ( very confident ). A higher score indicated a higher sense of future confidence ( M = 3.14, SD = 0.79). 3.2.2. Computer and information literacy. This scale was adopted from the International Computer and Information Literacy Study (ICILS) (Fraillon et al. 2014), consisting of two dimensions: ICT efficacy and ICT Skills. There were 23 items in the scale. ICT efficacy was measured by 11 items with a 4-point Likert scale from 1 ( Strongly disagree ) to 4 ( Strongly agree ), including “It is essential to me to work with a computer.” “I think using a computer is fun.” “Learning how to use a new computer program is very easy for me.” ( M = 2.89, SD = 0.58, Cronbach’s a = 0.89). ICT skills was measured by 12 items with a 3-point Likert scale from 1 ( I do not think I could do this ) to 3 ( I know how to do this ), including “Search for and find a file on your computer.” “Use software to find and get rid of viruses.” “Create or modify a document.” ( M = 1.97, SD = 0.55, Cronbach’s a = 0.89). 3.2.3. Learning motivation. The learning motivation scale (Amabile et al. 1994) was modified to assess the learning motivation of 8 th graders. The scale contained 20 items, including 10 intrinsic motivation items (e.g., “I often think about and urge myself to meet my own expectations or standards.” “How well I do on tests is secondary to studying itself, which I really enjoy.” “I like studying because studying itself allows me to gain a lot of knowledge.”) and 10 extrinsic motivation items (e.g., “I often work extra hard to meet the standards of achievement expected by my parents.” “I study hard because teachers usually praise students who work hard.” “It’s hard to hold my head up in front of my relatives and friends when I don't do well on exams.”). Respondents answered each question in the survey using a five-point Likert scale from 1 ( very much not true ) to 5 ( very much true ). Higher scores represented higher learning motivation (For the intrinsic motivation: M = 3.39, SD = 0.74, Cronbach’s a = 0.89; For the extrinsic motivation, M = 3.88, SD = 0.79, Cronbach’s a = 0.82; For overall motivation, M = 3.64, SD = 0.70, Cronbach’s a = 0.91). Previous studies have demonstrated that this scale had good reliability and validity (Amabile et al. 1994; Loo, 2001). 3.2.4. Active learning. The students’ active learning scale in this study was derived from the 2013 ICILS test (Fraillon et al. 2014). The active learning scale consisted of four question items (who mainly teaches you to do the following things: “Communicate over the Internet.” “Change computer equipment.” “Look up information on a computer.” and “online learning”). Respondents answered each question using a three-point Likert scale, with value 1 being “I never studied.” value 2 being “Teachers or family members taught me.” and value 3 being “Self-study.” The higher the score, the more active the students were in learning computers on their own ( M = 2.37, SD = 0.56, Cronbach’s a = 0.75). 3.3 Data analysis A serial mediation model was developed to investigate the chain mediating effect of learning motivation (M1: Mediator 1) and active learning (M2: Mediator 2) on the positive association between rural secondary school students’ future confidence (X: Independent variable) and their CIL (Y: Dependent variable). Structural equation modeling was used to test the serial mediation model through AMOS 29.0. The study sample was bootstrapped 5,000 times. 4. Results 4.1 Assessing common method bias Common bias was detected using Harman’s one-way method by conducting an exploratory factor analysis without rotation on the questionnaire measurement scale. This method analyzed the number of factors, the proportion of variance explained, and the proportionality between the variance explained by the first factor and the total variance explained. If the amount of variance explained by the first factor did not exceed 40% of the total amount of variance explained (Tang and Wen 2020), the common method bias was deemed insignificant. The variance explained collar of the first factor in this study accounted for 35.54% of the total variance, indicating that the common method bias was not significant for this data. 4.2 The model fit Based on the conceptual model and the relationships between the variables, a chain-mediated structural equation model with latent variables (Figure 1) was developed to investigate the mediating effect of learning motivation and active learning on the positive association between future confidence and CIL. Adhering to established guidelines, the structural equation model’s fit was assessed using the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR). These criteria were met, with RMSEA = 0.05, CFI = 0.94, TLI = 0.91, and SRMR = 0.04. It is noteworthy that the significance of the χ2 value may be influenced by the sample size, as demonstrated by Brown (2015). Given the substantial sample size employed in this study, the χ2 value was not employed to evaluate the model’s fit. 4.3 Hypothesis testing Structural equation modeling demonstrated a positive correlation between rural students’ future confidence and CIL ( β = 0.07, p = 0.002), motivation to learn ( β = 0.23, p < 0.001), as well as their engagement in active learning ( β = 0.05, p = 0.049) (see Table 2). Learning motivation ( β = 0.05, p = 0.020) and active learning (β = 0.60, p < 0.001) was also positively correlated with CIL (see Table 2). Learning motivation was positively associated with active learning ( β = 0.05, p = 0.015). Table 2 . Path coefficients among future confidence, CIL, learning motivation and active learning. Path β SE p Future confidence → CIL 0.07 0.02 0.002 Future confidence → Learning motivation 0.23 0.03 <0.001 Future confidence → Active learning 0.05 0.02 0.049 Learning motivation → CIL 0.05 0.03 0.020 Learning motivation → Active learning 0.05 0.02 0.015 Active learning → CIL 0.60 0.03 <0.001 Note: The table shows the standardized path coefficients. CIL = computer and information literacy. The sample was replicated 5,000 times to generate an approximate sampling distribution. The mean of these 5,000 effect estimates was calculated as the mean indirect effect. These estimates were ranked in order of magnitude, and the 95% confidence intervals for the mediated effects were estimated. The results demonstrated that future confidence significantly influenced learning motivation ( β = 0.012, p = 0.019), active learning ( β = 0.028, p = 0.047), and the combination of future confidence, learning motivation, and active learning ( β = 0.007, p = 0.015). These three pathways corresponded to confidence intervals that did not overlap, indicating the significance of the indirect effects (See Table 3). Table 3 . Results of mediation analysis of learning motivation and active learning on future confidence–CIL relation. Indirect effects Estimates p 95% CI Future confidence → Learning motivation → CIL 0.012 0.019 [0.002, 0.027] Future confidence → Active learning → CIL 0.028 0.047 [0.000 0.057] Future confidence → Learning motivation → Active learning → CIL 0.007 0.015 [0.001, 0.006] Note: The table shows the standardized path coefficients. CIL = computer and information literacy. In summary, the results presented in Tables 2 and 3 provide evidence for the following six hypotheses, which were all confirmed in the analyses. H1 was supported. Future confidence is positively correlated with CIL, ( β = 0.07, p = 0.002), H2 was supported. Future confidence is positively correlated with learning motivation ( β = 0.23, p < 0.001). H3 was supported. Learning motivation mediates the positive relationship between future confidence and CIL, ( β = 0.012, p = 0.019). H4 was supported. Future confidence is positively correlated with active learning ( β = 0.05, p = 0.049), which is also positively correlated with CIL ( β = 0.60, p < 0.001). H5 was supported. Active learning mediates the positive relationship between future confidence and CIL ( β = 0.028, p = 0.047). H6 was supported. Learning motivation mediates the positive relationship between future confidence and CIL through active learning ( β = 0.05, p = 0.015). Also, there is a chain mediating effect of learning motivation and active learning on the relationship between future confidence and CIL ( β = 0.007, p = 0.015). 5. Discussion This study, grounded in dispositional optimism theory and literature on educational learning, investigated the structural pattern between rural students’ confidence in the future and their computer and information literacy. Specifically, learning motivation and active learning were identified as mediators through which the relationship between future confidence and CIL could be established. Theoretically, this study provided empirical evidence that dispositional optimism among China’s rural students contributed to the development of CIL. It also explores and verified the relationships between learning motivation, active learning, and CIL. These findings offer valuable insights for enhancing CIL among rural students in China and promoting the overall development of these students in the digital era. 5.1 Future confidence and CIL(H1) In line with our first aim, our study demonstrated the positive relation from future confidence to computer and information literacy among rural students. Specifically, higher future confidence was related with better computer skills and digital performance. The finding is consistent with what was reported in previous studies that high levels of future confidence in individuals can help improve their mental and physical health (Lee et al. 2018; Ouyang et al. 2023 ). The current study suggests that high levels of future confidence among rural students contribute to CIL, just as dispositional optimists are more likely to be physically and mentally healthy and academically successful (Peterson 2000 ). It further demonstrated dispositional optimism theory, optimistic outlook not only results in more favorable life outcomes in physical and mental health (Carver et al. 2010 ; Scheier and Carver 1992 ) and academic success (Tetzner and Becker 2018 ), but also fosters CIL. The finding indicates that the rural students with high future confidence, show higher learning motivation (Chang et al. 2022 ), and pay more effort in information technology courses to enhance their CIL (Riswanti et al. 2022), so they may have better CIL performance. Therefore, enhancing future confidence of rural students is an effective intervention to reduce existing disparities in digital skills between rural and urban populations. 5.2 Mediating role of learning motivation (H2 and H3) Learning motivation mediated the relation between future confidence and computer and information literacy. Specifically, future confidence is positively related to learning motivation, and learning motivation is positively related to CIL. First, the higher the level of students’ future confidence, the stronger their learning motivation. This is consistent with the findings of previous studies that optimistic individuals who believe in positive outcomes tend to have increased motivation in pursuing their goals (Chen 2001 ; Ihsan et al. 2015 ; Zhu 2014 ). Our study elucidates that rural students with high future confidence are more inclined to establish ambitious goals in information technology education and are more willing to invest effort in achieving them. This confidence in their abilities motivates students to engage more fully in learning computer activities. Second, higher learning motivation among rural students is associated with improved CIL. This aligns with the fact that individuals with higher learning motivation exhibit better academic performance and digital literacy compared to those with lower motivation (Bakhtiarvand et al. 2011 ; Lilian 2022 ; Marth and Bogner 2019 ). New evidence was also found to support that highly motivated students effectively enhance their learning behavior, directing their learning objectives and sustaining it through computer-based learning activities. They demonstrate a greater willingness to invest time and effort in information technology courses, maintaining higher enthusiasm and sustained effort. As a result, they are more likely to achieve higher CIL levels than their peers. This result suggests that educators can effectively stimulate students’ learning motivation in computer-based learning environments through various interventions, such as enhancing the appeal of information technology and implementing reward and punishment mechanisms, thereby improving computer and information literacy. 