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Adaptation of Bachelor’s and Master’s Students in Engineering to a Changing Environment

Written By

Dzhamilia Nugmanova, Inna Kozlova and Roman Kupriyanov

Submitted: 15 October 2025 Reviewed: 01 December 2025 Published: 25 March 2026

DOI: 10.5772/intechopen.1014190

Industry 4.0 - Transforming the Future Beyond Manufacturing - Volume 2, Human-Centred, Organizational and Societal Transformations IntechOpen
Industry 4.0 - Transforming the Future Beyond Manufacturing - Vol... Edited by Miguel Delgado-Prieto

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Industry 4.0 - Transforming the Future Beyond Manufacturing - Volume 2, Human-Centred, Organizational and Societal Transformations [Working Title]

Dr. Miguel Delgado-Prieto and Dr. Luis Romeral Martinez

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Abstract

This chapter examines how undergraduate and master’s engineering students adapted to university life before and during the COVID-19 pandemic. The sample comprised 626 students majoring in physics and chemical engineering from two Russian universities (KFU and KNITU), with data collected in 2018 and 2021. Adaptation was assessed across four domains: sociocultural, physiological, sociopsychological, and academic. Before the pandemic, no significant differences were observed between student groups. However, during the pandemic, master’s students demonstrated higher adaptation levels, particularly in the sociocultural and physiological domains. The strengthened correlation between sociocultural and physiological adaptation during the pandemic (from 0.27 to 0.49 for master’s students and from 0.34 to 0.48 for bachelor’s students) suggests that students’ health and well-being became more closely linked to their sense of social and cultural integration under crisis conditions. Enhancing students’ adaptation thus requires a holistic strategy that integrates cultural belonging, peer networks, well-being, and study skills. In an era of rapid technological and social transformation, these dimensions are central – not peripheral – to preparing engineers whose adaptability underpins the workforce readiness demanded by Industry 4.0.

Keywords

  • adaptation
  • engineering education
  • bachelor’s students
  • master’s students
  • COVID-19
  • student well-being
  • sociocultural integration

1. Introduction

Industry 4.0 – marked by digitalization, automation, and global integration – has transformed the role of engineers. Technical knowledge alone is no longer sufficient; graduates must also demonstrate resilience, flexibility, and the ability to work in multicultural teams [1]. With technologies evolving almost as quickly as the length of a degree program, universities must prepare students for the constant reorientation of skills and knowledge [2–4].

In Russia, engineering education has traditionally emphasized strong theoretical training in physics, chemistry, and mechanical engineering. While this provides a solid scientific base, it often leaves students less prepared for the fast-changing realities of modern industry. Recent studies confirm that engineering students must adapt not only to academic demands but also to broader sociocultural environments [5].

For today’s engineering students, adaptation means more than meeting academic requirements. It includes mastering digital tools, keeping pace with technological innovation, and functioning effectively in collaborative environments. Flexible curricula and hands-on learning are crucial supports [6]. Employers increasingly rank adaptability alongside technical expertise: The Future of Jobs Report lists analytical thinking, resilience, flexibility, agility, leadership, and social influence among the top skills for the future workforce [7].

Research offers multiple perspectives on how students adapt to university life, underscoring the complexity of this process. Early models emphasized integration into academic and social contexts. Tinto argued that students’ persistence depends on the degree of their academic and social integration [8]. Building on this idea, Baker and Siryk developed the Student Adaptation to College Questionnaire (SACQ), which distinguishes three domains of adaptation: academic, social, and personal-emotional [9].

Subsequent approaches broadened the concept. Kim described adaptation as a dynamic interaction between individuals and unfamiliar environments, highlighting adaptation as an ongoing process rather than a single transition [10]. Vlasova interpreted adaptation as part of socialization and personality development, while Morozov emphasized behavioral patterns shaped by psychophysiological and sociopsychological factors [11, 12].

Further research pointed to the multicomponent character of adaptation. Rean identified behavioral, cognitive, and subjective-personal components, while Friedlander demonstrated the importance of social support, self-esteem, and stress management for first-year students [13, 14]. Martin and Ertzberger later emphasized resilience as the capacity to regulate thoughts, emotions, and behavior under conditions of novelty and uncertainty [15].

