International Open Access Journal Platform

1 Introduction

Contemporary higher education operates within digitally saturated environments where information flows are continuous and frequently algorithmically curated. Students engage daily with platforms that influence how knowledge is accessed, filtered, and interpreted. In this context, leadership extends beyond interpersonal coordination to include the capacity to navigate complex digital systems and make informed judgments amidst uncertainty. Data-intensive tools and learning analytics further embed decision-making processes within streams of quantified information (Selwyn, 2017; Williamson, 2017). Student leaders are therefore expected to assess credibility, interpret patterns, and respond strategically to rapidly evolving information conditions. These expectations align with broader accounts of digital transformation, which identify data-rich environments as reshaping the competencies requisite for effective participation in knowledge societies (McCarthy et al., 2023; Punie, 2017). Evidence also suggests that digital skill demands now include critical evaluation, information management, and adaptive engagement with online content (van Laar et al., 2019). Despite these developments, leadership preparation in universities remains largely anchored in conventional skill domains, with limited integration of competencies aligned to digitally mediated and data-intensive contexts.

Digital literacy and data-driven thinking have gained increasing attention as core competencies in higher education, although they are frequently conceptualized in fragmented ways. Digital literacy extends beyond operational skills to encompass the ability to locate, evaluate, integrate, and generate information within digital environments (Ng, 2012; Degner et al., 2022), alongside a critical awareness of the social and ethical dimensions of technology use (Zakir et al., 2025). It also involves the capacity to function as both a consumer and producer of digital content, requiring higher-order cognitive engagement and informed judgment (Georgopoulou et al., 2025). In parallel, data-driven thinking draws on data literacy, which involves interpreting, analyzing, and communicating data to support decision-making processes (Mandinach, 2025). This competence is increasingly recognized as essential in data-rich environments where individuals must transform raw information into actionable insights. Emerging research indicates that these capabilities contribute to enhanced learning effectiveness, analytical reasoning, and adaptive engagement in digital contexts (Vaszkun & Mihalkov, 2025). Overall, digital literacy enables navigation within complex information spaces, while data-driven thinking supports informed judgment grounded in evidence.

Despite growing interest in these competencies, existing scholarship tends to examine digital literacy and data-related capabilities independently, with limited attention to their combined role in leadership development. Studies on digital literacy frequently concentrate on students’ ability to access and evaluate information, with outcomes linked to academic performance and online participation (van Laar et al., 2019; Siddiq et al., 2016). In addition, studies on data literacy and analytics in education focus on evidence-based decision-making, often within institutional or professional contexts (Ifenthaler et al., 2019; Wolff et al., 2016). Leadership education, however, continues to prioritize interpersonal competence, teamwork, and communication, with comparatively less attention to how students interpret digital information or utilize data in shaping decisions (Seemiller, 2018). This separation limits understanding of how students integrate these competencies when engaging in leadership practices within digitally mediated environments. Empirical evidence derived from course-based contexts remains particularly scarce, as much of the literature adopts conceptual, policy-oriented, or skills-based perspectives without examining how these capabilities interact in authentic learning settings.

Building on these limitations, this study examines the associations among self-perceived digital literacy, data-driven thinking, and student leadership among college students following a course-based training that integrated leadership development and data-driven thinking skills. Accordingly, the study is guided by the following research questions:

RQ1: What are students’ self-perceived abilities in digital literacy, data-driven thinking, and student leadership following course-based training?

RQ2: To what extent are students’ self-perceived digital literacy and data-driven thinking abilities associated with their self-perceived student leadership following course-based training?

2 Methodology

2.1 Design and Participants

This study adopted a descriptive survey design to examine the associations among digital literacy, data-driven thinking, and student leadership within a course-based instructional context. Participants comprised 321 undergraduate students enrolled in a structured leadership training program at a large public university in China (54.9% male, 45.1% female; Mean age = 21.34 years). The course was designed to develop students’ leadership competencies through guided activities that incorporated elements of digital literacy and data-driven thinking, including information evaluation, evidence-based decision-making, and collaborative problem-solving tasks. Students were recruited at the beginning of the program, and participation in the study was voluntary. Before the commencement of data collection, all participants were informed about the purpose of the study, and informed consent was obtained in accordance with standard ethical procedures. The survey was administered following the completion of the training, thereby allowing for the assessment of students’ competencies within a post-instructional context.

2.2 Measures

Data were collected using a structured questionnaire comprising three scales: digital literacy, data-driven thinking, and student leadership. All items were measured on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The measures were adapted from established scales and subsequently refined to align with the instructional context of the course. Digital literacy was assessed using items that assessed students’ ability to locate, evaluate, and utilize digital information, as well as their awareness of ethical and responsible technology use. The scale focused on higher-order competencies, including critical evaluation of online content and effective engagement with digital tools. Data-driven thinking was measured using items that examined students’ capacity to interpret data, identify patterns, and make decisions based on evidence. The construct also captured analytical reasoning and the ability to translate data into actionable insights within learning tasks.

