Academic Adaptation and Performance Among International Students in China: The Mediating Role of Student Engagement

Abstract

Academic adaptation is widely recognized as a critical challenge for international students, with direct implications for their academic success and performance. While existing research has established a positive correlation between academic adaptation and performance, it has not adequately explored this relationship in the context of international students in China. Moreover, the potential mediating role of student engagement warrants further empirical investigation. To address this gap, this study employs a cross-sectional survey of 427 international students in China. The findings confirm a significant positive relationship between academic adaptation and academic performance. Moreover, student engagement was identified as a significant, albeit limited, mediator in this relationship. This result indicates that the effect of student engagement on academic performance may be more immediate, whereas its effect on academic adaptation may be prior. By elucidating this complex mediating pathway, this study advances our understanding of the processes linking adaptation to performance. It offers practical insights for educators seeking to enhance the international student experience.

1. Introduction

The internationalization of higher education refers to the integration of international, intercultural, and global perspectives across an institution’s educational goals, functions, and offerings [1], which aims to enhance educational quality and contribute to society. The internationalization of higher education has some significant impacts on international students’ academic performance, such as improved language skills [2], enhanced critical thinking skills [3], improved employability [4], and increased global competencies [5]. However, internationalization also presents challenges. Difficulties in academic adaptation and social integration can adversely affect international students’ academic performance [6]. Both academic adaptation and academic performance are significant in the educational domain. Moreover, student engagement is essential in higher education, affecting academic performance, retention rates, and overall student success, and it is believed that enhancing student engagement is essential for academic success and performance [7]. According to Schaufeli [8], student engagement can be a mediating variable to explore the relationship with other variables.

In recent years, China has emerged as a major global destination for international students. Since 2017, it has consistently ranked as the leading study-abroad destination in Asia and the third globally, trailing only the United States and the United Kingdom [9]. Despite this trend, research on the academic performance of international students in China remains limited, especially when compared to the focus on domestic student performance. While some studies have addressed this [10,11], with only a few studies having disclosed the academic performance of international students at certain universities in China, with qualitative data outweighing quantitative data [12], the existing work underscores a significant need for further investigation into the academic performance of the international student population.

Within the educational domain, both academic adaptation and academic performance are recognized as crucial. As Crede and Niehorster [13] state, academic adaptation is a vital determinant of academic performance. A substantial body of scholarship links cross-cultural adaptation directly to academic adaptation for international students, positing it as central to their overall adaptation [14]. This argument is exemplified by Zhu [15], who defines cross-cultural adaptation as encompassing psychological, social, and academic dimensions. Conversely, other researchers such as Dunn [16] argued that the primary challenges stem from the students’ academic roles, with their foreign status acting as a secondary influential factor.

Student engagement, widely regarded as a cornerstone of educational quality [17], serves as a key factor for enhancing learning experiences. Consistent with established models [18], effective academic adaptation promotes student engagement, which in turn enhances learning processes and outcomes. However, compared to their domestic peers, the investigation of international students’ engagement has been relatively overlooked. Research consistently indicates that international students often lack the requisite foundational knowledge and adaptive capabilities necessary for effective academic engagement, resulting in poor academic performance among international students in China [19,20,21].

This study aims to investigate these three factors about international students who are studying in Chinese universities, how the international students’ adaptation and student engagement affect their academic performance, and whether student engagement has a mediating impact on their academic performance by addressing the research questions listed below:

• Is there any significant influence of academic adaptation on academic performance among international students in China?
• Is there any significant influence of academic adaptation on student engagement among international students in China?
• Is there any significant influence of student engagement on academic performance among international students in China?
• Is there any significant mediating influence of student engagement on the relationship between academic adaptation and academic performance among international students in China?

2. Literature Review

2.1. Academic Performance

Academic performance is a crucial metric for assessing educational quality [22] and a significant predictor of student success in higher education [23]. The most prevalent indicators include grade point average (GPA) and course grades, encompassing cumulative GPA, major-specific GPA, individual course grades [24], and class ranking [25]. Beyond these, standardized test scores, such as entrance examinations and language proficiency assessments, provide uniform benchmarks for evaluating academic capabilities [26]. Research accomplishments, including publications, conference presentations, and thesis work, further demonstrate a student’s research competence and potential for academic innovation [27]. It cannot be neglected that learning achievement and learning gain are equal to academic performance. Additionally, academic progress metrics like credit completion rate and time to degree offer dynamic perspectives on student progress. As higher education evolves, the framework for assessing academic performance has expanded beyond traditional metrics. This study employs Zhu & Wang’s [28] comprehensive definition of academic performance, which draws from Xing’s [29] research. They conceptualize academic success as the quantifiable behavioral outcomes or improvements that occur during the learning process, assessable across three domains: knowledge acquisition, skills development, and personal growth.

