Study on Learning Adaptability of STEM Students in the New College Entrance Examination Context

Study on Learning Adaptability of STEM Students in the New College Entrance Examination Context | 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 Research Article Study on Learning Adaptability of STEM Students in the New College Entrance Examination Context Xiangyi Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5344859/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Over ten years of the New College Entrance Examination Reform, the flexible subject selection model has posed challenges to STEM (Science, Technology, Engineering, and Math) majors education in Chinese universities. The relaxation of subject requirements has led to diverse knowledge backgrounds among students, resulting in some lacking a solid foundation in these fields, which contributes to high failure rates and frequent major changes. This study reconstructs the dimensions of learning adaptability for STEM students, analyzing different adaptability types using data from 39 "985 Project" universities in China. Findings indicate two main types: low adaptability, characterized by low motivation and difficulty using intelligent tools, and high adaptability, which correlates with better adjustment but moderate interest in the major. Finally, this study offers targeted recommendations for improving the learning adaptability of STEM students. learning adaptability competences in STEM new college entrance examination STEM majors Figures Figure 1 Introduction Since 2014, the New College Entrance Examination Reform has been gradually implemented across China, with two primary models: the "3 + 3" and "3 + 2 + 1" systems. In the "3 + 3" model, the first "3" represents the compulsory subjects of Chinese, Mathematics, and English, while the second "3" allows students to choose any three subjects from Physics, Chemistry, Biology, Geography, History, and Politics. Following the initial trials of the "3 + 3" model, a significant decline in the number of students choosing Physics was observed. Consequently, subsequent reforms adopted the "3 + 2 + 1" model, where Physics and History became mandatory subjects, effectively addressing the issue of declining Physics enrollments. However, both models have led to a more interdisciplinary academic foundation among students compared to the traditional division between the sciences and humanities. Moreover, some university programs have relaxed subject requirements to ensure sufficient enrollment, resulting in students entering these programs without a solid foundation in the relevant subjects, leading to academic challenges. This issue is particularly pronounced in STEM majors, where research has shown that students need a strong foundation in Physics, Chemistry, and Biology during high school to adapt well to university-level studies in these fields (Yuan et al., 2023 ). Consequently, students with weaker foundations in these subjects are more likely to experience learning adaptability issues, leading to major changes or even dropout. In addition, students in science and engineering programs often have diverse academic backgrounds, with varying strengths across subjects, which is not always accommodated by the uniform curricula and teaching methods used by universities, hindering personalized development. Understanding the learning adaptability of STEM students under the New College Entrance Examination Reform, identifying the characteristics of different adaptability types, and determining effective measures to enhance their adaptability are crucial for reducing the incidence of course failures and major changes. However, recent research on learning adaptability has primarily focused on developing adaptability scales, with little attention given to the specific challenges faced by different academic majors or the analysis of various adaptability types. To address these issues, this study designs a set of learning adaptability dimensions tailored to STEM students under the New College Entrance Examination Reform, develops a corresponding assessment scale, categorizes different adaptability types, and provides a detailed analysis of the characteristics of each group, offering recommendations for improving learning adaptability in these majors. Related Work Conceptual Definition The concept of learning adaptability has been defined differently by various scholars. Most definitions are based on Piaget's theory of cognitive equilibrium, emphasizing the individual's ability to adapt to new information and situations by promptly adjusting learning strategies and behaviors to align with the external learning environment(1995,as cited in Feng,2006). For example, Larose et al.(1995) consider learning adaptability as a psychological and behavioral process in which learners, aiming to achieve good learning outcomes, continuously adjust themselves in response to changes in the learning environment and needs to maintain a balance between their internal learning state and the external environment. Another perspective defines learning adaptability as the process where individuals strive to adjust themselves to achieve a balance between their internal state and the learning environment, based on environmental and learning needs(López-Angulo et al.,2021). Xie et al.(2023) further define learning adaptability as the process in which learners, through sufficient interaction with the learning environment, continuously adjust their psychological state and learning behaviors to achieve a dynamic balance with the environment. She argues that in the era of intelligent learning, university students' adaptability should reflect both proactive adaptation to and active transformation of the learning environment. This study agrees with the definition of learning adaptability as a process of bidirectional dynamic balance. Based on this viewpoint, learning adaptability in STEM students is defined as the process by which these students, given their existing knowledge base and structure, continuously adjust their learning strategies and behaviors through full interaction with the learning environment to achieve a bidirectional dynamic balance, thereby attaining significant academic achievement and professional development. The extent to which STEM students can successfully adapt to learning, cultivate clear thinking patterns, and adopt rigorous learning attitudes will directly affect their academic performance and even career development. Measurement of Learning Adaptability Current research on learning adaptability is quite extensive, and different researchers have developed various measurement tools. Early studies often treated learning adaptability as one dimension within broader adaptability scales. For instance, Zitow (1984) developed the College Adjustment Rating Scale (CARS), which measures students' adaptability to college life stresses across three dimensions: learning, family, and social environments. Baker and Siryk ( 1999 ) combined learning, social, and emotional adaptability in the Student Adaptation to College Questionnaire (SACQ). However, these studies featured relatively few items on learning adaptability, with coarse-grained divisions. Zhou (1991) focused specifically on learning adaptability, developing the Academic Adaptation Test (AAT), which includes dimensions such as learning attitude, techniques, environment, and physical and mental health. However, this test mainly targets primary and secondary school students, with limited applicability. Feng et al. ( 2006 ), considering the educational model and learning methods in Chinese universities, developed a learning adaptability scale for college students. This scale, which boasts high reliability and validity with clear dimensional divisions, has been widely used in studies on Chinese college students' learning adaptability. In recent years, some studies have developed new scales considering recent educational phenomena. For example, Nan ( 2021 ), in the context of the new college entrance examination reform, developed a learning adaptability questionnaire for college students, covering five dimensions: curriculum and teaching arrangement, cognitive and learning abilities, learning engagement and professional knowledge, learning environment, and learning strategies. Xie et al. ( 2023 ) recognized the importance of intelligent learning environments in learning adaptability, restructured the learning environment dimension in the adaptability scale, and integrated human-machine collaboration concepts, measuring students' learning adaptability across learning motivation, ability, and environment. Although these self-developed scales are innovative, they often test the structural dimensions proposed by the study's specific subjects, which may lack generalizability. Learning Adaptability in STEM Students Currently, there is limited research specifically focused on learning adaptability in STEM students. Some studies analyze the learning adaptability of students within specific STEM fields. For example, Cao et al. (2019)found that medical students with high learning adaptability exhibit low academic burnout and strong learning immersion. Chen et al. (2024)found that AI-based learning environments can help engineering students improve their learning adaptability. Bazelais et al. ( 2016 )discovered that grit is not a significant predictor of success in physics. These studies are often highly specialized, with conclusions that are not widely generalizable. Additionally, these studies often fully adopt earlier learning adaptability scales without redesigning or modifying measurement dimensions for specific fields. Other studies treat STEM students' learning adaptability as an outcome within broader learning adaptability research. For instance, Li et al.(2023) found that STEM students show higher adaptability in online learning compared to other majors, while Chen et al.