5.3 Mediating role of active learning (H4 and H5) Active learning also mediated the relation between future confidence and computer and information literacy (CIL). Specifically, future confidence is positively related to active learning, which is also positively related to CIL. H4 and H5 were supported. First, the higher the level of rural students’ future confidence, the more actively they learn computers independently. The result is similar with the previous studies which indicated that confident students engage in learning activities more actively (Allen and Baughman 2016 ). This is owing to that students with sufficient future confidence are more likely to see the value of a task and thus be more engaged in Information Technology Courses. While acquiring digital skills, students are more inclined to adopt effective learning strategies, particularly self-regulated learning strategies, which enhance their computer performance. Furthermore, the more actively they engage in learning computer knowledge, the better their performance in CIL. This aligns with previous research findings (Chatwattana 2021 ; Hosseinitabaghdehi and Salehi 2018 ; Riswanti et al. 2022). This is because students who actively participate in their education are more likely to actively engage in computer learning activities through metacognition, motivation, and behavior. Moreover, they can take responsibility for their learning and persist despite challenges in learning computers, which often enable them to attain higher CIL. This result underscores the significance of fostering students’ awareness and ability for independent learning in computer education. 5.4 Learning motivation and active learning (H6) Two more key findings are: 1) learning motivation is positively correlated with active learning, and 2) there is a mediating effect of learning motivation and active learning on the relationship between future confidence and computer-integrated learning. In essence, rural students’ future confidence can enhance their learning motivation, which, in turn, motivates students to actively and independently participate in computer-related learning activities, ultimately improving rural students’ CIL. In the field of computer information science, previous studies have demonstrated that students’ learning motivation plays a crucial role in sustaining active learning (Altinpulluk et al. 2023 ; Pan 2020 ). This is because motivated students tend to learn actively and independently, including pursuing in-depth understanding and mastery of digital knowledge and skills. Active learning strategies provide students with opportunities to explore information and technology courses in depth. As they gain more personal satisfaction, sense of achievement, and self-fulfillment from active learning (Chang et al. 2014 ; Freeman et al. 2014 ), active learning can contribute to the overall development of rural students’ CIL. It is noteworthy that the positive correlation between learning motivation and active learning is also evident in the disciplines of mathematics and English. For instance, in the field of mathematics, a study demonstrated that students with higher learning motivation exhibited greater self-directed engagement in mathematical activities and achieved superior performance in the subject (Xia et al. 2022 ). Similarly, in the discipline of English, college students’ intrinsic and extrinsic motivations in English learning were found to be positively associated with self-directed learning behaviors (Li and Yu 2008 ). Zhang ( 2005 ) further suggested that students’ learning motivation could enhance the long-term sustainability of independent learning behaviors in English. Therefore, methods that promote learning motivation and facilitate active learning in other disciplines, such as setting clear objectives and developing personalized learning plans, and providing constructive feedback, may be applicable to enhance computer literacy instruction. This study, thus, provides novel perspectives for educators to enhance CIL pedagogy, fostering students’ confidence through these methods could stimulate their learning motivation, encourage them to actively participate in the learning process of computer knowledge and skills, and eventually enhance their CIL proficiency. 5.5 Theoretical and practical implications This study, rooted in Dispositional Optimism Theory and the existing literature on educational learning, has yielded substantial theoretical and practical implications for enhancing Computer and Information Literacy among China’s rural students. It offers valuable insights for educational practitioners and researchers seeking to improve CIL among rural students. By focusing on psychological resilience through fostering future confidence, coupled with strategic pedagogical approaches that cultivate learning motivation and active participation, this study suggests that it is possible to bridge the digital divide and empower rural students in the digital age. 5.5.1 Theoretical Implications : This study bridges the gap between dispositional optimism theory and computer information science, showcasing how rural students’ positive outlook can significantly enhance their computer and information literacy. It underscores the significance of cultivating this positive mindset in rural secondary school students, offering educators a valuable tool to indirectly improve computer and information literacy (CIL) through increased learning motivation and active learning strategies. The findings provide empirical support for Dispositional Optimism Theory. They suggest that future confidence, a key component of optimism, plays a substantial role in fostering CIL among rural students. Furthermore, this study demonstrates the mediating roles of learning motivation and active learning in the relationship between future confidence and CIL. This further deepens our understanding of how these psychological and behavioral factors contribute to CIL development. The study presents robust empirical evidence that optimistic rural students, due to their positive outlook, are more likely to develop stronger CIL. Therefore, this research contributes to our understanding of dispositional optimism’s role in the development of computer and information literacy. 5.5.2 Practical Implications : This research offers valuable insights for educational practices aimed at bridging the digital divide between urban and rural students in China. It emphasizes the need for educators to create positive learning environments that address the unique challenges faced by rural students in acquiring digital skills. Based on this research, educators can employ several strategies. Teachers and parents are encouraged to: 1) Nurture optimism among rural students, helping them envision a positive and accessible future for growth and opportunity. 2) Develop tailored strategies that ignite the desire to learn computer skills among rural students, considering their unique characteristics and environments. 3) Encourage independent learning and adopt active learning strategies that empower students to take ownership of their computer skill development. 4) Recognize and address the limited digital resources in rural areas by equipping students with fundamental knowledge and skills that can be further explored independently. The findings highlight the significance of enhancing future confidence among rural students to improve their computer literacy. Interventions aimed at boosting their optimism can be highly effective in reducing the digital gap between rural and urban communities. Educators must implement strategies to foster students’ learning motivation in computer-based learning environments. This can be achieved through engaging content, reward systems, and clear learning goals. Creating active learning environments with opportunities for self-directed exploration and independent learning is crucial. Educators can utilize techniques like metacognitive strategies, student-led projects, and constructive feedback to encourage active participation. 5.6 Limitations and future studies Despite the innovative findings and contributions of this study, there are several limitations that warrant consideration, as well as opportunities for future research to address them. First, the study relied exclusively on self-reported measures of Computer and Information Literacy (CIL), which may introduce subjective biases. Future research should incorporate objective assessments, such as standardized tests or performance-based evaluations, to provide a more accurate and comprehensive understanding of CIL among rural students. Second, the measurement of future confidence was restricted to a single-item indicator, which may limit the reliability and validity of the construct. Future studies should adopt multi-item scales to assess future confidence and dispositional optimism, as this would provide a more robust and nuanced understanding of these psychological factors. Additionally, exploring other dimensions of optimism, such as resilience and hope, could offer further insights into their influence on learning outcomes. Third, the study acknowledged the scarcity of digital resources in rural areas, such as inadequate access to stable internet connections, computers, and other technological devices. This limitation may affect the applicability and generalizability of the findings. Future research should examine the moderating effects of resource availability on the relationships between future confidence, learning motivation, active learning, and CIL. Longitudinal studies could also assess how improvements in digital infrastructure impact these relationships over time. Fourth, the cross-sectional design of this study limits causal inferences. Future studies should employ longitudinal or experimental designs to better understand the causal mechanisms underlying the relationships identified in this study. For instance, interventions aimed at enhancing future confidence and their subsequent effects on learning motivation, active learning, and CIL could be tested over time. Finally, the study focused exclusively on rural students in China, which may limit the generalizability of the findings to other contexts. Future research should explore whether the relationships observed in this study hold true in diverse cultural, economic, and educational settings. Comparative studies between urban and rural populations, as well as across different countries, could provide valuable insights into how contextual factors influence the development of CIL. 6. Conclusions This study provides groundbreaking insights into the development of Computer and Information Literacy among rural Chinese students by integrating Dispositional Optimism Theory into the realm of digital education. The findings reveal that future confidence—a key aspect of optimism—plays a pivotal role in enhancing CIL, with learning motivation and active learning serving as vital mediators in this process. These results highlight the transformative power of psychological resilience and self-directed learning in addressing the digital divide. One of the most innovative discoveries of this research is that fostering future confidence among rural students not only improves their outlook on life but also directly impacts their digital proficiency. Students with higher levels of future confidence are more motivated to learn and actively engage in computer-related activities, ultimately achieving superior CIL outcomes. This underscores the idea that a positive mindset can drive tangible educational gains, even in resource-limited environments. Moreover, this study discovers that learning motivation and active learning are not merely byproducts of optimism but essential mechanisms through which future confidence translates into improved digital skills. The relationship between these factors provides a solid foundation for designing targeted interventions aimed at empowering rural students to thrive in the digital era. By cultivating optimism, fostering intrinsic motivation, and promoting active, independent learning, educators can create an environment where rural students take ownership of their digital education. This approach not only equips students with essential skills but also instills in them a sense of agency and self-efficacy, crucial for navigating an increasingly digital world. Overall, this study not only advances theoretical understanding but also delivers practical strategies to uplift rural students in the digital age. By leveraging future confidence and active learning as catalysts for digital literacy, educators can empower rural students to overcome systemic barriers, enabling them to realize their potential and contribute meaningfully to the global digital economy. This research represents a crucial step forward in narrowing the digital divide and ensuring equitable access to opportunities in the 21st century Declarations Author Contribution Ling Li contributed to research design, field survey and data collection. Qiuyan Wang and Yu-Leung Ng contributed to data analysis and charting. Bu Zhong contributed to research design and writing.All authors reviewed the manuscript. Acknowledgement The written of the paper was supported by National Social Science and Humanity Foundation (18ZDA338), 111 program (B21036), Decision Making Laboratory for Western China Education and Human Development at Southwest University and IA Laboratory at Hong Kong Baptist. Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan004), Chongqing Social Science and Humanity Foundation (2022YC028). Data Availability The data that support the findings of this study are available from the first and corresponding authors upon reasonable request. 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J Curriculum Stud 44(3):299–321. https://doi.org/10.1080/00220272.2012.668938 Wang C, Schwab G, Fenn P, Chang M (2013) Self-efficacy and self-regulated learning strategies for English language learners: Comparison between Chinese and German college students. J Educational Dev Psychol 3(1):173. https://doi.org/10.5539/JEDP.V3N1P173 Wang Q, Lee KCS, Hoque KE (2020) The effect of classroom climate on academic motivation mediated by academic self-efficacy in a higher education institute in China. Int J Learn Teach Educational Res 19(8):194–213. https://doi.org/10.26803/ijlter.19.8.11 Wang R, Li D, Zhang J, Song G, Liu Q, Tang X (2022) The relationship between parent-adolescent communication and depressive symptoms: The roles of school life experience, learning difficulties and confidence in the future. Psychol Res Behav Manage 15:1295–1310. https://doi.org/10.2147/PRBM.S345009 Wang X (2014) The Correlation Between Autonomous Learning Motivation and Metacognitive Strategy for Non-English Majors. Foreign Lang Educ 35(5):72–75. https://10.16362/j.cnki.cn61-1023/h.2014.05.023 (In Chinese) Williams G (2014) Optimistic problem-solving activity: enacting confidence, persistence, and perseverance. ZDM Mathematics Education 46:407–422. https://doi.org/10.1007/s11858-014-0586-y Xia Q, Yin H, Hu R, Li X, Shang J (2022) Motivation, Engagement, and Mathematics Achievement: An Exploratory Study Among Chinese Primary Students. Sage Open 12(4):21582440221134609. https://doi.org/10.1177/21582440221134609 Xinhuanet (2022) Research Report on the Status and Needs of Digital Literacy Education in China's Rural Schools Released, Suggests Multi-Party Co-Construction of Cultivation System. https://www.news.cn/tech/20220708/0618ad827db445eeb4abfc60a7f99b4f/c.html Xu J, Li J, Yang J (2024) Self-regulated learning strategies, self-efficacy, and learning engagement of EFL students in smart classrooms: A structural equation modeling analysis. System 125:103451. https://doi.org/10.1016/j.system.2024.103451 Yang HM, Jiang L (2021) Digital economy, spatial effects and total factor productivity. Stat Res 38(4):3–15. https://doi.org/10.19343/j.cnki.11-1302/c.2021.04.001 (In Chinese) Yusuf M (2011) The impact of self-efficacy, achievement motivation, and self-regulated learning strategies on students' academic achievement. Procedia - Social Behav Sci 15:2623–2626. https://doi.org/10.1016/j.sbspro.2011.04.158 Zhao WL, Xie R (2020) Digital Inequalities and Social Stratification: Analysis of the Social Inequality Effect of Information Communication Technologies. Science Soc 10(1):32–45. https://doi.org/10.19524/j.cnki.10-1009/g3.2020.01.032 (In Chinese) Zhao L, Cao C, Li Y, Li Y (2022) Determinants of the digital outcome divide in E-learning between rural and urban students: Empirical evidence from the COVID-19 pandemic based on capital theory. Comput Hum Behav 130:107177. https://doi.org/10.1016/j.chb.2021.107177 Zhang LJ, Bao WY, Zhao LL (2024) Educational Mirror of the Intelligence Divide: An Underlying Perspectives on the Digital Transformation of Education. Mod Educational Technol 34(7):51–60 (In Chinese) Zhang DY (2005) English Learning Strategies and Autonomous Learning. Foreign Lang Educ 1:49–55. http://dx.doi.org/10.16362/j.cnki.cn61-1023/h.2005.01.017 (In Chinese) Zhong ZJ (2011) From access to usage: The divide of self-reported digital skills among adolescents. Comput Educ 56(3):736–746. https://doi.org/10.1016/j.compedu.2010.10.016 Zhu JR (2014) Study of the Relationship among middle school students academic Pressure, academic motivation, academic self-confidence and academic Achievement. Central China Normal University. (In Chinese) Additional Declarations No competing interests reported. Supplementary Files AppendixRawData.sav DataUseInstructions.docx Highlightsforreview.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6122977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427113632,"identity":"03347dbb-baca-45b3-9791-b48c362d9ebd","order_by":0,"name":"Ling Li","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":427113634,"identity":"046b2a48-8183-429f-a9e6-0a2a144ad297","order_by":1,"name":"Qiuyan Wang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Qiuyan","middleName":"","lastName":"Wang","suffix":""},{"id":427113635,"identity":"95480eef-e10f-4a67-8744-f14ca6a36003","order_by":2,"name":"Yu-Leung Ng","email":"","orcid":"","institution":"Hong Kong Baptist University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Leung","middleName":"","lastName":"Ng","suffix":""},{"id":427113637,"identity":"4eb817e6-6866-4e2f-93b4-5b18d69d1bf6","order_by":3,"name":"Bu Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACCSBmbGCQM+DhAXGZgSJsxGkxJl1L4gaitcjPbj728OuOw+nbec4ek2CosE5skG5LwKuFcc6xdGPZM4dzd/b2pUkwnElPbJA5dgCvFmaJHDNpybbDuRvO85hJMLYdTmyQSG/Aq4UNqiXdAKzlHxFaeIBaJD+2HU4wONsD1NIA0pKG32ESEmlp0oxn0g03nDmXbJEA9FibRFoCXi3yM5KPSf7cYS1vcCb34I0PNday/RJpBni1gAAzD4wFMp5QRIIB4w9iVI2CUTAKRsHIBQCz/UUL0Gld1wAAAABJRU5ErkJggg==","orcid":"","institution":"Hong Kong Baptist University","correspondingAuthor":true,"prefix":"","firstName":"Bu","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2025-02-27 17:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6122977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6122977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78447095,"identity":"eeff9805-c850-491f-a8fc-e424a776c61a","added_by":"auto","created_at":"2025-03-13 10:14:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76692,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual model for predicting computer and information literacy.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6122977/v1/74605d76b809c9a5ea90b3b9.png"},{"id":86449736,"identity":"36f14035-efe8-43f0-af17-9139926b0f90","added_by":"auto","created_at":"2025-07-10 18:46:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6122977/v1/75469133-37a1-46ab-8d16-aa0df9a8ea69.pdf"},{"id":78447100,"identity":"2ca79c00-42ce-4389-82d9-9d1d69dc879d","added_by":"auto","created_at":"2025-03-13 10:14:34","extension":"sav","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":248784,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixRawData.sav","url":"https://assets-eu.researchsquare.com/files/rs-6122977/v1/39b1a2c6ab425def4acb4b7e.sav"},{"id":78447097,"identity":"50fdd451-7944-4117-9088-eea0fb20ea2a","added_by":"auto","created_at":"2025-03-13 10:14:34","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11001,"visible":true,"origin":"","legend":"","description":"","filename":"DataUseInstructions.docx","url":"https://assets-eu.researchsquare.com/files/rs-6122977/v1/7328607f6f72ce2bd2fb9e97.docx"},{"id":78447096,"identity":"62f91c28-ac94-47bd-b300-e93ff1503b54","added_by":"auto","created_at":"2025-03-13 10:14:34","extension":"doc","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11776,"visible":true,"origin":"","legend":"","description":"","filename":"Highlightsforreview.doc","url":"https://assets-eu.researchsquare.com/files/rs-6122977/v1/6433139dbc1dafbdb8854f77.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimism in action: Future confidence fuels digital literacy development in China’s rural students.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRural students tend to face a series of overlooked educational disparities compared to their urban peer (Kryst et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), particularly in developing computer and information literacy (CIL) (Li et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These challenges arise from a range of socioeconomic factors, limited access to technology, and systemic inequalities in educational resources (Ren et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Scherer and Siddiq \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In China, rural students often contend with resource disparities that impede their ability to cultivate CIL \u0026ndash; skills increasingly essential for academic and professional success (Shi et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Computer and Information Literacy (CIL) is a prerequisite for students to effectively participate in the digital world. The compounded effects of poverty stigma and minimal exposure to technology further diminish their confidence in succeeding in a technology-driven world (Shi et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), making it crucial to address these underlying issues.\u003c/p\u003e \u003cp\u003eFuture confidence, defined as the belief in one's ability to achieve desired outcomes, is a critical psychological factor influencing students\u0026rsquo; motivation and engagement in learning (Karakose et al. 2015; Soner \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has consistently shown that students possessing higher confidence in the future are more likely to actively engage in their education and persist despite challenges (Allen and Baughman \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, rural students frequently demonstrate lower levels of future confidence perhaps due to their restricted access to educational resources (China Institute for Educational Finance \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), fewer opportunities for academic and career advancement, or systemic barriers such as geographic isolation (Bonilla-Mej\u0026iacute;a et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This lack of confidence can adversely affect their motivation to learn and their ability to develop essential skills like CIL, further perpetuating the cycle of disadvantage.\u003c/p\u003e \u003cp\u003eDespite ample research on resource disparities and their impact on educational outcomes (Li and Su \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there remains a notable gap in understanding how future confidence specifically affects CIL development among rural students. Most studies focus primarily on external factors, such as access to technology and infrastructure (Campos and Scherer \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Qin \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhong \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while neglecting the internal psychological factors that shape students\u0026rsquo; willingness and ability to engage with technology. Notably, the interaction between resource disparities and future confidence, as well as the long-term consequences of low future confidence on rural students\u0026rsquo; ability to acquire and apply CIL skills, are insufficiently explored.