2.2. Academic Adaptation

The conceptual foundation of academic adaptation originates from Tinto’s [30] research on student dropout, which posits that students must achieve academic and social integration to persist in their studies. Pioneering the operationalization of this concept, Baker and Siryk [31] transformed it into measurable, multidimensional indicators encompassing academic adjustment, social integration, personal-emotional adaptation, and institutional attachment, thereby expanding the research into psychosocial adaptation. More recently, Deng [32] defined academic adaptation as the process of adjusting one’s perception and behavior to fit the learning environment, overcome difficulties, and achieve academic success. One strand of scholarship equates cross-cultural adaptation with academic adaptation, considering it a significant component of the students’ overall adjustment [19,33]. Conversely, other researchers assert that the principal challenges arise from their identity as students, with their status as foreigners serving as a secondary influencing factor [34].

2.3. Student Engagement

Student engagement is widely conceptualized as a multifaceted construct encompassing behavioral, cognitive, and emotional dimensions [35]. Grounded in the work of scholars and frameworks such as Schaufeli et al. [8] and the NSSE [36], these dimensions offer a comprehensive lens for understanding how students interact with their learning environments. According to Fredricks et al. [7], it represents a state characterized by sustained energy, mental resilience, a positive emotional tone, and a deep, valued immersion in learning. Behavioral engagement, evidenced by regular attendance, active participation, and timely task completion, is consistently linked to positive academic outcomes like higher grades and lower dropout rates. Cognitive engagement involves the investment of intentional effort in deep learning, employing higher-order thinking and metacognitive strategies to overcome challenges and achieve a profound understanding. Educational practices that foster this dimension often emphasize active learning and problem-solving. Emotional engagement refers to students’ affective responses, including their sense of belonging, interest, and enthusiasm. Positive emotions enhance motivation and long-term commitment, whereas negative emotions like anxiety can be detrimental. Cultivating a supportive and inclusive school climate is crucial for nurturing this dimension.

2.4. Transformative Theory

Mezirow [37] introduced the concept of perspective transformation, which he subsequently developed into the theory of transformative learning within adult education. He asserted that adults, influenced by their cumulative experiences, frequently establish enduring cognitive, evaluative, and behavioral patterns. While these established frameworks constitute personal knowledge assets, they can also impede the acquisition of new information, rendering adult learning fundamentally distinct from that of children in formal settings [38]. Mezirow emphasized that adults must engage in transformative learning by opening themselves to new perspectives and altering these entrenched patterns. Only through such a process can they effectively enhance their capacity for adaptation. Transformative learning theory, a significant topic in adult education, provides a crucial framework for understanding the academic adaptation of international students. The theory has been progressively applied and broadened in this context. For instance, KuMi-Yeboah [39] observed that African international graduate students in the United States acclimated to their new academic environment by critically reflecting on their original academic concepts and learning methods, engaging in academic activities, and interacting with faculty and peers.

2.5. Hypotheses and Conceptual Model

2.5.1. The Relationship Between Academic Adaptation and Academic Performance

There is a strong correlation between academic adaptation and academic performance. Converging evidence from previous studies identifies academic adaptation as a primary predictor of academic performance [40]. Students who successfully acclimate to their educational environment by adopting effective study habits and coping strategies typically attain superior results [41]. These adaptation challenges often stem from systemic disparities between home and host universities, exacerbated by language barriers, differing pedagogical approaches, and sociocultural factors [42,43]. Conversely, difficulties in adapting manifest in inadequate academic performance. These shortcomings can be evidenced by challenges such as an inability to manage academic workloads, low tolerance for pressure, and fractured interpersonal relationships with supervisors, as reported by international students and their advisors.

H1. 

There is a significant influence of international students’ academic adaptation on academic performance in China.