Student leadership was assessed using items that measured students’ perceived ability to guide group activities, make informed decisions, and contribute to collaborative outcomes. The scale captured both cognitive and behavioral aspects of leadership enacted during the course. To ensure clarity and contextual relevance, all items were reviewed and slightly adapted to reflect the leadership training activities. A pilot test was conducted with a small group of students to confirm item clarity and appropriateness before full administration.

2.3 Reliability of the Measures

The internal consistency of the three scales was examined using Cronbach’s alpha. The results showed acceptable reliability for all constructs. The digital literacy scale had a Cronbach’s alpha of 0.78, the data-driven thinking scale had an alpha of 0.81, and the student leadership scale had an alpha of 0.84. These values indicate that the items within each scale were sufficiently consistent for use in the subsequent descriptive and correlation analyses. Overall, the reliability results support the suitability of the questionnaire for examining students’ self-perceived abilities in digital literacy, data-driven thinking, and student leadership within the course-based training context.

2.4 Analysis

The analysis was conducted in two stages. First, descriptive statistics were computed to examine participants’ responses to the scale items, including mean scores and standard deviations, to provide an overview of students’ perceptions across digital literacy, data-driven thinking, and leadership. This step offered an initial understanding of how the constructs were distributed within the sample. Second, a correlation analysis was conducted to assess the associations among the three constructs. A correlation matrix was generated to examine the strength and direction of relationships among digital literacy, data-driven thinking, and student leadership. This analysis directly addressed the second research question by identifying the extent to which each construct was related to student leadership, as well as the degree of association between digital literacy and data-driven thinking within the post-training context.

3 Results

RQ1: What are students’ perceptions of digital literacy, data-driven thinking, and student leadership following course-based training?

To address the above research question, descriptive statistics were employed, including mean and standard deviation. The results revealed generally high levels of agreement across the three constructs (see Table 1). For digital literacy, students reported strong capabilities in evaluating online information (M = 4.12, SD = 0.63), utilizing digital tools for academic tasks (M = 4.18, SD = 0.61), and integrating information from multiple sources (M = 4.10, SD = 0.66). Slightly lower ratings were observed for identifying biased content (M = 3.55, SD = 0.68) and considering ethical issues (M = 3.82, SD = 0.58). For data-driven thinking, students indicated high engagement in utilizing data to support decisions (M = 4.08, SD = 0.67), identifying patterns (M = 4.09, SD = 0.65), and basing conclusions on evidence (M = 4.20, SD = 0.60). Comparatively lower responses were observed for interpreting charts and tables (M = 3.76, SD = 0.62) and explaining decisions using data (M = 3.41, SD = 0.64).

For student leadership, respondents reported strong agreement in guiding group goals (M = 4.17, SD = 0.61), making informed decisions (M = 4.13, SD = 0.63), and taking initiative (M = 4.09, SD = 0.66). Lower ratings were observed for contributing to problem-solving (M = 3.71, SD = 0.59) and coordinating group tasks (M = 3.44, SD = 0.62). Overall, the results indicate consistently positive perceptions across digital literacy, data-driven thinking, and leadership subsequent to the course-based training.

Table 1 Perceptions of Digital Literacy, Data-Driven Thinking, and Leadership Skills

RQ2: To what extent are digital literacy and data-driven thinking associated with student leadership following course-based training?

A correlation analysis was conducted to examine the associations among digital literacy, data-driven thinking, and student leadership following the course-based training. The results indicated significant positive relationships among all three constructs (see Table 2). Digital literacy was positively associated with student leadership (r = 0.62, p < 0.01). Similarly, data-driven thinking showed a positive relationship with student leadership (r = 0.65, p < 0.01). In addition, digital literacy was positively correlated with data-driven thinking (r = 0.58, p < 0.01). Overall, the findings indicate that digital literacy and data-driven thinking are closely linked with student leadership, supporting their relevance within leadership development in digitally mediated environments.

Table 2 Correlation between Digital Literacy, Data-Driven Thinking, and Student Leadership

Note: N = 321; **p < 0.01.

4 Discussion

The findings indicate that students reported generally strong perceptions of digital literacy following the training, particularly in evaluating online information, integrating multiple sources, and utilizing digital tools for academic tasks. These findings suggest that structured learning experiences can support the development of higher-order digital competencies that extend beyond basic technical use. Prior work has shown that digital literacy develops most effectively when learners engage in tasks that require critical evaluation and synthesis of information within authentic contexts (Ng, 2012; van Laar et al., 2019). Conversely, comparatively lower ratings for identifying biased content and considering ethical issues point to dimensions of digital literacy that are more complex and less immediately responsive to short-term instruction. This finding is consistent with research evidence demonstrating that critical and ethical awareness requires sustained engagement and repeated exposure to ambiguous information environments (Schüller, 2020).