\u003c/p\u003e \u003cp\u003eTo address the gaps, our research investigates the relationships between future confidence, learning motivation, active learning, and CIL among 8th graders (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,393) in rural China, exploring the mediating effects of learning motivation and active engagement in computer knowledge on the future confidence\u0026ndash;CIL relationship. The findings are essential for gaining valuable insights into the interplay between psychological and contextual factors, ultimately informing targeted interventions aimed at promoting equitable access to technology and education for rural students.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe development of students\u0026apos; abilities to utilize digital technologies has gained international recognition as a critical educational goal (Fraillon and Duckworth 2024). For instance, the European Union has set a target to ensure that the percentage of 8\u003csup\u003eth\u003c/sup\u003e graders with low computer and information literacy (CIL) remains below 15% by 2030 (European Commission 2020). Similarly, countries like China are committed to enhancing digital literacy among primary and secondary school students (Guangming Online 2024). Despite these efforts, many Chinese rural students continue to struggle with digital skills, significantly lagging their urban peers in areas such as digital content creation, digital learning, and information literacy (Li et al. 2023; Zhang et al. 2024). This disparity poses a risk of hindering rural students from effectively participating in the digital economy and sharing digital resources in the future (Zhao and Xie 2020), underscoring the urgent need to explore strategies for promoting CIL among rural students.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.1. The current study\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eConsidering these challenges, our study centers on rural 8\u003csup\u003eth\u003c/sup\u003e graders living in Southwest China\u0026rsquo;s Guangxi Zhuang Autonomous Region, a region characterized by a diverse population of ethnic minorities and areas that are economically underdeveloped (Yang and Jiang 2021). Guangxi serves as a representative model of rural conditions in China, where many rural students lack stable internet access and sufficient digital resources, such as desktops, laptops, and tablets, due to low family income and inadequate technological infrastructure in schools (China Institute for Educational Finance 2023). Thus, it is likely that this limited exposure to digital technology may result in a lack of future confidence among these students, a psychological factor that could significantly impact students\u0026rsquo; CIL development (Hatlevik et al. 2018; Levine and Donitsa-Schmidt 1998). Given the challenges in accessing rural student samples for research, previous studies on the interplay between confidence and CIL have predominantly focused on urban students, potentially exacerbating existing disparities in digital skills between rural and urban student groups.\u003c/p\u003e\n\u003cp\u003eWhile existing literature has examined various factors affecting CIL, including school, family, and individual psychological influences (Campos and Scherer 2024; Fraillon et al. 2014; Qin 2015; San et al. 2018), psychological factors directly related to students\u0026apos; CIL development warrant further exploration. Factors such as computer anxiety (Gao and Gao 2011), computer self-efficacy (Lee and Huang 2014), and poverty stigma (Shi et al. 2024) have all been found to influence CIL significantly. However, future confidence, a crucial psychological variable, remains understudied. Drawing on dispositional optimism theory (Scheier and Carver 1985), which posits that future confidence positively correlates with academic performance (Meisha and Al-Dabbagh 2021; Stankov et al. 2012), innovative activity (Herz et al. 2014), and mental health (Wang et al. 2022), this study posits that future confidence can enhance students\u0026apos; learning motivation and engagement in information technology education, thereby improving their CIL performance.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2 Theoretical lens: Dispositional optimism\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eDispositional optimism theory posits that people often demonstrate a general tendency to expect positive outcomes in their lives. As a result, optimistic individuals are more likely to believe that good things will happen to them, which can lead to increased motivation and effort in pursuing goals. The theory suggests that this optimistic outlook not only influences emotional well-being but also correlates with better coping strategies in the face of adversity, ultimately resulting in more favorable life outcomes. According to the dispositional optimism theory (Scheier and Carver 1985), future confidence is positively associated with academic success, physical and mental health etc. Likewise, considering that students with future confidence may show higher learning motivation (Chang et al. 2022), and invest more effort in information technology education to enhance their competency in computer knowledge learning (Riswanti Rini et al. 2022), potentially leading to improved CIL.\u003c/p\u003e\n\u003cp\u003ePrior studies had identified the positive impact of dispositional optimism on academic success (Chemers et al. 2001; Tetzner and Becker 2015). Dispositional optimism belongs to a type of optimism, also known as grand optimism (Scheier and Carver 1985). Dispositional optimism is described as the \u0026ldquo;extent to which people hold generalized favorable expectancies for their future\u0026rdquo; (Carver et al. 2010, p. 879). This positive outlook appears to help optimistic people to develop more positively in several areas of life, including work, health, social relations, and education (Ramli et al. 2023). It is because dispositional optimism helps guide human thoughts and behavior across multiple contexts, including educational context (Carver and Scheier 2001). Students\u0026rsquo; future confidence is an important indicator of whether they are disproportionately optimistic (Joutsenniemi et al. 2013; Stankevicius et al. 2014). Future confidence could be both motivated and motivating (Peterson 2000), it can influence students\u0026rsquo; behavior through the regulation of cognitive, motivational, physical and mental response processes (Che 2002). More specifically, students with sufficient future confidence usually hold a positive attitude toward the world. They tend to make positive predictions about future events. In many cases, they can generate more vivid mental images of positive events, leading to having a stronger sense of pre-experiencing positive events.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis positive mindset significantly impacts students\u0026rsquo; motivation and learning behaviors in the academic setting. Students who are confident about their future demonstrate higher academic engagement and greater persistence in achieving their academic goals (Carver and Scheier 2001). They actively participate in the learning process and exert sustained effort (Segerstrom 2007). Consequently, students with high future confidence are more likely to attain academic, competitive, and employment success (Peterson 2000).\u003c/p\u003e\n\u003cp\u003eDispositional optimism theory is equally applicable in CIL education. A study of middle school students found that their positive perceptions of computer abilities are significantly related to computer performance, and this positive relation was mediated by computer learning motivation (Christoph et al. 2015). Another study investigated 7\u003csup\u003eth\u003c/sup\u003e through 12\u003csup\u003eth\u003c/sup\u003e grades and verified the positive relationship between computer confidence, computer attitudes, and computer knowledge. Specifically, computer confidence and computer attitudes were positively related to each other, and that higher scores on both were associated with higher computer knowledge (Levine and Donitsa-Schmidt 1998).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, in the CIL scholarship, one theoretical perspective that is surprisingly absent is the dispositional optimism theory. Prior research had identified that future confidence can positively relate to students\u0026rsquo; academic performance (Kleitman and Moscrop 2010; Ramli et al. 2023) through learning motivation (Lens et al. 2001; Fern\u0026aacute;ndez and Brenlla 2023) and active learning (Yusuf 2011). High future confidence increases motivation and effort in pursuing goals in learning process, ultimately resulting in more successful learning outcomes. This is owing to future confidence is the quintessential expression of optimism, a kind of optimistic mindset can often help produce positive results (Williams 2014). However, to the best of our knowledge, few studies have attempted to examine rural secondary school students\u0026rsquo; future confidence and their CIL, and the chain mediating roles played by learning motivation and active learning. To address the literature gap, employing dispositional optimism theory and considering literature on educational learning, we test whether future confidence may relate to enhanced CIL indirectly via learning motivation and active learning in the process of CIL acquisition.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3 Future confidence and CIL\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eInformation and communication technology (ICT) are having an increasing impact on our daily lives, work, and social interactions. Developing CIL is essential for actively participating in today\u0026rsquo;s globalized and increasingly digitalized societies (Voogt \u0026amp;and Roblin 2012). The development of CIL in middle school students is an important part in basic education. Referring to the International Computer and Information Literacy Study (ICILS) definition of CIL, this study defined CIL as \u0026ldquo;an individual\u0026rsquo;s ability to utilize computers for investigative, creative, and communicative purposes in order to participate effectively in family, school, workplace, and community activities\u0026rdquo; (Fraillon et al. 2013, pp. 17). Drawing on previous studies (Shi et al. 2024), this study selected The Basic ICT Skills Scale, the Advanced ICT Skills Scale, and the ICT Self-Efficacy Scale to measure CIL of 8\u003csup\u003eth\u003c/sup\u003e grade middle school students in Guangxi Zhuang Autonomous Region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the educational context, future confidence, as an essential psychological state, is especially vital to CIL performance. Building on dispositional optimism theory (Scheier and Carver 1985), middle school students with sufficient future confidence usually keep positive prediction about future and trust they can be successful in the future. This confidence is a valuable psychological and emotional resource, which is helpful to develop middle school students\u0026rsquo; ICT self-efficacy. Shi et al. (2024) found students\u0026rsquo; self-efficacy is positively related to their ICT self-efficacy. In addition, this positive outlook also brings about constructive behaviors. Positive students put more effort into their studies (Tan and Tan 2014), including learning ICT skills. Existing studies have explored various factors similar to future confidence contributing to CIL. For example, a study of 8\u003csup\u003eth\u003c/sup\u003e graders found that students\u0026rsquo; self-efficacy, covering self-confidence and expectations for the future, was positively correlated with their CIL (Hatlevik et al. 2018). Hence, this study poses the first hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e: Chinese rural students\u0026rsquo; future confidence is positively related to their CIL.