2.5.2. The Relationship Between Academic Adaptation and Student Engagement

A well-established body of research posits that successful academic adaptation is a critical antecedent to elevated student engagement. This relationship is empirically supported: academic adaptation significantly predicts student engagement [44,45], with increased adaptability being substantially correlated with active involvement in academics [46]. Specifically, successful adapters exhibit positive engagement behaviors, such as active participation and strong instructor relationships [47]. Conversely, inadequate adaptation often results in negative engagement, marked by apathy and a detrimental disengagement from academic life.

H2. 

There is a significant influence of international students’ academic adaptation on student engagement in China.

2.5.3. The Relationship Between Student Engagement and Academic Performance

Previous research has shown that student engagement is related to student retention and academic performance [36,48]. According to previous empirical consensus, student engagement is widely recognized as a critical factor influencing academic performance in higher education. A substantial body of research establishes that higher levels of engagement are positively correlated with improved academic performance and lower dropout rates [49]. Moreover, this relationship is further reinforced by findings that engagement is a robust predictor of student success [50]. Furthermore, Boulton et al. [51] acknowledge student engagement as a significant factor influencing student success and performance in higher education. Evidence from the Chinese context confirms that, demonstrating that sustained student engagement is significantly associated with greater educational gains, higher performance rankings, and continuous academic improvement [52,53].

H3. 

There is a significant influence of student engagement on academic performance among international students in China.

2.5.4. Mediating Role of Student Engagement

Research on student engagement originated with Schaufeli’s [8] conceptual distinction between learning engagement and work engagement. Galve-González [54] demonstrated that engagement mediates the effects of social integration and self-regulation on academic satisfaction and dropout intentions. Moreover, Godsk and Møller [55] emphasize the mediating function of learning engagement in converting technology-enhanced learning into enhanced outcomes. Chinese scholarship provides consistent validation. Student engagement has been operationalized as a mediator between gratitude and achievement [56], between self-efficacy and achievement [57], as well as between environment and learning gain [58]. Such evidence confirms the mediating pathway of student engagement to boost achievement and retention. In this study, student engagement explored its appropriateness as a mediating construct for clarifying how academic adaptation translates into performance within international student populations.

H4. 

There is a significant mediating influence of student engagement on the relationship between international students’ academic adaptation and academic performance in China.

2.5.5. Conceptual Framework

A conceptual framework represents the core ideas and their connections that structure a research inquiry [59]. Figure 1 shows a diagrammatic illustration of the conceptual framework, which is based on the literature review. The conceptual framework of this research developed from “Transformative Theory” “Model of Academic adaptation [32]” “Model of Student engagement [7]” and “Model of Academic performance [29]”, in which the variables involved are international students’ academic adaptation as the independent variable, academic performance as the dependent variable, and student engagement as the mediating variable.

3. Data and Methodology

3.1. Measuring Instruments

This study utilized three self-report instruments. The academic adaptation of international students was measured using a scale adapted from Deng’s [32] academic adaptation scale for international students in China. The instrument comprises 34 items across six dimensions: (a) self-perception (6 items, e.g., I am satisfied with my current learning status.), (b) learning motivation (6 items, e.g., I want to learn Chinese to stay and work in China in the future.), (c) learning attitude (5 items, e.g., I actively participate in extracurricular academic activities.), (d) academic ability (5 items, e.g., I can use library’s resources to search for journals or literature.), (e) learning environment (6 items, e.g., The university classrooms, study rooms, laboratories, and other teaching facilities can meet my learning needs.), and (f) teaching method (6 items, e.g., I can keep up with the teaching progress of my teachers.), reported the overall Cronbach’s alpha of 0.946, and Cronbach’s alpha for each dimension ranged from 0.828 to 0.923, indicating good internal consistency reliability.

Student engagement was assessed with a scale adapted from Wang [50], which consists of 12 items, rated on 3 dimensions: (a) behavioral engagement (4 items, e.g., I always review what was taught right after class.), (b) cognitive engagement (5 items, e.g., I always question what I have learned.), and (c) emotional engagement (3 items, e.g., I am curious about what I learn.). The overall Cronbach’s alpha was 0.892, with individual dimension coefficients of 0.868, 0.854, and 0.805, respectively, indicating strong reliability in the measurement.

