Nada Mallah Boustani
Loubna Mourtajji2,3- 1Faculty of Business and Administration, Saint Joseph University, Beirut, Lebanon
- 2Marketing Department, University Institute of Technology of Oise, Creil, France
- 3Business and Administration Management Department, University Institute of Technology of Oise, Beauvais, France
Introduction: This study examines the adaptability and resilience of undergraduate students at a French university during an abrupt transition to online learning triggered by a crisis. Grounded in resilience theory and the Technology Acceptance Model (TAM), the research investigates students’ perceptions of digital learning environments and their capacity to cope with academic disruption. It further situates the analysis within Connectivism and Technological Pedagogical Content Knowledge (TPCK) frameworks to assess the transformative potential of information and communication technologies (ICTs) in fostering inclusive and equitable education.
Methods: A post-crisis quantitative survey was administered to 402 students enrolled at the Institut Universitaire de Technologie (IUT) in Oise. The questionnaire captured students’ experiences, emotional responses, and perceptions of online learning practices during the crisis. Factor analysis was conducted on key survey items (Q7) to identify underlying dimensions related to digital learning experiences.
Results: The factor analysis revealed three principal dimensions shaping students’ learning experiences: teacher innovation in digital pedagogy, students’ emotional responses, and practical challenges associated with online learning. The findings indicate that social support networks and engagement in virtual communities played a significant role in helping students manage academic difficulties. Moreover, learning perceptions differed according to the level of study, with master’s students demonstrating higher levels of engagement and adaptability compared to undergraduates.
Discussion: The results underscore the critical role of pedagogical innovation, emotional support, and technical infrastructure in building sustainable and resilient digital learning environments. ICTs emerge not merely as tools for ensuring instructional continuity in times of crisis, but as enablers of pedagogical equity and institutional transformation. This study contributes to ongoing debates on sustainable and inclusive education by highlighting how well-supported digital ecosystems can enhance resilience and accessibility within higher education.
1 Introduction and context of the study
The advent of the COVID-19 pandemic has brought about a radical and rapid transformation of the global educational landscape. This health crisis has forced educational institutions, from elementary school to universities, to switch to emergency remote learning (ERL) teaching formats. In this article, we consistently use the term emergency remote learning to distinguish it from formal online or distance learning programs that are intentionally designed and pedagogically prepared (Rapanta et al., 2020): It’s not exactly distance learning, it’s something else given the context of the crisis and the subsequent return to normality after the lockdown. Bates et al. (2020) approached this transition by highlighting not only the technical and pedagogical challenges, but also the unprecedented opportunity to innovate, quite rapidly, in teaching methods.
This study focuses on the adaptability of undergraduate students at a French university institute, the first to close its doors during the pandemic. The sustainability in our study refers to social and pedagogical sustainability during crisis-imposed transitions. It aims to understand students’ perceptions of online learning and the strategies they adopted to ensure continuity in their learning. While the COVID-19 pandemic has prompted much research into emergency remote education around the world, such as in Malaysia (Al-Kumaim et al., 2021; Gopinathan et al., 2022), in UAE (Al-Chami et al., 2022), Indonesia (Surani and Hamidah, 2020), Poland (Stecula and Wolniak, 2022), USA (Means and Neisler, 2021), Germany (Schlenz et al., 2020), India (Khan et al., 2020), studies in France remain relatively limited. This study suggests an in-depth exploration of how students have adapted to using digital technologies in a challenging context, based on resilience theory and focusing on their ability to overcome challenges.
Our study focuses on the first students to experience confinement in France. They were on winter vacation when, on the eve of resumption, they received a communiqué from the university president requiring them not to attend classes. Students and teaching staff were left wondering why they were subject to these restrictions, while other institutes and campuses in other regions were not.
Students affected by these initial confinements had to adapt suddenly to a new reality of isolation and distance learning, totally unforeseen, without adequate preparation or support. This may have led to feelings of loneliness and exclusion, as well as challenges in accessing educational resources and communicating effectively with teachers and classmates. In addition, the disparity of measures taken from one region to another may have seemed arbitrary and unfair, exacerbating feelings of frustration and incomprehension among these students.
The pandemic revealed structural weaknesses and inequalities in the education system. Daniel (2020) has highlighted how different institutions have responded to these challenges. Some have successfully implemented innovative solutions to maintain pedagogical continuity, while others have struggled, highlighting disparities in access to technological and pedagogical resources. This study aims to explore these diverse experiences to understand how emergency remote teaching has been perceived and how effective the different strategies implemented by students have been. Some were able to adapt and showed a certain resilience, while others dropped out and gave up. Some students encountered technical, pedagogical, organizational or communication problems with distance learning, a practice that has its own specificities. On the other hand, others see it as an opportunity to explore and experiment with new approaches to teaching (Descamps et al., 2020). A number of studies (Anita et al., 2021) have shown that the type of learning offered, and the role played by teachers and educational teams, is a major factor in dropping out of school, when combined with a difficult family situation. Numerous scientific publications have reported or evaluated what happened then and afterward. Few studies, however, have attempted to conceptualize and give meaning to ways of managing this crisis.
The premise of this research is that, in a disrupted educational context such as the COVID-19 pandemic, resilience may be associated to the way students perceive and use technology for learning. Indeed, a resilient student might be more inclined to perceive technology positively in line with the Technology Acceptance Model (Davis, 1986) as an effective way to continue learning and improve communication with peers despite adverse circumstances. Conversely, a non-resilient student will tend to perceive the situation negatively and reject the adoption of any new technology (Zoom, Moodle). This interaction between technology perception and individual resilience creates fertile ground for technological adoption and adaptation in difficult situations. Conversely, digital networking, social support and participation in virtual groups (WhatsApp, Discord, Snapchat) strengthen students’ resilience, enabling them to better manage academic challenges and adapt more easily to the use of new educational technologies.