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.4 Learning motivation and active learning\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eLearning motivation is a key factor contributing to students\u0026rsquo; academic performance (Fraser and Killen 2005) and CIL development (Mortimore and Wall 2009). Learning motivation has always been the focus of educational psychology research (Filgona et al. 2020), referring to improving an individual\u0026rsquo;s learning behavior, directing the purpose of learning, and maintaining this learning activity, thereby adjusting and strengthening the student\u0026rsquo;s inner journey or inner psychological state (Tollefson 2000). Depending on where the incentive comes from, it might be intrinsic or extrinsic (Murayama 2022). Intrinsic motivation is motivated by personal gratification, interest, or enjoyment in the activity itself. Extrinsic motivation stems from outside influences or rewards including money, recognition, grades, or admiration (Murayama 2022). Learning motivation can be invoked to explain differences in learning behavior (Beckmann and Heckhausen 2018; S\u0026aacute;nchenz-Bol\u0026iacute;var and Mart\u0026iacute;nez-Mart\u0026iacute;nez 2022). Usually, students who are highly motivated apply extra effort and exhibit more positive emotions in the face of adversity, and achieve better outcomes (Elliot et al. 1999; Feng et al. 2023; Li et al. 2022). In contrast, students who lack learning motivation put in less effort, which subsequently leads to poor academic performance (Fraser and Killen 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificant positive relationship has been found between learning motivation and academic success. A study conducted among university students revealed a positive relationship between learning motivation and academic performance, with effort acting as a mediating variable (Goodman et al. 2011). Ma et al. (2023) observed positive correlations between motivation, English learning strategies, and academic achievement. A study demonstrated that college students\u0026rsquo; learning motivation strengthened the positive relationship between learning approaches and academic achievement (Bakhtiarvand et al. 2011). In the field of information technology education, learning motivation, as an important psychological factor characterizing an individual, is also a key factor influencing CIL. A study corroborated a positive relationship between motivational belief strategies and digital literacy competency, which signified the important role of self-motivation in promoting digital literacy as well as preparing students to be a part of the digital future (Lilian 2022). A study conducted among college students indicated that the higher the intrinsic motivation of students, the better their digital literacy (Marth and Bogner 2019). A study found both intrinsic and extrinsic academic motivation were found to be positively related to information literacy self-efficacy (Ross et al. 2016), and information literacy self-efficacy was positively correlated with CIL (Hatlevik et al. 2018).\u003c/p\u003e\n\u003cp\u003eUnderstanding the relationship between future confidence and learning motivation is critical to comprehending students\u0026rsquo; CIL performance. Students with high future confidence are more likely to establish ambitious academic goals and demonstrate a strong commitment to reaching them (Che 2002; Ryan 2017). This confidence in their talents inspires them to participate more thoroughly in learning activities, employ effective learning tactics, and persevere in the face of adversity (Karakose et al. 2023; Soner 2019; Wang et al. 2020). The relationship between confidence and learning motivation has been supported by empirical research. A study reported that academic self-efficacy is a positive predictor of both academic motivation, self-control and self-management of university students (Soner 2019). Chen (2001) has pointed out that students\u0026rsquo; confidence in their competence positively predicts their motivation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, both students\u0026rsquo; future confidence and learning motivation are correlated with enhanced CIL. Meanwhile, future confidence positively relates to learning motivation. Thus, this study poses the following research hypotheses:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e: Future confidence is positively related to learning motivation while learning motivation is positively related to CIL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e: Learning motivation mediates the positive relationship between future confidence and CIL among rural students.\u003c/p\u003e\n\u003cp\u003eExcept for the learning motivation as the mediating variable, active learning is also vital to foster CIL (Anthonysamy et al. 2020; Lee et al. 2015). Different phrases have been used to describe active learning, including self-directed learning, self-planned learning, self-teaching, autonomous learning, and independent study (Ainoda et al. 2005; Brockett and Hiemstra 1991). In this study, active learning is described self-planned learning as a learning activity with the characteristics of being self-initiated and occurring in isolation (Hiemstra 1976) and self-education (Brookfield 1986). Accordingly, active learning of computer knowledge is defined as students\u0026rsquo; self-directed and independent learning of computer knowledge, including searching information, communicating with others, and learning via the Internet. It can be viewed as both a competency and a learning activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConfidence is recognized as a key factor influencing learners\u0026rsquo; active learning of knowledge (Panadero 2017; Usher 2012; Xu et al. 2024). Students with high confidence are more likely to see the value of a task and thus be more engaged in learning. Allen and Baughman (2016) found that students with more confidence and knowledge are more willing to learn actively and independently. A study conducted among Chinese and German college students showed positive relationships between self-efficacy, use of self-regulated learning strategies, and English language test scores (Wang et al. 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the field of computer information science, the level of students\u0026rsquo; active learning of computer knowledge is positively related to the level of their CIL. A study demonstrated that students\u0026rsquo; self-directed learning of computer knowledge contributed positively to their digital literacy (Riswanti Rini et al. 2022). Another study on doctoral students revealed that the higher the students\u0026rsquo; active learning scores, the higher their level of information literacy, and that information literacy mediates the relationship between active learning and self-efficacy (Hosseinitabaghdehi and Salehi 2018). \u0026nbsp;Massive open online courses can improve students\u0026rsquo; active learning and thus strengthen their digital skills (Chatwattana 2021). As if students have sufficient confidence, they are eager to learn computer knowledge and may continue their computer studies actively, so they can effectively acquire digital skills. Accordingly, this study poses the following research hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e: Future confidence is positively related to active learning, which is also positively related to CIL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e: Active learning mediates the relationship between future confidence and CIL.\u003c/p\u003e\n\u003cp\u003eActive learning is a key positive predictor of CIL, while its existence presupposes that students have a high degree of confidence in themselves and sufficient motivation to learn. Motivation is expected to be an important basis for judging whether a student is a self-directed learner. Studies have pointed out that motivation is an important predictor of active learning. For example, Pang (2001) found that students\u0026rsquo; intrinsic motivation is a prerequisite for active learning. Wang (2014) discovered that learning motivation is a key factor relating to improved English active learning ability. Ryan and Deci (2000) found that both intrinsic and extrinsic motivations were important factors influencing autonomy-driven learning. Lei et al. (2024) found that the two dimensions of learning motivation, namely internal learning motivation and external learning motivation, had a significant positive impact on learning engagement.\u003c/p\u003e\n\u003cp\u003eBased on the theory of dispositional optimism and relevant literature on educational learning, having a high degree of future confidence tends to regulate students\u0026rsquo; learning motivation (Fern\u0026aacute;ndez and Brenlla 2023; Lens et al. 2001) and active learning of knowledge (Yusuf 2011), ultimately contributing to enhanced CIL (Levine and Donitsa-Schmidt 1998). Combining the above hypotheses, we predict that rural secondary school students\u0026rsquo; confidence in the future would relate to enhanced CIL indirectly via their increased learning motivation and active engagement in the process of learning computer knowledge and skills. Thus, we propose the following research hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e: There is a chain mediating effect of learning motivation and active learning on the relationship between future confidence and CIL.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003ch2\u003e\u003cem\u003e3.1 Sampling and procedure\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe data came from the project of Education Quality Evaluation of Junior High Schools in the rural school districts of a city in Southwest China\u0026rsquo;s Guangxi Zhuang Autonomous Region. This project was sponsored and funded by the city\u0026rsquo;s education authorities. None of the data had been used in any other research projects. For this study, 6,997 students were recruited from 106 rural schools, who completed the Programme for International Student Assessment (PISA) with the help of local school administrators and teachers. After removing incomplete and missing data, 2,393 8\u003csup\u003eth\u003c/sup\u003e graders (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eFemale\u003c/sub\u003e = 1,338, 55.9%) were included in the study. Their demographics are reported in Table 1.\u003c/p\u003e\n\u003cp\u003eThe study proposal was reviewed and granted approval by the Institutional Review Board of the first author\u0026rsquo;s university prior to the commencement of data collection. A researcher who had been properly trained provided an overview of the survey to the schools, parents, and legal guardians, and subsequently gave their approval. Students were informed about the survey, and only those who provided their consent participated in the study. Students completed the paper-and-pencil survey as part of a class assignment during regular school hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The sample demographics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eN\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e%\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eUrban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eRural areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNo registration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eHan nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eZhuang nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eComputers at home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNo computers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eOne or more computers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eUniversity or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFather\u0026rsquo;\u003csup\u003e\u0026nbsp;\u003c/sup\u003es education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eUniversity or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMother\u0026rsquo;\u003csup\u003e\u0026nbsp;\u003c/sup\u003es job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFather\u0026rsquo;\u003csup\u003e\u0026nbsp;\u003c/sup\u003es job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eM\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eICT skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eICT efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eLearning motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eActive learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eFuture confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2 Measures\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e3.2.1. Future confidence.