Content area
This mixed-methods study, informed by Social Cognitive Theory, investigates the interplay of technological self-efficacy, intrinsic motivation, and contextual factors in shaping e-learning strategy use and overall satisfaction among advanced EFL university students. A quantitative survey of 147 students provided broad insights, which were enriched by a qualitative case study of three learners offering detailed perspectives. Quantitative results revealed significant positive correlations between technological self-efficacy and both e-learning strategy use and overall satisfaction, as well as between contextual factors and these same outcomes. While intrinsic motivation showed significant correlations with strategy use and satisfaction, it did not directly predict overall satisfaction in the regression analysis. However, this highlights its role in shaping the learners’ choice and application of strategies. The regression model also showed that both technological self-efficacy and positive perceptions of contextual factors were significant direct predictors of overall satisfaction. The analysis of qualitative data underscored the importance of diverse learning strategies (cognitive, metacognitive, and social-affective) and highlighted key contextual factors, such as structured curricula, formative assessments, interactive instruction, and opportunities for peer interaction. Furthermore, findings indicated that years of study influenced e-learning strategy use, and that prior online learning experience was linked to overall satisfaction. These findings emphasize the need for e-learning environments that foster technological self-efficacy and positive perceptions of contextual factors, and that support intrinsic motivation through engagement and challenge. Further research is recommended to explore these complex interactions in varied learning contexts and with larger and more diverse samples to identify specific patterns and developmental trajectories in e-learning.
Introduction
The rapid global expansion of e-learning has transformed educational landscapes, particularly in language acquisition, shifting towards learner-centered approaches (Kang et al. 2020; Montebello 2021; Suárez and El-Henawy 2023). Online language programs have experienced significant growth, underscoring the need to understand the factors that shape learners’ experiences and outcomes in these digital settings (Choi et al. 2023; Mcilwraith and Fortune 2016). While research acknowledges the importance of learner-specific variables like motivation, attitudes, and learning strategies in language proficiency (Dörnyei and Ushioda 2013), the unique affordances of e-learning environments introduce further complexities (Dizon 2023; Liu and Yu 2022). This necessitates examining not only these learner-specific variables but also how they interact with contextual factors – the elements of the learning environment such as curriculum design, assessment practices, and modes of instruction (Blake 2013; Kang et al. 2020). These contextual factors can either support or hinder learners’ technological self-efficacy and intrinsic motivation, thereby influencing their engagement and success in e-learning environments (Al Doghan and Piaralal 2024; Merhi and Meisami 2024). For instance, limited access to reliable internet or a lack of adequately designed online resources can diminish learners’ confidence in using technology for learning (reducing TSE) and decrease their inherent interest in the learning process (lowering intrinsic motivation) (Alieto et al. 2024; Mhlanga 2024; Paran et al. 2024; Rafiq et al. 2024).
This study focuses on understanding the relationships between two specific learner variables—technological self-efficacy (learners’ beliefs in their ability to use technology effectively for learning) and intrinsic motivation (the desire to learn driven by internal interest and enjoyment) – and their influence on the use of e-learning strategies (deliberate actions learners take to enhance their language learning), among advanced EFL university students in Egypt. While prior research has explored these elements individually, a precise understanding of how technological self-efficacy and intrinsic motivation interact to influence the specific selection and application of e-learning strategies, and thus learning success, among advanced EFL learners remains limited. This study, therefore, investigates this interplay in a specific context, aiming to provide insights that can inform the development of more effective e-pedagogical practices tailored to the needs of advanced language learners in similar developing-nation settings.
Achieving proficiency in a second language (L2) is a complex process influenced by numerous factors, including instructional methods, learning environments, and individual learner differences. Among the most crucial learner-internal factors are the approaches and techniques individuals consciously employ to manage their own learning. These techniques, often referred to as language learning strategies, encompass a wide range of cognitive, metacognitive, affective, and social actions taken to enhance learning and language use. Research indicates a correlation between the effective use of language learning strategies and higher language proficiency (Feng and Liu 2022; Pawlak 2021; Teng 2022; Wenden 1991). Consequently, it is crucial that learners are provided with learning experiences and opportunities to effectively develop and implement these strategies. Understanding how learners utilize these strategies, particularly within university-level online English language programs in Egypt, remains a vital area for investigation to inform more effective pedagogical practices.
Research question
How do technological self-efficacy and intrinsic motivation relate to the e-learning strategies employed by advanced EFL university students in Egypt?
Hypotheses:
H1: A positive correlation exists between technological self-efficacy and the range of e-learning strategies employed by advanced EFL university students in Egypt.
H2: A positive correlation exists between intrinsic motivation and the range of e-learning strategies employed by advanced EFL university students in Egypt.
Literature review
The influence of learner variables on second language acquisition (SLA) is a well-established area of research, with internal factors such as beliefs, attitudes, motivations, and learning strategies playing a crucial role (Busch 2010; Dörnyei and Ushioda 2011). In e-learning, where learners navigate unfamiliar technologies and adapt to new learning modalities, these factors are especially critical (Blake 2013; Kang et al. 2020). This review explores the interplay of technological self-efficacy and intrinsic motivation with e-learning strategies within the context of advanced EFL learning, addressing the complex and dynamic nature of such an interplay. It recognizes the importance of both deliberate (instructed) and incidental learning experiences in supporting learner development, as such experiences provide a rich context for learning and skill enhancement.
Theoretical framework: the social cognitive theory of learning
This study is informed by Social Cognitive Theory (SCT) (Bandura 1986), which posits that learning is a dynamic interaction of personal factors (beliefs, attitudes, motivation), environmental factors (social interactions, feedback, learning resources), and behavioral factors (learning strategies, self-regulation). SCT offers a particularly relevant framework for understanding the learning experience in online settings, where learner interactions are facilitated and mediated by technology, and where learners must develop self-directedness and autonomy (Godwin-Jones 2019; Reinders and Hubbard 2013). SCT highlights the dynamic relationship between these three factors, emphasizing the fact that modifications in one can potentially affect changes in the others, thus resulting in a dynamic and constantly evolving learning experience.
Specifically, SCT provides insights into the ways in which a learner’s belief in their ability to learn can be as crucial as the learning environment itself. This theory emphasizes the importance of observational learning, whereby learners are not only influenced by the direct feedback from their environment but also through observation, by witnessing the behaviors of others in their learning environment. Moreover, self-efficacy and self-regulation are also central to SCT. Self-efficacy refers to learners’ beliefs in their ability to succeed in a given task, which significantly impacts their motivation and engagement. Self-regulation, on the other hand, involves the ability to monitor, adjust, and control one’s learning processes, a skill essential for navigating e-learning environments autonomously.
SCT can also explain how a student’s technological self-efficacy (TSE) influences their interaction with and adoption of e-learning strategies (Montaño-González 2020). When students believe in their capacity to manage digital environments effectively, they are more likely to actively explore and utilize various digital resources and approaches. Similarly, intrinsic motivation in e-learning can drive the persistence needed for independent learning and implementation of various learning strategies. The dynamic interaction between these internal factors and elements in the learning environment has an influence on learning experiences. Therefore, this theoretical approach offers a valuable lens for understanding how a learner’s belief in their ability to use technology and their internal drive to learn can have an influence on their engagement in e-learning. Drawing from this theoretical perspective, this study aims to deepen our understanding of how learners process and respond to e-learning experiences.
Technological self-efficacy, intrinsic motivation and e-learning strategies
Understanding how learners engage with the online learning environment requires a closer examination of the interplay between key learner variables and their influence on e-learning strategy adoption. In this vein, three critical areas are here considered: technological self-efficacy, intrinsic motivation, and the specific e-learning strategies employed by learners. Their respective roles and interconnected influence in the learning process are highlighted.
Technological self-efficacy (TSE)
TSE refers to a learner’s belief in their ability to use technology successfully for learning (Compeau and Higgins 1995). Learners with high TSE tend to embrace technology for language acquisition (Hanif et al. 2018) and have a greater capacity to view technology as a useful tool that can expand access to multiple learning resources (Pan 2020). The literature shows that when learners feel confident using technological tools, they tend to become more autonomous in their learning (Macfadyen and Dawson 2010), a core principle in the Social Cognitive theory of learning. Studies have also shown that higher technological self-efficacy is linked to more positive attitudes towards technology-mediated learning, creating a positive cycle where competence with technology results in greater engagement (Chen et al. 2023).