(2020) found that freshmen in STEM fields exhibit slightly lower adaptability than their counterparts in the humanities and social sciences, with a higher rate of major changes. Alipio(2020) found that STEM students with high adaptability outperform those in the humanities and social sciences with low adaptability. However, as STEM students' learning adaptability is only an outcome in these studies, the analysis is often superficial, relying heavily on descriptive data. In summary, most researchers tend to classify the dimensions of college students' learning adaptability, generally dividing them into self-adaptation and adaptation to the learning environment. This study agrees with these classification dimensions but finds that further refinement reveals overlapping and intersecting issues, indicating a need for integration. Specifically, research on STEM students' learning adaptability remains preliminary, with a lack of specialized studies and shallow conclusions. This study, focusing on students who participated in the new college entrance examination from 2020 to 2023 and subsequently chose STEM majors, integrates existing measurement dimensions and, based on text mining results from STEM professional training objectives, reconstructs the dimensions of learning adaptability measurement for STEM students. It also analyzes and summarizes the characteristics of students and groups with different types of adaptability, providing recommendations for the development of STEM programs in the context of the new college entrance examination reform. Research Design Construction of Structural Dimensions This study categorizes the learning adaptability of STEM students into two main aspects: self-adaptation and adaptation to the learning environment. Self-adaptation refers to the process in which learners proactively adjust their learning strategies and behaviors according to their learning needs during the interaction with the learning environment. The elements of self-adaptation include learning motivation, professional interest, and learning ability. Learning motivation comprises intrinsic and extrinsic motivation. Intrinsic motivation refers to the learner's efforts to learn driven by the pursuit of challenges and curiosity; extrinsic motivation refers to learning driven by factors outside the learning activities, such as gaining recognition from others or obtaining a diploma. Additionally, given the complexity and tediousness of some courses in science and engineering majors, students need to possess a high level of enthusiasm for the field to develop deep learning motivation and continue to excel in the professional domain. Previous research defines students' emotional experiences based on cognitive foundations towards learning and learning situations as learning attitudes. This study posits that the learning attitude of STEM students is primarily reflected in their strong professional interest in their major, thus identifying professional interest as a key dimension in measuring learners' learning adaptability. Learning ability is a critical indicator for assessing whether students can successfully complete their studies and adapt to future life and work. The educational objectives of STEM majors determine the orientation of students' knowledge, abilities, and quality development and serve as an essential basis for measuring the learning ability of students in these majors. This study creatively proposes using the TF-IDF algorithm to perform text mining on the educational objectives of national characteristic STEM majors from 39 Chinese 985 universities and generate a word cloud (Fig. 1 ). The text mining mentions that STEM students should possess a solid theoretical foundation (fundamental knowledge, basic skills, professional knowledge), knowledge application and practical ability (problem-solving, practical ability, analysis, technical development), scientific research and knowledge innovation ability (innovation awareness, scientific research, innovation ability, critical thinking ability), knowledge management ability (literature search, information security, information acquisition), and meta-learning ability (lifelong learning, autonomous learning). Learners' adaptation to the learning environment includes adapting to the teaching methods, arrangements, and school management in the university teaching model, as well as adapting to the intelligent learning environment in the context of the intelligent era. This includes whether learners can adapt to and utilize intelligent learning platforms and tools and accurately retrieve learning resources from vast amounts of online data. In summary, under the background of the new college entrance examination reform, the structural dimensions for measuring the learning adaptability of STEM students are summarized as follows. Table 1 Structural Dimensions of Learning Adaptability Measurement for STEM Students Adaptation Type Dimension Sub-dimension Self-adaptation Learning Motivation Intrinsic Motivation Extrinsic Motivation Learning Ability Theoretical Foundation Knowledge Application and Practice Scientific Research and Knowledge Innovation Knowledge Management Meta-learning Professional Interest Environmental Adaptation Teaching Model Learning Environment Scale Development Based on the measurement dimensions of learning adaptability for STEM students constructed above, and referencing research results from Feng(2006), Amabile(1994,as cited in Yu,2017), Xie(2023), and others, this study designed the "Learning Adaptability Scale for STEM Students." The scale is structured around two main dimensions: self-adaptation and environmental adaptation. Self-adaptation includes forming the learning motivation, the learning ability, and the professional interest; environmental adaptation includes adjusting the teaching model and the learning environment. Moreover, in developing items for the learning ability dimension, this study considers the skills required by learners in the intelligent era, such as managing knowledge using intelligent learning tools and solving problems encountered in learning with these tools. The final scale comprises 39 items, including 33 items measured on a five-point Likert scale across different dimensions, and 6 demographic items: gender, grade level, ethnicity, place of household registration, whether the student has experienced the new college entrance examination, and selected subjects under the new examination reform. Data Sources In the pilot phase of the scale, 83 university students majoring in STEM who had chosen their major after the new college entrance examination reform were recruited from the Shanghai region of China as study participants. In the official measurement phase, the participants were 202 university students majoring in STEM who had chosen their major after the reform, recruited from various universities across China. Among the formal measurement sample, 51.49% were freshmen, 30.69% were sophomores, 10.4% were juniors, and 7.43% were seniors. Additionally, 50.99% of the participants were female. Geographically, there were 37 participants from Northeast China, 61 from North China, 40 from East China, 19 from South China, 22 from Central China, 6 from Northwest China, and 17 from Southwest China. Reliability and Validity Testing The scale's reliability was tested, resulting in an overall Cronbach’s alpha coefficient of 0.947. The coefficients for the learning motivation were 0.911, for learning ability 0.928, for professional interest 0.892, for learning environment 0.867, and for teaching model 0.918. These results suggest that the learning adaptability scale for STEM students, along with its subscales, has high reliability and good internal consistency. For validity testing, confirmatory factor analysis was performed using Amos. Model fit was assessed using maximum likelihood estimation, evaluating the model's fit using indices such as the chi-square to degrees of freedom ratio (CMIN/DF), root mean square residual (RMR), and root mean square error of approximation (RMSEA). Specific data are presented in Table 2 . Comparing these with the fit indices standards, all indicators met the required fit criteria, indicating that the model constructed in this study has good fit and the scale has strong structural validity. Table 2 Model Fit Test Results Fit Indices Fit Standard Fit Value Fit Judgment Absolute Fit CMIN P > 0.05 0.088 Yes \(\:{{\chi\:}}^{2}/\text{d}\text{f}\) 1< \(\:{x}^{2}\) / \(\:\text{d}\text{f}\) <3 1.089 Yes SRMR < 0.05 0.040 Yes RMSEA 0.9 0.989 Yes IFI > 0.9 0.990 Yes TLI > 0.9 0.988 Yes AGFI > 0.8 0.850 Yes Parsimonious Fit PGFI > 0.5 0.745 Yes PNFI > 0.5 0.805 Yes PCFI > 0.5 0.900 Yes Research Results Main Adaptation Types of STEM Students Under the Background of the New College Entrance Examination Reform To thoroughly analyze the types of learning adaptability among STEM students under the new college entrance examination reform and to explore the characteristics of different groups, this study employed a clustering algorithm for detailed classification of the subjects. First, to select the most suitable classification algorithm for the subjects, the silhouette coefficient was used to evaluate the effectiveness of various clustering algorithms, ultimately choosing the Mean Shift clustering method, which had the highest silhouette coefficient. Mean Shift is a non-parametric clustering algorithm that achieves clustering by moving data points to find the regions with the highest density. Compared to other algorithms, Mean Shift has the following advantages: 1) It does not require pre-setting the number of clusters, which is a significant advantage over algorithms like K-Means. 2) It is suitable for clusters of any shape, not limited to spherical distributions. 