Introduction

A comprehensive understanding of the various psychological factors that impact academic performance is vital for promoting student success and well-being1,2,3. Key psy chological factors, including self-efficacy, motivation, anxiety, and stress, significantly influence students’ academic outcomes4,5,6. A considerable body of literature in educational psychology has long recognized that academic achievement is a complex result of the interaction of psychological, environmental, and socioeconomic factors. Studies have shown that parental education, household income, and school resources significantly influence learning outcomes and cognitive engagement7,8,9. Similarly, environmental variables such as classroom climate, peer interaction, and institutional support have been found to mediate the effects of motivation and self-efficacy on academic success. Many of these investigations have employed validated and reliable tools such as the Academic Motivation Scale (AMS), the General Self-Efficacy Scale (GSES), and the State–Trait Anxiety Inventory (STAI), demonstrating robust psychometric reliability in assessing these constructs. Although numerous studies in cognitive and educational psychology have examined the interplay among psychological, environmental, and socioeconomic factors influencing academic performance; often employing validated instruments such as the Academic Motivation Scale (AMS), the General Self-Efficacy Scale (GSES), and the State–Trait Anxiety Inventory (STAI), few have attempted to integrate these constructs within a unified quantitative prioritization framework10,11,12,13,14,15. The current study builds on this foundation by applying the Analytic Hierarchy Process (AHP) not as a novel invention, but as a structured decision-support method to systematically assess and rank the relative influence of psychological factors using participants’ comparative judgments.

Self-efficacy, an individual’s confidence in their ability to accomplish tasks, significantly influences students’ engagement and motivation in their studies. Students with high self-efficacy are more likely to set ambitious goals and implement effective learning strategies, thereby improving academic performance16,17,18,19,20. Motivation, encompassing both intrinsic and extrinsic types, drives students to actively participate in their coursework and surmount challenges. Intrinsic motivation, driven by genuine curiosity and a passion for learning, often leads to enhanced cognitive engagement and improved academic results. On the other hand, extrinsic motivation, which involves seeking rewards or avoiding negative consequences, can also impact academic success21.

Anxiety and stress, however, can impede performance by affecting focus and cognitive function22. Elevated levels of test anxiety can contribute to poor performance due to distractions and nervousness. To mitigate the effects of these psychological factors, supportive interventions such as counselling and stress management programs can be employed, significantly boosting students’ academic achievements and overall educational experience. Academic performance is a multifaceted construct influenced by both internal and external factors. Internally, students’ study habits, motivation, self-efficacy, and cognitive strategies play pivotal roles in their academic success23. Study habits, including time management and organizational skills, directly impact academic outcomes by influencing how students approach their coursework. Motivation, comprising both intrinsic and extrinsic types, impacts students’ drive and commitment to their studies. Self-efficacy, the belief in one’s abilities, determines how students approach academic challenges and manage their learning tasks. Factors such as family background, school environment, and teacher quality significantly impact academic performance. Family support, socio-economic status, and parental education contribute to students’ learning experiences and opportunities7. Despite these advancements, there remains limited integration of these multidimensional influences within a single analytical framework. Prior studies have typically examined psychological, environmental, or socioeconomic factors separately. The present study addresses this gap by using the Analytic Hierarchy Process (AHP) to prioritize key psychological variables comparatively, providing a structured synthesis that complements rather than replaces traditional psychometric approaches. This approach enables a more comprehensive understanding of how internal psychological dimensions interact with broader contextual influences on academic performance.