Synthesizing these perspectives, Nugmanova et al. proposed a framework that views student adaptation as a multidimensional process encompassing physiological, sociocultural, sociopsychological, and academic domains. This model captures both individual characteristics and environmental demands, as well as the dynamic interaction between them [16].

1.1 The COVID-19 pandemic as a catalyst for change

The COVID-19 pandemic introduced unprecedented challenges that tested students’ adaptive capacities. The sudden shift to online instruction disrupted established pedagogical practices and intensified problems related to social integration, mental health, and access to learning resources [17, 18]. In Russia, the pandemic became a driver of rapid digitalization in higher education. Universities redesigned teaching and assessment strategies, expanded the use of platforms such as Zoom and Microsoft Teams, and required students to adjust to new learning formats almost overnight [19]. While distance learning was initially seen as a temporary measure, by the autumn of 2020, it had become a new educational reality demanding sustained adaptive effort [20].

The crisis also revealed deep inequalities. Limited access to technology and insufficient digital competencies hindered many students’ learning, with those from lower socioeconomic backgrounds facing the greatest difficulties [21, 22]. Thus, the pandemic not only accelerated digital transformation but also underscored the diverse conditions under which students experience adaptation.

1.2 Adaptation challenges for bachelor’s students

For bachelor’s students, adaptation is closely tied to the transition from school to university, a period marked by significant developmental and social changes. Entering higher education requires mastering new academic expectations, integrating into unfamiliar peer groups, and adjusting to new cultural and institutional norms.

Research on first-year students confirms the complexity of this stage. Friedlander showed that adaptation depends strongly on social support, self-esteem, and the ability to cope with stress [14]. These factors are particularly relevant for undergraduates, who often face challenges in establishing new support networks and managing academic demands simultaneously.

Age-related characteristics intensify these difficulties. Nebylitsyn described late adolescence as a period of heightened reactivity to external stimuli, while Hagquist noted the increased vulnerability of older adolescents to psychological problems [23, 24]. This makes undergraduates more sensitive to stressors arising from both academic transitions and changes in their social environment.

Thus, for bachelor’s students, adaptation encompasses not only academic integration but also emotional regulation and social belonging. Theories that highlight the role of stress, social support, and personal-emotional adjustment [14, 25] are particularly applicable to understanding this group’s challenges.

1.3 Adaptation challenges for master’s students

Master’s students face a different set of adaptation challenges than undergraduates. Having already completed a first degree, they usually possess experience in navigating academic demands and institutional structures. Their adaptation, therefore, centers less on transition into university life and more on professionalization, research activity, and social responsibility.

Theoretical perspectives on socialization and personal development help explain this stage. Ananyev identified the years up to age 25 as a crucial period of personality formation and entry into professional maturity [26]. Similarly, Vlasova framed adaptation as part of socialization, while Morozov emphasized the behavioral and sociopsychological patterns that shape students’ adjustment [11, 12]. These views underscore that, for master’s students, adaptation is tied not only to academic tasks but also to the consolidation of professional identity and social roles.

However, adaptation at this level is not without obstacles. Deniz and Yilmaz listed several factors that can hinder adaptation: regional traditions and languages, as well as practical issues such as housing and peer relationships [27]. We added to this the differences in national education systems and institutional expectations [16]. Such issues may complicate the adaptation process even for students with prior university experience, particularly in international or interdisciplinary programs.

In sum, adaptation among master’s students is best understood through theories that highlight socialization, professional development, and the integration of academic and personal roles. While their challenges differ from those of undergraduates, they remain multifaceted and demand sustained flexibility.

1.4 Research focus of this chapter

Building on these distinctions, this chapter examines how physics and chemical engineering students – at both the undergraduate and master’s levels – adapt to university life. The analysis draws on case studies from Kazan Federal University (KFU) and Kazan National Research Technological University (KNITU), covering the period before and during the COVID-19 pandemic.

Adaptation is considered across four interrelated domains – academic, sociocultural, sociopsychological, and physiological – following the multidimensional framework proposed by Nugmanova et al. [28]. Within this framework, bachelor’s and master’s students confront different challenges: undergraduates focus on transition, social integration, and emotional regulation, while master’s students emphasize professional skills, professional development, and social responsibility. These contrasts are summarized in Figure 1, which presents the main adaptation challenges for each group.

Figure 1.

Adaptation challenges: bachelor’s and master’s students.