Academic performance was measured using a scale based on Zhu and Wang [28], which was designed to assess three aspects of academic performance, including (a) knowledge (3 items, e.g., I extend my knowledge to other subject areas.), (b) skill (3 items, e.g., I always think critically and analytically.), and (c) personal development (3 items, e.g., I clarify my future development and plans.). All instruments employ five-point Likert scales, from 1 (Strongly Disagree) to 5 (Strongly Agree). The measure demonstrated strong reliability, with a Cronbach’s alpha of 0.89 for the overall scale, and 0.73, 0.83, and 0.80 for its three dimensions, respectively.

3.2. Procedure and Respondent

The study utilized purposive sampling, targeting international undergraduates in China who were sophomores and over the age of 18. This focus was informed by the theoretical understanding that the second year constitutes a critical post-transition stage for deepened academic integration. As such, this cohort was selected for its potential to yield considered insights into the interplay between academic adaptation, engagement, and performance. The data collection procedure was anonymous, and the volunteers could not answer the questions before clicking “agree” with the participant information and informed consent form.

The data were collected through an online survey administered via WeChat. A structured questionnaire was designed and disseminated using “Wenjuanxing”, which is seamlessly integrated with WeChat. To target the specific cohort of sophomore international students, the survey link was distributed through relevant student community networks. 500 questionnaires were distributed, and 427 were validly collected.

As detailed in

Table 1. The profile of respondents.

Description	Frequency (n = 427)	Percentage (%)
Gender
Male	225	52.69%
Female	202	47.31%
Age
19–20	147	34.43%
21–22	175	40.98%
23–24	82	19.20%
25–26	23	5.39%
Nationality
India	47	11.01%
South Korea	47	11.01%
Pakistan	21	4.92%
Thailand	50	11.71%
Bangladesh	15	3.51%
Tanzania	10	2.34%
Ghana	12	2.81%
Indonesia	41	9.6%
Nigeria	16	3.75%
Russia	29	6.79%
Malaysia	26	6.09%
Kenya	10	2.34%
Vietnam	46	10.77%
Others	57	13.35%
Major Category
Engineering	117	27.40%
Western Medicine	32	7.49%
Management	62	14.52%
Economics	54	12.65%
Chinese language	77	18.03%
Chinese Medicine	25	5.85%
Science	42	9.84%
Others	18	4.22%
Chinese proficiency
HSK3	80	18.74%
HSK4	267	62.53%
HSK5	78	18.27%
HSK6	2	0.47%
Medium of Instruction
Chinese	272	63.7%
English	105	24.56%
Bilingual	50	11.7%

, the study sample comprised 427 participants, with a majority being male (52.69%, n = 225) compared to female (47.31%, n = 202). In terms of nationality, the nationalities represented were Thai (11.71%), Korean and Indian (11.01% each), Vietnamese (10.77%), with the remaining groups each below 7%. Regarding academic disciplines, Engineering was the most pursued field (27.40%), ahead of Chinese Language Studies (18.03%), Management (14.52%), and Engineering & Technology (11.71%). In terms of Chinese proficiency, HSK 4 was the most common level (62.53%), significantly surpassing HSK 3 (18.74%), HSK 5 (18.27%), and HSK 6 (0.47%). Finally, regarding the medium of instruction, courses were primarily conducted in Chinese (63.7%), with English (24.56%) and a bilingual approach (11.7%) being less common.

4. Results

Due to its many advantages over alternative approaches, the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was adopted in this research, which facilitates a comprehensive evaluation of both the measurement model (assessing reliability and validity) and the structural model (testing the research hypotheses). Data analysis was conducted using SPSS version 29 and Smart-PLS 4.1.

4.1. Measurement Model

This research proposes a Reflective-Formative Hierarchical Component Model (HCM), since three multidimensional scales were used. In this model, higher-order latent variables are formatively composed of their lower-order dimensions, each of which is measured reflectively by its corresponding observed indicators. The evaluation of the measurement model begins with the first-order model depicted in Figure 2, which involves evaluating three essential criteria: indicator reliability, internal consistency, and convergent validity.

The results of the first-order measurement model demonstrated strong reliability and validity, as shown in

Table 2. The measurement model assessment (first order).

