The involvement of young people in STEM fields is a crucial goal for a developing society. At the same time, a key challenge remains in understanding the factors influencing career choices in these areas, particularly among women (Heilbronner, 2013). In this context, it is essential to examine the determinants that drive individuals toward STEM careers.

Among the various factors influencing career choice—such as sociocultural, economic, and educational aspects—psychological factors (including personal, motivational, emotional, and cognitive components) play a pivotal role. Prior research has identified several key influences on career selection, including: the presence or absence of cognitive abilities required for acquiring professionally significant knowledge (Rottinghaus et al., 2018); psychological factors related to an individual’s motivation and personality traits (Watt et al., 2012); and the evaluation of career choice motives within specific economic contexts (Pleşa, 2019). These elements form a complex, interdependent system of psychological characteristics that shape professional aspirations and decisions.

Research indicates a complex interaction between cognitive abilities and motivational factors in task performance. Cognitive ability is positively linked to intrinsic motivation and effort in cognitive tasks (Ramme et al., 2022). However, this relationship may vary depending on task complexity and individual differences (Kossowska, 2007). Rather than general trait motivation, context-specific achievement motivation has been found to moderate the connection between cognitive ability and performance (Hirschfeld et al., 2004). Students with different cognitive abilities may prioritize distinct motivational drivers, such as self-development or professional achievement (Churikov & Kagan, 2023). While cognitive and motivational factors are often studied separately, integrating both aspects in research is beneficial, as it offers a more comprehensive understanding of information processing and task performance (Kossowska et al., 2014).

Research consistently shows a significant relationship between motivation and academic performance across different educational levels. Intrinsic motivation positively correlates with academic achievement for students of all ages (Liu et al., 2022; Goodman et al., 2011). While external motivation's impact varies, its influence on performance tends to increase as students grow older (Liu et al., 2022). Effort acts as a partial mediator between both intrinsic and external motivation and academic performance (Goodman et al., 2011). Students who attribute success to internal factors like ability and hard work generally perform better academically (Berry & Plecha, 1999). Additionally, positive affect states, such as confidence and enthusiasm, are associated with better test performance (Berry & Plecha, 1999). The relationship between motivation and academic performance is complex, involving various mediating factors like effort, engagement, anxiety, and learning strategies (Liu et al., 2022). Given the importance of motivation in academic success,it is necessary to pay more attention to motivational factors in education.

The structure of relationships between cognitive abilities, academic performance and motivational factors, however, remains unknown.

One of the most promising methodologies for studying the structure of relationships between psychological factors is network analysis. This approach enables researchers to view the system holistically, analyzing not only individual traits but also the connections between them. By applying network analysis, it is possible to determine the centrality of specific traits within the psychological network and to differentiate correlations from potential causal mechanisms (Borsboom, Denny, et al., 2021).

The present study aims to investigate the network structure linking cognitive abilities, motivational factors, and academic performance in first-year university students. Understanding these interconnections will provide deeper insights into the mechanisms underlying academic success and career orientation, offering valuable implications for education and career guidance strategies.

We conducted a network analysis to examine the structure of cognitive and motivational variables in two student (18-21 years old, mean age 18.67, SD = 2.09), groups with different educational profiles: STEM (2306 persons, 47% females) and humanitarian (3355 persons, 79% females). The dataset included variables related to academic performance, cognitive abilities, and motivational influences. The networks were estimated using the EBICglasso method with nonparanormal transformation, and thresholding was applied to enhance specificity. To identify clusters within the networks, we employed the Clique Percolation method with a weighted approach (k = 3–6). Separate networks were estimated for students in STEM and humanities tracks, and minimum spanning trees (MSTs) were extracted for each group to assess core structural differences. Network Comparison Test (NCT) was used to evaluate differences between groups in terms of global strength, individual edge weights, and centrality measures (strength, expected influence, betweenness). Additionally, MST structures were compared to identify shared and unique connections.

Analysis revealed no significant global structural differences between the two networks (M = 0.129, p = 0.269), indicating similar overall topology. Global expected influence was also comparable (S = 0.515, p = 0.058), suggesting relatively stable node importance across networks. However, intrinsic organization differs. A deeper investigation of network structure in the two groups reveals two principal independent factors: cognitive abilities and motivational influences. Academic achievement is predominantly associated with cognitive abilities, while anxiety towards mathematics (AMAS) is linked to motivational aspects, potentially indicating its mediating function. The choice of STEM-related fields appears to be driven primarily by cognitive factors and correlates with academic success. Interestingly, verbal intelligence does not align with either of the two principal factors, suggesting its distinct role in cognitive processing. The analysis of MST indicates structural similarity between humanities and STEM students, highlighting a common underlying motivational framework. However, some differences emerge. Within the motivational cluster of STEM students, external factors—such as traditions, the influence of renowned figures, and peer interactions—play a central role. Moreover, a key contradiction arises within the STEM group: students experience tension between self-determined choices, parental expectations, and financial incentives. This might indicate the influence of external motivational factors within STEM students—such as societal expectations, financial incentives, and role models—points to the broader role of cultural and environmental factors in shaping educational and career trajectories. Despite differences in motivation, the structural organization of cognitive abilities remains largely consistent across groups. Overall, these insights highlight the complex interplay between cognitive and motivational factors in academic success and career choices, emphasizing the necessity of considering both cognitive and motivational determinants in academic performance and achievement modeling. The study was supported by RSF, project №24-18-01102

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