Beyond engagement and autonomy, learners who believe in their abilities to learn and use technology are also more likely to persist when encountering challenges and feel more motivated to explore new technologies for learning purposes, now immensely scaffolded by artificial intelligence and chatbots (Alrajhi 2024; Al Shamsi et al. 2022; Belk 2021). Furthermore, Calafato (2023) found a positive correlation between technological self-efficacy and the use of technology-mediated learning strategies in a study of multilingual university students learning Arabic as a foreign language. This suggests that confidence in using technology can lead to more effective and diverse strategy use.
However, learners with low TSE may experience apprehension towards technology-based tasks, possibly resulting in avoidance behaviors (Macfadyen and Dawson 2010), reinforcing the importance of fostering a positive digital learning environment (Akram and Li 2024). Research suggests that such learners may also struggle with self-directed learning in e-learning settings that rely heavily on independent technology use and navigation (Li and Wu 2023; Li et al. 2025). Additionally, they may be less likely to experiment with new technological approaches to learning, which can hinder their development as autonomous learners (Bandura 1997; Lai et al. 2022). As such, there is a need for educators to address challenges in learning environments that might discourage or inhibit engagement with technology (Selwyn 2016; Yeganeh et al. 2025) and promote the use of digital platforms, tools, and resources in a user-friendly and supportive environment (Kirschner and van Merriënboer 2013).
Intrinsic motivation
Motivation, a fundamental aspect of learning, strongly impacts SLA (Dörnyei 2001; Dörnyei 2014). Intrinsic motivation stems from an internal desire for enjoyment, interest, or a sense of achievement, and research indicates that intrinsically motivated learners are more likely to succeed (Deci and Ryan 2000). This inner drive motivates learners to engage more deeply with the content, actively seeking out knowledge that they perceive as enjoyable or relevant to their personal goals (Alrajhi 2020; Keller and Suzuki 2004). In the context of e-learning, research shows that intrinsically motivated learners are more proactive and engaged, seeking out online resources and participating actively in online learning communities (Dörnyei 2001; Gao et al. 2025). Studies have also found that learners with higher intrinsic motivation tend to exert more effort in learning activities and are more persistent in the face of obstacles, which leads to more meaningful engagement with the target content (Guo et al. 2025; Guo et al. 2025).
This proactivity extends to making use of different types of strategies, which results in deeper engagement with learning, more efficient use of available time, and a greater degree of learning autonomy (Dizon and Tang 2020. This highlights the fact that creating a positive e-learning environment which is both challenging and engaging has the capacity to enhance motivation and, subsequently, improve language learning outcomes. Therefore, understanding and fostering intrinsic motivation among advanced EFL learners is important for educators designing e-learning content and experiences that learners find rewarding.
The field of second language acquisition has long recognized the active role learners play in their own developmental trajectories. Language learning strategies (LLS), defined by Oxford (1990) as “specific actions taken by the learner to make learning easier, faster, more enjoyable, more self-directed, more effective, and more transferrable to new situations” (p. 8), represent a key aspect of this learner agency. Investigating the impact of these strategies has been a significant research focus. Research indicates a correlation between the effective use of language learning strategies and higher language proficiency (Feng and Liu 2022; Pawlak 2021; Teng 2022; Wenden 1991). Consequently, it is crucial that learners are provided with learning experiences and opportunities to effectively develop and implement these strategies. This foundational link underscores the potential benefits of strategy-based instruction and highlights the need to understand the complex interplay between strategy use, individual differences (such as motivation and self-efficacy), and specific learning tasks.
E-learning strategies
Language learning strategies are deliberate, conscious actions learners take to improve their language proficiency (Oxford 1990). These strategies are categorized into:
Cognitive strategies
These involve manipulating language information directly (e.g., using dictionaries, vocabulary practice, extensive reading) (Cohen 2014; Oxford 2013). In the context of e-learning, cognitive strategies might include using online flashcards, engaging with interactive exercises, practising vocabulary through apps, or engaging in online reading programs that are specifically designed to promote vocabulary acquisition (Lin and Lin 2019; Zou et al. 2021). Such specific cognitive approaches include focusing on word meaning, breaking down new words into smaller components, and associating new vocabulary with previously acquired concepts (Izmalkova and Blinnikova 2024; Teng 2022). These approaches, when implemented in e-learning environments, allow learners to explore content at their own pace and focus on individual learning needs (Kohnke et al. 2021; Sun et al. 2023).
Metacognitive strategies
These involve planning and monitoring the learning process (e.g., setting goals, evaluating progress, adapting learning approaches) (Cohen 2014; Oxford 2013). Examples in e-learning might include using online tools to track learning progress or adjust learning plans based on identified weaknesses, setting specific learning objectives, allocating time for different activities, and reviewing previously learned material to assess progress. Such a conscious, self-directed learning behavior requires an awareness of the nature of the tasks that are undertaken, as well as a readiness to respond to new challenges. In such circumstances, the metacognitive approach becomes essential for guiding the learning process.
Social-affective strategies
These focus on interactions and emotional management (e.g., seeking feedback, collaborating, managing anxiety). In online contexts, this might involve seeking feedback in online forums or engaging in online collaboration projects with peers that lead to the development of communicative skills, while encouraging the use of language in authentic and enjoyable learning environments (Lai et al. 2022; Yeganeh et al. 2025). Such social-affective strategies enable learners to develop a sense of community, share their experiences, provide and receive feedback, and participate in activities that promote positive emotional experiences and reduce anxiety (Guo et al. 2025; Liu et al. 2025). These strategies enable learners to better navigate their learning challenges and to foster a supportive learning community (Selwyn 2016; Kirschner and van Merriënboer 2013).
Effective use of language learning strategies has been shown to correlate with higher language proficiency (Teng 2022; Pawlak 2021; Wenden 1991). It is crucial, therefore, that learners are given access to learning experiences and opportunities to develop and implement these strategies effectively.
Specific e-learning contexts and frameworks
The use of technology in education has expanded significantly (Voogt et al. 2013), with e-learning platforms offering increased flexibility and resources that traditional classroom settings cannot match. This is particularly true in developing nations, where e-learning has the capacity to provide access to high-quality English language instruction (Alrajhi 2023; Latif 2018; Shaalan 2022). Online learning platforms can provide learners with a broader range of resources, personalized instruction, and opportunities for peer interaction that surpass the limitations of conventional classroom settings (Shaalan 2022). Furthermore, e-learning has the potential to effectively address the growing demand for individualized learning experiences, catering to the unique needs and learning styles of diverse student populations (El-Sheikh 2020).
However, the impact of contextual factors, such as cultural and socioeconomic backgrounds, should not be ignored, and the lack of access to reliable internet connections and technology remains a real challenge for some learners (Mahmud and Gope 2009; El-Seoud et al. 2014; Adel 2017). Learners’ experiences with e-learning are often impacted by various social factors, and access to technology and reliable internet can significantly influence their motivation and engagement with online learning. For advanced EFL learners, such as university students, specific frameworks are of importance. Research suggests that fostering technological self-efficacy and intrinsic motivation is crucial for success in such environments (Pan 2020; Derakhshan and Fathi 2023). Creating a supportive e-learning environment that enhances learner engagement and promotes self-directed learning is essential for optimal outcomes (Okojie et al. 2019).
In e-learning environments, learner self-efficacy (Bandura 1997) is crucial for engagement, persistence, and achievement (Al Doghan and Piaralal 2024), with higher computer self-efficacy correlating with positive attitudes towards technology (Compeau and Higgins 1995; Pan 2020), and links to learning strategies (Montaño-González 2020) and student-teacher relationships impacting motivation (Akram and Li 2024).
Extramural English
This is a key concept in this study, referring to learners’ engagement with English beyond the classroom (Sundqvist and Sylvén 2016), such as through gaming or watching English-language media (Bräuer and Mazarakis 2024; Jabbari and Eslami 2019; Keller and Suzuki 2004). This can be considered a valuable opportunity to experience English as it is used in real-world contexts. Extant research (Calafato and Clausen 2024; Reinhardt and Han 2021) highlight the importance of vocabulary learning strategies used when gaming, alongside gaming frequency. Furthermore, research on extramural English has revealed a positive link between various out-of-class activities and vocabulary knowledge and reading skills (Brevik 2016; Ebadi et al. 2023; Rød and Calafato 2023; Sundqvist and Wikström 2015). These studies emphasize the importance of exposure to authentic language and engagement in various extracurricular activities as it impacts on their learning (Lee and Lu 2023; Rød and Calafato 2023; Yang and Wu 2015).