This leads to the following research questions:

  1. What differences exist in the adaptation components of bachelor’s and master’s engineering students?

  2. How did the COVID-19 pandemic affect the adaptation of bachelor’s and master’s engineering students?

  3. Did the relationships among adaptation components change differently for bachelor’s and master’s engineering students during the pandemic?

The answers to these questions aim to provide new insights for engineering educators and university administrators, with the goal of designing learning environments that not only transmit knowledge but also strengthen students’ resilience and capacity to adapt in the era of Industry 4.0.

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2. Method

2.1 Study design

The study employed a non-experimental, comparative design with natural grouping variables [29–31]. Data were collected in two waves: before the COVID-19 pandemic and during the pandemic, which allowed the context of the pandemic to be treated as an additional grouping variable.

The study included two grouping variables: level of higher education (bachelor’s or master’s) and period (before vs. during the COVID-19 pandemic). The measured variables were the four components of adaptation: academic, sociocultural, sociopsychological, and physiological. The analysis further examined the relationships between these components.

Participants completed the Assessment of Students’ Adaptation to University questionnaire [32]. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Kazan Federal University (Russia). The research was carried out in three stages:

  • Stage 1 (2018–2019): Baseline study at Kazan Federal University (KFU) and Kazan National Research Technological University (KNITU), with students completing paper questionnaires.

  • Stage 2 (2020–2021): Follow-up study at the same universities during the COVID-19 pandemic, using online questionnaires distributed via Google Forms.

  • Stage 3: Statistical analysis of survey data using STATISTICA.

Based on the theoretical framework and study design, the following hypotheses were formulated:

  1. Bachelor’s and master’s students differ in their adaptation to university.

  2. The COVID-19 pandemic influenced the adaptation of bachelor’s and master’s students in engineering in distinct ways.

  3. The COVID-19 pandemic altered the relationships between adaptation components for bachelor’s and master’s students.

To address these hypotheses, the study was guided by the following research objectives:

  1. To compare the adaptation components of bachelor’s and master’s engineering students.

  2. To examine how the COVID-19 pandemic affected these adaptation components in both groups.

  3. To analyze how the relationships between adaptation components changed for bachelor’s and master’s students during the pandemic.

2.2 Participants

The study included 626 first-year engineering students in physics and chemical engineering programs at Kazan Federal University (KFU) and Kazan National Research Technological University (KNITU). Data were collected before the COVID-19 pandemic (2018) and during the pandemic (2021). The average age of bachelor’s students was 18.6  years, while master’s students averaged 22.1  years. Participation was voluntary, with students informed of the study’s aims and providing consent before completing the questionnaire.

2.3 Instruments

The study utilized the Assessment of Students’ Adaptation to University questionnaire [32], which includes 25 items rated on a 7-point Likert scale and covers four domains: sociocultural, physiological, sociopsychological, and academic adaptation. Validation studies reported good internal reliability, with Cronbach’s α values ranging from 0.78 to 0.84 [32, 33].

Sociocultural adaptation reflects students’ engagement with university culture, campus life, and the broader social environment [34–39]. A sample item is: “How well are you informed about the university’s social and cultural activities?”

Physiological adaptation captures perceptions of health, rest, nutrition, and the physical comfort of learning conditions [40]. A sample item is: “Assess your general physical condition since beginning your studies at the university.”

Sociopsychological adaptation assesses emotional well-being, peer integration, social comfort, and perceived support [41, 42]. These constructs align with findings that highlight the positive role of self-esteem, social support, and institutional belonging [43]. A sample item is: “Assess your overall emotional state since you started studying at the university.”

Academic adaptation refers to adjustment to study demands and learning routines [44, 45]. The scale is designed to assess several aspects of this process. These include students’ motivation to learn, the effectiveness of learning, time management skills, presentation skills, comprehension of academic texts, and readiness for future professional growth [28, 46]. A sample item is: “Assess your ability to plan time for independent work during the semester.”

The questionnaire makes it possible to analyze each adaptation component separately, as well as calculate an overall adaptation index by averaging scores across the four domains.

2.4 Data collection and analysis

Data were collected in classrooms before the pandemic and online during the pandemic. Students were informed of the study’s aims and provided consent prior to participation. Completion of the questionnaire took, on average, 15–20  minutes. All statistical analyses were performed using the STATISTICA software package. Descriptive statistics (means and standard deviations) were first calculated for each of the adaptation components. To compare differences between bachelor’s and master’s students, independent-samples Student’s t-tests were employed. The normality of the distributions was assessed prior to testing, and homogeneity of variances was examined using Levene’s test. Statistical significance was set at p < 0.05.