These postulates are questioned based on generic elements such as gender, age, level of education (Bachelor Year 1, 2, or 3) and contextual elements such as the use and availability of digital tools, family support and stress. To analyze these aspects, the research draws on resilience theory, used to understand how students coped with the psychological and emotional challenges associated with this transition. This theory offers a robust, multidimensional framework for examining students’ online learning experience during the COVID-19 pandemic.
2 Theoretical background
2.1 TAM
Our conceptual model is based on the Technology Acceptance Model (Davis, 1986) which conceptualizes the acceptance of technology by users (Venkatesh and Davis, 2000). Meta-analysis of the TAM model (King and He, 2006; Yousafzai et al., 2007) make it possible to become aware of the extent of the use of this model, in varied contexts and situations worldwide.
In terms of education, studies have notably tested the adoption and acceptance (or not) of innovative technologies both from the point of view of teachers (Scherer et al., 2019) and students (Mohammadi, 2015; Ibrahim et al., 2017; Rafique et al., 2020). However, the use and acceptance of the tools offered is done on a voluntary basis. On the contrary, the TAM model is used here in a constrained context created by the appearance of the pandemic and the obligation to resort to distance learning overnight without preparation or training. Specific situations have been tested in Jordan (Almaiah et al., 2020), Vietnam (Ho et al., 2020), Indonesia (Siron et al., 2020; Sukendro et al., 2020; Mailizar et al., 2021) or even in Germany (Vladova et al., 2021). We propose to test an extended TAM model in France, during the period of the COVID-19 pandemic within the first public higher education establishment to have closed its doors.
2.2 Resilience theory
Resilience theory is a complex, multidimensional and multidisciplinary concept. It has its roots in psychology, and has been mobilized in fields ranging from logistics (Aloui et al., 2021), clinical research to management, finance and ecology, and on several levels from the individual, the family, teams (Brykman and King, 2021) to the organization (Hepfer and Lawrence, 2022). This “seductive yet intimidating” concept has been embraced since the global financial crisis of 2008 and the food price crisis of 2008 (Barrett et al., 2021). It has been studied as a condition, as a process, and as an outcome, leading to different methods and empirical results (Fisher et al., 2019).
It focuses on the ability of an individual, community or system to recover from adversities, defined as any difficult, stressful or traumatic experience or situation. This can include events such as personal tragedies (Hoelterhoff and Chung, 2017), death, professional challenges (van Doorn and Hülsheger, 2015), natural disasters (Aldunce et al., 2014; Williams and Shepherd, 2016; Gralepois, 2023), or adverse social circumstances such as the COVID-19 pandemic. In the context of our study, the pandemic is an example of adversity, not just for students, but for the entire global population. It brought with it unprecedented health, economic and social challenges. The crisis imposed severe restrictions, such as confinement and social distancing measures, profoundly affecting students’ daily lives, psychological wellbeing and social interactions. Economic disruption and the loss of student jobs have increased financial insecurity and left some students in precarious conditions. In addition, the constant threat to health and bereavement due to the loss of loved ones exacerbated their stress and anxiety. Resilience in this context involves adapting to all these circumstances, maintaining or recovering mental wellbeing, and being able to navigate an uncertain and constantly changing environment.
Fisher et al. (2019) emphasized the need to consider the temporality of resilience. Indeed, he distinguishes between an event that lasts over time and an event of short duration, a one-off event and a repetitive event. This temporality influences the ability to adapt, which is the second dimension of resilience. It covers the ability to adjust positively in the face of adversity. Students who adapted positively to the pandemic and coped with the changes, were able to recover more quickly and adopt all the tools offered by the institution, or even optimize them. Resilient students will not hesitate to develop support networks, which in turn will further strengthen their resilience.
2.3 Theory of innovation in education
Innovation has become a central driver of transformation in higher education, particularly in environments disrupted by technological acceleration and global crises. As teaching increasingly extends beyond the physical classroom, reaching students across diverse social, geographic, and economic contexts, universities face the imperative of equipping learners with competencies that allow them to operate in a volatile, digital, and interconnected world. Innovation, therefore, is not limited to adopting new tools; it involves rethinking pedagogical models, curricula, and institutional strategies to foster adaptability, critical thinking, and lifelong learning.
Several theoretical frameworks provide foundations for understanding how innovation can be integrated meaningfully into educational practice. The Technological Pedagogical Content Knowledge (TPACK) framework (Mishra and Koehler, 2006) emphasizes that effective digital-age teaching requires the intersection of content expertise, pedagogical methods, and technological tools. This framework aligns strongly with findings from Boustani and Sayegh (2021) study on online learning during COVID-19, which showed that facilitating conditions and perceived learning value enhanced online learning acceptance among Lebanese students, while technological access particularly unstable internet connectivity remained a major barrier. These results illustrate that innovation requires both pedagogical and infrastructural support.
Connectivism, a theory of learning for the digital era, views knowledge as distributed across networks and emphasizes digital literacy, information navigation, and the ability to learn continuously across platforms (Siemens, 2004). These principles are reflected in Boustani (2023) study on employability during and after COVID-19, which found that graduates now require networked learning skills, adaptability, and soft skills competencies increasingly valued by employers in Lebanon and beyond. The findings show that innovation in education must address not only content but also students’ ability to operate in digital ecosystems shaped by AI, automation, and remote work models.