Construct	Item	Outer Loading	Cronbach’s Alpha	CR	AVE
Academic Adaptation			0.984	0.985	0.657
Self-perception	SB01	0.878	0.926	0.927	0.732
	SB02	0.852
	SB03	0.902
	SB04	0.814
	SB05	0.825
	SB06	0.826
Learning motivation	SB07	0.726	0.894	0.903	0.655
	SB08	0.860
	SB09	0.698
	SB10	0.842
	SB11	0.858
	SB12	0.817
Learning attitude	SB13	0.808	0.898	0.900	0.712
	SB14	0.804
	SB15	0.868
	SB16	0.889
	SB17	0.825
Academic ability	SB18	0.870	0.944	0.945	0.816
	SB19	0.896
	SB20	0.938
	SB21	0.901
	SB22	0.893
Learning environment	SB23	0.881	0.958	0.958	0.825
	SB24	0.931
	SB25	0.918
	SB26	0.922
	SB27	0.913
	SB28	0.865
Teaching method	SB29	0.922	0.954	0.957	0.815
	SB30	0.934
	SB31	0.935
	SB32	0.849
	SB33	0.826
	SB34	0.923
Student Engagement			0.967	0.968	0.735
Behavioral engagement	SC01	0.904	0.912	0.915	0.742
	SC02	0.813
	SC03	0.889
	SC04	0.807
	SC05	0.860
Cognitive engagement	SC06	0.906	0.944	0.945	0.900
	SC07	0.916
	SC08	0.922
	SC09	0.915
Emotional engagement	SC10	0.942	0.938	0.938	0.842
	SC11	0.959
	SC12	0.938
Academic Performance			0.958	0.959	0.687
Knowledge	SD01	0.884	0.908	0.909	0.784
	SD02	0.891
	SD03	0.892
	SD04	0.853
Skills	SD05	0.861	0.927	0.927	0.820
	SD06	0.912
	SD07	0.922
	SD08	0.915
Personal development	SD09	0.908	0.896	0.899	0.763
	SD10	0.892
	SD11	0.866
	SD12	0.809

. According to Hair et al. [60], the internal consistency of all constructs was confirmed, with both Cronbach’s alpha and composite reliability values surpassing the 0.7 criterion. Furthermore, convergent validity was established, as the Average Variance Extracted (AVE) for every construct exceeded 0.5 and all outer loadings were high and significant.

After evaluating the first-order measurement model, the second-order measurement model was evaluated for reliability, internal consistency, convergent validity, and discriminant validity. The structural configuration is illustrated in Figure 3, and the corresponding results are presented in

Table 3. The measurement model assessment (second order).

Construct	Item	Item Parcel	Outer Loading	Cronbach’s Alpha	CR	AVE
Academic Adaptation	SB01-06	aa1	0.927	0.969	0.969	0.865
	SB07-12	aa2	0.906
	SB13-17	aa3	0.925
	SB18-22	aa 4	0.933
	SB23-28	aa5	0.939
	SB29-34	aa6	0.950
Student Engagement	SC01-05	se1	0.944	0.947	0.947	0.904
	SC06-09	se2	0.957
	SC10-12	se3	0.952
Academic Performance	SD01-04	ap1	0.924	0.927	0.928	0.873
	SD05-08	ap2	0.930
	SV09-12	ap3	0.949

and

Table 4. Discriminant validity.

	Academic Adaptation	Academic Performance	Student Engagement
Academic Ability	0.933	0.816	0.885
Behavioral Engagement	0.875	0.805	0.944
Cognitive Engagement	0.845	0.800	0.957
Emotional Engagement	0.873	0.822	0.952
Knowledge	0.835	0.924	0.796
Learning Attitude	0.925	0.834	0.867
Learning Environment	0.939	0.835	0.837
Learning Motivation	0.906	0.856	0.793
Personal development	0.858	0.949	0.809
Self-perception	0.927	0.869	0.807
Skills	0.843	0.930	0.781
Teaching Method	0.950	0.839	0.883
$\sqrt{\mathit{A}\mathit{V}\mathit{E}}$	Academic Adaptation	Academic Performance	Student Engagement
Academic Adaptation	0.930
Academic Performance	0.904	0.935
Student Engagement	0.909	0.851	0.951

.

According to Hair et al. [60], the second-order measurement model was assessed for reliability, internal consistency, and convergent validity. As shown in Table 3, all outer loadings were high, ranging from 0.906 to 0.957 across the constructs of academic adaptation, student engagement, and academic achievement, indicating strong indicator reliability. Internal consistency was confirmed as both Cronbach’s Alpha and Composite Reliability (CR) values for all constructs exceeded the 0.70 threshold, with results ranging from 0.927 to 0.969. Furthermore, convergent validity was established, as the Average Variance Extracted (AVE) for each construct was well above the recommended 0.50 level, with values of 0.865 for academic adaptation, 0.904 for student engagement, and 0.873 for academic performance.