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3. Results

3.1 Hypothesis 1

Table 1 presents the overall comparison of adaptation between bachelor’s and master’s students in engineering, based on data collected both before and during the COVID − 19 pandemic. Significant differences were found on several subscales. In sociocultural adaptation, bachelor’s students reported a lower mean score (4.67) than master’s students (4.97, p < 0.01). A similar pattern was observed for physiological adaptation (4.49 vs. 4.80, p < 0.01). Differences also emerged on the overall adaptation index, with master’s students scoring higher than bachelor’s students (5.01 vs. 4.84, p < 0.05). By contrast, no significant differences were found in sociopsychological adaptation (4.53 vs. 4.51, p  =  0.853) or academic adaptation (4.33 vs. 4.46, p  =  0.093).

Adaptation Mean bachelors, n = 450 SD Mean masters, n = 176 SD t-value p Cohen’s d
Sociocultural 4.67 1.17 4.97 1.14 −2.65 0.008** 0.26
Physiological 4.49 1.06 4.80 1.09 −3.05 0.002** 0.29
Sociopsychological 4.53 0.96 4.51 0.90 0.18 0.853 0.02
Academic 4.33 0.79 4.46 0.86 −1.68 0.093 0.16
Overall 4.84 0.85 5.01 0.86 −2.08 0.038* 0.20

Table 1.

Comparative analysis of adaptation components among bachelor’s (n = 450) and master’s (n = 176) engineering students (t-tests).

Note: SD = standard deviation; *statistically significant differences: *p < 0.05; **p < 0.01.


The table presents mean scores, standard deviations (SD), t-values, p-values, and Cohen’s d effect sizes for four components of adaptation – sociocultural, physiological, sociopsychological, and academic – as well as an overall index. Higher mean values indicate stronger adaptation. Statistically significant differences (*p < 0.05; **p < 0.01) appeared for master’s students in sociocultural, physiological, and overall adaptation levels. The corresponding Cohen’s d effect sizes are small (|d| = 0.26–0.29) but also indicate the practical significance of differences in the adaptation components of bachelor’s and master’s students. These findings indicate that while bachelor’s and master’s students do not differ significantly in academic or sociopsychological adaptation, master’s students demonstrate stronger sociocultural and physiological adjustment, as well as higher overall adaptation. Hypothesis H1 is therefore supported.

3.2 Hypothesis 2

As shown in Table 2, before the COVID-19 pandemic, there were almost no statistically significant differences in adaptation between bachelor’s and master’s students. The only exception was the sociocultural adaptation subscale, where master’s students scored higher (4.95 vs. 4.65, p < 0.05). No significant differences emerged in physiological, sociopsychological, or academic adaptation, nor in the overall adaptation index, suggesting that, under normal conditions, bachelor’s and master’s students adapted to university life in broadly similar ways.

Adaptation Mean bachelor's before the Covid-19 pandemic, n = 240 SD Mean masters before the Covid-19 pandemic, n = 96 SD t-value p Cohen’s d
Sociocultural 4.65 1.18 4.95 0.99 −2.24 0.025* 0.28
Physiological 4.61 0.99 4.57 0.88 0.35 0.722 0.04
Sociopsychological 4.56 0.89 4.44 0.86 1.15 0.252 0.14
Academic 4.23 0.78 4.30 0.68 −0.72 0.469 0.10
Overall 4.85 0.81 4.88 0.69 −0.34 0.730 0.04

Table 2.

Comparative analysis of adaptation components among bachelor’s (n  =  240) and master’s (n = 96) engineering students before the COVID-19 pandemic (t-tests).

Note: SD = standard deviation; * statistically significant differences; * p < 0.05.


Mean scores, SDs, t-values, p-values, and Cohen’s d are reported for the four adaptation components and the overall index. Higher means denote stronger adaptation. A statistically significant difference (p < 0.05) was observed only in the sociocultural component. The Cohen’s d effect size indicates that the difference in sociocultural adaptation between master’s and bachelor’s students is small (|d| = 0.28), while the differences in all other components are negligible.