Virtual reality (VR) and e-learning
The use of virtual reality (VR) as a means to enhance e-learning experiences has grown in recent years (Panconesi and Guida 2021). A meta-analysis by Qiu et al. (2024) found a positive effect of VR-based learning on EFL performance, particularly in the development of communicative skills. VR can also provide a more immersive learning environment with increased opportunities for interaction with digital content (Chen et al. 2019). This interactivity and the capability to provide learners with a sense of ‘presence’ have resulted in increased learner engagement and motivation, resulting in more positive learning experiences and better learning outcomes. The use of VR has resulted in a growing understanding of how self-efficacy and motivation can play a significant role in learning within these environments, and that technology plays a critical role in how learners utilize different strategies (Reinhardt 2018).
Such specific learning environments, while different from the context of our study, suggest the potential for technology to enhance learning through the facilitation of self-efficacy, motivation, and learning strategy use. At the same time, research into the application of VR in EFL highlights the importance of incorporating task-based approaches to ensure that learners’ engagement is not solely focused on technology but also on the learning process (Pan 2020). This approach prioritizes the task itself, thereby promoting real-world applications of learning, while using VR’s affordances to facilitate such applications (Dizon 2023).
The unique landscape of e-learning in developing nations
The educational landscape in many developing nations is undergoing a rapid shift towards e-learning, driven by the increasing availability of online resources and the growing demand for English proficiency in a globalized economy (Shaalan 2022). While e-learning offers exciting possibilities for enhancing language education in these regions, navigating this transition requires a nuanced understanding of the unique challenges and opportunities present.
Cultural and socioeconomic factors
Learners’ experiences with e-learning are shaped by a complex interplay of cultural and socioeconomic factors. For example, access to reliable internet connectivity and technology infrastructure remains a significant challenge, particularly for learners in rural areas (Mahmud and Gope 2009). Additionally, cultural values and attitudes towards technology can influence learners’ motivation and engagement with online learning (El-Seoud et al. 2014; El-Seoud et al. 2013). Some learners may prefer traditional, classroom-based instruction, highlighting the need to bridge the gap between conventional and online learning approaches (El-Seoud et al. 2014). The study by Zalat et al. (2021) underscores the importance of addressing technological challenges in e-learning and highlights the critical role of motivation in e-learning in such contexts.
Despite these challenges, e-learning presents significant opportunities for increasing access to high-quality English language instruction in developing nations. Online learning platforms can provide learners with a broader range of resources, personalized instruction, and opportunities for peer interaction that surpass the limitations of conventional classroom settings (Shaalan 2022). Furthermore, e-learning has the potential to effectively address the growing demand for individualized learning experiences, catering to the unique needs and learning styles of diverse student populations (El-Sheikh 2020).
The need for research in context
The rapid growth of e-learning in many developing nations like Egypt, driven by increased access to online resources and a heightened demand for English proficiency in a globalized world (Shaalan 2022; Ismail and Gohar 2023), warrants a closer examination of the intersection of learner variables, technology use, and e-learning strategies in specific contexts, such as the one in our study. It is important to recognize that prior experiences and preferences for traditional learning may not always translate well into online spaces (El-Seoud et al. 2014; El-Sheikh 2020; Jennings 2021), and that e-learning should account for differences in learner background, technical proficiency, and motivation.
Effective e-pedagogical practices that cater to diverse learning styles and needs require both an acknowledgment of these differences and the provision of necessary support and guidance. Accordingly, this literature review underscores the importance of cultivating technological self-efficacy and intrinsic motivation, and the establishment of supportive e-learning environments for effective implementation of diverse learning strategies.
Method
This study employed a mixed-methods approach, combining a quantitative survey with a qualitative case study to explore the complex dynamics of e-learning among advanced EFL university students in Egypt. This approach was chosen to provide both broad, generalizable insights into the phenomena under investigation and in-depth understanding of individual experiences. The study comprised two distinct phases: a quantitative phase followed by a qualitative phase, with integration of findings achieved through data transformation.
Quantitative phase
This study employed a convergent parallel mixed-methods design to explore the complex dynamics of e-learning among advanced EFL university students in Egypt. This approach was chosen to provide both broad, generalizable insights (via the quantitative survey) and in-depth understanding of individual experiences (via the qualitative case study). In a convergent design, quantitative and qualitative data are collected concurrently, analyzed separately, and then merged to compare and contrast the findings, seeking confirmation/convergence, elaboration/explanation, and complementarity. The study comprised two distinct phases: a quantitative phase followed by a qualitative phase, with integration of findings achieved through side-by-side comparison and data transformation, as described below.
Participants
A cohort of 147 participants were recruited through email invitations sent to all students enrolled in the online English language program at a public Egyptian university, ensuring voluntary and confidential participation. The final sample comprised 147 advanced EFL university students, including 100 females and 47 males. The age of participants ranged from 19 to 24 (M = 21.1, SD = 1.7), and their level of language proficiency, was classified as ‘advanced’ based on their enrollment in the final year of their undergraduate English language and literature program, completion of at least three years of formal English language instruction, and a minimum score of B2 on the Common European Framework of Reference for Languages (CEFR) placement test. All of the participants also had varying degrees of prior experience with online learning, ranging from no prior experience to extensive prior experience in other fully-online or blended learning programs.
Surveys
The “Exploring the Dynamics of E-Learning in Advanced English Language Learning” survey comprised six sections. The survey instrument, designed to explore the dynamics of e-learning, included six sections: demographic information (gender, age, year of study, and prior online learning experience), technological self-efficacy (measured with five items assessing confidence in using technology for learning), intrinsic motivation (measured with five items gauging internal drive to learn English), e-learning strategies (measured with ten items assessing the use of cognitive, metacognitive, and social-affective approaches), contextual factors (measured with ten items evaluating the impact of curriculum, assessments, virtual instruction, and peer interaction), and overall satisfaction (measured with five items assessing general satisfaction with the online program). A 5-point Likert scale (ranging from strongly disagree to strongly agree) was used for all sections except demographics, where multiple-choice questions were used.
The survey instrument was developed based on a thorough review of relevant literature and designed to reflect the study’s theoretical framework (Social Cognitive Theory), with its content validity ensured through expert review from three specialists in e-learning and language acquisition, resulting in minor modifications to improve item clarity. The survey was then piloted with 20 students from the same population to identify any ambiguities or issues prior to full-scale data collection. Pilot test data were excluded from the final analysis, and the findings were used to finalize the survey instrument and its layout. Reliability of the survey was confirmed using the split-half method, which yielded a coefficient of 0.85, and further corroborated with the Kuder-Richardson Formula (KR-20), which yielded a coefficient of 0.88 for the multiple-choice items. The survey was administered online over a two-week period using a dedicated survey platform, and the collected data were subsequently analyzed statistically using descriptive and inferential methods in SPSS, with the inferential analysis including correlational analyses and a regression model, the results of which are detailed in the corresponding section.
Qualitative phase
The subsequent qualitative phase employed a case study design, focusing on a small sample of three advanced EFL learners enrolled in the same online English language program at the Public Egyptian university. The case study approach allowed for in-depth exploration of the nuanced and subjective experiences of these few participants within the specific context of their online learning journeys, yielding a rich understanding of their individual motivations, perceptions, and strategy use. This small sample size was chosen to allow for a more focused and manageable in-depth analysis and to allow for an iterative, intensive investigation of individual cases. It allowed the researcher to allocate more time and resources to the analysis of each case to develop an understanding of how variables manifested themselves within the lived experiences of a few participants, rather than a broad picture of general trends. This is a common strategy in case study research, where the focus is on detail and understanding within a specific context, rather than generalizability.
Participants: Participants were purposefully selected from the online English language program at a public Egyptian university, thus representing advanced EFL learners as defined by enrollment in the final year of their undergraduate English language and literature program, completion of at least three years of formal English language instruction, and a minimum score of B2 on the Common European Framework of Reference for Languages (CEFR) placement test. Access to participants was facilitated through the researcher’s professional network.