Finally, experiential learning theory (Kolb, 1984) underscores the role of real-world engagement and reflection in developing meaningful, transferable skills. The integration of technological expectations, including AI-enabled tools, further shows how innovation shapes students’ readiness for emerging economic realities.
In the dynamic field of education, where traditional boundaries are being redefined by technological advancements, the role of innovation has taken center stage, revolutionizing both the learning experience and the art of teaching. In the era of online learning, the challenge and unique opportunity to transmit knowledge, but also to inspire a new generation to embrace change, adaptability, and a thirst for innovation. Universities, as incubators of tomorrow’s professionals, have the heavy responsibility of producing graduates who not only master fundamental theories, but are also prepared to navigate the complexities of a rapidly changing global marketplace. Technological Pedagogical Content Knowledge (TPCK) is a framework that emphasizes the integration of technology, pedagogy, and content knowledge. It focuses on how teachers can effectively use technology to improve teaching and learning experiences. It recognizes the importance of aligning technology with specific subject areas and teaching approaches in order to improve educational outcomes (Mishra and Koehler, 2006). Connectivism is a learning theory based on the importance of networked learning and the role of technology in facilitating learning. It recognizes that knowledge is distributed across networks and emphasizes the skills needed to navigate and make connections in digital spaces. Connectivism emphasizes the development of digital literacy, network analysis and learning across different platforms and resources (Siemens, 2004).
Across our research portfolio, these theories converge to show that innovation emerges through the integration of technology with pedagogy, the creation of active and experiential learning environments, and the development of digital, cognitive, and entrepreneurial competencies aligned with a rapidly changing global and local context. These insights are essential for building sustainable and resilient educational environments capable of preparing students for the uncertainties of the 21st-century landscape.
2.4 Research suggested conceptual model
The research raises an important point about the unique situation created by the crises due to the COVID-19 pandemic, particularly regarding student acceptance of online courses. Indeed, the pandemic placed students in a position where they had few or no alternatives to emergency remote learning, which could influence their perception and acceptance of this teaching modality. The model focuses on the adoption by students of distance learning solutions made available by the establishment (Moodle, course sessions via Zoom for example) depending on their perceived usefulness and ease of use. We also integrate into our study the attitude of students toward solutions emanating from their initiative to exchange and recreate a semblance of a class or work group (Google drive, Snapchat, WhatsApp). Indeed, it appears that the social dimension (Ho et al., 2020; Vladova et al., 2021) can affect students’ attitudes. The state of social isolation in which students found themselves without preparation is thus considered within the model. Figure 1 presents the conceptual model, highlighting the interrelationships between resilience, stress, and social ties in shaping the adoption of technology-mediated education during periods of crisis.
Figure 1. Conceptual model.
In terms of moderating variables, we particularly study the impact of the level of studies on the attitude and adoption of these solutions by students. This population has the particularity of being very comfortable with technology; many hours are spent behind screens, constantly switching from the real world to the virtual world. For them, the real world and the virtual world complement each other, and the use of distance learning solutions should therefore not pose an operational problem (Carlson, 2005; Jones et al., 2010). However, voluntary vs. coerced use can lead to changes in attitude and behavior. Students in the 1st year of post-baccalaureate studies may in fact have a different approach from more experienced students, who already know the training, its habits, and its expectations, and who are therefore more reassured about their ability to follow the requirements. Furthermore, the use under constraint the COVID-19 pandemic of distance learning solutions and the digital tools made available can lead students to a form of technological dependence, or even depression—we then speak of technological addiction (Sherer and Levounis, 2022). Numerous studies testify to this (Alimoradi et al., 2022), in France (Barrault et al., 2019), in Italy (Volpe et al., 2022), in sub-Saharan Africa (Sserunkuuma et al., 2023) and in Ethiopia (Mengistu et al., 2023). The fact of having been impacted by the illness (oneself or loved ones), of having felt anxiety or of having thought about stopping one’s studies are all aspects considered in the research model.
3 Materials and methods
3.1 Instrument description
This study employed a questionnaire survey comprising three main sections:
The first section deals with respondents’ perceptions of distance learning courses. Material conditions of access are considered, as well as students’ confidence in distance learning courses. The question of stress is also addressed.
Second section looks at the conditions under which distance learning was introduced at the University (Beauvais and Creil Campus), and how students used and appropriated (or not) the tools. The organizational adaptation and resilience shown by the respondents are questioned through several open-ended questions. Although requiring specific processing, these modalities made it possible to gather spontaneous responses revealing the students’ practice.
The third part consists of a complete data sheet. In addition to gaining a better understanding of the respondents through classic socio-demographic criteria and criteria linked to studies and living conditions during the pandemic, the researchers were also interested in knowing whether the respondents had been directly affected by the COVID-19 pandemic.
More precisely, nine questions deal with students’ experience in emergency remote education during pandemic, either positive or negative. The respondents had to answer the questions using a 5-point central Likert scale (Likert, 1932): 1 = strongly disagree, 2 = disagree, 3 = no opinion, 4 = agree, and 5 = strongly agree.
3.2. Data collection
This descriptive cross-sectional study was conducted on a non-probabilistic sample comprising 402 participants from a university in France, 345 questionnaires were validated. Data collection spanned from February to March 2022 (after the lockdown), utilizing a computer/web-based methodology on www.limesurvey.com.
The questionnaire was administered to students in a variety of courses (Marketing Techniques, Business and Administration Management, Environmental Health and Safety, Logistics and Transport Management), at different levels (from L1 to L3), in both initial training and apprenticeship schemes. They came from either the Creil Campus (the 1st establishment to close its doors following the COVID-19 pandemic) or the Beauvais Campus of the University of Picardy. Descriptive statistics of the sample are illustrated in Figure 2.