During the COVID-19 pandemic, however, the distribution of adaptation scores shifted (Table 3). On every subscale, master’s students reported higher mean values than bachelor’s students. Statistically significant differences were found in sociocultural adaptation (4.98 vs. 4.68, p < 0.05), physiological adaptation (4.95 vs. 4.42, p < 0.01), and the overall adaptation index (5.10 vs. 4.84, p < 0.05). In academic adaptation, the difference approached but did not reach significance (4.56 vs. 4.39, p = 0.090). These results indicate that during the pandemic, master’s students adapted more successfully than bachelor’s students, particularly in sociocultural and physiological domains.

Adaptation Mean bachelors during the COVID-19 pandemic, n = 210 SD Mean masters during the Covid-19 pandemic, n = 80 SD t-value p Cohen’s d
Sociocultural 4.68 1.18 4.98 1.23 −2.06 0.040* 0.25
Physiological 4.42 1.10 4.95 1.18 −3.88 0.0001** 0.46
Sociopsychological 4.50 1.01 4.6 0.94 −0.38 0.707 0.10
Academic 4.39 0.80 4.56 0.94 −1.7 0.090 0.19
Overall 4.84 0.88 5.10 0.95 −2.34 0.019* 0.28

Table 3.

Comparative analysis of adaptation components among bachelor’s (n = 210) and master’s (n = 80) engineering students during the COVID-19 pandemic (t-tests).

Note: SD = standard deviation; * statistically significant differences * = p < 0.05; ** = p < 0.01.


The table includes mean scores, SDs, t-values, p-values, and Cohen’s d for each adaptation component and the overall index. Higher means indicate stronger adaptation. Significant differences (p < 0.05; **p < 0.01) were found in the sociocultural, physiological, and overall components. The Cohen’s d effect of the difference was small for sociocultural and overall adaptation (|d| ≈ 0.25–0.28) and approaching medium for physiological adaptation (|d| ≈ 0.46).

A comparison of the two periods highlights how the adaptation landscape changed. Before COVID-19, bachelor’s and master’s students were broadly similar, with only a modest sociocultural advantage for master’s students. During the pandemic, however, clear differences emerged: master’s students consistently reported higher scores across all components, with significant advantages in sociocultural and physiological adaptation as well as in overall adaptation. This shift suggests that, while adaptation was comparable under normal conditions, master’s students demonstrated greater resilience in the disruptive context of the pandemic.

An additional observation concerns domains where the relative positions of the groups changed. Before the pandemic, bachelor’s students scored marginally higher in sociopsychological adaptation (4.56 vs. 4.44, p = 0.252) and in physiological adaptation (4.61 vs. 4.57, p = 0.722). During the pandemic, however, the pattern reversed: master’s students reported slightly higher scores in sociopsychological adaptation (4.60 vs. 4.50, p = 0.707) and substantially higher scores in physiological adaptation (4.95 vs. 4.42, p < 0.01). Although the differences in sociopsychological adaptation remained non-significant, the reversal in physiological adaptation reached statistical significance, highlighting this domain as especially sensitive to the pandemic context.

3.3 Hypothesis 3

To examine whether the COVID-19 pandemic altered the relationships between adaptation components, we first considered the relationship of each component with the overall adaptation index. In contrast to Hypotheses 1 and 2, which presented comparisons beginning with bachelor’s students, the order here is reversed because the changes in interrelationships were more pronounced among master’s students. As shown in Table 4, before the pandemic, the overall adaptation of master’s students was most strongly associated with physiological (r = 0.82) and sociopsychological adaptation (r = 0.83), while links with sociocultural (r = 0.65) and academic adaptation (r = 0.74) were weaker. This indicates that, under normal conditions, the adaptation of master’s students depends mainly on health, well-being, and emotional support.

Adaptation Sociocultural Physiological Sociopsychological Academic Overall
Sociocultural 1.00 0.27 0.42* 0.51* 0.65*
Physiological 0.27 1.00 0.66* 0.47* 0.82*
Sociopsychological 0.42* 0.66* 1.00 0.36* 0.83*
Academic 0.51* 0.47* 0.36* 1.00 0.74*
Overall 0.65* 0.82* 0.83* 0.74* 1.00

Table 4.

Correlations among adaptation components in master’s students before the COVID-19 pandemic.

Note:* – statistically significant correlations; * = p < 0.05.