Data collection: Data collection involved semi-structured interviews, observations of online learning activities, and document analysis:
Semi-structured interviews: Individual semi-structured interviews were conducted with each of the three participants, lasting approximately 45–60 min each and taking place online. The interview guide comprised open-ended questions designed to elicit detailed information about participants’ e-learning strategies, motivations, and beliefs.
Observations: The online learning platform was observed to gain insight into participant engagement and interaction patterns during online learning activities, as well as their use of different features of the online program. The focus was on their use of learning strategies in the real-time context of online learning sessions. The observation period spanned three weeks, with 3–4 h of observation carried out each week.
Document analysis: Course materials, submitted assignments, and self-reflection journals were collected from the three participants. The goal was to provide further insights into their learning processes, motivations, and beliefs by examining their actual engagement with coursework and their thoughts, experiences, and reflections as documented in their assignments and journal entries. The data collection focused on materials produced by learners between weeks 1 and 6 of the program.
Qualitative data analysis: All qualitative data was analyzed using thematic analysis, following a systematic process of: (1) familiarization with the data, (2) generating initial codes, (3) searching for overarching themes, (4) reviewing themes for coherence, (5) defining and naming themes, and (6) producing the final report. This rigorous process enabled a nuanced and contextualized understanding of participant experiences.
Integration of qualitative and quantitative findings
The integration of qualitative and quantitative data in this mixed-methods study was achieved primarily through data transformation and side-by-side comparison. First, a side-by-side comparison was conducted, as presented in the joint display table in the Discussion section. This involved juxtaposing key quantitative findings (e.g., regression coefficients, correlations) with relevant qualitative themes and representative quotes to identify areas of convergence, divergence, and complementarity. The qualitative findings, derived from textual data (e.g., interviews and journal entries), were thematically coded to identify recurring themes related to technology self-efficacy, motivation, and e-learning strategies. Specifically, the themes presented in Tables 5 and 6 (“E-Learning Strategies Employed by Participants” and “Impact of Contextual Factors on E-Learning Experiences”) were used. The frequency with which these themes appeared in the qualitative data was then quantified (as reported in the tables).
These frequencies, while not directly used in statistical analyses, provided a qualitative measure of the prominence of each theme. For example, the high frequency of the “Vocabulary Acquisition” theme within the “Cognitive Strategies” category (20 instances, 25% weighted percentage) indicates that this was a particularly salient aspect of participants’ e-learning experiences. These theme frequencies were then considered alongside the quantitative results (correlations, regression coefficients) to see how the prominence of certain qualitative themes might help explain the strength of the quantitative relationships. This approach allowed for a nuanced understanding of how the qualitative experiences related to the quantitative findings.
To enhance the rigor of the study, several strategies were employed, particularly addressing the limitations of a small qualitative sample size:
Triangulation: Data from interviews, observations, and document analysis were triangulated within the qualitative phase to provide a comprehensive and nuanced understanding of the participants’ experiences, ensuring the findings were corroborated with multiple sources.
Member checking: In the qualitative phase, preliminary findings were shared with participants to ensure the accuracy and validity of the researchers’ interpretations. This allowed for participant verification of the researchers’ interpretations of their experiences.
Rich description: The qualitative section included detailed descriptions of the participants’ experiences and context to facilitate a more detailed and nuanced understanding of findings and their implications.
Findings
The following sections present the results of this mixed-methods study, beginning with the quantitative findings. Data were collected from 147 advanced EFL university students in Egypt, defined as those in their final year of undergraduate English studies, having at least three years of formal instruction, and achieving a minimum B2 CEFR level. These results offer insights into the relationships between technological self-efficacy, intrinsic motivation, contextual factors, e-learning strategy use, and overall satisfaction within this specific population.
Findings from the quantitative phase
The survey study, administered to 147 advanced EFL university students in Egypt, provided valuable insights into the factors influencing their online learning experiences. These students were classified as ‘advanced’ based on their enrollment in the final year of their undergraduate English language and literature program, completion of at least three years of formal English language instruction, and a minimum score of B2 on the Common European Framework of Reference for Languages (CEFR) placement test. The following sections present descriptive and inferential statistics summarizing the key findings.
Correlational analysis
Table 1 presents the Pearson correlation coefficients (r) examining the relationships between the study’s main variables, along with associated p-values and 95% confidence intervals (CIs).
Table 1. Pearson correlations between the dimensions of the survey.
Correlation | r | p-value | 95% CI |
|---|---|---|---|
Technological self-efficacy & e-learning strategy use | 0.68 | <0.001 | [0.56, 0.77] |
Intrinsic motivation & e-learning strategy use | 0.42 | <0.001 | [0.27, 0.55] |
Technological self-efficacy & overall satisfaction | 0.75 | <0.001 | [0.66, 0.82] |
Intrinsic motivation & overall satisfaction | 0.53 | <0.001 | [0.39, 0.64] |
Contextual factors & e-learning strategy use | 0.48 | <0.001 | [0.34, 0.60] |
Contextual factors & overall satisfaction | 0.72 | <0.001 | [0.62, 0.79] |
p-values are two-tailed; CIs indicate lower and upper bounds.
As shown in Table 1, all correlations were statistically significant, indicating positive associations between: (1) technological self-efficacy and e-learning strategy use, (2) intrinsic motivation and e-learning strategy use, (3) technological self-efficacy and overall satisfaction, (4) intrinsic motivation and overall satisfaction, (5) contextual factors and e-learning strategy use, and (6) contextual factors and overall satisfaction. These findings suggest that higher levels of technological self-efficacy, intrinsic motivation, and positive perceptions of contextual factors are all associated with both greater use of e-learning strategies and greater satisfaction with online learning experiences. All correlations were in the moderate to large range.
Multiple regression analysis
To assess the predictive power of technological self-efficacy, intrinsic motivation, and contextual factors on overall satisfaction, a multiple linear regression analysis was performed. Table 2 presents the results, including unstandardized coefficients (B), standard errors (SE), standardized coefficients (β), t-statistics, exact p-values, and 95% confidence intervals (CI). The model had a statistically significant overall fit, F(3, 143) = 84.69, p < 0.001, with an R² of 0.64 and an adjusted R² of 0.63. The model accounted for 63% of the variance in overall satisfaction. Diagnostics indicated no issues with multicollinearity, with Variance Inflation Factor (VIF) values ranging from 1.12 to 1.34. A power analysis conducted post-hoc using G*Power (Faul et al. 2007) assuming alpha at 0.05, determined that the statistical power was high (1 − β = 1.00).
Table 2. Multiple regression analysis predicting overall satisfaction with online English language learning.
Predictor variable | B | SE | β | t | p-value | 95% CI lower | 95% CI upper |
|---|---|---|---|---|---|---|---|
Technological self-efficacy | 0.41 | 0.05 | 0.45 | 7.81 | <0.001 | 0.31 | 0.51 |
Intrinsic motivation | 0.11 | 0.09 | 0.12 | 1.20 | 0.23 | −0.07 | 0.29 |
Contextual factors | 0.35 | 0.05 | 0.38 | 7.21 | <0.001 | 0.26 | 0.45 |
p-values are two-tailed; CIs indicate lower and upper bounds.
As shown in Table 2, both technological self-efficacy (B = 0.41, SE = 0.05, β = 0.45, t = 7.81, p < 0.001, 95% CI [0.31, 0.51]) and positive perceptions of contextual factors (B = 0.35, SE = 0.05, β = 0.38, t = 7.21, p < 0.001, 95% CI [0.26, 0.45]) were found to be statistically significant direct predictors of overall satisfaction with the online learning program. Intrinsic motivation did not reach statistical significance as a direct predictor of satisfaction (B = 0.11, SE = 0.09, β = 0.12, t = 1.20, p = 0.23, 95% CI [−0.07, 0.29]), although a separate analysis revealed that it was a significant predictor of e-learning strategy use (β = 0.56, p < 0.001). This suggests that while intrinsic motivation plays a role in learning engagement and effort, its direct impact on overall satisfaction is less pronounced compared to a student’s confidence in using technology and their assessment of their e-learning environment.
Independent samples t-tests
Independent samples t-tests were conducted to examine potential gender differences in technological self-efficacy and intrinsic motivation. Table 3 presents these results:
Table 3. T-tests for gender differences in self-efficacy and motivation.