Theoretical background

In educational and psychological research, decision-making tools such as regression models, structural equation modeling (SEM), and the Analytic Hierarchy Process (AHP) have been employed to analyze and rank the diverse factors that influence academic performance. While AHP is an established and validated technique, its use in synthesizing multiple psychological constructs within a unified comparative framework remains comparatively underexplored. By combining participants’ subjective evaluations with quantitative prioritization, AHP serves as a complementary tool to the rich body of psychometric research that has examined motivation, anxiety, self-efficacy, and related constructs24. AHP serves as a versatile decision-making tool that aids in prioritization and decision-making processes based on various criteria by deconstructing a problem into a hierarchical framework of factors25. This method is increasingly employed in diverse fields, including education, to analyze and rank factors that impact academic performance. By using AHP, researchers and educators can objectively assess and compare the significance of factors such as student motivation, teaching quality, and parental involvement. This structured method fosters more informed decision-making and resource allocation, paving the way for enhanced strategies that promote improved educational outcomes26. The application of AHP enables clear visualization of the contribution of various factors to the overall performance, facilitating the identification of crucial areas for intervention and improvement by stakeholders27,28. Consequently, the growing popularity of AHP highlights its efficacy in addressing complex decision-making challenges and optimizing various processes through a comprehensive analysis of influencing factors. Understanding the impact of psychological factors on academic performance is crucial, as these factors provide valuable insights into students’ educational success or challenges29. Table 1 presents the advantages and disadvantages of various factors that impact academic achievement.

Table 1 Factors influencing academic achievement: a comparison of positive and negative effects.
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The reviewed literature collectively demonstrates the versatility of the Analytic Hierarchy Process (AHP) in addressing diverse educational challenges. Across these studies, AHP has been used to evaluate teaching priorities, assess learning environments, integrate technology in instruction, and support decision-making related to curriculum design, infrastructure, and educational resource allocation. The consistent application of AHP across various contexts underscores its methodological strength in systematically ranking factors based on expert or participant judgment. By enabling both quantitative rigor and qualitative insight, AHP allows researchers to translate subjective evaluations into measurable priorities, thereby enhancing the precision of educational analysis. Building on these contributions, the current study utilizes the AHP to prioritize key psychological factors that influence academic performance among university students. Table 2 provides a concise summary of previous applications of AHP in educational research, illustrating how this method has evolved and been validated in multiple contexts.

Table 2 A summary of previous studies applying AHP in educational settings.
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The novelty of this study lies in its application of the Analytic Hierarchy Process (AHP) to systematically evaluate and prioritize psychological factors influencing academic performance. While traditional approaches often analyze these factors in isolation, AHP provides a structured, quantitative framework for understanding their relative significance and interactions. By deconstructing complex issues such as motivation, self-efficacy, and stress into manageable components, AHP allows for a nuanced analysis of their impact on academic outcomes. This method integrates subjective and quantitative data, offering a comprehensive view of how these psychological dimensions contribute to student success. Furthermore, the study’s use of AHP facilitates the development of targeted interventions, enabling educators to design evidence-based strategies that address the most influential factors. This approach advances theoretical understanding and offers practical solutions for enhancing academic performance and student well-being, marking a significant departure from more conventional, less systematic analyses. Therefore, while AHP itself is an established and validated technique, the contribution of this study lies in its integration of well-recognized psychological variables within a comparative hierarchical framework applied directly to university learners. This approach enables a clearer understanding of how motivational, affective, and self-regulatory dimensions interact to shape academic outcomes, complementing earlier research that has essentially treated these factors in isolation.

Methodology

Participants and data collection

This study involved 200 university students (150 undergraduates and 50 postgraduates) from Yan’an University, China. Participants were selected using a purposive–convenience sampling method, a widely accepted approach in educational and psychological research that emphasizes accessibility and willingness to participate. The purposive component ensured the inclusion of students from various academic disciplines and study levels, while the convenience component facilitated voluntary participation. Recruitment notices were distributed through the university’s online portals, department bulletin boards, and classroom announcements, inviting interested students to complete an online screening form to confirm their eligibility. Inclusion criteria required participants to be:

  1. i.

    Full-time students currently enrolled in undergraduate or postgraduate programs.

  2. ii.

    Between 18 and 30 years of age; and.

  3. iii.

    Willing to provide informed consent to participate voluntarily.

Exclusion criteria included:

  1. i.

    Students on academic leave or suspension.

  2. ii.

    Individuals with self-reported psychological or cognitive conditions that might interfere with survey comprehension; and.

  3. iii.