Figure 2. Demographic characteristics of the survey respondents.
All procedures complied with ethical standards for research involving human participants. Participation was voluntary and anonymous, and informed consent was obtained from all respondents prior to completing the survey. Students were informed of the aims of the study, the confidential handling of their data, and their right to withdraw at any moment without consequence. The items used to assess students’ experience in emergency remote education during the pandemic are summarized in Table 1. The questionnaire was anonymous, and the administration of the questionnaire was carried out in the presence of a teacher. The standardization of administration is guaranteed by the protocol developed, with students invited to complete the questionnaire electronically during a work session in a computer room. Rigorous adherence to ethical principles was maintained throughout the questionnaire design and data collection processes.
Table 1. Questions used to assess students’ experience in emergency remote education during pandemic.
3.3 Statistical analysis
Statistical analysis in this study was carried out using SPSS Version 28 by IBM, Inc. (Armonk, NY, United States). Fundamental descriptive statistics were applied, and statistical tests were employed to assess variations among groups of variables. Parametric tests were favored due to their heightened strength and efficacy compared to non-parametric alternatives. In this scenario, a factor analysis was performed and throughout all tests, a significance level of 5% was upheld (p < 0.05). The study was carried out post the COVID-19 pandemic, the students were interviewed even though they had returned to “normal” teaching conditions. They were thus able to report their attitude and past behavior (and not behavioral intentions). This study thus makes it possible to test the validity of the TAM model in a context not of prospective but of a posteriori behavior. For all statistical analyses was used the SPSS. Some basic descriptive statistics were used, such as frequency tables for sociodemographic variables and for the nine questions used in the questionnaire to assess students’ experience in online education during pandemic.
An exploratory analysis of the items used in the questionnaire was performed using Factor Analysis (FA) with method of Principal Component Analysis (PCA). Prior to the analysis, it was confirmed whether the data were suitable for the FA analysis (Broen et al., 2015). The second criteria is the value of the Kaiser-Meyer-Olkin (KMO) measure of the adequacy, whose values should indicate adequacy according to the following scale: excellent for 0.9 ≤ KMO ≤ 1.0, Good for 0.8 ≤ KMO < 0.9, Acceptable for 0.7 ≤ KMO < 0.8, Tolerable for 0.6 ≤ KMO < 0.7, Bad for 0.5 ≤ KMO < 0.6, and unacceptable for KMO < 0.5. The third criteria was the significance of the Bartlett’s test, considering a 5% level of significance (Kaiser and Rice, 1974).
After the confirmation that data were suitable to apply FA, this was performed with PCA methods and Varimax rotation. The Kaiser Normalization was used to extract the relevant factors, with eigenvalues above 1. To investigate the percentage of variance explained by the factors extracted, were used the communalities (Broen et al., 2015). When classifying the variables (questions) according to the factors extracted by FA, the variables with factor loadings whose absolute value was lower than 0.4 were excluded, for having a low relevance to the factor (Stevens, 2009; Rohm and Swaminathan, 2004).
4 Data analysis and results
4.1 Qualitative analysis results
Qualitative analysis, carried out on open-ended questions administered to all 402 students, highlights students’ ability to adapt to unexpected circumstances (the COVID-19 pandemic) and find creative ways to maintain contact with peers and teachers, despite physical isolation. The formation of self-help groups via platforms such as WhatsApp facilitated not only the academic sharing of resources and information but also served as emotional and psychological support, providing a space to share in addition to coursework, experiences and concerns. Indeed, the results underline the importance of online mutual aid and collaborative work for emotional support and validation of their experiences. Students were able to continue learning effectively thanks to the pooling of resources (document sharing) and the organization of virtual study sessions, demonstrating the importance of such practices in reassuring them and reducing stress. These collaborative strategies ensured moral support for the students and had a positive impact on their mental health and resilience.
Networking via digital platforms is crucial to building and maintaining dynamic e-learning communities. This approach illustrates students’ pragmatic adaptation and testifies to their ability to innovate in the way they learn and to resist external constraints, thus embodying the principles of resilience theory. These verbatims reveal that students’ resilience is manifested not only in their ability to persevere individually, but also in their ability to regroup and collaborate in a disrupted educational context.
4.2 Quantitative analysis results
4.2.1 Sample characterization
A total of 345 valid responses were obtained, as summarized in Table 2 the sample is slightly skewed toward females (53.3%) compared to males (46.7%). A significant majority (98.3%) of respondents fall within the age range of 18–25 years old, indicating a predominantly young participant group. The highest proportion of respondents are in the “Bachelor Year 3” category (49.3%), followed by “Bachelor Year 1” (32.2%) and “Bachelor Year 2” (18.6%). This suggests a diverse representation across different stages of higher education. Most respondents (81.4%) reside in Oise region, indicating a localized participant group. A considerable portion (78.6%) live with their parents, highlighting a significant dependence on family support.
Table 2. Example of verbatims.
4.2.1.1 Impacts of the COVID-19 pandemic
Nearly half of the respondents (47.8%) were directly affected by the COVID-19 pandemic, while a higher percentage (82.9%) had relatives affected. This indicates a widespread impact within their social circles.
4.2.1.2 Distance learning experience
Over 60% experienced anxiety during periods of distance learning, indicating potential challenges in adjusting to remote education. Around a third (31.9%) considered stopping their studies during this period, reflecting the difficulties they faced.
4.2.1.3 Technology and connectivity
The majority (92.8%) had the necessary IT tools for distance learning, but a notable portion (7.2%) did not, potentially affecting their educational experience. A significant number (62.6%) faced connectivity issues, which could have impacted their ability to engage effectively in remote learning.