Variable | Group | M | SD | t | df | p-value | Cohen’s d |
|---|---|---|---|---|---|---|---|
Technological self-efficacy | Males | 3.85 | 0.72 | 1.23 | 145 | 0.22 | 0.21 |
Females | 4.01 | 0.67 | |||||
Intrinsic motivation | Males | 3.72 | 0.64 | 0.87 | 145 | 0.39 | 0.14 |
Females | 3.81 | 0.70 |
p-values are two-tailed.
As seen in Table 3, there were no statistically significant differences between males and females in technological self-efficacy (t(145) = 1.23, p = 0.22, Cohen’s d = 0.21) or intrinsic motivation (t(145) = 0.87, p = 0.39, Cohen’s d = 0.14). The effect sizes (Cohen’s d) also showed small effects. These findings indicate that gender does not appear to be a major factor influencing these variables within this sample.
One-way ANOVA
One-way ANOVAs were performed to assess the influence of year of study and prior experience with online learning on e-learning strategy use and overall satisfaction. Table 4 presents these results, including F-statistics, degrees of freedom, exact p-values, and partial eta-squared (η²). A power analysis conducted post-hoc using G*Power (Faul et al. 2007) assuming alpha at 0.05, determined that the statistical power for the ANOVA with year of study (1 − β = 0.76) and prior experience with online learning (1 − β = 0.90) were moderate and high, respectively.
Table 4. One-way ANOVAs examining the influence of year of study and prior experience on e-learning strategy use and overall satisfaction.
Variable | Factor levels | F | df | p-value | η² |
|---|---|---|---|---|---|
E-learning strategy use | Year of study | 3.21 | 3, 143 | 0.02 | 0.06 |
Overall satisfaction | Prior experience | 4.56 | 3, 143 | 0.01 | 0.09 |
p-values are two-tailed.
Table 4 shows a statistically significant difference in e-learning strategy use across different years of study (F(3, 143) = 3.21, p = 0.02, η² = 0.06), suggesting that students in their senior year might employ a wider range of learning strategies compared to first-year students (however, post-hoc tests are required to establish which groups are significantly different). There was also a statistically significant difference in overall satisfaction based on prior experience with online learning (F (3, 143) = 4.56, p = 0.01, η² = 0.09), indicating that students with more extensive prior online experience tend to report higher satisfaction levels.
The quantitative findings from the survey data indicate that: (a) students with high self-efficacy, motivation, and positive perceptions of their e-learning context are more likely to adopt different learning strategies and express greater satisfaction with the online learning environment; (b) while technological self-efficacy and contextual factors had a stronger direct predictive power on overall satisfaction, intrinsic motivation was a significant predictor of e-learning strategy use; (c) gender did not significantly impact self-efficacy or intrinsic motivation in this study’s population; and (d) both years of study and prior experiences with e-learning had an impact on e-learning strategy usage and satisfaction, respectively. Taken together, these findings underscore the importance of self-efficacy, motivation, and positive learning environments for effective e-learning among advanced EFL students.
The quantitative findings indicate that, among our advanced EFL learners in Egypt, a combination of technological self-efficacy and positive perceptions of contextual factors in their online learning environment are the strongest predictors of overall satisfaction. While intrinsic motivation correlates positively with both strategy use and satisfaction, its direct influence on overall satisfaction is less pronounced than the other two factors. Moreover, the findings suggest that students’ experience with online learning plays a role in their overall satisfaction, while their year of study is related to how varied their strategies are when learning in an e-learning environment. The lack of a significant gender difference suggests that, at least in this population, these constructs are not influenced by gender.
Taken together, these findings underscore the significant importance of technological self-efficacy and positive perceptions of contextual factors when fostering overall satisfaction. Moreover, they suggest that while intrinsic motivation can be instrumental in supporting learner engagement and effort, it appears to have less direct influence on overall satisfaction with an online language program compared to the other factors studied.
Qualitative findings
This study explored the complex interplay between technological self-efficacy, intrinsic motivation, and e-learning strategy use among three advanced EFL learners enrolled in an online English language program at a university in Egypt. These learners met the criteria for ‘advanced’ status, as established earlier in this study. Data collected through semi-structured interviews, observations, and document analysis revealed recurring themes related to e-learning strategies, attitudes, motivations, and the connection between learner variables and e-learning success.
E-learning strategies: a diverse toolkit
The analysis of qualitative data indicated that the three participating advanced EFL learners utilized a diverse range of e-learning strategies. Table 5 presents the identified themes, subthemes, and representative examples of the learning strategies used, with each category having several subthemes, suggesting a multifaceted approach to online learning. Additionally, the theme frequencies reflect how often a particular strategy category was mentioned by participants, while the inter-rater reliability ensures the consistency of coding.
Table 5. E-learning strategies employed by participants.
Strategy category | Themes | Subthemes | Examples | Theme frequency (weighted %) |
|---|---|---|---|---|
Cognitive | Vocabulary acquisition | Utilizing online tools | Utilizing online dictionaries and resources for vocabulary acquisition | 20 (25%) |
Vocabulary practice | Using flashcards and online games for vocabulary practice | |||
Grammar rules | Online resources | Using online grammar tools and engaging in online grammar exercises | 10 (12.5%) | |
Practice | Practicing grammar through online exercises | |||
Reading comprehension | Extensive reading | Engaging in extensive reading of English materials online | 10 (12.5%) | |
Focused Reading | Actively engaging with texts, paying close attention to understanding | |||
Writing practice | Exercises | Practicing writing through online exercises and assignments | 8 (10%) | |
Collaboration | Practicing writing through collaborative writing projects | |||
Technology as a tool | Accessing information | Using technology to access information | 12 (15%) | |
Skill development | Using technology to practice skills and connecting with others | |||
Metacognitive | Self-monitoring | Self-assessment | Identifying strengths and weaknesses and reflecting on learning processes and progress | 8 (10%) |
Goal setting | Setting and evaluating goals | |||
Planning and organization | Study schedule | Planning study sessions effectively and scheduling time for online activities | 4 (5%) | |
Prioritizing tasks | Setting and prioritizing learning goals. | |||
Adapting learning approaches | Resource seeking | Modifying strategies based on individual needs and seeking additional resources | 6 (7.5%) | |
Adjustment | Adjusting learning strategies based on challenges that are encountered | |||
Learner agency | Active choice | Taking ownership of learning by actively choosing strategies | 2 (2.5%) | |
Social-affective | Collaboration | Online discussion | Participating in online discussions and group activities | 5 (6.25%) |
Project work | Working collaboratively on projects, often through online platforms | |||
Feedback seeking | Peer feedback | Seeking feedback from peers through online forums or during virtual sessions | 3 (3.75%) | |
Instructor feedback | Seeking feedback from instructors through online forums or during virtual sessions | |||
Self-reflection | Emotional reflection | Reflecting on learning processes, emotions, and challenges | 2 (2.5%) | |
Challenges analysis | Analyzing successes and failures to inform future strategies | |||
Emotional management | Reducing anxiety | Using learning strategies to reduce feelings of anxiety or frustration associated with online learning | 2 (2.5%) | |
Supportive learning environment | Social interaction | Participating in online discussions and group discussions to connect with other learners | 4 (5%) | |
Support | Feeling a sense of belonging in the online environment and receiving encouragement and support from peers and instructors | |||
Motivation and goal setting | Personal goals | Having a clear vision for their English learning goals and staying motivated by focusing on the benefits | 3 (3.75%) | |
Aspirations | Staying motivated by focusing on their aspirations for future success |
Theme frequencies, as presented in Table 5, reflect how often each theme and subtheme appeared across the analyzed qualitative data. The weighted percentages were calculated by dividing the frequency of each theme by the total number of coded instances across all themes, and then multiplying by 100. This weighting accounts for the varying number of codes within each strategy category, providing a more accurate representation of the relative prominence of each theme. To ensure the reliability of the coding process, inter-rater reliability was calculated using Fleiss’ kappa, yielding a value of 0.91. This indicates a high degree of agreement between the two independent researchers who coded the themes and subthemes.
The learners demonstrated a proactive approach to managing the unique challenges and opportunities of online education by actively employing a diverse range of learning strategies.