3) It has good robustness and stability, effectively handling noise and outliers. The learning adaptability dimension data of students after clustering is shown in Table 3 . The study sample can be divided into two distinct adaptability groups based on the five dimensions of learning adaptability, with significant differences in scores across these dimensions. These groups can be summarized as low adaptability and high adaptability types. Each adaptability type shows imbalances in scores across the five dimensions. Analyzing the number of students in each type of learning adaptability, the proportion of low adaptability students (63.86%) is higher than that of high adaptability students (36.14%), indicating that the overall adaptability of students is moderate. Table 3 Students with Different Types of Learning Adaptability Clustering Situation Cluster Center Proportion Learning Motivation Learning Ability Professional Interest Learning Environment Teaching Model Low Adaptability 56.56 61.96 58.40 57.44 57.23 63.86% High Adaptability 83.47 85.10 81.51 85.71 88.29 36.14% Characteristics of High Adaptability Students A preliminary analysis of high adaptability students was conducted. In terms of gender, 46.58% of these students are male and 53.42% are female. Geographically, 38.16% are from the eastern region, 35.53% from the central region, 15.79% from the western region, and 10.53% from the northeastern region. Regarding grade level, 47.37% are freshmen, 28.95% are sophomores, 11.84% are juniors, and 11.84% are seniors. Analyzing the elective subjects under the new college entrance examination for this group reveals that 10.53% of the students selected traditional science subjects: physics, chemistry, and biology. Among the students who chose STEM majors, 55.26% selected political science, and 43.42% selected physics in the college entrance examination. The most common subject combination (11.84%) among these students was physics, political science, and biology. The dimension with the lowest score was professional interest (81.51). Most students indicated that "I don't feel that my major suits me, and it was not my voluntary choice." The highest scoring dimension was the teaching model (88.29), which includes items reflecting the students' adaptation to university teachers' teaching styles, effective use of their own time in university, and less nostalgia for high school life. In the learning ability dimension, the scores for each item were close to the maximum, with relatively lower scores on the item "adapting to and using smart learning tools to solve problems." Characteristics of Low Adaptability Students A preliminary analysis of low adaptability students was conducted. In terms of gender, 50.39% of low adaptability students are male and 49.61% are female. Geographically, 45.24% of these students are from the eastern region, 16.67% from the central region, 15.08% from the western region, and 23.02% from the northeastern region. Regarding grade level, 53.97% are freshmen, 31.75% are sophomores, 9.52% are juniors, and 4.76% are seniors. Analyzing the elective subjects under the new college entrance examination for this group reveals that only 8.73% of the students selected traditional science subjects. Among these students, 50.7% selected political science, and only 33.33% selected physics. The most common subject combination (15.08%) was political science, chemistry, and biology, which was the most frequently selected combination in this group. The dimension with the lowest score was learning motivation (56.56). The external learning motivation scored higher than the internal motivation, reflecting that the primary motivators for these learners were external factors such as achieving higher scores and gaining others' approval. The dimension with the highest score was the learning ability (61.96), with the highest-scoring item being "using smart learning tools to organize knowledge," and the lowest being "adapting to and using smart learning tools to solve problems." Discussion There are similarities in subject selection among the two types of students under the new college entrance examination reform, but there is a significant gap in overall learning adaptability scores. Specifically, in different dimensions of the learning adaptability, both types of students exhibit imbalances in their scores, facing different issues in the learning adaptability: low adaptability students lack internal learning motivation and struggle to adapt to and use smart learning tools to solve problems, while high adaptability students show strong adaptation to university teaching models but display moderate professional interests. Similarities in Subject Selection Across Different Types of Students Regarding subject selection under the new college entrance examination reform, few students majoring in STEM simultaneously selected traditional science subjects: physics, chemistry, and biology (9.41%). However, more than half of the sample students selected political science (52.48%). For high adaptability students, the most common subject combination was physics, political science, and biology; for low adaptability students, it was political science, chemistry, and biology. Both groups frequently selected political science and biology, which may be related to the utilitarian tendencies in subject selection. This method of subject selection can easily lead to an incomplete knowledge structure for STEM students, making it more challenging for them to adapt to the complex and interdisciplinary courses in their major fields of study. Significant Differences in Overall Scores Between Different Types of Students, With Imbalances Across Dimensions There is a substantial gap in scores across all dimensions between high adaptability and low adaptability students. It is evident that there are significant differences in learning adaptability across various dimensions between high and low adaptability students. Moreover, both types of students show imbalances in their scores across dimensions, with noticeable score gaps between the five dimensions. This result differs from the research of Nan(2021), who suggests the existence of a balanced type of learning adaptability with smaller score gaps across dimensions. This discrepancy may be due to differences in the study subjects. Low Adaptability Students Lack Internal Learning Motivation and Struggle to Adapt to and Use Smart Learning Tools to Solve Problems Low adaptability STEM students mainly exhibit low internal learning motivation. This suggests that for STEM students, a lack of sufficient learning motivation may lead to reduced learning interests and poor learning outcomes. Internal motivation, as opposed to external motivation, is more likely to enhance the learning adaptability of STEM students. This finding aligns with the research results of Yu et al.(2017), who found that the internal learning motivation has a direct positive impact on academic achievement. At the same time, these students perform poorly on items related to adapting to and using smart learning tools to solve problems. This may be related to the heavy use of paper-based learning resources and the potential restrictions on electronic device usage by parents, resulting in limited exposure to smart learning tools during high school. Upon entering university, students need to learn to use smart learning tools such as smartphones, computers, and tablets for studying, and to find the necessary resources from vast amounts of electronic learning materials. If they cannot adapt to or use these smart learning tools, it will be even more difficult to solve real-world problems using them. High Adaptability Students Excel in Adapting to University Teaching Model but Show Moderate Professional Interest High adaptability STEM students perform well in adapting to university the teaching model, meaning they can better adapt to university teachers' lecture styles, management methods, and efficiently manage their abundant free time in university, while rarely indulging in nostalgia for high school life. The teaching model is an indispensable factor in learning adaptability for STEM students. Research indicates that there are significant differences between university and high school science subjects in terms of teaching concepts, methods, and content(Sabirova et al.,2020). Therefore, better adaptation to university the teaching model can effectively enhance the learning adaptability of STEM students. However, this group of students scored the lowest in the professional interest among the five aspects of learning adaptability. This suggests that while these students can adapt well to university learning, they lack clear goals, experiencing a conflict between interests and employment, and between specialties and goals. This finding is consistent with the conclusions of Du et al.(2016), who believe that this phenomenon arises because "students' passive and institutionalized learning life before high school leads to a lack of clear learning plans and autonomous learning abilities." Higher Environmental Adaptation Scores in High Learning Adaptability Students, Higher Self-Adaptation Scores in Low Learning Adaptability Students Although STEM students with high learning adaptability tend to score higher across all dimensions compared to those with low learning adaptability, their performance in environmental adaptation is particularly noteworthy. This indicates that students with higher adaptability are better suited to the teaching mode and learning environment of universities. This finding is consistent with the conclusions of Wang et al.(2009), who emphasized that learning adaptability is significantly influenced by environmental factors. In contrast, students with lower learning adaptability tend to score higher in self-adaptation than in environmental adaptation, suggesting that these students exhibit relatively lower flexibility in adjusting to the objective environment. Chen et al.