While many students had access to educational resources, challenges related to the COVID-19 pandemic, such as anxiety, connectivity issues, and familial impact, significantly influenced their educational experiences during the period of distance learning.
Table 3 summarizes these results.
Table 3. Characteristics of the sample.
4.2.2 Level of agreement of the participants with the questions
Table 4 shows the percentage of responses obtained for each of the questions according to the five scores used to measure the level of agreement (from 1 = strongly disagree to 5 = strongly agree).
Table 4. Frequency of responses to the nine questions about students’ experience in online education during pandemic.
The results indicated that for most of the questions a high number of participants did not express an opinion (percentages of score 3 ranging from a minimum of 15.9% for question Q6 to a maximum of 35.9% for Q2). For most of the items, the participants scored 1 or 2, corresponding to disagreement or strong disagreement, respectively, while for a few questions the participants expressed agreement (higher percentages of scores 4 and 5), like for questions Q1, Q6, and Q9.
4.2.3 Factor analysis results
In Table 5, it shows that one of the criteria related with the correlation matrix that should identify possible correlations between the variables. Factor analysis was conducted to identify underlying relationships between observed variables. In this research we presented a correlation matrix indicating the relationships between different variables related to distance learning experiences. The first phase was to verify the assumptions that the data were suitable to apply FA. The first one was confirmed since the correlation matrix showed some correlations between the variables (2 values of r higher than 0.5, the highest being 0.571 for the correlation between Q1 and Q4). A KMO value over 0.5 and a significance level for the Bartlett’s test below 0.05 suggest there is substantial correlation in the data.
Table 5. Correlation matrix.
The Kaiser-Meyer-Olkin (KMO) measure assesses the sampling adequacy for the analysis (Table 6). A value of 0.746 suggests a moderate to high degree of adequacy, indicating that the data is suitable for factor analysis. Bartlett’s Test of Sphericity tests whether the correlation matrix is an identity matrix. The low p-value (0) suggests that there are significant correlations between variables. Regarding the second criteria, the value of KMO was found to be 0.746, which corresponds to acceptable (Kaiser and Rice, 1974).
Table 6. KMO and Bartlett’s test.
Finally, the third criteria were also confirmed, since the Bartlett’s test was significant (p < 0.001), confirming the rejection of the null hypothesis “H0: The correlation matrix is equal to the identity matrix.”
After confirmation of the assumptions, Factor Analysis was applied as described earlier. The anti-image matrix revealed that all values of the correlations were over 0.5, and therefore neither of the variables should be excluded from the analysis (the lowest of the values was 0.632 for variable Q1, and the highest was 0.851 for variable Q8) (Table 7).
Table 7. Anti-image correlation.
In Table 8, total Variance Explained indicates the percentage of total variance in the original variables explained by each component. It helps in understanding the relative importance of each component in capturing the variability of the data whereas the cumulative percentage of variance explained by the components represents the total amount of variance explained by the current component and all previous components. It helps in deciding how many components to retain for analysis based on the desired level of explained variance.
Table 8. Total variance explained.
In this case, the first component explains 31.52% of the variance, and subsequent components add to the cumulative percentage. The first component explains the largest share of variance in the data, with an eigenvalue of 2.837. The second and third components also account for meaningful portions of variance, with eigenvalues of 1.533 and 1.017, contributing to cumulative percentages of 48.557 and 59.862%, respectively. Together, the first three components explain 59.86% of the total variance in the dataset. Although a cumulative variance close to 60% is considered acceptable for exploratory factor analysis in educational and social research, it nonetheless indicates that a substantial proportion of unexplained variance remains. Therefore, while this three-factor structure provides a reasonable representation of the underlying constructs, future studies incorporating additional variables or latent factors may enhance explanatory power and improve model precision.
From this factor analysis, the authors identified patterns and relationships among variables. In this context, it can be used to understand the underlying factors influencing students’ perceptions and experiences with distance learning. The component matrix obtained from the exploratory factor analysis is displayed in Table 9.
Table 9. Component matrix.
Component 1 is associated with factors related to the overall experience and outcomes of distance learning courses. It has high positive correlations with variables such as “confidence in the teaching of distance learning courses” (0.742) and “fear of failing your semester/year because of online courses?” (−0.591), indicating that higher confidence in teaching and concerns about academic progression during distance learning are strongly associated with this component. It also has negative correlations with variables related to stress during distance learning, such as “feeling of stress during the distance learning period” (−0.536), suggesting that higher stress levels are inversely related to this component.
Component 2 captures factors related to teacher innovation and support in the context of distance learning. It has high positive correlations with variables such as “teachers have demonstrated innovation in their distance learning” (0.527) and “teachers have used specific supports for distance learning courses” (0.480), indicating that perceptions of teacher innovation and support are strongly associated with this component.
Component 3 reflects a mix of factors related to emotional responses and social dynamics experienced during distance learning. It has positive correlations with variables such as “fear of failing your semester/year because of emergency remote courses” (0.456) and “distance learning strengthened the social cohesion of the class” (0.406), suggesting that fears about academic progression and perceptions of enhanced social cohesion are associated with this component. It also has positive correlations with variables related to stress during distance learning, such as “feeling of stress during the distance learning period” (0.447), indicating that higher stress levels are positively related to this component.
In Table 10, rotated Component Matrix aids in better interpretability, emphasizing certain variables’ stronger relationships with specific components presenting a clearer structure after a varimax rotation, aiming to simplify the interpretation of the components.
Table 10. Rotated component matrix.