Simultaneously, discriminant validity was evaluated through cross-loading analysis and the Fornell-Larcker criterion [60]. The results in Table 4 show that all indicators had the highest loadings on their assigned constructs [61], and the square root of each construct’s AVE was greater than its inter-construct correlations, thereby supporting discriminant validity. Thereby, discriminant validity was supported.

4.2. Structural Model

In this part, the variance inflation factor (VIF) values for the structural model were primarily examined to assess multicollinearity, with the results presented in

Table 5. Collinearity issue.

	Academic Adaptation	Academic Performance	Student Engagement
Academic Adaptation		5.776	1.000
Academic Performance
Student Engagement		5.776

. The VIF for Student Engagement was below the threshold of 3, while those for Academic Adaptability and Academic Performance, though exceeding the more conservative threshold of 5, remained within the acceptable limit of 10 [62]. Therefore, multicollinearity is not a concern in this study.

According to Chin [63], the predictive accuracy of the structural model was evaluated using the coefficient of determination (R²). As shown in

Table 6. Coefficient of Determination (R²).

	R-Square	R-Square Adjusted	Level
Academic Performance	0.823	0.822	High
Student Engagement	0.827	0.826	High

, the model demonstrates exceptionally high explanatory power, the minimal difference between the R² and adjusted R² values suggests a robust and parsimonious model without overfitting. The elevated R² value observed in this study can be attributed to the model’s well-supported theoretical pathway, in which student engagement mediates the relationship between academic adaptation and academic performance. Specifically, the academic adaptation component is adapted from Deng’s model [32] for international students, which originated from the first Chinese university student academic adaptation model by Feng et al. [64]. The academic performance component is based on the structure of Educational and Personal Growth by Kuh [65], as operationalized in self-reported learning gains scales [36]. The student engagement component is underpinned by the classic theoretical framework proposed by Fredricks et al. [7]. Consequently, the model’s strong theoretical foundation and clear structure offer a coherent explanation for the high proportion of variance explained (R²).

4.3. Research Hypotheses

To test the research hypotheses, this study examines the relationships between academic adaptation, student engagement, and academic performance.

Table 7. Direct and indirect effect and Hypotheses Testing.

Hypothesis	Relationship	β Value	SD	T Statistics	p Values	CI95%
H1	Academic Adaptation -> Academic Performance	0.754	0.059	12.741	0.000	[0.635, 0.865]
H2	Academic Adaptation -> Student Engagement	0.909	0.010	95.550	0.000	[0.889, 0.926]
H3	Student Engagement -> Academic Performance	0.166	0.062	2.688	0.007	[0.049, 0.285]
H4	Academic Adaptation -> Student Engagement -> Academic Performance	0.151	0.056	2.701	0.007	[0.045, 0.259]

presents the findings of the hypotheses.

4.3.1. Is There Any Significant Influence of Academic Adaptation on Academic Performance Among International Students in China?

Academic adaptation has a significant positive and direct effect on academic performance (β = 0.754, t = 12.741, p ≤ 0.05) at a significance level of 0.1, which H1 was validated. This indicates that for every one-standard-deviation increase in a student’s academic adaptation, their academic performance rises by approximately three-quarters of a standard deviation. The magnitude of this relationship is strikingly consistent with prior research. Particularly, it closely mirrors the results of Mao et al. [43], who found adaptation explained in a different sample. The consistency across studies underscores the vital role of academic adaptation in international students’ academic success.

4.3.2. Is There Any Significant Influence of Academic Adaptation on Student Engagement Among International Students in China?

Academic adaptation has a direct and positive effect on student involvement, as indicated by (β = 0.909, t = 95.550, p ≤ 0.05) at a significance level of 0.1. The 95% confidence interval for the adjusted bias excluded the zero value. As a result, H2 was supported. This indicates that when international students adapt well academically, their academic performance improves. In the previous study, it is also found that international students’ academic adaptation had a direct impact on student engagement replication with 318 international students who study in China reported an almost identical standardized coefficient (β = 0.450, p ≤ 0.05) between “academic adaptation” and “student engagement” [66]. The converging evidence confirms the pivotal role of academic adaptation in fostering student engagement.