(2020) found that adapting to the learning environment can effectively enhance learning motivation, which is an aspect of self-adaptation. Therefore, changes in environmental adaptation will also impact self-adaptation, further underscoring the critical role of environmental factors in shaping university students' learning adaptability. Recommendations Based on the research findings, this study proposes the following recommendations to enhance the learning adaptability of STEM students. Integrate Learning Adaptability Assessment into Diagnostic and Formative Evaluations Universities should enhance the diagnostic and formative evaluation systems for STEM students by incorporating learning adaptability assessments as a critical component. Given the diverse knowledge structures and subject strengths of students under the new college entrance examination system, integrating adaptability testing into diagnostic and formative evaluations allows institutions and instructors to quickly gauge students’ learning abilities, interests, and other key learning indicators at different stages. This approach helps identify issues beyond academic performance and facilitates targeted interventions. Nan et al. (2021) suggest conducting foundational knowledge diagnostic tests for new university students, with expert teams evaluating their academic preparedness and adjusting course arrangements accordingly. The dimensions of adaptability measured in this study provide valuable reference points for these evaluations, enabling educators to identify learning issues at various stages and adjust teaching methods and plans based on students' adaptability. Optimizing this evaluation approach enhances overall learning adaptability and supports individualized educational support, ultimately promoting students' holistic development and academic success. Implement Targeted Interventions for Students with Learning Adaptability Issues For students struggling with learning adaptability, precise intervention measures are essential. The study found that students with low adaptability often face challenges in adjusting to the learning environment. Universities should establish comprehensive adaptability support programs to help students acclimate to the academic setting, including training on study skills, time management, and detailed introductions to university resources such as libraries and learning centers. These initiatives aim to enhance students' understanding of university expectations. For students struggling with self-adaptation, institutions should guide them to explore their chosen disciplines' development trends and frontiers. Early identification of the root causes of poor adaptability, such as lack of interest in their major, can lead to timely decisions about changing disciplines. If issues stem from learning ability or motivation rather than interest, personalized support, mentoring, and peer learning communities can help improve learning skills and motivation. Deepen Educational Reforms to Enhance Learning Adaptability in STEM Students Post-Reform In response to the general adaptability challenges faced by STEM students under the new college entrance examination system, there is a need to deepen educational reforms by systematically revising curriculum and teaching arrangements. While some researchers advocate a return to traditional subject selection models, emphasizing a strong foundation in physics, chemistry, and biology for better adaptation to university-level STEM programs(Liu & Tang,2024). This study suggests refining the current reform rather than negating its advantages.It is crucial to recognize the benefits of diversified choices and personalized development brought by the new examination reform while addressing the unique demands of STEM majors for foundational knowledge. High school curricula should align with university requirements, especially for foundational subjects in STEM. High school guidance should encourage prospective STEM students to select relevant subjects, and curricula might incorporate interdisciplinary courses to foster comprehensive scientific thinking and cross-disciplinary skills. Universities should tailor their education to students' backgrounds, providing foundational courses in the first year for those with weaker backgrounds to strengthen their knowledge base. Incorporating inquiry-based and project-based learning methods can further bridge the gap between theory and practice, enhancing students’ engagement and practical skills. Finally, a robust feedback mechanism should be established to continuously monitor and assess the impact of the new examination reform on students' learning adaptability. This system would enable timely adjustments to curricula and teaching strategies, ensuring that educational reforms effectively enhance students’ adaptability and academic achievement. In conclusion, while the direction of the new college entrance examination reform is correct, its implementation must be adjusted to address the distinct requirements of various majors. A systematic and forward-looking approach will maximize the positive effects of the reform, enhancing students' academic performance and professional competence in the university context. Conclusion This study reconstructed the measurement dimensions of learning adaptability for STEM students and validated the effectiveness of these dimensions through a scale. Furthermore, the study utilized the Mean Shift clustering method to classify STEM students into high and low adaptability types based on the various dimensions of learning adaptability, summarizing the characteristics of each type. The study found that while there are similarities in subject selection among the different types of students under the new college entrance examination, there are significant differences in learning adaptability, with varying levels of adaptability across dimensions. The study provided a detailed analysis of these differences and offered recommendations to improve the learning adaptability of STEM students, enriching theoretical research and practical applications in learning adaptability, with significant theoretical and practical implications. In the future, this study will continue to expand the sample size and explore the integration of research dimensions with ChatGPT to engage students in real-time interactions through open-ended questions. This approach will allow for timely and in-depth analysis of students' learning adaptability and provide improvement strategies to enhance their learning outcomes. However, the study requires a larger sample size and longer measurement periods to improve the accuracy of the results. In future work, the study will increase the sample size to cover a more comprehensive student population and continuously optimize and refine the research model. Declarations Ethics declarations Ethics approval and consent to participate The Ethics Committee of Tongji University approved this study .The authors affirm that the work described is original research, has not been published previously, and is not under consideration for publication elsewhere. Informed consent was obtained from all individual participants included in the study. Consent for publication We hereby provide consent for the publication of the manuscript detailed above, including any accompanying images or data contained within the manuscript that may directly or indirectly disclose our identities. Competing interests The authors declare that they have no competing interests. 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Investigation on the current situation of college students' learning adaptability. *Theory and Practice of Education, 27*, 62-64. Xie, Y. J., Cao, D. P., Sun, T., & Yang, L. B. (2019). The effects of academic adaptability on academic burnout, immersion in learning, and academic performance among Chinese medical students: A cross-sectional study. *BMC Medical Education, 19*, 1-8. Xie, Y. R., Qiu, Y., Luo, W. J., et al. (2023). Research on the reshaping of college students’ learning adaptation in the intelligent era: Connotation, model, and measurement. *E-Education Research, 44*(03), 13-20. https://doi.org/10.13811/j.cnki.eer.2023.03.002 Yu, Q., Liu, J. L., & Zhao, Y. (2017). The impact mechanism of teacher support on students’ learning motivation and academic achievement. *Journal of Tianjin University (Social Science Edition), 19*(6), 542-547. Yuan, D., Tang, M., & Yu, X. (2023). Research on the impact of the new college entrance examination subject selection policy on the connection between high school and university education: An exploration of the relationships among policy recognition, high school learning engagement, and university major adaptability. *China Higher Education Research, 2023*(01), 43-50. https://doi.org/10.16298/j.cnki.1004-3667.2023.01.08 Zitzow, D. (1984). The college adjustment rating scale. *Journal of College Student Personnel, 25*(2), 160-164. Additional Declarations No competing interests reported. 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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-5344859","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383266145,"identity":"722419b5-15da-4398-a43b-ea124b7a0b94","order_by":0,"name":"Xiangyi Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCcYGhgQQQ/7xwQcfDGzsSNDCkJZsOKMgLZkILXBWjpk0z4dDQBMIAPnZzW0SD2ru2G04cMDY2MbgADMD++GjG/BpMbhzsNkg4diz5A0HGxIf5xjc4WPgSUu7gVeLRGLjgwS2w8kGhxkOG+cYPGNmkOAxw6tFfkZiw4GEf0AtxxjbpC0MDjM2ENLCcANoS2LbYTuDM8xs0gzEaDG4kdhskNh3OEHyBhuzYY9BWjIbIb/Iz0h/Jvnj22F7vhv8Hx/8+GNjx89++Bh+h0FB4oIDUBYbMcpBwF6+gVilo2AUjIJRMOIAAHzGU3+TuvjHAAAAAElFTkSuQmCC","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Xiangyi","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-10-28 07:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5344859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5344859/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70173153,"identity":"e4560686-ae50-4f0e-aa42-8701f3020df4","added_by":"auto","created_at":"2024-11-29 06:59:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279510,"visible":true,"origin":"","legend":"\u003cp\u003eText Mining of Educational Objectives in STEM Majors\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5344859/v1/a182ca1b837c2f5ce090112a.png"},{"id":70173708,"identity":"cfe6d696-fd37-4baf-a0f5-cb1f6a008e6d","added_by":"auto","created_at":"2024-11-29 07:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":928053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5344859/v1/5f47b75f-bcbc-4216-a0f4-3f91d34ce878.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on Learning Adaptability of STEM Students in the New College Entrance Examination Context","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince 2014, the New College Entrance Examination Reform has been gradually implemented across China, with two primary models: the \u0026quot;3\u0026thinsp;+\u0026thinsp;3\u0026quot; and \u0026quot;3\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;1\u0026quot; systems. In the \u0026quot;3\u0026thinsp;+\u0026thinsp;3\u0026quot; model, the first \u0026quot;3\u0026quot; represents the compulsory subjects of Chinese, Mathematics, and English, while the second \u0026quot;3\u0026quot; allows students to choose any three subjects from Physics, Chemistry, Biology, Geography, History, and Politics. Following the initial trials of the \u0026quot;3\u0026thinsp;+\u0026thinsp;3\u0026quot; model, a significant decline in the number of students choosing Physics was observed. Consequently, subsequent reforms adopted the \u0026quot;3\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;1\u0026quot; model, where Physics and History became mandatory subjects, effectively addressing the issue of declining Physics enrollments. However, both models have led to a more interdisciplinary academic foundation among students compared to the traditional division between the sciences and humanities.