4.2.3.1 Component 1 “innovation and support”
Emphasizes teacher-related factors like innovation and specific support for distance learning. It has high positive correlations with variables such as “teachers have used specific supports for distance learning courses” (0.855) and “teachers have demonstrated innovation in their distance learning” (0.841), indicating that perceptions of teacher support and innovation are strongly associated with this component.
4.2.3.2 Component 2 “emotional aspects”
Focuses on emotional aspects such as stress, confidence in listening, fear of academic success, and perceptions of exam reflection. Represents emotional responses, including stress and concerns about academic success. It has positive correlations with variables such as “feeling of stress during the distance learning period” (0.852) and “confidence in listening to distance learning courses” (0.507), suggesting that higher stress levels and confidence in listening skills during distance learning are associated with this component.
4.2.3.3 Component 3 “academic anxiety over pedagogical trust”
Relates more to factors like group work difficulty, confidence in pedagogy, and confidence in distance course listening. This component appears to represent a mix of factors related to the experience of distance learning and its impact on academic progression and confidence in teaching. It has positive correlations with variables such as “fear of failing your semester/year because of online courses” (0.830) and “confidence in listening to distance learning courses” (0.415), indicating that fears about academic progression and confidence in teaching are associated with this component. The negative loading of confidence in teaching (−0.478) indicates vulnerability and reduced trust rather than resilience; therefore, this factor was renamed Academic Anxiety and Pedagogical Trust. This suggests an inverse relationship between confidence in teaching and this Academic Anxiety and Pedagogical Trust.
By having a refined perspective on how different variables group together in terms of underlying factors, each component seems to represent specific aspects of the students’ experiences with distance learning: teacher-related aspects, emotional responses, and practical difficulties. The factor analysis reveals three distinct components that encapsulate various aspects of students’ experiences with distance learning. These results are in line with recent studies such as Ferreras-Garcia et al. (2022) who compare the on-line university students and the face-to-face university students (as this study). They highlight that the face-to-face university students were affected more in terms of the results of their learning process than development of competences. This study emphasizes the critical role of teachers and their innovative strategies, the emotional impact on students, and the practical challenges encountered during distance learning, three aspects of the learning process.
A series of additional group-based analyses were conducted to examine whether experiences of emergency remote learning differed according to demographic characteristics. A one-way ANOVA was performed to test differences across study levels (Bachelor Year 1, Year 2, and Year 3) for each of the three factor scores. Results indicated no statistically significant differences for Innovation and Support, F(2, 342) = 1.04, p = 0.356, Emotional Aspects, F(2, 342) = 0.16, p = 0.855, or Academic Anxiety and Pedagogical Trust, F(2, 342) = 1.60, p = 0.204. These findings suggest that students reported similar academic and emotional experiences across study years during emergency remote learning. Full results are presented in Table 11.
Table 11. One-way ANOVA comparing factor scores across study levels.
Independent samples t-tests were then conducted to examine differences by gender. No statistically significant differences emerged for Innovation and Support, t(343) = −0.92, p = 0.358, or Emotional Aspects, t(343) = 0.57, p = 0.568. However, a significant difference was found for Academic Anxiety and Pedagogical Trust, t(343) = −2.97, p = 0.003, with female students reporting higher levels of anxiety and lower confidence in teaching quality. These results are presented in Table 12.
Table 12. Independent samples t-tests comparing factor scores by gender.
5 Discussion
Factors influencing students’ online learning experiences during the COVID-19 pandemic in France focus on the challenges and opportunities associated with emergency remote learning. The study aimed to assess student attitude with emergency remote learning, particularly during the COVID-19 pandemic including access to technology, value of learning, facilitating conditions and behavioral intentions. Kawane et al. (2025) show that strengthening online higher education in disaster risk reduction requires aligning technological design with students’ real capacities and emotional wellbeing, a perspective that resonates strongly with the arguments discussed in this article, where the authors emphasized how resilient and sustainable educational environments emerge when digital technologies are integrated with an awareness of learners’ contextual, infrastructural, and socio-emotional realities. We can argue that facilitating conditions and learning value have been identified as having a positive impact on emergency remote learning, leading to an acceptance of emergency remote engagement even beyond the pandemic. Additionally, variations in learning values were observed across different education levels, with graduate students showing higher values. The study highlighted the importance of educational and technical support from institutions to enhance students’ emergency remote learning experience. The contribution of the study aligns with the CCPT framework as it addresses the integration of technology (emergency remote learning platforms) with pedagogy (effective teaching strategies) and content knowledge (implementation program implementation) in the context of emergency remote learning during a crisis. This work highlights the importance of teachers’ understanding of how technology can be used effectively in specific content areas. Connectivism theory recognizes the changing dynamics of learning in the networked digital age. The research is interested in how management education adapts in times of health crisis compared to traditional technical training, especially since the COVID-19 pandemic has accelerated access to new tools and news realities of emergency remote learning. The study draws on theories of innovation to examine the role of innovation in higher education.
The independent samples t-tests showed no significant gender differences for Innovation and Support or Emotional Aspects, suggesting that male and female students perceived technological usability, teacher responsiveness, and emotional support similarly during emergency remote learning. However, a significant difference emerged for Academic Anxiety and Pedagogical Trust, t(343) = −2.973, p = 0.003, with female students reporting higher anxiety and lower trust in teaching quality. This aligns with recent studies showing that female university students generally report higher levels of academic worry and stress in online or hybrid modalities, especially when assessment procedures are unclear or workload increases unexpectedly (Aristovnik et al., 2020). Research has also demonstrated that women tend to perceive greater pressure to perform well academically, and online learning environments may intensify this pressure due to increased self-regulation demands and reduced face-to-face reassurance from instructors (Besser et al., 2022). Furthermore, trust in teachers and institutions appears to be more strongly associated with emotional wellbeing among female students than male students during remote learning (Baticulon et al., 2021). Although this effect should be interpreted cautiously, as the present data rely on self-reported perceptions rather than clinical measures, it highlights the importance of gender-sensitive pedagogical strategies during digital transitions. Strengthening communication clarity, providing timely feedback, and ensuring transparent grading criteria may help reduce the disproportionate academic anxiety experienced by female students.