For example, one student stated, “I find that using online dictionaries and grammar resources really helps me to understand new vocabulary and grammar rules.” (Cognitive Strategy: Vocabulary Acquisition; Utilizing online tools) This statement is representative of the participants’ views on the usefulness of external resources for comprehension. Another participant noted, “I try to plan my study time carefully and break down the course materials into manageable chunks. This helps me stay focused and on track,” highlighting their application of metacognitive strategies (Planning and Organization: Study Schedule). Furthermore, the importance of social-affective strategies was highlighted by another participant: “I really appreciate being able to participate in online discussions and get feedback from my peers and instructors. It helps me learn from different perspectives and feel more connected to the learning community.” (Social-Affective Strategies: Feedback Seeking: Peer Feedback) These representative quotes provide direct examples of strategy use across all three categories.
Attitudes, motivations, and beliefs influencing strategy use
The analysis supported the connection between positive attitudes, high intrinsic motivation, and strong self-beliefs and the use of diverse e-learning strategies.
Positive attitudes
Participants’ positive attitudes towards e-learning stemmed from the flexibility, accessibility, and interactive nature of the online platform, with ‘flexibility’ being a commonly mentioned theme (mentioned 10 times). One representative example of the theme, as voiced by Participant 1: “I really enjoy the flexibility of online learning. It allows me to study at my own pace and choose the learning materials that best suit my needs.” Observations also highlighted consistent engagement with the interactive features of the platform, such as Participant 2, who “consistently used the interactive whiteboard to participate in discussions and ask clarifying questions,” showing a clear preference for interaction. These findings show an ability to adapt to the demands of the online environment, and an agency in adapting their learning strategies to their needs.
Intrinsic motivation
Participants’ intrinsic motivation appeared to be an important factor influencing their engagement in the online learning environment, with the theme of “personal growth” and “future opportunities” emerging as reasons behind their commitment to their learning (mentioned 14 times). As Participant 3 noted in their course reflection: “I’m really committed to improving my English because I want to study abroad in the future. I believe that if I work hard, I can achieve my goals.” This often appeared to translate into consistent effort and a willingness to explore various learning materials and strategies, as was apparent in all three participants’ engagement with extra materials beyond the required course content.
Strong beliefs
Participants who expressed stronger beliefs in their ability to learn English also tended to demonstrate more confidence in using various e-learning strategies (theme mentioned 8 times), which lead to greater persistence and a willingness to seek support when needed. A participant stated: “I know I can learn English if I put in the effort. I’m not afraid to ask for help when I need it,” demonstrating a strong self-belief that enabled them to learn and seek out support when needed.
However, not all aspects were positive as the theme of “Challenges and Frustrations” emerged (mentioned 13 times), acknowledging the difficulties learners face in navigating e-learning environments, including technical issues, a lack of structure, and feelings of isolation, all of which often negatively impacted engagement and motivation.
Technological self-efficacy and intrinsic motivation: shaping strategy choices
An in-depth analysis of participants’ self-reflection journals using in-vivo coding explored the relationship between technological self-efficacy, intrinsic motivation, and e-learning strategy use. In-vivo coding focused on identifying and classifying the specific language used by participants, thus enabling a closer look into their individual experiences. The findings are presented according to the themes that emerged from the participants’ coded journals, with theme frequency indicating how often the themes were mentioned.
Positive attitudes and engagement: Participant 1’s journal demonstrated a pattern of positive themes (mentioned 14 times), characterized by words and phrases such as “enjoy,” “love,” “confident,” “motivated,” “diverse strategies,” “community,” “accessible,” “flexible,” and “convenient,” indicating a strong positive attitude, high intrinsic motivation, and belief in their ability to learn English.
Mixed attitudes and struggles: Participant 2’s journal conveyed a mixed pattern (mentioned 10 times), with terms such as “not sure,” “isolating,” “busy,” “sidetracked,” and “engaging,” illustrating a somewhat mixed attitude, less pronounced intrinsic motivation, and an uncertain belief in their ability to succeed.
Negative attitudes and lack of engagement: Participant 3 used mostly negative terms (mentioned 13 times) like “dislike,” “distractions,” “minimum amount of work,” and “not sure,” which revealed their negative attitude, low intrinsic motivation, and weak belief in their ability to learn. These patterns indicated a connection between attitudes, motivation, and strategy use.
The findings highlight that learners with positive attitudes, high intrinsic motivation, and strong self-beliefs are more likely to engage in e-learning and explore diverse strategies. In contrast, those with negative attitudes, low intrinsic motivation, and weak self-beliefs may find e-learning less appealing and are less likely to explore a wide array of learning strategies. A supportive e-learning environment, characterized by strong social interaction, positive feedback, and encouragement, may serve to enhance motivation, engagement, and ultimately, success in online learning, while also helping to offset the challenges that learners face in navigating an online learning environment.
Technological self-efficacy, intrinsic motivation, and e-learning success
Although a quantitative correlation could not be established due to the small qualitative sample, the study’s qualitative analysis suggested a connection between the use of diverse e-learning strategies, technological self-efficacy, intrinsic motivation, and a perceived sense of success (often described as improved fluency or confidence)
Strategic engagement and self-efficacy: Those who consistently employed a wide range of cognitive, metacognitive, and social-affective strategies, coupled with a strong belief in their ability to use online resources, reported greater confidence and fluency in their English language skills.
One participant stated, “I can feel myself becoming more fluent and confident when speaking English. I’m able to express myself more clearly and easily,” indicating an improvement in skills and confidence as a result of active learning. A different perspective was shared by another participant, whose limited confidence and motivation resulted in feelings that “I’m not sure if I’m really improving. It’s hard to stay motivated.” This demonstrates the importance of self-belief in language learning.
Intrinsic motivation and perseverance: Participants highly motivated to improve their English (based on their personal goals or future aspirations) not only engaged in more strategic learning but also demonstrated greater self-efficacy and perseverance.
A participant’s journal entry demonstrated such a perspective: “I believe in myself and my ability to learn English. I’m committed to working hard and making progress.” This contrasts with other learners, who expressed negative self-belief and low motivation, “I’m not sure if I can actually learn English. It seems too difficult.” This demonstrates the impact of both motivation and self-belief on a learner’s view of their ability to progress in their studies.
These findings indicate that a positive learning environment plays an important role in cultivating both self-efficacy and motivation. Both of these appear to be important contributors to e-learning success.
Shaping e-learning experiences: the impact of contextual factors
The qualitative data suggests that the design and structure of the online learning environment substantially influence learners’ experiences, influencing their strategy use, motivation, and overall engagement. Key contextual factors included curriculum design, assessment practices, virtual classroom instruction, and opportunities for peer interaction. Table 6 presents the identified themes, subthemes, representative quotes, and frequencies, as well as the interrater reliability.
Table 6. Impact of contextual factors on e-learning experiences.
Contextual factor | Theme | Subtheme | Representative quote | Theme frequency (weighted %) |
|---|---|---|---|---|
Curriculum design | Clear structure & design | Learning objectives | “I really appreciate how the course is structured. It’s broken down into manageable modules, and there are clear objectives for each one. It makes it much easier for me to stay on track and stay motivated.” | 10 (18.18%) |
Organization | The course structure is easy to follow and well-organized | |||
Lack of structure | Lack of clarity | “I’m not sure what I’m supposed to be learning. It feels like there’s a lot of information, but it’s not organized very well. I’m not sure what the key goals are or how I’m supposed to be using the materials.” | 7 (12.72%) | |
Confusion | Students feeling overwhelmed by disorganized information. | |||
Web-based assessments | Regular feedback | Positive impact | “I like how there are quizzes and exercises after each module. It helps me to see what I’ve learned and where I need to focus my efforts. It’s also really encouraging to see my progress over time.” | 11 (20%) |
Progress tracking | The assessments help me track my progress and understand my weak areas. | |||
Need for more feedback | Feedback frequency | “I wish there were more opportunities to get feedback on my work. It would be helpful to know how I’m doing more regularly.” | 6 (10.91%) | |
Nature of feedback | I need more specific feedback on areas where I’m not progressing | |||
Virtual classroom instruction | Real-time support | Clarification | “I enjoy being able to ask questions and get immediate feedback from the instructor. It makes a big difference in my understanding and my motivation.” | 8 (14.55%) |
Support | Having the instructor available for help improves my experience greatly | |||
Technical issues | Technology difficulties | “It’s hard to follow along sometimes when the technology isn’t working. I miss the face-to-face interaction of a traditional classroom.” | 5 (9.09%) | |
Lack of interaction | Technical issues resulted in reduced communication with instructors | |||
Peer influence | Collaboration | Peer interaction | “The online forum is great! It’s a place where we can discuss our ideas, ask questions, and get feedback from other students. It’s really helpful to learn from other perspectives and see how others are approaching the material.” | 4 (7.27%) |
Feedback | Opportunities for feedback from peers are valuable and help me see a wider perspective on concepts. | |||
Need for structure | Structured group work | “I think it would be helpful if there were more opportunities for group work. Sometimes, it’s hard to get involved in the online forum.” | 4 (7.27%) | |
Limited opportunities | The current platform has few options to connect with others and learn collaboratively. |
Theme frequencies, as presented in Table 6, reflect how often each theme and subtheme appeared across the analyzed qualitative data. The weighted percentages were calculated by dividing the frequency of each theme by the total number of coded instances across all themes and multiplying by 100. This method accounts for the varying number of codes within each contextual factor category, providing a more accurate representation of the relative importance of each theme. To ensure the reliability of the coding process, inter-rater reliability was calculated using Fleiss’ kappa, yielding a value of 0.88. This indicates a high degree of agreement between the two independent researchers who coded the themes and subthemes.