\u003c/p\u003e\n\u003cp\u003eMoreover, some university programs have relaxed subject requirements to ensure sufficient enrollment, resulting in students entering these programs without a solid foundation in the relevant subjects, leading to academic challenges. This issue is particularly pronounced in STEM majors, where research has shown that students need a strong foundation in Physics, Chemistry, and Biology during high school to adapt well to university-level studies in these fields (Yuan et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, students with weaker foundations in these subjects are more likely to experience learning adaptability issues, leading to major changes or even dropout. In addition, students in science and engineering programs often have diverse academic backgrounds, with varying strengths across subjects, which is not always accommodated by the uniform curricula and teaching methods used by universities, hindering personalized development.\u003c/p\u003e\n\u003cp\u003eUnderstanding the learning adaptability of STEM students under the New College Entrance Examination Reform, identifying the characteristics of different adaptability types, and determining effective measures to enhance their adaptability are crucial for reducing the incidence of course failures and major changes. However, recent research on learning adaptability has primarily focused on developing adaptability scales, with little attention given to the specific challenges faced by different academic majors or the analysis of various adaptability types. To address these issues, this study designs a set of learning adaptability dimensions tailored to STEM students under the New College Entrance Examination Reform, develops a corresponding assessment scale, categorizes different adaptability types, and provides a detailed analysis of the characteristics of each group, offering recommendations for improving learning adaptability in these majors.\u003c/p\u003e"},{"header":"Related Work","content":"\u003ch2\u003eConceptual Definition\u003c/h2\u003e\u003cp\u003eThe concept of learning adaptability has been defined differently by various scholars. Most definitions are based on Piaget's theory of cognitive equilibrium, emphasizing the individual's ability to adapt to new information and situations by promptly adjusting learning strategies and behaviors to align with the external learning environment(1995,as cited in Feng,2006). For example, Larose et al.(1995) consider learning adaptability as a psychological and behavioral process in which learners, aiming to achieve good learning outcomes, continuously adjust themselves in response to changes in the learning environment and needs to maintain a balance between their internal learning state and the external environment. Another perspective defines learning adaptability as the process where individuals strive to adjust themselves to achieve a balance between their internal state and the learning environment, based on environmental and learning needs(López-Angulo et al.,2021). Xie et al.(2023) further define learning adaptability as the process in which learners, through sufficient interaction with the learning environment, continuously adjust their psychological state and learning behaviors to achieve a dynamic balance with the environment. She argues that in the era of intelligent learning, university students' adaptability should reflect both proactive adaptation to and active transformation of the learning environment.\u003c/p\u003e\u003cp\u003eThis study agrees with the definition of learning adaptability as a process of bidirectional dynamic balance. Based on this viewpoint, learning adaptability in STEM students is defined as the process by which these students, given their existing knowledge base and structure, continuously adjust their learning strategies and behaviors through full interaction with the learning environment to achieve a bidirectional dynamic balance, thereby attaining significant academic achievement and professional development. The extent to which STEM students can successfully adapt to learning, cultivate clear thinking patterns, and adopt rigorous learning attitudes will directly affect their academic performance and even career development.\u003c/p\u003e\u003ch3\u003eMeasurement of Learning Adaptability\u003c/h3\u003e\u003cp\u003eCurrent research on learning adaptability is quite extensive, and different researchers have developed various measurement tools. Early studies often treated learning adaptability as one dimension within broader adaptability scales. For instance, Zitow (1984) developed the College Adjustment Rating Scale (CARS), which measures students' adaptability to college life stresses across three dimensions: learning, family, and social environments. Baker and Siryk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) combined learning, social, and emotional adaptability in the Student Adaptation to College Questionnaire (SACQ). However, these studies featured relatively few items on learning adaptability, with coarse-grained divisions. Zhou (1991) focused specifically on learning adaptability, developing the Academic Adaptation Test (AAT), which includes dimensions such as learning attitude, techniques, environment, and physical and mental health. However, this test mainly targets primary and secondary school students, with limited applicability. Feng et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), considering the educational model and learning methods in Chinese universities, developed a learning adaptability scale for college students. This scale, which boasts high reliability and validity with clear dimensional divisions, has been widely used in studies on Chinese college students' learning adaptability. In recent years, some studies have developed new scales considering recent educational phenomena. For example, Nan (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in the context of the new college entrance examination reform, developed a learning adaptability questionnaire for college students, covering five dimensions: curriculum and teaching arrangement, cognitive and learning abilities, learning engagement and professional knowledge, learning environment, and learning strategies. Xie et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recognized the importance of intelligent learning environments in learning adaptability, restructured the learning environment dimension in the adaptability scale, and integrated human-machine collaboration concepts, measuring students' learning adaptability across learning motivation, ability, and environment. Although these self-developed scales are innovative, they often test the structural dimensions proposed by the study's specific subjects, which may lack generalizability.\u003c/p\u003e\u003ch3\u003eLearning Adaptability in STEM Students\u003c/h3\u003e\u003cp\u003eCurrently, there is limited research specifically focused on learning adaptability in STEM students. Some studies analyze the learning adaptability of students within specific STEM fields. For example, Cao et al. (2019)found that medical students with high learning adaptability exhibit low academic burnout and strong learning immersion. Chen et al. (2024)found that AI-based learning environments can help engineering students improve their learning adaptability. Bazelais et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)discovered that grit is not a significant predictor of success in physics. These studies are often highly specialized, with conclusions that are not widely generalizable. Additionally, these studies often fully adopt earlier learning adaptability scales without redesigning or modifying measurement dimensions for specific fields. Other studies treat STEM students' learning adaptability as an outcome within broader learning adaptability research. For instance, Li et al.(2023) found that STEM students show higher adaptability in online learning compared to other majors, while Chen et al.(2020) found that freshmen in STEM fields exhibit slightly lower adaptability than their counterparts in the humanities and social sciences, with a higher rate of major changes. Alipio(2020) found that STEM students with high adaptability outperform those in the humanities and social sciences with low adaptability. However, as STEM students' learning adaptability is only an outcome in these studies, the analysis is often superficial, relying heavily on descriptive data.\u003c/p\u003e\u003cp\u003eIn summary, most researchers tend to classify the dimensions of college students' learning adaptability, generally dividing them into self-adaptation and adaptation to the learning environment. This study agrees with these classification dimensions but finds that further refinement reveals overlapping and intersecting issues, indicating a need for integration. Specifically, research on STEM students' learning adaptability remains preliminary, with a lack of specialized studies and shallow conclusions. This study, focusing on students who participated in the new college entrance examination from 2020 to 2023 and subsequently chose STEM majors, integrates existing measurement dimensions and, based on text mining results from STEM professional training objectives, reconstructs the dimensions of learning adaptability measurement for STEM students. It also analyzes and summarizes the characteristics of students and groups with different types of adaptability, providing recommendations for the development of STEM programs in the context of the new college entrance examination reform.