    Respondents who did not complete the questionnaire in full or failed the consistency check (CR > 0.1) during AHP validation.

The final sample comprised 52% female and 48% male students, with a mean age of 22.4 years (SD = 2.1). Participants represented a diverse range of academic disciplines, including education (22%), physical sciences (19%), social sciences (25%), engineering (21%), and humanities (13%), ensuring a balanced cross-section of the university population. Given the subjective nature of the Analytic Hierarchy Process (AHP), it was crucial to ensure that all participants had a sufficient conceptual understanding of the psychological factors they were evaluating. Before the survey, participants attended a brief online orientation session (approximately 15 min) led by the researchers, which explained the meaning of each psychological construct (e.g., motivation, self-efficacy, anxiety, emotional well-being, cognitive styles, and self-regulation) using precise definitions, real-life academic examples, and visual aids. A short comprehension check was followed to confirm understanding before participants proceeded to the pairwise comparison tasks. This preparatory step minimized bias and ensured that participants’ judgments were informed and conceptually consistent. Yan’an University approved the survey content, and participation was entirely voluntary. No identifying information was collected, and confidentiality was strictly maintained throughout. This approach ensured that participants’ evaluations were both ethically sound and methodologically robust, providing credible data for the subsequent AHP analysis.

A structured questionnaire was designed to systematically assess the psychological factors that influence academic performance. The questionnaire aimed to capture students’ perceptions of the relative importance of six key factors: motivation, self-regulation, anxiety, cognitive styles, emotional well-being, and self-efficacy. Each factor was assessed using a 9-point Likert scale to measure the intensity of their experiences and perceptions, ranging from 1 (least significant) to 9 (most important). The survey was administered online through a user-friendly platform, allowing participants to provide their input in a standardized format. This approach ensured consistency in data collection and minimized manual input errors. The collected data were imported into specialized Analytic Hierarchy Process (AHP) software for analysis. The software aggregated pairwise comparisons from the participants, calculated consistency ratios, and synthesized their input to determine the relative priority of each factor. Responses with a consistency ratio (CR) above 0.1 were reviewed and refined to ensure the reliability and validity of the final rankings.

Variables

This study examined six key psychological variables hypothesized to influence academic performance: Motivation, Self-Efficacy, Anxiety, Emotional Well-Being, Self-Regulation, and Cognitive Styles. Each variable was operationally defined based on established theoretical frameworks in educational and psychological research.

  1. a)

    Motivation refers to the internal and external drivers that stimulate students’ engagement and persistence in academic tasks. Both intrinsic and extrinsic forms were considered.

  2. b)

    Self-efficacy represents students’ confidence in their ability to achieve academic goals and manage learning challenges effectively.

  3. c)

    Anxiety reflects feelings of worry and apprehension related to academic performance, including test anxiety and general academic stress.

  4. d)

    Emotional Well-Being captures students’ overall emotional state, including their capacity to manage stress and maintain a positive outlook during academic pursuits.

  5. e)

    Self-regulation denotes students’ ability to plan, monitor, and control their learning behaviors, goals, and study habits.

  6. f)

    Cognitive Styles describe preferred ways of processing information—ranging from analytical to holistic approaches, that influence learning strategies and problem-solving.

These variables were integrated into the Analytic Hierarchy Process (AHP) model as the primary criteria for pairwise comparison and ranking.

Procedure

Data collection followed a structured and standardized procedure designed to ensure participant understanding, consistency, and accuracy of responses. After obtaining ethical approval and participant consent, the researchers conducted an online orientation session to explain the study’s purpose and clarify each psychological variable using practical academic examples. Participants were then guided through a brief comprehension check to ensure they had a conceptual understanding before completing the AHP-based questionnaire. The questionnaire was administered online using a secure platform. It included pairwise comparisons of the six psychological variables on a 9-point Likert scale, where 1 indicated equal importance and 9 represented extreme importance of one factor over another. Each participant compared all variable pairs in relation to their perceived impact on academic performance. Upon completion, responses were screened for consistency using the AHP Consistency Ratio (CR). Any responses with CR values above 0.1 were excluded from the final analysis to maintain reliability. The data collection process spanned two weeks, ensuring ample time for voluntary participation across multiple departments.