The table presents Pearson correlation coefficients between the sociocultural, physiological, sociopsychological, and academic components and the overall adaptation index. Most correlations are statistically significant (p < 0.05), except for the sociocultural–physiological pair, which is not significant. Statistically significant correlations are marked with an asterisk.

During the pandemic (Table 5), overall adaptation became more closely tied to academic adaptation (r  =  0.88) and sociocultural adaptation (r = 0.77), while links with sociopsychological adaptation (r = 0.88) and physiological adaptation (r  =  0.80) remained strong. This suggests that, in crisis conditions, successful adaptation for master’s students increasingly depended on academic functioning and integration into the sociocultural environment.

Adaptation Sociocultural Physiological Sociopsychological Academic Overall
Sociocultural 1.00 0.49* 0.60* 0.60* 0.77*
Physiological 0.49* 1.00 0.60* 0.57* 0.80*
Sociopsychological 0.60* 0.60* 1.00 0.72* 0.88*
Academic 0.60* 0.57* 0.72* 1.00 0.88*
Overall 0.77* 0.80* 0.88* 0.88* 1.00

Table 5.

Correlations among adaptation components in master’s students during the COVID-19 pandemic.

Note: * – statistically significant correlations; * = p < 0.05.


Table  5 presents the Pearson correlation coefficients between the four adaptation components and the overall index. All correlations are positive and statistically significant (p < 0.05), with the coefficients generally higher than those observed before the COVID − 19. The change in correlations in the structure of adaptations before and during COVID − 19 among master’s students is presented more clearly in Figure 2.

Figure 2.

Correlation graph of the adaptation structure before and during COVID-19 in master’s students.

For bachelor’s students, a similar pattern was observed. Before COVID-19 (Table 6), their overall adaptation was most strongly linked to sociopsychological adaptation (r = 0.85) and academic adaptation (r = 0.81), with weaker ties to sociocultural adaptation (r  =  0.75) and physiological adaptation (r = 0.74). Thus, in normal conditions, bachelor’s students’ adaptation relied more heavily on emotional stability and academic adjustment.

Adaptation Sociocultural Physiological Sociopsychological Academic Overall
Sociocultural 1.00 0.34* 0.56* 0.52* 0.75*
Physiological 0.34* 1.00 0.54* 0.51* 0.74*
Sociopsychological 0.56* 0.54* 1.00 0.58* 0.85*
Academic 0.52* 0.51* 0.58* 1.00 0.81*
Overall 0.75* 0.74* 0.85* 0.81* 1.00

Table 6.

Correlations among adaptation components in bachelor’s students before the COVID-19 pandemic.

Note: * – statistically significant correlations; * = p < 0.05.


Table 6 shows Pearson correlation coefficients among the four adaptation components and the overall index, specifically for bachelor’s students. All correlations are positive and statistically significant (p < 0.05), with moderate to strong coefficients across the adaptation components.

During the pandemic (Table 7), these relationships shifted only slightly. The overall index showed strengthened associations with sociocultural (r = 0.77) and physiological adaptation (r = 0.77), while links with sociopsychological (r = 0.84) and academic adaptation (r = 0.79) remained strong but were slightly weaker than before. This indicates that health and cultural integration became somewhat more central to bachelor’s students’ overall adaptation during the pandemic.

Adaptation Sociocultural Physiological Sociopsychological Academic Overall
Sociocultural 1.00 0.48* 0.56* 0.53* 0.77*
Physiological 0.48* 1.00 0.54* 0.50* 0.77*
Sociopsychological 0.54* 0.54* 1.00 0.57* 0.84*
Academic 0.53* 0.50* 0.57* 1.00 0.79*
Overall 0.77* 0.77* 0.84* 0.79* 1.00

Table 7.

Correlations among adaptation components in bachelor’s students during the COVID-19 pandemic.

Note: * – statistically significant correlations; * = p < 0.05.


As in Table 6, Table 7 presents Pearson correlation coefficients among the four adaptation components and the overall index for bachelor’s students, but during the pandemic. All correlations are positive and statistically significant (p < 0.05), and most coefficients are slightly higher than those observed before the pandemic.

The change in correlations in the structure of adaptations before and during COVID-19 among bachelor’s students is presented in Figure 3.

Figure 3.

Correlation graph of the adaptation structure before and during COVID-19 in bachelor’s students.