A similar differentiation is evident in data-driven thinking. Students reported strong engagement in utilizing data to support decisions, identifying patterns, and basing conclusions on evidence, while lower ratings were observed for interpreting data representations and explaining decisions using data. This suggests that students are able to engage with data at a procedural or recognition level, but encounter greater difficulty when required to translate data into structured reasoning and communication. Existing research has documented similar distinctions, wherein learners demonstrate competence in recognizing patterns but show weaker performance in interpreting and articulating data-driven insights (Wolff et al., 2016). These findings indicate that data-related competencies develop unevenly across components and may require more explicit instructional scaffolding to support interpretation and explanation.

The findings provide further insight into how these competencies align with student leadership. Digital literacy showed a strong positive association with student leadership, suggesting that students who are more capable of navigating digital information environments also report stronger leadership engagement. This relationship can be understood in terms of the increasing integration of digital contexts into collaborative and decision-making processes, where leaders are expected to evaluate information, coordinate knowledge, and respond to evolving inputs. Evidence from prior studies suggests that digital competence is closely linked with collaborative performance and informed decision-making in group settings (Siddiq et al., 2016; van Laar et al., 2019). The present findings extend this line of work by situating digital literacy within leadership practice, indicating that these competencies are intertwined within contemporary learning environments.

Data-driven thinking demonstrated an even stronger association with student leadership, highlighting the centrality of evidence-based reasoning in leadership processes. Students who reported stronger abilities in interpreting and utilizing data also reported higher levels of leadership engagement, suggesting that analytical competence supports decision quality and group coordination. This finding aligns with research showing that effective leadership increasingly involves the capacity to interpret data, justify decisions, and guide actions based on evidence (Liden et al., 2025). In addition, the strong association between digital literacy and data-driven thinking indicates that these competencies are interconnected, with digital environments providing the context in which data is accessed and interpreted. The findings suggest that leadership development in contemporary settings requires the integration of both digital and data-related competencies, as they jointly underpin how students engage, decide, and act within complex learning environments (Hamzah et al., 2025).

5 Conclusion

This study examined students’ perceptions of digital literacy, data-driven thinking, and student leadership following a course-based training, and further explored the associations among these constructs. The findings indicate that students reported generally high levels of competence across all three domains, with stronger performance in evaluative, analytical, and action-oriented aspects. Lower ratings in areas such as identifying bias, interpreting data representations, and coordinating group tasks point to specific dimensions that remain less developed. In addition, the results demonstrate significant positive associations among digital literacy, data-driven thinking, and student leadership, indicating that these competencies are closely aligned within the post-instructional context. The findings suggest that leadership in contemporary learning environments is increasingly shaped by students’ capacity to navigate digital information and engage in evidence-based reasoning.

These findings carry important implications for curriculum design and instructional practice. Leadership training programs may benefit from a more deliberate integration of digital and data-related competencies, particularly through tasks that require students to evaluate information, interpret data, and justify decisions within collaborative settings. Greater attention is also needed to support the development of critical and ethical dimensions of digital literacy, as well as students’ ability to communicate data-informed reasoning. From a pedagogical perspective, this calls for structured scaffolding, iterative practice, and opportunities for reflection embedded within leadership activities. Future research may extend this work by examining changes over time or comparing different instructional approaches to better understand how these competencies develop and interact across diverse learning contexts.

6 Limitations

This study has several limitations. First, the findings are based on self-report data. The results, therefore, represent students’ self-perceived abilities in digital literacy, data-driven thinking, and student leadership, rather than direct performance-based measures of these constructs. Future studies could include task-based assessments, classroom observations, or performance rubrics to provide stronger evidence of actual ability. Second, the study used a descriptive and correlational design. Although the data were collected after a course-based training program, the absence of a pre-test, control group, or longitudinal follow-up means that the findings should not be interpreted as evidence of instructional effectiveness or causal change. The results show how the three constructs were associated in a post-training context.

Third, the study did not conduct exploratory factor analysis or confirmatory factor analysis. This decision was informed by the scope of the paper and the use of scales with a limited number of items. Since the purpose of the study was not to develop or validate a new measurement instrument, the analysis focused on internal consistency, descriptive patterns, and construct associations. Cronbach’s alpha values showed acceptable reliability across the three scales, which provided sufficient support for their use in this preliminary conference study. However, future research should examine the factorial structure of the measures through EFA and CFA, especially if the scales are used in larger studies or across different institutional contexts. Finally, the sample was drawn from one university and one course-based training context. This limits the generalisability of the findings. Further studies involving multiple institutions, different academic disciplines, and more diverse student groups would help clarify whether similar patterns are observed across broader higher education settings.

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