\u003c/p\u003e"},{"header":"Research Design","content":"\u003ch2\u003eConstruction of Structural Dimensions\u003c/h2\u003e\u003cp\u003eThis study categorizes the learning adaptability of STEM students into two main aspects: self-adaptation and adaptation to the learning environment. Self-adaptation refers to the process in which learners proactively adjust their learning strategies and behaviors according to their learning needs during the interaction with the learning environment. The elements of self-adaptation include learning motivation, professional interest, and learning ability. Learning motivation comprises intrinsic and extrinsic motivation. Intrinsic motivation refers to the learner's efforts to learn driven by the pursuit of challenges and curiosity; extrinsic motivation refers to learning driven by factors outside the learning activities, such as gaining recognition from others or obtaining a diploma. Additionally, given the complexity and tediousness of some courses in science and engineering majors, students need to possess a high level of enthusiasm for the field to develop deep learning motivation and continue to excel in the professional domain. Previous research defines students' emotional experiences based on cognitive foundations towards learning and learning situations as learning attitudes. This study posits that the learning attitude of STEM students is primarily reflected in their strong professional interest in their major, thus identifying professional interest as a key dimension in measuring learners' learning adaptability.\u003c/p\u003e\u003cp\u003eLearning ability is a critical indicator for assessing whether students can successfully complete their studies and adapt to future life and work. The educational objectives of STEM majors determine the orientation of students' knowledge, abilities, and quality development and serve as an essential basis for measuring the learning ability of students in these majors. This study creatively proposes using the TF-IDF algorithm to perform text mining on the educational objectives of national characteristic STEM majors from 39 Chinese 985 universities and generate a word cloud (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The text mining mentions that STEM students should possess a solid theoretical foundation (fundamental knowledge, basic skills, professional knowledge), knowledge application and practical ability (problem-solving, practical ability, analysis, technical development), scientific research and knowledge innovation ability (innovation awareness, scientific research, innovation ability, critical thinking ability), knowledge management ability (literature search, information security, information acquisition), and meta-learning ability (lifelong learning, autonomous learning).\u003c/p\u003e\u003cp\u003eLearners' adaptation to the learning environment includes adapting to the teaching methods, arrangements, and school management in the university teaching model, as well as adapting to the intelligent learning environment in the context of the intelligent era. This includes whether learners can adapt to and utilize intelligent learning platforms and tools and accurately retrieve learning resources from vast amounts of online data.\u003c/p\u003e\u003cp\u003eIn summary, under the background of the new college entrance examination reform, the structural dimensions for measuring the learning adaptability of STEM students are summarized as follows.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural Dimensions of Learning Adaptability Measurement for STEM Students\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation Type\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub-dimension\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-adaptation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Motivation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntrinsic Motivation\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtrinsic Motivation\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Ability\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTheoretical Foundation\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge Application and Practice\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScientific Research and Knowledge Innovation\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnowledge Management\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeta-learning\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional Interest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Adaptation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeaching Model\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Environment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eScale Development\u003c/h2\u003e\u003cp\u003eBased on the measurement dimensions of learning adaptability for STEM students constructed above, and referencing research results from Feng(2006), Amabile(1994,as cited in Yu,2017), Xie(2023), and others, this study designed the \"Learning Adaptability Scale for STEM Students.\" The scale is structured around two main dimensions: self-adaptation and environmental adaptation. Self-adaptation includes forming the learning motivation, the learning ability, and the professional interest; environmental adaptation includes adjusting the teaching model and the learning environment. Moreover, in developing items for the learning ability dimension, this study considers the skills required by learners in the intelligent era, such as managing knowledge using intelligent learning tools and solving problems encountered in learning with these tools. The final scale comprises 39 items, including 33 items measured on a five-point Likert scale across different dimensions, and 6 demographic items: gender, grade level, ethnicity, place of household registration, whether the student has experienced the new college entrance examination, and selected subjects under the new examination reform.\u003c/p\u003e\u003ch3\u003eData Sources\u003c/h3\u003e\u003cp\u003eIn the pilot phase of the scale, 83 university students majoring in STEM who had chosen their major after the new college entrance examination reform were recruited from the Shanghai region of China as study participants. In the official measurement phase, the participants were 202 university students majoring in STEM who had chosen their major after the reform, recruited from various universities across China. Among the formal measurement sample, 51.49% were freshmen, 30.69% were sophomores, 10.4% were juniors, and 7.43% were seniors. Additionally, 50.99% of the participants were female. Geographically, there were 37 participants from Northeast China, 61 from North China, 40 from East China, 19 from South China, 22 from Central China, 6 from Northwest China, and 17 from Southwest China.\u003c/p\u003e\u003ch3\u003eReliability and Validity Testing\u003c/h3\u003e\u003cp\u003eThe scale's reliability was tested, resulting in an overall Cronbach’s alpha coefficient of 0.947. The coefficients for the learning motivation were 0.911, for learning ability 0.928, for professional interest 0.892, for learning environment 0.867, and for teaching model 0.918. These results suggest that the learning adaptability scale for STEM students, along with its subscales, has high reliability and good internal consistency.\u003c/p\u003e\u003cp\u003eFor validity testing, confirmatory factor analysis was performed using Amos. Model fit was assessed using maximum likelihood estimation, evaluating the model's fit using indices such as the chi-square to degrees of freedom ratio (CMIN/DF), root mean square residual (RMR), and root mean square error of approximation (RMSEA). Specific data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Comparing these with the fit indices standards, all indicators met the required fit criteria, indicating that the model constructed in this study has good fit and the scale has strong structural validity.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFit Standard\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFit Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFit Judgment\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAbsolute Fit\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMIN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP \u0026gt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\chi\\:}}^{2}/\\text{d}\\text{f}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{d}\\text{f}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncremental Fit\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParsimonious Fit\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePNFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCFI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Research Results","content":"\u003cp\u003e \u003cb\u003eMain Adaptation Types of STEM Students Under the Background of the New College Entrance Examination Reform\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo thoroughly analyze the types of learning adaptability among STEM students under the new college entrance examination reform and to explore the characteristics of different groups, this study employed a clustering algorithm for detailed classification of the subjects. First, to select the most suitable classification algorithm for the subjects, the silhouette coefficient was used to evaluate the effectiveness of various clustering algorithms, ultimately choosing the Mean Shift clustering method, which had the highest silhouette coefficient. Mean Shift is a non-parametric clustering algorithm that achieves clustering by moving data points to find the regions with the highest density. Compared to other algorithms, Mean Shift has the following advantages: 1) It does not require pre-setting the number of clusters, which is a significant advantage over algorithms like K-Means. 2) It is suitable for clusters of any shape, not limited to spherical distributions. 3) It has good robustness and stability, effectively handling noise and outliers.