A closer look at specific interrelationships highlights how the pandemic reshaped the adaptation system. For master’s students, sociocultural adaptation emerged as a central hub: its correlations strengthened with physiological adaptation (0.27 → 0.49, Δ = + 0.22), sociopsychological adaptation (0.42 → 0.60, Δ = + 0.18), academic adaptation (0.51 → 0.60, Δ = + 0.09), and overall adaptation (0.65 → 0.77, Δ = + 0.12). This shows that under pandemic conditions, integration into the social and cultural environment became more strongly interwoven with health, well-being, and academic performance. For bachelor’s students, correlations among components remained relatively stable, though the link between sociocultural and physiological adaptation grew from 0.34 to 0.48 (Δ = + 0.14), suggesting that health and well-being became more connected to social and cultural integration.

Taken together, these results confirm Hypothesis 3: the COVID-19 pandemic altered the relationships between adaptation components for both groups, but the changes were more pronounced for master’s students than for bachelor’s students.

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4. Discussion and conclusions

Taken together, the results of Hypotheses 1–3 show that adaptation to university among engineering students is shaped by the level of study and becomes more differentiated under crisis conditions. Before the pandemic, bachelor’s and master’s students were broadly similar across most components, with a statistically significant difference observed only in sociocultural adaptation, where master’s students scored higher. During the pandemic, however, master’s students consistently reported stronger adaptation, especially in sociocultural and physiological domains, while sociocultural adaptation itself emerged as a central hub for overall adjustment. The limited pre-pandemic differences and the subsequent widening of gaps during the pandemic can be explained by the developmental vulnerability of late adolescence: as Nebylitsyn described, this period is characterized by heightened reactivity to external stimuli, and Hagquist emphasized adolescents’ increased susceptibility to psychological difficulties [23, 24]. This led to a decrease in physiological adaptation during COVID-19 among undergraduate students [47]. In turn, the sociocultural advantage of master’s students reflects their prior experience of social interaction, peer support, and familiarity with university life, which contribute to higher self-ratings of adaptation [48, 49].

Sociocultural adaptation emerges as a pivotal dimension in our study. It was the only domain where master’s students consistently outperformed bachelor’s students both before and during the pandemic (H1–H2), and it became more tightly interwoven with other domains of adaptation for master’s students, where correlations with physiological, sociopsychological, and academic adaptation all strengthened during COVID-19 (H3). For bachelor’s students, too, the link between sociocultural and physiological adaptation gained importance, suggesting that cultural integration was increasingly connected to well-being in crisis conditions. This finding is consistent with recent studies confirming the importance of the sociocultural environment and direct interaction with teachers and peers in sustaining student adaptation during the pandemic [50, 51]. “Sociocultural adaptation” appears as one of the keywords in 2025 year’s bibliometric research on the topic, linked to concepts such as “psychological adaptation” and “international students” [52]. The authors report an annual publication growth rate of 4.71%. The steady rise in academic attention confirms that sociocultural adaptation is not only a construct of growing theoretical interest but also an empirically central factor in how engineering students adjust to university life, particularly under conditions of uncertainty and disruption.

The pandemic also significantly disrupted the educational experience of bachelor’s students, reducing opportunities for collaboration, limiting access to laboratories, and undermining their sense of belonging to university life [53–55]. This highlights the importance of structured mentoring systems, teamwork-based online classes, health promotion initiatives, and resilience training as preventive measures to support vulnerable groups, particularly undergraduates, during social and educational disruptions. The strengthened correlation between sociocultural and physiological adaptation during the pandemic (from 0.27 to 0.49 for master’s students and from 0.34 to 0.48 for bachelor’s students) indicates that students’ health and well-being became more closely tied to their sense of social and cultural integration at the university under crisis conditions.

Building on our previous findings on gender differences, we propose that female undergraduates in engineering may represent the most vulnerable group, whose adaptation is especially at risk under conditions of disruption [56, 57]. This perspective resonates with the wider inequalities revealed by the pandemic: limited digital competencies, unequal access to technology, and socioeconomic disadvantage all hindered students’ ability to adapt, underscoring that adaptation is not experienced under uniform conditions [58]. In this sense, the pandemic highlighted both the centrality of resilience in engineering education and the uneven vulnerabilities that shape how students can achieve it [59, 60].