\u003c/em\u003e Future confidence was measured using a single item: \u0026ldquo;How confident are you about your future?\u0026rdquo; Respondents rated their future confidence on a scale from 1 (\u003cem\u003enot confident at all\u003c/em\u003e) to 4 (\u003cem\u003every confident\u003c/em\u003e). A higher score indicated a higher sense of future confidence (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 3.14, \u003cem\u003eSD\u003c/em\u003e = 0.79).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.2. Computer and information literacy.\u003c/em\u003e This scale was adopted from the International Computer and Information Literacy Study (ICILS) (Fraillon et al. 2014), consisting of two dimensions: ICT efficacy and ICT Skills. There were 23 items in the scale. ICT efficacy was measured by 11 items with a 4-point Likert scale from 1 (\u003cem\u003eStrongly disagree\u003c/em\u003e) to 4 (\u003cem\u003eStrongly agree\u003c/em\u003e), including \u0026ldquo;It is essential to me to work with a computer.\u0026rdquo; \u0026ldquo;I think using a computer is fun.\u0026rdquo; \u0026ldquo;Learning how to use a new computer program is very easy for me.\u0026rdquo; (\u003cem\u003eM\u003c/em\u003e = 2.89, \u003cem\u003eSD\u003c/em\u003e = 0.58, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e = 0.89). ICT skills was measured by 12 items with a 3-point Likert scale from 1 (\u003cem\u003eI do not think I could do this\u003c/em\u003e) to 3 (\u003cem\u003eI know how to do this\u003c/em\u003e), including \u0026ldquo;Search for and find a file on your computer.\u0026rdquo; \u0026ldquo;Use software to find and get rid of viruses.\u0026rdquo; \u0026ldquo;Create or modify a document.\u0026rdquo; (\u003cem\u003eM\u003c/em\u003e = 1.97, \u003cem\u003eSD\u003c/em\u003e = 0.55, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e= 0.89).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.3. Learning motivation.\u0026nbsp;\u003c/em\u003eThe learning motivation scale (Amabile et al. 1994) was modified to assess the learning motivation of 8\u003csup\u003eth\u003c/sup\u003e graders. The scale contained 20 items, including 10 intrinsic motivation items (e.g., \u0026ldquo;I often think about and urge myself to meet my own expectations or standards.\u0026rdquo; \u0026ldquo;How well I do on tests is secondary to studying itself, which I really enjoy.\u0026rdquo; \u0026ldquo;I like studying because studying itself allows me to gain a lot of knowledge.\u0026rdquo;) and 10 extrinsic motivation items (e.g., \u0026ldquo;I often work extra hard to meet the standards of achievement expected by my parents.\u0026rdquo; \u0026ldquo;I study hard because teachers usually praise students who work hard.\u0026rdquo; \u0026ldquo;It\u0026rsquo;s hard to hold my head up in front of my relatives and friends when I don\u0026apos;t do well on exams.\u0026rdquo;). Respondents answered each question in the survey using a five-point Likert scale from 1 (\u003cem\u003every much not true\u003c/em\u003e) to 5 (\u003cem\u003every much true\u003c/em\u003e). Higher scores represented higher learning motivation (For the intrinsic motivation: \u003cem\u003eM\u003c/em\u003e = 3.39, \u003cem\u003eSD\u003c/em\u003e = 0.74, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e = 0.89; For the extrinsic motivation, \u003cem\u003eM\u003c/em\u003e = 3.88, \u003cem\u003eSD\u003c/em\u003e = 0.79, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e = 0.82; For overall motivation, \u003cem\u003eM\u003c/em\u003e = 3.64, \u003cem\u003eSD\u003c/em\u003e = 0.70, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e = 0.91). Previous studies have demonstrated that this scale had good reliability and validity (Amabile et al. 1994; Loo, 2001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.4. Active learning.\u003c/em\u003e The students\u0026rsquo; active learning scale in this study was derived from the 2013 ICILS test (Fraillon et al. 2014). The active learning scale consisted of four question items (who mainly teaches you to do the following things: \u0026ldquo;Communicate over the Internet.\u0026rdquo; \u0026ldquo;Change computer equipment.\u0026rdquo; \u0026ldquo;Look up information on a computer.\u0026rdquo; and \u0026ldquo;online learning\u0026rdquo;). Respondents answered each question using a three-point Likert scale, with value 1 being \u0026ldquo;I never studied.\u0026rdquo; value 2 being \u0026ldquo;Teachers or family members taught me.\u0026rdquo; and value 3 being \u0026ldquo;Self-study.\u0026rdquo; The higher the score, the more active the students were in learning computers on their own (\u003cem\u003eM\u003c/em\u003e = 2.37, \u003cem\u003eSD\u003c/em\u003e = 0.56, \u003cem\u003eCronbach\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e = 0.75).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.3 Data analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eA serial mediation model was developed to investigate the chain mediating effect of learning motivation (M1: Mediator 1) and active learning (M2: Mediator 2) on the positive association between rural secondary school students\u0026rsquo; future confidence (X: Independent variable) and their CIL (Y: Dependent variable). Structural equation modeling was used to test the serial mediation model through AMOS 29.0. The study sample was bootstrapped 5,000 times.\u003c/p\u003e"},{"header":"4. Results","content":"\u003ch2\u003e\u003cem\u003e4.1 Assessing common method bias\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eCommon bias was detected using Harman\u0026rsquo;s one-way method by conducting an exploratory factor analysis without rotation on the questionnaire measurement scale. This method analyzed the number of factors, the proportion of variance explained, and the proportionality between the variance explained by the first factor and the total variance explained. If the amount of variance explained by the first factor did not exceed 40% of the total amount of variance explained (Tang and Wen 2020), the common method bias was deemed insignificant. The variance explained collar of the first factor in this study accounted for 35.54% of the total variance, indicating that the common method bias was not significant for this data.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2 The model fit\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eBased on the conceptual model and the relationships between the variables, a chain-mediated structural equation model with latent variables (Figure 1) was developed to investigate the mediating effect of learning motivation and active learning on the positive association between future confidence and CIL. Adhering to established guidelines, the structural equation model\u0026rsquo;s fit was assessed using the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR). These criteria were met, with RMSEA = 0.05, CFI = 0.94, TLI = 0.91, and SRMR = 0.04. It is noteworthy that the significance of the \u0026chi;2 value may be influenced by the sample size, as demonstrated by Brown (2015). Given the substantial sample size employed in this study, the \u0026chi;2 value was not employed to evaluate the model\u0026rsquo;s fit.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3 Hypothesis testing\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eStructural equation modeling demonstrated a positive correlation between rural students\u0026rsquo; future confidence and CIL (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.07, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.002), motivation to learn (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.23, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), as well as their engagement in active learning (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.049) (see Table 2). Learning motivation (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.020) and active learning \u003cem\u003e(\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.60, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) was also positively correlated with CIL (see Table 2). Learning motivation was positively associated with active learning (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Path coefficients among future confidence, CIL, learning motivation and active learning.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFuture confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFuture confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLearning motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFuture confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eActive learning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eLearning motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eLearning motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eActive learning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eActive learning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The table shows the standardized path coefficients. CIL = computer and information literacy.\u003c/p\u003e\n\u003cp\u003eThe sample was replicated 5,000 times to generate an approximate sampling distribution. The mean of these 5,000 effect estimates was calculated as the mean indirect effect. These estimates were ranked in order of magnitude, and the 95% confidence intervals for the mediated effects were estimated. The results demonstrated that future confidence significantly influenced learning motivation (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.012, \u003cem\u003ep\u003c/em\u003e = 0.019), active learning (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.028, \u003cem\u003ep\u003c/em\u003e = 0.047), and the combination of future confidence, learning motivation, and active learning (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.007, \u003cem\u003ep\u003c/em\u003e = 0.015). These three pathways corresponded to confidence intervals that did not overlap, indicating the significance of the indirect effects (See Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e.\u0026nbsp;Results of mediation analysis of learning motivation and active learning on future confidence\u0026ndash;CIL relation.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eIndirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eFuture confidence\u0026nbsp;\u0026rarr;\u0026nbsp;Learning motivation\u0026nbsp;\u0026rarr;\u0026nbsp;CIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[0.002, 0.027]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eFuture confidence\u0026nbsp;\u0026rarr;\u0026nbsp;Active learning\u0026nbsp;\u0026rarr;\u0026nbsp;CIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[0.000 0.057]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eFuture confidence\u0026nbsp;\u0026rarr;\u0026nbsp;Learning motivation\u0026nbsp;\u0026rarr;\u0026nbsp;Active learning\u0026nbsp;\u0026rarr;\u0026nbsp;CIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[0.001, 0.006]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The table shows the standardized path coefficients. CIL = computer and information literacy.\u003c/p\u003e\n\u003cp\u003eIn summary, the results presented in Tables 2 and 3 provide evidence for the following six hypotheses, which were all confirmed in the analyses.\u003c/p\u003e\n\u003cp\u003eH1 was supported. Future confidence is positively correlated with CIL, (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.07, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.002),\u003c/p\u003e\n\u003cp\u003eH2 was supported. Future confidence is positively correlated with learning motivation (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.23, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eH3 was supported. Learning motivation mediates the positive relationship between future confidence and CIL, (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.012, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.019).