\u003c/p\u003e\u003cp\u003eThe learning adaptability dimension data of students after clustering is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The study sample can be divided into two distinct adaptability groups based on the five dimensions of learning adaptability, with significant differences in scores across these dimensions. These groups can be summarized as low adaptability and high adaptability types. Each adaptability type shows imbalances in scores across the five dimensions. Analyzing the number of students in each type of learning adaptability, the proportion of low adaptability students (63.86%) is higher than that of high adaptability students (36.14%), indicating that the overall adaptability of students is moderate.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudents with Different Types of Learning Adaptability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClustering Situation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCluster Center\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Motivation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearning Ability\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfessional Interest\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLearning Environment\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeaching Model\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Adaptability\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.86%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Adaptability\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.14%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eCharacteristics of High Adaptability Students\u003c/h2\u003e\u003cp\u003eA preliminary analysis of high adaptability students was conducted. In terms of gender, 46.58% of these students are male and 53.42% are female. Geographically, 38.16% are from the eastern region, 35.53% from the central region, 15.79% from the western region, and 10.53% from the northeastern region. Regarding grade level, 47.37% are freshmen, 28.95% are sophomores, 11.84% are juniors, and 11.84% are seniors. Analyzing the elective subjects under the new college entrance examination for this group reveals that 10.53% of the students selected traditional science subjects: physics, chemistry, and biology. Among the students who chose STEM majors, 55.26% selected political science, and 43.42% selected physics in the college entrance examination. The most common subject combination (11.84%) among these students was physics, political science, and biology.\u003c/p\u003e\u003cp\u003eThe dimension with the lowest score was professional interest (81.51). Most students indicated that \"I don't feel that my major suits me, and it was not my voluntary choice.\" The highest scoring dimension was the teaching model (88.29), which includes items reflecting the students' adaptation to university teachers' teaching styles, effective use of their own time in university, and less nostalgia for high school life. In the learning ability dimension, the scores for each item were close to the maximum, with relatively lower scores on the item \"adapting to and using smart learning tools to solve problems.\"\u003c/p\u003e\u003ch2\u003eCharacteristics of Low Adaptability Students\u003c/h2\u003e\u003cp\u003eA preliminary analysis of low adaptability students was conducted. In terms of gender, 50.39% of low adaptability students are male and 49.61% are female. Geographically, 45.24% of these students are from the eastern region, 16.67% from the central region, 15.08% from the western region, and 23.02% from the northeastern region. Regarding grade level, 53.97% are freshmen, 31.75% are sophomores, 9.52% are juniors, and 4.76% are seniors. Analyzing the elective subjects under the new college entrance examination for this group reveals that only 8.73% of the students selected traditional science subjects. Among these students, 50.7% selected political science, and only 33.33% selected physics. The most common subject combination (15.08%) was political science, chemistry, and biology, which was the most frequently selected combination in this group.\u003c/p\u003e\u003cp\u003eThe dimension with the lowest score was learning motivation (56.56). The external learning motivation scored higher than the internal motivation, reflecting that the primary motivators for these learners were external factors such as achieving higher scores and gaining others' approval. The dimension with the highest score was the learning ability (61.96), with the highest-scoring item being \"using smart learning tools to organize knowledge,\" and the lowest being \"adapting to and using smart learning tools to solve problems.\"\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are similarities in subject selection among the two types of students under the new college entrance examination reform, but there is a significant gap in overall learning adaptability scores. Specifically, in different dimensions of the learning adaptability, both types of students exhibit imbalances in their scores, facing different issues in the learning adaptability: low adaptability students lack internal learning motivation and struggle to adapt to and use smart learning tools to solve problems, while high adaptability students show strong adaptation to university teaching models but display moderate professional interests.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSimilarities in Subject Selection Across Different Types of Students\u003c/h2\u003e \u003cp\u003eRegarding subject selection under the new college entrance examination reform, few students majoring in STEM simultaneously selected traditional science subjects: physics, chemistry, and biology (9.41%). However, more than half of the sample students selected political science (52.48%). For high adaptability students, the most common subject combination was physics, political science, and biology; for low adaptability students, it was political science, chemistry, and biology. Both groups frequently selected political science and biology, which may be related to the utilitarian tendencies in subject selection. This method of subject selection can easily lead to an incomplete knowledge structure for STEM students, making it more challenging for them to adapt to the complex and interdisciplinary courses in their major fields of study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSignificant Differences in Overall Scores Between Different Types of Students, With Imbalances Across Dimensions\u003c/h2\u003e \u003cp\u003eThere is a substantial gap in scores across all dimensions between high adaptability and low adaptability students. It is evident that there are significant differences in learning adaptability across various dimensions between high and low adaptability students. Moreover, both types of students show imbalances in their scores across dimensions, with noticeable score gaps between the five dimensions. This result differs from the research of Nan(2021), who suggests the existence of a balanced type of learning adaptability with smaller score gaps across dimensions. This discrepancy may be due to differences in the study subjects.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLow Adaptability Students Lack Internal Learning Motivation and Struggle to Adapt to and Use Smart Learning Tools to Solve Problems\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLow adaptability STEM students mainly exhibit low internal learning motivation. This suggests that for STEM students, a lack of sufficient learning motivation may lead to reduced learning interests and poor learning outcomes. Internal motivation, as opposed to external motivation, is more likely to enhance the learning adaptability of STEM students. This finding aligns with the research results of Yu et al.(2017), who found that the internal learning motivation has a direct positive impact on academic achievement.\u003c/p\u003e \u003cp\u003eAt the same time, these students perform poorly on items related to adapting to and using smart learning tools to solve problems. This may be related to the heavy use of paper-based learning resources and the potential restrictions on electronic device usage by parents, resulting in limited exposure to smart learning tools during high school. Upon entering university, students need to learn to use smart learning tools such as smartphones, computers, and tablets for studying, and to find the necessary resources from vast amounts of electronic learning materials. If they cannot adapt to or use these smart learning tools, it will be even more difficult to solve real-world problems using them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHigh Adaptability Students Excel in Adapting to University Teaching Model but Show Moderate Professional Interest\u003c/h2\u003e \u003cp\u003eHigh adaptability STEM students perform well in adapting to university the teaching model, meaning they can better adapt to university teachers' lecture styles, management methods, and efficiently manage their abundant free time in university, while rarely indulging in nostalgia for high school life. The teaching model is an indispensable factor in learning adaptability for STEM students. Research indicates that there are significant differences between university and high school science subjects in terms of teaching concepts, methods, and content(Sabirova et al.,2020). Therefore, better adaptation to university the teaching model can effectively enhance the learning adaptability of STEM students.\u003c/p\u003e \u003cp\u003eHowever, this group of students scored the lowest in the professional interest among the five aspects of learning adaptability. This suggests that while these students can adapt well to university learning, they lack clear goals, experiencing a conflict between interests and employment, and between specialties and goals. This finding is consistent with the conclusions of Du et al.(2016), who believe that this phenomenon arises because \"students' passive and institutionalized learning life before high school leads to a lack of clear learning plans and autonomous learning abilities.\"\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigher Environmental Adaptation Scores in High Learning Adaptability Students, Higher Self-Adaptation Scores in Low Learning Adaptability Students\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough STEM students with high learning adaptability tend to score higher across all dimensions compared to those with low learning adaptability, their performance in environmental adaptation is particularly noteworthy. This indicates that students with higher adaptability are better suited to the teaching mode and learning environment of universities. This finding is consistent with the conclusions of Wang et al.