Educational Resources for Emergency Remote Learning: has taken the place of the traditional teaching method. Students can experience another perspective through Educational Resources for Emergency Remote Learning. The new teaching technique comes with several problems. Educational institutions are working to compensate for lost learning while seeking solutions to the problems caused by the lockdown. Universities need resources to compensate for lost learning when they reopen. The creator of innovative educational tools may choose to focus on customizing the needs of the workforce to solve the problem of accessibility for all students from different economic backgrounds. Given the current situation, education systems around the world need to invest in the professional development of teachers, particularly in the areas of ICT and effective pedagogy. The COVID-19 pandemic has shown us that teachers and students/learners need to be trained in the use of various Educational Resources for Emergency Remote Learning. It highlights the importance of digital literacy, networked learning, and the ability to navigate and connect to resources in Educational Resources for Emergency Remote Learning.
5.1 Overcoming digital divide
While students demonstrated notable adaptability and emotional resilience throughout the transition to emergency remote learning, it is important to acknowledge that resilience alone could not compensate for material inequalities. Many students continued to face structural barriers such as unstable connections of the Internet, limited access to digital devices, and inadequate study spaces at home. These constraints directly affected participation, engagement, and academic performance, regardless of individual motivation or coping strategies. In this sense, resilience did not eliminate material inequities; rather, it operated within the limits imposed by technological and socio-economic conditions. This underscores that psychological or social coping mechanisms must be complemented by institutional support and infrastructural investment to ensure equitable learning opportunities during digital transitions. Based on the findings, following suggestions could be made:
5.1.1 Embrace technology-assisted learning
Given the growing importance of technology in education, it is imperative that educational institutions fully embrace technology-assisted learning. This includes providing students with essential resources, such as access to devices and reliable internet connectivity, to facilitate effective emergency remote learning experiences. Additionally, institutions should invest in training and supporting educators so that they can effectively use technology in their teaching methods and create engaging and interactive emergency remote learning environments. The principles of the Technological Pedagogical Content Knowledge (TPCK) framework (Koehler and Mishra, 2009) can guide educators in effectively integrating technology into their teaching practices.
5.1.2 Promote educational innovations
This involves integrating active learning strategies, experiential learning opportunities, and real-world case studies into curricula. By integrating innovative teaching methods such as flipped classrooms, project-based learning, and simulations, higher education institutions can cultivate critical thinking, problem-solving skills, and knowledge application. Constructivist learning theories (Piaget, 1985) can support these strategies, emphasizing the importance of active engagement in the learning process. The principles of responsive curriculum design (Hussein et al., 2022) emphasize the need for curricula to remain adaptable and align with contemporary demands.
5.1.3 Promote collaboration and networking
Encouraging interdisciplinary collaboration and fostering a supportive ecosystem, in line with the theory of communities of practice (Wenger, 1998) can lead to new ideas, shared learning experiences and innovative solutions.
5.1.4 Promote student support and engagement
As emergency remote learning gains importance, it is imperative to put in place adequate mechanisms for student support and engagement. Institutions should find ways of consistent communication and interaction between students and teachers. Emergency remote forums, virtual office hours, and peer collaboration platforms can facilitate active participation and address student questions and concerns. Recognizing the social-emotional wellbeing of students, institutions should ensure access to mental health counseling or support services, as advocated by student development theory (Evans et al., 2020).
This article also integrated resilience as a catalyst to influence the adoption of new technology (Zoom) in the event of a global health crisis. Indeed, adaptability, perseverance and social bonding enabled students to have a positive attitude toward technology. The most resilient students used a variety of innovative teaching tools and were able to adapt to the resulting changes. Resilience is not just the ability to survive and overcome crisis, but also the willingness to explore new solutions.
5.1.5 Promote unique solutions
Studies suggest that some people can cope with adversity better than others and that there are differences in the resilience of men and women, influenced by social, economic and cultural factors. Research mentions (Novella and Arribillaga, 2014) that there are differences in the level of resilience, particularly among teenagers and students. However, this survey showed that there is no difference between male and female students in their ability to be resilient and to adopt new digital tools. This makes it possible to envisage unique solutions for all students, regardless of their gender or age (as long as they are all in the same age bracket).
5.1.6 Promote social ties and student community
Developing students’ behavioral and cognitive plasticity, managing their stress and developing social ties are important foundations for encouraging the adoption of innovative teaching methods. Improving resilience in students involves a range of strategies and interventions designed to strengthen their ability to cope with challenges and adapt to change. For example, strengthening positive relationships between students and teachers and promoting a sense of community and belonging within the institution fosters a caring educational environment. In addition, the development of mentoring and support groups reduces the fear associated with crisis situations and encourages learning.
5.1.7 Promote gamification
We propose to apply the principle of gamification (Deterding et al., 2011a; Deterding et al., 2011b; Huotari and Hamari, 2012; Barata et al., 2017) to distance learning actions. Gamification consists of adopting the specific characteristics of games in a completely different context (Krath et al., 2021), in particular by integrating the notions of competition and reward (in the form of credits or points). By stimulating students more and making them more involved in the training, they could participate actively. This can be as simple as speaking up, putting the camera in front of them, asking questions and sharing comments, all spontaneously (and not at the express request of teachers).