\u003c/p\u003e\n\u003cp\u003eH4 was supported. Future confidence is positively correlated with active learning (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.049), which is also positively correlated with CIL (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.60, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eH5 was supported. Active learning mediates the positive relationship between future confidence and CIL (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.028, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.047).\u003c/p\u003e\n\u003cp\u003eH6 was supported. Learning motivation mediates the positive relationship between future confidence and CIL through active learning (\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.015). Also, there is a chain mediating effect of learning motivation and active learning on the relationship between future confidence and CIL (\u003cem\u003e\u0026beta; =\u0026nbsp;\u003c/em\u003e0.007, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.015).\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study, grounded in dispositional optimism theory and literature on educational learning, investigated the structural pattern between rural students\u0026rsquo; confidence in the future and their computer and information literacy. Specifically, learning motivation and active learning were identified as mediators through which the relationship between future confidence and CIL could be established. Theoretically, this study provided empirical evidence that dispositional optimism among China\u0026rsquo;s rural students contributed to the development of CIL. It also explores and verified the relationships between learning motivation, active learning, and CIL. These findings offer valuable insights for enhancing CIL among rural students in China and promoting the overall development of these students in the digital era.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Future confidence and CIL(H1)\u003c/h2\u003e \u003cp\u003eIn line with our first aim, our study demonstrated the positive relation from future confidence to computer and information literacy among rural students. Specifically, higher future confidence was related with better computer skills and digital performance. The finding is consistent with what was reported in previous studies that high levels of future confidence in individuals can help improve their mental and physical health (Lee et al. 2018; Ouyang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The current study suggests that high levels of future confidence among rural students contribute to CIL, just as dispositional optimists are more likely to be physically and mentally healthy and academically successful (Peterson \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). It further demonstrated dispositional optimism theory, optimistic outlook not only results in more favorable life outcomes in physical and mental health (Carver et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Scheier and Carver \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and academic success (Tetzner and Becker \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but also fosters CIL. The finding indicates that the rural students with high future confidence, show higher learning motivation (Chang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and pay more effort in information technology courses to enhance their CIL (Riswanti et al. 2022), so they may have better CIL performance. Therefore, enhancing future confidence of rural students is an effective intervention to reduce existing disparities in digital skills between rural and urban populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Mediating role of learning motivation (H2 and H3)\u003c/h2\u003e \u003cp\u003eLearning motivation mediated the relation between future confidence and computer and information literacy. Specifically, future confidence is positively related to learning motivation, and learning motivation is positively related to CIL. First, the higher the level of students\u0026rsquo; future confidence, the stronger their learning motivation. This is consistent with the findings of previous studies that optimistic individuals who believe in positive outcomes tend to have increased motivation in pursuing their goals (Chen \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ihsan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhu \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our study elucidates that rural students with high future confidence are more inclined to establish ambitious goals in information technology education and are more willing to invest effort in achieving them. This confidence in their abilities motivates students to engage more fully in learning computer activities.\u003c/p\u003e \u003cp\u003eSecond, higher learning motivation among rural students is associated with improved CIL. This aligns with the fact that individuals with higher learning motivation exhibit better academic performance and digital literacy compared to those with lower motivation (Bakhtiarvand et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lilian \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Marth and Bogner \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). New evidence was also found to support that highly motivated students effectively enhance their learning behavior, directing their learning objectives and sustaining it through computer-based learning activities. They demonstrate a greater willingness to invest time and effort in information technology courses, maintaining higher enthusiasm and sustained effort. As a result, they are more likely to achieve higher CIL levels than their peers. This result suggests that educators can effectively stimulate students\u0026rsquo; learning motivation in computer-based learning environments through various interventions, such as enhancing the appeal of information technology and implementing reward and punishment mechanisms, thereby improving computer and information literacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Mediating role of active learning (H4 and H5)\u003c/h2\u003e \u003cp\u003eActive learning also mediated the relation between future confidence and computer and information literacy (CIL). Specifically, future confidence is positively related to active learning, which is also positively related to CIL. H4 and H5 were supported. First, the higher the level of rural students\u0026rsquo; future confidence, the more actively they learn computers independently. The result is similar with the previous studies which indicated that confident students engage in learning activities more actively (Allen and Baughman \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This is owing to that students with sufficient future confidence are more likely to see the value of a task and thus be more engaged in Information Technology Courses.\u003c/p\u003e \u003cp\u003eWhile acquiring digital skills, students are more inclined to adopt effective learning strategies, particularly self-regulated learning strategies, which enhance their computer performance. Furthermore, the more actively they engage in learning computer knowledge, the better their performance in CIL. This aligns with previous research findings (Chatwattana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hosseinitabaghdehi and Salehi \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Riswanti et al. 2022). This is because students who actively participate in their education are more likely to actively engage in computer learning activities through metacognition, motivation, and behavior. Moreover, they can take responsibility for their learning and persist despite challenges in learning computers, which often enable them to attain higher CIL. This result underscores the significance of fostering students\u0026rsquo; awareness and ability for independent learning in computer education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Learning motivation and active learning (H6)\u003c/h2\u003e \u003cp\u003eTwo more key findings are: 1) learning motivation is positively correlated with active learning, and 2) there is a mediating effect of learning motivation and active learning on the relationship between future confidence and computer-integrated learning. In essence, rural students\u0026rsquo; future confidence can enhance their learning motivation, which, in turn, motivates students to actively and independently participate in computer-related learning activities, ultimately improving rural students\u0026rsquo; CIL. In the field of computer information science, previous studies have demonstrated that students\u0026rsquo; learning motivation plays a crucial role in sustaining active learning (Altinpulluk et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pan \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is because motivated students tend to learn actively and independently, including pursuing in-depth understanding and mastery of digital knowledge and skills. Active learning strategies provide students with opportunities to explore information and technology courses in depth. As they gain more personal satisfaction, sense of achievement, and self-fulfillment from active learning (Chang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Freeman et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), active learning can contribute to the overall development of rural students\u0026rsquo; CIL.\u003c/p\u003e \u003cp\u003eIt is noteworthy that the positive correlation between learning motivation and active learning is also evident in the disciplines of mathematics and English. For instance, in the field of mathematics, a study demonstrated that students with higher learning motivation exhibited greater self-directed engagement in mathematical activities and achieved superior performance in the subject (Xia et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, in the discipline of English, college students\u0026rsquo; intrinsic and extrinsic motivations in English learning were found to be positively associated with self-directed learning behaviors (Li and Yu \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Zhang (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) further suggested that students\u0026rsquo; learning motivation could enhance the long-term sustainability of independent learning behaviors in English. Therefore, methods that promote learning motivation and facilitate active learning in other disciplines, such as setting clear objectives and developing personalized learning plans, and providing constructive feedback, may be applicable to enhance computer literacy instruction. This study, thus, provides novel perspectives for educators to enhance CIL pedagogy, fostering students\u0026rsquo; confidence through these methods could stimulate their learning motivation, encourage them to actively participate in the learning process of computer knowledge and skills, and eventually enhance their CIL proficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Theoretical and practical implications\u003c/h2\u003e \u003cp\u003eThis study, rooted in Dispositional Optimism Theory and the existing literature on educational learning, has yielded substantial theoretical and practical implications for enhancing Computer and Information Literacy among China\u0026rsquo;s rural students. It offers valuable insights for educational practitioners and researchers seeking to improve CIL among rural students. By focusing on psychological resilience through fostering future confidence, coupled with strategic pedagogical approaches that cultivate learning motivation and active participation, this study suggests that it is possible to bridge the digital divide and empower rural students in the digital age.