The analysis suggests that a well-designed e-learning environment, incorporating clear objectives, effective assessment practices, engaging virtual instruction, and ample opportunities for peer interaction, contributes significantly to supporting learners’ experiences and learning outcomes.
These qualitative findings regarding the impact of contextual factors provide a deeper understanding of why these factors were significant predictors of overall satisfaction in the quantitative analysis (β = 0.38, p < 0.001). The emphasis on clear structure and design, regular feedback, real-time support from instructors, and opportunities for peer collaboration directly corresponds to the elements that contribute to a positive and effective e-learning environment, as identified in the quantitative data. This connection supports the overall conclusion that a well-designed e-learning environment is important for learner satisfaction and success.
Curriculum design
The data shows that a clear and well-structured curriculum with engaging online materials, interactive activities, and clear learning objectives was often seen as crucial for promoting student engagement and motivation (Theme: Clear Structure & Design). Participants found this “easier to stay on track and stay motivated.” In contrast, those who felt that there was a lack of structure also reported that “I’m not sure what I’m supposed to be learning. It feels like there’s a lot of information, but it’s not organized very well,” indicating that a lack of structure negatively affected student learning experience (Theme: Lack of Structure).
Web-based assessments
Formative assessments that provided regular feedback on participants’ progress encouraged a more proactive approach to learning and fostered a sense of accomplishment (Theme: Regular Feedback). Participants found these quizzes to be both helpful and encouraging in highlighting areas that they could improve in and tracking their overall progress. However, those who found it difficult to engage with the assessment practices expressed a need for more frequent feedback or the need to know “how I’m doing more regularly” (Theme: Need for More Feedback), suggesting that the type of feedback offered is a potentially influential factor.
Virtual classroom instruction
Live online sessions with instructors were found to provide real-time support, clarification of concepts, and opportunities for interaction (Theme: Real-time Support), leading to a more positive experience. Some participants noted that virtual classroom sessions were “really helpful,” as they enabled participants “to ask questions and get immediate feedback from the instructor,” which, in turn, resulted in greater understanding and motivation. Conversely, technical difficulties during virtual classroom sessions resulted in less engagement (Theme: Technical Issues) with one learner noting “It’s hard to follow along sometimes when the technology isn’t working. I miss the face-to-face interaction of a traditional classroom.”
Peer influence
Collaboration with peers through online forums and group discussions was found to be valuable in fostering a supportive learning environment (Theme: Collaboration). The online forum was described as “great,” with learners noting, “It’s a place where we can discuss our ideas, ask questions, and get feedback from other students. It’s really helpful to learn from other perspectives and see how others are approaching the material.” However, not all participants found the interaction opportunities to be sufficient, as there was also the sense that there was a “need for more structured collaboration” or that “it’s hard to get involved in the online forum” (Theme: Need for Structure).
These findings emphasize the significance of creating a well-designed e-learning environment that promotes technological self-efficacy and intrinsic motivation while also considering the impact of curriculum design, assessment practices, virtual classroom instruction, and opportunities for peer collaboration. A well-structured curriculum, regular feedback, interactive instruction, and opportunities for peer collaboration were all identified as important contributing factors for promoting student engagement, motivation, and overall success in online learning.
Discussion
This study employed a mixed-methods approach to explore the complex interplay of technological self-efficacy, intrinsic motivation, and contextual factors in shaping e-learning experiences and influencing language acquisition among advanced EFL university students in Egypt. These students were, as previously defined, in their final year of undergraduate English studies, had completed at least three years of formal instruction, and achieved a minimum B2 CEFR level. By combining the depth of a qualitative case study with the breadth of a quantitative survey, this study provides a detailed understanding of the interplay of these factors, both confirming and extending existing research.
Learner variables and e-learning strategies
Both the qualitative and quantitative findings of this study highlight the interconnectedness of learner variables and their influence on the adoption of e-learning strategies. As Table 5 shows, participants used a diverse range of e-learning strategies, consistent with prior research emphasizing the multifaceted nature of effective language learning (Oxford 1990; Cohen 2014). These strategies were clearly categorized into cognitive, metacognitive, and social-affective, which aligns with established frameworks in the SLA field (Wenden 1991; Oxford 2013).
The survey results further substantiated these findings by revealing statistically significant positive correlations between technological self-efficacy and e-learning strategy use (r = 0.68, p < 0.001) and between intrinsic motivation and e-learning strategy use (r = 0.42, p < 0.001), thus providing further evidence in support of Hypotheses 1 and 2. These findings are consistent with SCT, which suggests that self-efficacy, which is a significant aspect of a learner’s personal attributes, can influence their choice and use of learning strategies (Bandura 1986). Further supporting these findings is a meta-analysis conducted by Huang et al. (2021), which demonstrated that when learners have higher levels of self-efficacy, they tend to use a wider range of learning strategies, and that positive attitudes towards technology, combined with high levels of self-efficacy, can facilitate a more engaged learning process.
While our study confirms a positive relationship between intrinsic motivation and the use of e-learning strategies, the quantitative results also indicate that intrinsic motivation did not directly predict overall satisfaction, with the regression analysis revealing that intrinsic motivation does not significantly predict overall satisfaction (β = 0.12, p = 0.23). This finding is, at first, inconsistent with prior research suggesting a strong and direct link between motivation and success in SLA (Dörnyei 2001; Deci and Ryan 2000); however, it does not negate the importance of motivation in e-learning. Instead, the study suggests that motivation’s influence on satisfaction is more nuanced, and might be indirectly facilitated by self-efficacy, which was a strong predictor of both overall satisfaction and strategy use.
The qualitative data, however, reveal the indirect role of intrinsic motivation. Participant 3’s journal entries, and the reflections of other participants, filled with phrases like “committed to improving” and emphasizing “future opportunities,” show how intrinsic motivation drives engagement with specific learning strategies, even if it doesn’t directly impact overall satisfaction with the program itself. This suggests a pathway where intrinsic motivation fuels effort and persistence in using strategies, which may then, in turn, contribute to satisfaction, albeit indirectly.
As such, the qualitative findings indicate that motivation, goal setting, and a positive belief in one’s ability to succeed in language learning were important components of the e-learning experiences, aligning with extant research (Chien et al. 2020). As one participant wrote, “I’m really committed to improving my English because I want to study abroad in the future. I believe that if I work hard, I can achieve my goals.” This connection between motivation and future aspirations is also present in other studies (e.g., Keller and Suzuki 2004). However, unlike some previous research (e.g. Pan 2020), our study suggests that the direct influence of intrinsic motivation on satisfaction is less pronounced than that of technological self-efficacy and the contextual factors shaping online learning environments.
The role of contextual factors
Both the qualitative and quantitative analyses of our study underscore the significant role of contextual factors in shaping e-learning experiences, as can be seen in Table 6. These findings align with research that emphasizes the importance of a well-designed learning environment (Blake 2013; Kang et al. 2020). The survey results confirmed the importance of well-structured and engaging curricula, with students reporting higher satisfaction and strategy use when the learning materials and objectives were perceived as clear and interesting. This finding supports studies by Jennings (2021) which highlight the importance of structured and organized learning environments in promoting student engagement.