5.1.8 Manage anxiety
Students’ anxiety caused by the crisis can have a major impact on their learning experience. When anxiety is moderate, it can sometimes act as a motivating and concentrating force. However, when anxiety becomes excessive, it can have a detrimental effect on the learning process. Anxious students can experience a lack of confidence in their abilities, which can lead them to avoid distance learning situations or become less actively involved in their training. Resilient students, on the other hand, know how to manage their emotions effectively and are more involved. Resilience encourages an optimistic but realistic view of situations. It enables students to see beyond immediate difficulties and perceive challenges as opportunities for growth and learning. Students cultivate social support networks with peers, teachers and family. These relationships provide emotional and practical support that can alleviate anxiety and foster a sense of security.
Although resilience can encourage the adoption of new technologies, it does not necessarily guarantee an effective transition to distance learning. Other factors, such as access to technological resources and the level of digital literacy, play an equally important role. Resilience alone cannot overcome all the practical and technological obstacles encountered during this transition.
6 Conclusion
In conclusion, our study on the factors influencing students’ emergency remote learning experiences during the COVID-19 pandemic in France sheds light on crucial aspects that impact the effectiveness of emergency remote education. The investigation focused on the student point of view, addressing key elements such as access to technology, the value of learning, facilitating conditions, and behavioral intentions. Facilitating conditions and learning value emerged as pivotal factors positively affecting emergency remote learning, suggesting a potential acceptance of emergency remote engagement even beyond the pandemic.
Our findings also revealed variations in learning values across different education levels, with graduate students exhibiting higher values. The study underscored the significance of educational and technical support from institutions in enhancing the overall emergency remote learning experience. The research aligns with the CCPT framework, emphasizing the integration of technology (emergency remote learning platforms), pedagogy (effective teaching strategies), and content knowledge (implementation program implementation) during the crisis. The Connectivism theory recognized the evolving dynamics of learning in the networked digital age.
Moreover, the study explored that maintaining and strengthening social ties through digital interactions played a key role in improving students’ resilience during the COVID-19 pandemic. Students were forced to draw on external resources (peers, teachers and educational teams) to alleviate stress and build resilience.
For future work, it is imperative to delve deeper into the factors influencing students’ emergency remote learning experiences, particularly in more diverse cultural contexts and educational settings. Longitudinal research would allow the evolution of attitudes, behaviors, and emotional responses to be tracked over time, offering valuable insight into the sustained impact of emergency remote learning beyond the initial crisis. In addition, examining the effectiveness of targeted interventions, such as tailored digital learning tools and professional development in ICT and pedagogy for teachers may provide practical solutions to strengthen online learning environments (Boustani et al., 2024). Further investigation into socio-economic disparities, digital access, and support structures will be essential to ensuring greater equity of opportunity. Finally, it should be noted that although the three extracted components explained 59.86% of the total variance (Table 8), an acceptable proportion for exploratory studies, a substantial share of unexplained variance remains. Future research incorporating additional variables or latent factors may therefore enhance explanatory power and provide a more comprehensive view of students’ digital learning experiences. As emergency remote education continues to evolve, ongoing research must remain adaptive, integrating emerging technologies and innovative pedagogical approaches to respond to the dynamic challenges of digital teaching and learning.
Because the sample derives from the first institution to close and while our study provides valuable insights, it is not without limitations. The research primarily focused on the French context, and findings may not be universally applicable. Additionally, the study’s reliance on self-reported data could introduce response biases. The retrospective design also introduces potential recall bias. As students were surveyed in 2022 about experiences during the early months of the COVID-19 pandemic in 2020, their perceptions may have shifted due to later academic experiences, emotional distance from the crisis, or memory reconstruction. This temporal gap could therefore influence the accuracy and reliability of self-reported attitudes. Future research should consider diverse cultural contexts and employ a mix of qualitative and quantitative methodologies for a comprehensive understanding of emergency remote learning experiences.
In conclusion, the COVID-19 pandemic has accelerated the need for innovative approaches to education. Addressing the identified challenges and leveraging the opportunities will be pivotal in shaping a resilient and effective emergency remote learning landscape for the future.
Conflict of insterest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The requirement of ethical approval was waived by the LEFMI laboratoire for research in Amiens France for the studies involving humans because the study doesn’t involve direct tests on human it involved the students perception of online group work; moreover, the students gave their consent to answer and the questionnaire was anonymous. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
NB: Conceptualization, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. LM: Data curation, Investigation, Resources, Supervision, Validation, Writing – original draft. NA-C: Conceptualization, Investigation, Project administration, Resources, Writing – original draft.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords: digital technologies, distance learning, emergency remote learning, online learning, resilience theory, technology acceptance model
Citation: Boustani NM, Mourtajji L and Arts-Chiss N (2026) Learning to adapt: educational technologies and the construction of sustainable and resilient learning environments. Front. Educ. 10:1686368. doi: 10.3389/feduc.2025.1686368
Received: 15 August 2025; Revised: 30 November 2025; Accepted: 09 December 2025;
Published: 13 January 2026.
Edited by:
Mayra Urrea-Solano, University of Alicante, SpainReviewed by:
Musawer Hakimi, Osmania University, IndiaIngrid Kirschning, University of the Americas Puebla, Mexico
Tomo Kawane, Hiroshima University, Japan
Copyright © 2026 Boustani, Mourtajji and Arts-Chiss. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nada Mallah Boustani, bmFkYS5tYWxsYWhib3VzdGFueUB1c2ouZWR1Lmxi