4.3.3. Is There Any Significant Influence of Student Engagement on Academic Performance Among International Students in China?

Student engagement has a direct impact on academic performance, as indicated by a positive effect (β = 0.166, t = 2.688, p ≤ 0.05) at the 0.1 significance level. Despite its modest effect size, student engagement exerts a significant positive direct influence on academic performance, rendering it a crucial contributing factor. As a result, H3 was confirmed. This suggests that higher levels of engagement are associated with improved grades, yet the strength of this relationship is relatively weak. This is the same as the previous research from McClenney et al. [49], which showed greater chances of achieving good academic results. This weak correlation can be explained by the assessment structure in Chinese universities, where heavily weighted final exams may incentivize last-minute surface study over sustained, meaningful engagement, thereby diminishing the measurable impact of engagement on grades.

4.3.4. Is There Any Significant Mediating Influence of Student Engagement on the Relationship Between International Students’ Academic Adaptation and Academic Performance Among International Students in China?

Student engagement serves as a mediator in the relationship between academic adaptation and academic performance. A significant positive direct relationship is observed between academic adaptation and academic performance (β = 0.754, t = 12.741, p ≤ 0.05). Additionally, a significant positive indirect relationship is identified involving academic adaptation, student engagement, and academic performance (β = 0.151, t = 2.701, p ≤ 0.05), at a significance level of 0.1. The 95% confidence interval for the adjusted bias excluded zero, suggesting that both direct and indirect correlations were significant and positive in this study [60,67]. A mediation effect was identified, thereby supporting H4. Compared to prior research by Ma et al. [58], this study advances the understanding of how academic adaptation impacts academic performance via student engagement among international students in China. The impact of student engagement on academic performance among international students in China is relatively weak. Because the participants have just become sophomores after completing their first year of university, the impact of student engagement may be diluted by academic adaptation, resulting in less impact on academic performance.

5. Conclusions and Implications

Based on the PLS-SEM analysis of the measurement and structural models, the measurement model’s analysis results are reliable and can be used for further measurement. The findings established the direct and indirect effects of academic adaptation, student engagement, and academic performance. The results indicate that academic adaptation and student engagement individually have a direct impact on academic performance, while student engagement partially mediates the relationship between academic adaptation and academic performance. Existing literature on international students’ academic adaptation has often exhibited a myopic focus on their cross-cultural identity, framing adaptation primarily as a facet of acculturation [20,68]. In contrast, this study reconceptualizes academic adaptation as a fundamentally educational phenomenon whose complexities are principally driven by academic systems and learning processes rather than cultural dissonance alone. Therefore, this research develops and evaluates an integrative model that connects academic adaptation, student engagement, and academic performance, which expands the application of student development theory [65,69] in the unique academic ecosystem of international students and Chinese higher education.

However, it is crucial to consider its potential limitations, which will lead to a more comprehensive understanding of the applicability and depth of the research conclusions.

First, there are several methodological limitations. The cross-sectional study design limits our ability to make absolute inferences about causal relationships between variables. The relationship between academic adaptation, student engagement, and academic performance is likely dynamic and mutually influential. Longitudinal studies would be more effective in elucidating their trajectories and reciprocal effects over time.

Second, the specificities of the research sample necessitate caution in generalizing the conclusions. Within China, significant subgroup differences may exist across institution types (e.g., research-intensive universities versus applied technology colleges) and students’ countries of origin (e.g., Belt and Road Initiative countries versus Western nations). Future research could employ multi-group analyses to compare pathways among students from diverse cultural backgrounds, disciplines, or institutional types, thereby enriching our understanding of the complexities inherent in the academic adaptation process.

Based on the above research findings and limitations, this study can generate more targeted policy and practical recommendations. First, systematize and proactively advance academic adaptation support. In orientation programs, provide subject-specific workshops on academic norms like Chinese citation formats and lab report writing, and assign “academic buddies”, who are senior international students or dedicated mentors. Second, establish a fast track for academic feedback. Establish regular, informal academic consultation times for international students, focusing on their learning difficulties and adaptation challenges, and providing timely formative feedback. Third, proactively and strategically integrate into the academic community. Encourage students to consciously view academic adaptation as a learning strategy that requires proactive management, rather than a passive experience [70]. This includes proactively contacting mentors, selectively joining academic societies, and forming course study groups with local students.