(2009), who emphasized that learning adaptability is significantly influenced by environmental factors. In contrast, students with lower learning adaptability tend to score higher in self-adaptation than in environmental adaptation, suggesting that these students exhibit relatively lower flexibility in adjusting to the objective environment. Chen et al.(2020) found that adapting to the learning environment can effectively enhance learning motivation, which is an aspect of self-adaptation. Therefore, changes in environmental adaptation will also impact self-adaptation, further underscoring the critical role of environmental factors in shaping university students' learning adaptability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eBased on the research findings, this study proposes the following recommendations to enhance the learning adaptability of STEM students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIntegrate Learning Adaptability Assessment into Diagnostic and Formative Evaluations\u003c/h2\u003e \u003cp\u003eUniversities should enhance the diagnostic and formative evaluation systems for STEM students by incorporating learning adaptability assessments as a critical component. Given the diverse knowledge structures and subject strengths of students under the new college entrance examination system, integrating adaptability testing into diagnostic and formative evaluations allows institutions and instructors to quickly gauge students\u0026rsquo; learning abilities, interests, and other key learning indicators at different stages. This approach helps identify issues beyond academic performance and facilitates targeted interventions.\u003c/p\u003e \u003cp\u003eNan et al. (2021) suggest conducting foundational knowledge diagnostic tests for new university students, with expert teams evaluating their academic preparedness and adjusting course arrangements accordingly. The dimensions of adaptability measured in this study provide valuable reference points for these evaluations, enabling educators to identify learning issues at various stages and adjust teaching methods and plans based on students' adaptability. Optimizing this evaluation approach enhances overall learning adaptability and supports individualized educational support, ultimately promoting students' holistic development and academic success.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplement Targeted Interventions for Students with Learning Adaptability Issues\u003c/h2\u003e \u003cp\u003eFor students struggling with learning adaptability, precise intervention measures are essential. The study found that students with low adaptability often face challenges in adjusting to the learning environment. Universities should establish comprehensive adaptability support programs to help students acclimate to the academic setting, including training on study skills, time management, and detailed introductions to university resources such as libraries and learning centers. These initiatives aim to enhance students' understanding of university expectations.\u003c/p\u003e \u003cp\u003eFor students struggling with self-adaptation, institutions should guide them to explore their chosen disciplines' development trends and frontiers. Early identification of the root causes of poor adaptability, such as lack of interest in their major, can lead to timely decisions about changing disciplines. If issues stem from learning ability or motivation rather than interest, personalized support, mentoring, and peer learning communities can help improve learning skills and motivation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDeepen Educational Reforms to Enhance Learning Adaptability in STEM Students Post-Reform\u003c/h2\u003e \u003cp\u003eIn response to the general adaptability challenges faced by STEM students under the new college entrance examination system, there is a need to deepen educational reforms by systematically revising curriculum and teaching arrangements. While some researchers advocate a return to traditional subject selection models, emphasizing a strong foundation in physics, chemistry, and biology for better adaptation to university-level STEM programs(Liu \u0026amp; Tang,2024).\u003c/p\u003e \u003cp\u003eThis study suggests refining the current reform rather than negating its advantages.It is crucial to recognize the benefits of diversified choices and personalized development brought by the new examination reform while addressing the unique demands of STEM majors for foundational knowledge. High school curricula should align with university requirements, especially for foundational subjects in STEM. High school guidance should encourage prospective STEM students to select relevant subjects, and curricula might incorporate interdisciplinary courses to foster comprehensive scientific thinking and cross-disciplinary skills.\u003c/p\u003e \u003cp\u003eUniversities should tailor their education to students' backgrounds, providing foundational courses in the first year for those with weaker backgrounds to strengthen their knowledge base. Incorporating inquiry-based and project-based learning methods can further bridge the gap between theory and practice, enhancing students\u0026rsquo; engagement and practical skills.\u003c/p\u003e \u003cp\u003eFinally, a robust feedback mechanism should be established to continuously monitor and assess the impact of the new examination reform on students' learning adaptability. This system would enable timely adjustments to curricula and teaching strategies, ensuring that educational reforms effectively enhance students\u0026rsquo; adaptability and academic achievement.\u003c/p\u003e \u003cp\u003eIn conclusion, while the direction of the new college entrance examination reform is correct, its implementation must be adjusted to address the distinct requirements of various majors. A systematic and forward-looking approach will maximize the positive effects of the reform, enhancing students' academic performance and professional competence in the university context.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reconstructed the measurement dimensions of learning adaptability for STEM students and validated the effectiveness of these dimensions through a scale. Furthermore, the study utilized the Mean Shift clustering method to classify STEM students into high and low adaptability types based on the various dimensions of learning adaptability, summarizing the characteristics of each type. The study found that while there are similarities in subject selection among the different types of students under the new college entrance examination, there are significant differences in learning adaptability, with varying levels of adaptability across dimensions. The study provided a detailed analysis of these differences and offered recommendations to improve the learning adaptability of STEM students, enriching theoretical research and practical applications in learning adaptability, with significant theoretical and practical implications.\u003c/p\u003e \u003cp\u003eIn the future, this study will continue to expand the sample size and explore the integration of research dimensions with ChatGPT to engage students in real-time interactions through open-ended questions. This approach will allow for timely and in-depth analysis of students' learning adaptability and provide improvement strategies to enhance their learning outcomes. However, the study requires a larger sample size and longer measurement periods to improve the accuracy of the results. In future work, the study will increase the sample size to cover a more comprehensive student population and continuously optimize and refine the research model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Tongji University approved this study .The authors affirm that the work described is original research, has not been published previously, and is not under consideration for publication elsewhere. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hereby provide consent for the publication of the manuscript detailed above, including any accompanying images or data contained within the manuscript that may directly or indirectly disclose our identities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlipio, M. (2020). 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Research on the impact of the new college entrance examination subject selection policy on the connection between high school and university education: An exploration of the relationships among policy recognition, high school learning engagement, and university major adaptability. *China Higher Education Research, 2023*(01), 43-50. https://doi.org/10.16298/j.cnki.1004-3667.2023.01.08\u003c/li\u003e\n \u003cli\u003eZitzow, D. (1984). The college adjustment rating scale. *Journal of College Student Personnel, 25*(2), 160-164.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"learning adaptability, competences in STEM, new college entrance examination, STEM majors","lastPublishedDoi":"10.21203/rs.3.rs-5344859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5344859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOver ten years of the New College Entrance Examination Reform, the flexible subject selection model has posed challenges to STEM (Science, Technology, Engineering, and Math) majors education in Chinese universities. The relaxation of subject requirements has led to diverse knowledge backgrounds among students, resulting in some lacking a solid foundation in these fields, which contributes to high failure rates and frequent major changes. This study reconstructs the dimensions of learning adaptability for STEM students, analyzing different adaptability types using data from 39 \"985 Project\" universities in China. Findings indicate two main types: low adaptability, characterized by low motivation and difficulty using intelligent tools, and high adaptability, which correlates with better adjustment but moderate interest in the major. 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