Data analysis

Data analysis was conducted using specialized Analytic Hierarchy Process (AHP) software to compute the relative importance (priority weights) of each psychological variable. The analysis followed these main steps:

  1. 1.

    Constructing the Pairwise Comparison Matrix: Responses from all participants were aggregated to form a collective matrix comparing each psychological variable.

  2. 2.

    Normalizing the Matrix: Each entry was divided by the column sum to standardize comparisons.

  3. 3.

    Calculating Priority Weights: The mean of each row in the normalized matrix determined the relative importance of each variable.

  4. 4.

    Assessing Consistency: The Consistency Index (CI) and Consistency Ratio (CR) were calculated to confirm the logical coherence of participants’ judgments. CR values below 0.1 were deemed acceptable.

  5. 5.

    Ranking Psychological Factors: Final weights were used to rank the six variables in order of their influence on academic performance.

This multi-step process ensured that the results were both mathematically sound and conceptually valid, providing an evidence-based hierarchy of psychological factors affecting academic achievement, which is comprehensively explained in the supplementary file as Appendix I.

Results and discussion

Once the matrix is constructed, we compute the column sums and normalize the matrix by dividing each entry by the sum of its corresponding column (i.e., column-wise normalization). The bottom ‘Column sums’ row in Table 3 reports these values; the normalized columns sum to 1.00, ensuring a valid basis for deriving priority weights.

Table 3 Consolidated pairwise matrix (left), column sums, and column-wise normalized matrix (right).
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Table 3 consolidates the raw pairwise comparisons, the correct column sums, and the column-wise normalized matrix used to derive the priority weights reported in Table 4.

Table 4 Priority weights (row means of the column-wise normalized matrix).
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Based on the analysis of the normalized matrix and the computed priority weights (see Tables 3 and 4), the ranking and relative importance of the psychological factors influencing academic performance can be clearly identified. Motivation holds the highest priority weight (0.439), confirming its central role in driving students’ engagement, persistence, and overall academic success. High levels of motivation encourage students to invest sustained effort, overcome learning challenges, and maintain focus on long-term educational goals. This finding highlights the importance of creating learning environments that promote both intrinsic and extrinsic motivation through supportive teaching practices and engaging academic experiences. The second most influential factor is Anxiety (0.218). Although moderate levels of anxiety can enhance focus and performance, excessive anxiety often disrupts concentration and cognitive functioning. This result highlights the importance of providing psychological support services and implementing stress management strategies that help students maintain optimal levels of academic anxiety. Self-efficacy ranks third (0.148), reflecting the significance of students’ confidence in their ability to achieve academic goals. Students with strong self-efficacy are more likely to adopt effective learning strategies, persist through challenges, and achieve better academic outcomes. Educational interventions that build students’ belief in their competence can therefore have a direct positive effect on academic achievement. Emotional Well-Being holds a weight of 0.097, signifying its contribution to resilience, motivation, and engagement. A positive emotional state enhances students’ capacity to cope with academic demands, emphasizing the need for universities to integrate mental health promotion and emotional support programs. Cognitive Styles and Self-Regulation, with weights of 0.056 and 0.042, respectively, are less dominant yet still meaningful contributors. Cognitive styles shape how students process information and approach problem-solving, while self-regulation reflects their ability to plan, monitor, and adjust learning behaviors. Although their direct impact appears smaller, both factors play complementary roles in supporting sustained academic effort and effective study habits.