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.5.1 Theoretical Implications\u003c/em\u003e: This study bridges the gap between dispositional optimism theory and computer information science, showcasing how rural students\u0026rsquo; positive outlook can significantly enhance their computer and information literacy. It underscores the significance of cultivating this positive mindset in rural secondary school students, offering educators a valuable tool to indirectly improve computer and information literacy (CIL) through increased learning motivation and active learning strategies.\u003c/p\u003e \u003cp\u003eThe findings provide empirical support for Dispositional Optimism Theory. They suggest that future confidence, a key component of optimism, plays a substantial role in fostering CIL among rural students. Furthermore, this study demonstrates the mediating roles of learning motivation and active learning in the relationship between future confidence and CIL. This further deepens our understanding of how these psychological and behavioral factors contribute to CIL development.\u003c/p\u003e \u003cp\u003eThe study presents robust empirical evidence that optimistic rural students, due to their positive outlook, are more likely to develop stronger CIL. Therefore, this research contributes to our understanding of dispositional optimism\u0026rsquo;s role in the development of computer and information literacy.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.5.2 Practical Implications\u003c/em\u003e: This research offers valuable insights for educational practices aimed at bridging the digital divide between urban and rural students in China. It emphasizes the need for educators to create positive learning environments that address the unique challenges faced by rural students in acquiring digital skills.\u003c/p\u003e \u003cp\u003eBased on this research, educators can employ several strategies. Teachers and parents are encouraged to:\u003c/p\u003e \u003cp\u003e1) Nurture optimism among rural students, helping them envision a positive and accessible future for growth and opportunity.\u003c/p\u003e \u003cp\u003e2) Develop tailored strategies that ignite the desire to learn computer skills among rural students, considering their unique characteristics and environments.\u003c/p\u003e \u003cp\u003e3) Encourage independent learning and adopt active learning strategies that empower students to take ownership of their computer skill development.\u003c/p\u003e \u003cp\u003e4) Recognize and address the limited digital resources in rural areas by equipping students with fundamental knowledge and skills that can be further explored independently.\u003c/p\u003e \u003cp\u003eThe findings highlight the significance of enhancing future confidence among rural students to improve their computer literacy. Interventions aimed at boosting their optimism can be highly effective in reducing the digital gap between rural and urban communities. Educators must implement strategies to foster students\u0026rsquo; learning motivation in computer-based learning environments. This can be achieved through engaging content, reward systems, and clear learning goals. Creating active learning environments with opportunities for self-directed exploration and independent learning is crucial. Educators can utilize techniques like metacognitive strategies, student-led projects, and constructive feedback to encourage active participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Limitations and future studies\u003c/h2\u003e \u003cp\u003eDespite the innovative findings and contributions of this study, there are several limitations that warrant consideration, as well as opportunities for future research to address them.\u003c/p\u003e \u003cp\u003eFirst, the study relied exclusively on self-reported measures of Computer and Information Literacy (CIL), which may introduce subjective biases. Future research should incorporate objective assessments, such as standardized tests or performance-based evaluations, to provide a more accurate and comprehensive understanding of CIL among rural students.\u003c/p\u003e \u003cp\u003eSecond, the measurement of future confidence was restricted to a single-item indicator, which may limit the reliability and validity of the construct. Future studies should adopt multi-item scales to assess future confidence and dispositional optimism, as this would provide a more robust and nuanced understanding of these psychological factors. Additionally, exploring other dimensions of optimism, such as resilience and hope, could offer further insights into their influence on learning outcomes.\u003c/p\u003e \u003cp\u003eThird, the study acknowledged the scarcity of digital resources in rural areas, such as inadequate access to stable internet connections, computers, and other technological devices. This limitation may affect the applicability and generalizability of the findings. Future research should examine the moderating effects of resource availability on the relationships between future confidence, learning motivation, active learning, and CIL. Longitudinal studies could also assess how improvements in digital infrastructure impact these relationships over time.\u003c/p\u003e \u003cp\u003eFourth, the cross-sectional design of this study limits causal inferences. Future studies should employ longitudinal or experimental designs to better understand the causal mechanisms underlying the relationships identified in this study. For instance, interventions aimed at enhancing future confidence and their subsequent effects on learning motivation, active learning, and CIL could be tested over time.\u003c/p\u003e \u003cp\u003eFinally, the study focused exclusively on rural students in China, which may limit the generalizability of the findings to other contexts. Future research should explore whether the relationships observed in this study hold true in diverse cultural, economic, and educational settings. Comparative studies between urban and rural populations, as well as across different countries, could provide valuable insights into how contextual factors influence the development of CIL.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study provides groundbreaking insights into the development of Computer and Information Literacy among rural Chinese students by integrating Dispositional Optimism Theory into the realm of digital education. The findings reveal that future confidence\u0026mdash;a key aspect of optimism\u0026mdash;plays a pivotal role in enhancing CIL, with learning motivation and active learning serving as vital mediators in this process. These results highlight the transformative power of psychological resilience and self-directed learning in addressing the digital divide. One of the most innovative discoveries of this research is that fostering future confidence among rural students not only improves their outlook on life but also directly impacts their digital proficiency. Students with higher levels of future confidence are more motivated to learn and actively engage in computer-related activities, ultimately achieving superior CIL outcomes. This underscores the idea that a positive mindset can drive tangible educational gains, even in resource-limited environments.\u003c/p\u003e \u003cp\u003eMoreover, this study discovers that learning motivation and active learning are not merely byproducts of optimism but essential mechanisms through which future confidence translates into improved digital skills. The relationship between these factors provides a solid foundation for designing targeted interventions aimed at empowering rural students to thrive in the digital era. By cultivating optimism, fostering intrinsic motivation, and promoting active, independent learning, educators can create an environment where rural students take ownership of their digital education. This approach not only equips students with essential skills but also instills in them a sense of agency and self-efficacy, crucial for navigating an increasingly digital world. Overall, this study not only advances theoretical understanding but also delivers practical strategies to uplift rural students in the digital age. By leveraging future confidence and active learning as catalysts for digital literacy, educators can empower rural students to overcome systemic barriers, enabling them to realize their potential and contribute meaningfully to the global digital economy. This research represents a crucial step forward in narrowing the digital divide and ensuring equitable access to opportunities in the 21st century\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLing Li contributed to research design, field survey and data collection. Qiuyan Wang and Yu-Leung Ng contributed to data analysis and charting. Bu Zhong contributed to research design and writing.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe written of the paper was supported by National Social Science and Humanity Foundation (18ZDA338), 111 program (B21036), Decision Making Laboratory for Western China Education and Human Development at Southwest University and IA Laboratory at Hong Kong Baptist. Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan004), Chongqing Social Science and Humanity Foundation (2022YC028).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the first and corresponding authors upon reasonable request. Data is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAinoda N, Onishi H, Yasuda Y (2005) Definitions and goals of self-directed learning in contemporary medical education literature. 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(In Chinese)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Future confidence, Computer and information literacy, Learning motivation, Active learning, Dispositional optimism theory","lastPublishedDoi":"10.21203/rs.3.rs-6122977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6122977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRural students often experience lower levels of future confidence compared to their urban peers due to limited access to resources, which can hinder their learning motivation, active learning, and ultimately, their computer and information literacy (CIL). Addressing a critical gap in the literature, this study (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,393) draws on Dispositional Optimism Theory and educational learning research to examine the chain mediating effects of learning motivation and active learning on the relationship between future confidence and CIL among rural 8th graders in China. The findings revealed three key insights: (1) future confidence is positively associated with CIL; (2) learning motivation and active learning independently mediate the relationship between future confidence and CIL; and (3) learning motivation and active learning together form a chain mediation effect, further strengthening the future confidence\u0026ndash;CIL link. These results provide a novel theoretical model to understand how students\u0026rsquo; psychological attributes and learning behaviors influence their digital literacy. Practically, the study offers actionable strategies for educators in rural China, such as fostering students\u0026rsquo; future confidence, promoting intrinsic learning motivation, and encouraging active participation in computer-based learning activities. The findings provide a pathway to improving rural students\u0026rsquo; CIL and bridging the digital divide between rural and urban students.\u003c/p\u003e","manuscriptTitle":"Optimism in action: Future confidence fuels digital literacy development in China’s rural students.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 10:14:29","doi":"10.21203/rs.3.rs-6122977/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52e4da24-617b-40e6-912f-0d1d29304649","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45510580,"name":"Social science/Education"},{"id":45510581,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-07-10T18:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 10:14:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6122977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6122977","identity":"rs-6122977","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}