This study demonstrates that adaptation to university life is a multi-component process sensitive to both the educational stage and external disruptions. The COVID-19 pandemic accentuated differences between bachelor’s and master’s students: while their adaptation was broadly similar before the crisis, master’s students showed clear advantages during it, particularly in sociocultural and physiological adaptation. Moreover, sociocultural adaptation emerged as a central hub, becoming more strongly linked with academic, sociopsychological, and health-related domains under pandemic conditions. For engineering education, these findings underline that resilience cannot be reduced to academic performance alone. Strengthening students’ adaptation requires a holistic strategy that integrates cultural belonging, peer networks, well-being, and study skills. In an era of rapid technological and social change, these dimensions are not peripheral but central to preparing engineers whose adaptability directly conditions the workforce’s readiness for Industry 4.0 [2].

A major advantage of this study is its survey methodology, which enabled the collection of a wide range of student perspectives across two universities and two time periods. Nevertheless, the study also has limitations. As the instrument relied on self-assessment and reflection, responses may have been shaped by temporary factors such as the respondents’ mental or physical state. Although the large sample size mitigates the impact of individual variability, the broader sociopsychological context of the pandemic may still have influenced how students evaluated their adaptation.

Despite these limitations, the findings provide several practical implications. By identifying vulnerable aspects of student adaptation – particularly the central role of sociocultural adjustment and the heightened challenges faced by undergraduates – universities can design targeted interventions to mitigate the negative consequences of large-scale disruptions. Continuous monitoring of adaptation throughout the semester is especially important for bachelor’s students, who appear more susceptible to academic and social challenges. Early identification of at-risk individuals would make it possible to provide timely, tailored support, thereby strengthening resilience and improving overall adaptation outcomes.

Table 8 illustrates how the components of university adaptation evolve into key professional competencies required in Industry 4.0 environments. Each component of adaptation aligns with distinct workforce behaviors (see columns 1 and 2 of Table 8) and challenges encountered during the early stages of engineering careers. Academic adaptation underpins continuous learning, cognitive flexibility, and the ability to navigate complex technological and organizational systems. Sociocultural adaptation extends beyond interpersonal communication to include cross-cultural competence, collaboration across diverse teams, and integration into multicultural organizational environments – skills increasingly vital in globalized and digitally networked workplaces. Sociopsychological adaptation promotes motivation, emotional regulation, and persistence under uncertainty, supporting professional resilience and effective self-management. Finally, physiological adaptation ensures sustained well-being and productivity under intensive or hybrid work conditions, emphasizing the importance of health-supportive practices and workload management.

Each of these components also corresponds to typical workplace stressors and can be strengthened through targeted organizational support actions (columns 3 and 4) – such as mentoring structures, inclusive community-building, professional development, and health promotion initiatives – thereby facilitating a smoother transition from university to professional life.

Adaptation Workplace behaviors Typical stressors Support actions
Academic Continuous learning, flexible task management, and adaptation to new technologies. Rapid skill obsolescence, performance pressure, complex project environments. Structured onboarding, ongoing professional development opportunities.
Sociocultural Teamwork, communication across groups, integration into organizational culture. Cross-functional collaboration challenges, unfamiliar cultural norms, limited social connectedness. Mentoring networks, inclusive community-building, peer support programs.
Sociopsychological Motivation, emotional self-regulation, resilience during uncertainty. High workload variability, performance evaluation stress, feedback sensitivity. Access to mental health resources, reflective supervision, or coaching.
Physiological Sustaining well-being and productivity under demanding or hybrid work conditions. Fatigue, limited recovery opportunities, and ergonomic challenges in remote work. Workload management policies, health promotion initiatives, ergonomic support.

Table 8.

Workforce bridge: mapping university adaptation to early-career behaviors, stressors, and support actions.

Together, these correspondences show that university adaptation forms the basis of professional readiness and long-term well-being. As a dynamic and transferable competence, adaptation can be cultivated through both educational and workplace practices, reinforcing the continuity between learning and professional development.

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The authors used ChatGPT (OpenAI, 2025) for language polishing and clarity improvements. All intellectual content, analysis, and interpretations are solely the responsibility of the authors.

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Written By

Dzhamilia Nugmanova, Inna Kozlova and Roman Kupriyanov

Submitted: 15 October 2025 Reviewed: 01 December 2025 Published: 25 March 2026

© 2026 The Author(s). Licensee IntechOpen. This content is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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