The Consistency Ratio (CR) results for the six psychological factors demonstrate a high level of reliability in the pairwise comparisons conducted through the Analytic Hierarchy Process (AHP). As shown in Table 5, the overall CR value for the 6 × 6 comparison matrix is 0.042, which is well below the acceptable threshold of 0.10. This indicates that the participants’ judgments were logically coherent and that the relative weightings among factors are statistically consistent. The low CR value confirms that the decisions made in comparing Motivation, Anxiety, Self-Efficacy, Emotional Well-Being, Cognitive Styles, and Self-Regulation are internally consistent and reliable. Consequently, the rankings derived from the normalized matrix and priority weights (Tables 3 and 4) can be regarded as valid reflections of participants’ collective perceptions. The high level of consistency across all comparisons strengthens the overall robustness of the AHP results and reinforces confidence in the accuracy of the derived priority weights.

Table 5 Consistency statistics for the 6 × 6 pairwise matrix.
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Discussion

The results of the Analytic Hierarchy Process (AHP) analysis underscore the significant impact of Motivation and Self-Efficacy on academic performance, aligning with existing literature. Motivation, with the highest priority weight of 0.343, highlights its critical role in educational success. This finding is consistent with research by Zimmerman (2015), who emphasized that intrinsic and extrinsic motivation are crucial for academic achievement. They argue that students who are intrinsically motivated exhibit higher engagement and better performance due to their genuine interest in the subject matter50. Self-efficacy is closely related to a priority weight of 0.338, supporting the findings of Locke (1997), who identified self-efficacy as a key predictor of academic success. Bandura’s theory posits that students with high self-efficacy are more likely to set challenging goals, persist in the face of difficulties, and achieve better academic outcomes51. This aligns with the current study’s results, which underscore the importance of fostering students’ confidence and belief in their capabilities.

The findings of this study align with prior research emphasizing the central roles of motivation, self-efficacy, and emotional well-being in academic achievement, while also complementing studies that underscore the importance of environmental and socioeconomic contexts. For instance, Ebipere (2022) demonstrated that socioeconomic background and family support substantially affect students’ capacity for motivation and self-regulation, suggesting that psychological and environmental dimensions are mutually reinforcing rather than independent. Similarly, Okwuduba et al. (2021) reported that emotional intelligence and social learning environments contribute to academic performance alongside individual self-efficacy and motivation. The current results reinforce these patterns by quantifying the relative weights of psychological factors and demonstrating their hierarchical importance within this broader ecosystem of influences.

It is essential to acknowledge that previous research in educational psychology has extensively investigated psychological and contextual factors that affect performance using validated measurement instruments (e.g., AMS, STAI, GSES, PANAS). The present study complements this body of work by employing AHP to synthesize participants’ comparative judgments rather than replacing psychometric evaluation. This approach offers an alternative decision-analytic perspective that translates qualitative perceptions into quantifiable priorities, thereby enhancing the interpretability of policy and educational practices.

Anxiety, with a priority weight of 0.261, corroborates the dual role described by Sarason, G. (1984), who observed that moderate anxiety can enhance performance by increasing alertness and focus52. The priority weight for Emotional Well-being is 0.226, which aligns with the literature on the role of emotional health in academic performance. Research by Schaufeli & Bakker (2004) indicates that positive emotions and a supportive environment significantly contribute to academic resilience and engagement53. This result underscores the importance of a favorable emotional climate in educational settings. Self-regulation, with a priority weight of 0.188, aligns with the findings of Zimmerman (2015), who emphasized that effective self-regulation, including goal-setting and disciplined study habits, is crucial for academic success. However, it may not be as impactful as motivation and self-efficacy50. The lower weight for Cognitive Styles (0.142) suggests that while cognitive styles influence learning processes, they have a lesser direct effect on academic performance than motivation and self-efficacy. The low Consistency Ratios (CRs) across all factors, ranging from 0.008 to 0.024, indicate high consistency in the pairwise comparisons, validating the reliability of the judgments. In summary, the AHP analysis confirms the significant roles of motivation and self-efficacy, consistent with existing literature, and highlights the importance of managing anxiety and fostering emotional well-being. The findings also emphasize the need for targeted interventions that enhance these key factors to improve academic outcomes. The strong consistency in the judgments further supports the validity and reliability of these results.

While providing valuable insights into the factors affecting academic performance through the Analytic Hierarchy Process (AHP), the study is subject to several limitations. One primary limitation is the inherent subjectivity of AHP in conducting pairwise comparisons and assigning priority weights to various psychological factors. Additionally, the AHP methodology requires consistent and accurate input from participants, which can be particularly challenging to ensure. While AHP is a robust tool for decision-making and prioritization, these limitations highlight the need for caution in interpreting results and suggest that future research should incorporate a broader range of perspectives and employ additional methodologies to validate and enrich the findings.

Future work and recommendations

The Analytic Hierarchy Process (AHP) is a robust multi-criteria decision-making (MCDM) tool that structures complex problems into a hierarchy of goals, criteria, sub-criteria, and alternatives, enabling both qualitative and quantitative evaluations. By combining expert judgment with mathematical consistency checks, AHP allows decision-makers to prioritize multiple factors and assess trade-offs systematically. This method can be effectively applied across diverse research domains represented by the listed articles. Biomedical Research and Cancer Therapeutics focuses on innovative strategies for disease treatment, including stem cell therapies, targeted drug delivery, and tissue engineering. Nanomaterials and Catalysis for Energy Applications explores advanced materials and catalytic processes to enhance energy conversion and environmental sustainability. Environmental and Earth Sciences investigates natural processes and human impacts on ecosystems, including soil, rock, and climate dynamics. Artificial Intelligence and Visual Question Answering emphasizes the development of intelligent algorithms for data analysis, predictive modeling, and image-based reasoning tasks. Blockchain and Secure Data Systems address secure information sharing, decentralized ledgers, and the optimization of collaborative networks. Control Systems and Engineering Technologies covers the design and stability analysis of mechanical, electrical, and vehicular systems under dynamic conditions. Mathematics and Computational Theory provide theoretical foundations and computational techniques for modeling, simulation, and problem-solving in complex systems. Economics, ESG, and Management Studies analyze organizational performance, sustainability metrics, and decision-making in socio-economic contexts. Social Networks and Behavioral Studies examine human behavior, social interactions, and psychological phenomena using computational and observational approaches. Finally, Specialized Theoretical Applications focuses on specific analytical frameworks and statistical methodologies to address niche scientific and practical problems.

Conclusion

The Analytic Hierarchy Process (AHP) analysis provided clear insights into the psychological factors most strongly influencing academic performance. Among the six factors examined, Motivation (0.439) emerged as the most influential, followed by Anxiety (0.218) and Self-Efficacy (0.148). This hierarchy highlights the pivotal roles of sustained motivation, effective anxiety management, and confidence in one’s capabilities in driving academic achievement. High motivation fuels engagement and persistence, while self-efficacy enhances students’ confidence in setting ambitious goals, sustaining effort, and overcoming obstacles. At the same time, moderate levels of anxiety can sharpen focus and performance, whereas excessive anxiety can hinder concentration and learning, highlighting the need for balanced stress-management strategies. Emotional Well-Being (0.097), Cognitive Styles (0.056), and Self-Regulation (0.042) also contribute to academic performance, albeit to a lesser extent. Emotional well-being supports resilience and engagement; cognitive styles influence how students process and apply information; and self-regulation underpins the discipline needed for goal-directed learning. While these factors show smaller numerical weights, they interact with the more dominant variables to shape students’ overall academic experiences. To translate these findings into practice, educators and administrators should prioritize initiatives that strengthen motivation and self-efficacy while providing targeted interventions to manage academic anxiety. Learning environments should incorporate activities that promote intrinsic motivation, such as autonomy, relevance, and mastery, alongside supportive feedback that builds confidence and competence. Mental-health programs aimed at stress regulation and emotional resilience are equally vital to sustaining students’ performance and well-being. In conclusion, this study underscores the significance of an integrated psychological framework in enhancing academic outcomes. By addressing the most influential factors, motivation, anxiety, and self-efficacy, within emotionally supportive, cognitively adaptive, and self-regulatory learning environments, educational institutions can foster both academic excellence and holistic student development. The findings emphasize that cultivating psychological well-being is not peripheral but central to achieving long-term educational success.