Similarly, the importance of regular feedback provided through formative assessments was highlighted, with students valuing assessments that provided insights into their progress and their areas that needed improvement, which echoes the findings by Dandan (2023). This is further reinforced by our qualitative findings, where participants often emphasized how a lack of structure could cause confusion and hinder the learning process. Moreover, findings indicated that live online sessions with instructors and opportunities for peer interaction are also important factors in promoting learning, with many reporting the importance of a supportive learning community.
The quantitative finding that contextual factors significantly predicted satisfaction is explained by the qualitative themes of ‘Clear Structure & Design’ and ‘Regular Feedback.’ Participants’ comments about appreciating the organized modules and helpful quizzes demonstrate why these contextual factors matter for a positive e-learning experience. For example, the qualitative data revealed that learners valued the clarity provided by well-defined learning objectives, stating, “I really appreciate how the course is structured…It makes it much easier for me to stay on track and stay motivated.” This directly links the quantitative finding of the importance of contextual factors to the specific qualitative experience of learners benefiting from a well-organized curriculum.
The qualitative findings directly support these claims, with participants highlighting that “I really appreciate being able to participate in online discussions and get feedback from my peers and instructors.” These findings also reinforce the significance of social interaction as an important part of the e-learning environment, which is line with previous studies that highlight the need to create opportunities for collaboration to foster a sense of belonging and to promote collaborative learning (Salmon 2004; Haneda 2006). As these findings suggest, well-structured curricula, regular feedback, interactive instruction, and opportunities for peer collaboration, all play an important role in fostering successful online learning (Shaalan 2022; Willems 2013). These are also consistent with the tenets of SCT, which sees both positive and negative interactions within the learning environment as influential to the learning process, and shows the interplay of environmental and personal factors.
The regression results highlight that technological self-efficacy and contextual factors were strong direct predictors of overall satisfaction, whereas intrinsic motivation’s influence was less direct (but still important) in terms of directly impacting overall satisfaction. The quantitative finding that technological self-efficacy strongly predicted overall satisfaction (β = 0.45, p < 0.001) is illuminated by the qualitative data. Participant 1’s frequent use of online resources, and the general pattern observed across participants of utilizing online tools for vocabulary acquisition and grammar practice, along with positive comments about their ‘confidence’ using technology, demonstrate how this self-efficacy translates into a more positive e-learning experience. This connection is further exemplified by statements such as, “I find that using online dictionaries and grammar resources really helps me to understand new vocabulary and grammar rules,” highlighting the practical application of technological self-efficacy in enhancing learning. This finding highlights the significance of developing students’ confidence and competence in using technology, and fostering well-designed and supportive online learning environments, including appropriate assessment and feedback strategies. It also aligns with previous findings that support the importance of scaffolding digital literacy and skills to enhance the positive impacts of e-learning (Kang et al. 2020).
To illustrate the integration of these findings, Table 7 presents a joint display summarizing the key connections between the quantitative results and the qualitative themes.
Table 7. Integration of quantitative and qualitative findings on e-learning experiences.
Quantitative finding | Qualitative theme(s) | Representative quote(s) | Explanation of connection |
|---|---|---|---|
Technological self-efficacy significantly predicted overall satisfaction (β = 0.45, p < 0.001). | Positive Attitudes and Engagement; Technological Confidence | “I find that using online dictionaries and grammar resources really helps me to understand new vocabulary and grammar rules.” “I’m confident using the online platform.” | The qualitative data show how technological self-efficacy translates into positive experiences. Learners who feel confident using technology actively utilize online resources, leading to greater satisfaction. |
Intrinsic motivation did not significantly predict overall satisfaction (β = 0.12, p = 0.23). | Intrinsic Motivation and Perseverance; Goal Setting | “I’m really committed to improving my English because I want to study abroad in the future. I believe that if I work hard, I can achieve my goals.” | While not a direct predictor of satisfaction, intrinsic motivation drives engagement with learning strategies. The qualitative data show how learners’ personal goals and aspirations fuel their effort and persistence. |
Contextual factors significantly predicted overall satisfaction (β = 0.38, p < 0.001). | Clear Structure & Design; Regular Feedback; Peer Interaction | “I really appreciate how the course is structured…It makes it much easier for me to stay on track.” “I like how there are quizzes…It helps me to see what I’ve learned.” “The online forum is great…to discuss ideas.” | The qualitative themes reveal which contextual factors are most important and why. Clear structure, regular feedback, and peer interaction provide support and enhance the learning experience, leading to greater satisfaction. |
Positive correlation between TSE and e-learning strategy use (r = 0.68, p < 0.001) | Cognitive; Vocabulary Acquisition; Utilizing online tools, Grammar Rules; Online resources | Utilizing online dictionaries and resources for vocabulary acquisition “I find that using online dictionaries and grammar resources really helps me to understand new vocabulary and grammar rules” | Technological self-efficacy and contextual factors were associated, where learners showed greater confidence and perceived support. This created a conducive environment for the active utilization of e-learning strategies, as demonstrated by the use of digital tools for vocabulary and grammar practice. |
Positive correlation Contextual Factors & E-Learning Strategy Use (0.48, <0.001) | Social-Affective; Collaboration; Online Discussion | “The online forum is great! It’s a place where we can discuss our ideas, ask questions, and get feedback from other students. It’s really helpful to learn from other perspectives and see how others are approaching the material.” | Learners showed a clear preference for utilizing contextual resources and e-learning strategies. This highlighted their confidence in leveraging digital platforms and engaging with supportive learning environments, which collectively contributed to a more interactive and effective learning experience. |
As Table 7 illustrates, the quantitative and qualitative findings converge to reveal a multi-faceted relationship between learner characteristics, contextual factors, and e-learning experiences. The direct link between technological self-efficacy and overall satisfaction, established quantitatively, is given depth and meaning by the qualitative data, showing how this confidence translates into active engagement with online resources. While intrinsic motivation’s direct impact on satisfaction was not statistically significant, the table highlights its potentially important indirect role, driving the use of learning strategies. Furthermore, the table clarifies which contextual factors are most influential and why, demonstrating that clear structure, regular feedback, and peer interaction are important components of a positive e-learning environment. Finally, the table demonstrates a clear link between technological self-efficacy, contextual factors and the use of a wide range of learning strategies. In essence, the integrated findings suggest that a combination of technological confidence, a supportive learning environment, and internally driven engagement with learning strategies are important contributors to successful e-learning outcomes.
Individual nuances and the need for personalized approaches
While the overarching patterns emphasize the importance of the factors mentioned above, the qualitative case studies revealed individual nuances in how students approached online learning. The qualitative data also highlighted that differences exist in learners’ preferences, with some favoring self-directed learning, and others thriving in more structured environments. This is consistent with existing research on learner autonomy, which suggests that no one learning approach is inherently superior to another (Wenden 1991; Teng 2022).
Therefore, these findings support the need for educators to adopt a learner-centered approach that offers flexibility, diverse options, and choice when it comes to learning materials and activities, to cater to individual differences in learning styles and preferences. This may require a re-evaluation of the content of learning programs to give learners a greater sense of ownership over the learning experience and support the principle of learner autonomy, which many see as beneficial for success in e-learning environments (Warschauer and Kern 2000; Meskill and Anthony 2015).
Limitations and future research
While this study provides valuable insights into the relationships between technological self-efficacy, intrinsic motivation, contextual factors, and e-learning outcomes among advanced EFL learners, several limitations should be considered. First, the qualitative data was derived from a small sample of three participants, which, while allowing for an in-depth and rich analysis, limits the generalizability or transferability of the specific qualitative themes and experiences to broader populations. Second, the survey participants represented advanced EFL university students enrolled in a specific online English language program in Egypt, which presents a potential selection bias. These factors may restrict the generalizability of the findings beyond similar populations or contexts. Third, the study relied primarily on self-reported data, which can be influenced by social desirability or response biases, potentially leading to inflated correlations or overly positive reports of self-efficacy and satisfaction. Finally, while this study measured a range of learner and contextual factors, it may not have accounted for all potentially influential variables (such as specific instructional techniques used within the platform, or individual learning preferences beyond those measured). Future research could address these limitations by expanding the sample size of the qualitative phase, gathering data from more diverse populations of EFL learners, and employing longitudinal data collection methods, which could provide more robust findings and a deeper understanding of the dynamics of e-learning.
Conclusion
In conclusion, this research provides valuable insights into the complex interplay of learner variables and contextual factors shaping e-learning experiences and influencing language acquisition outcomes among advanced EFL university students. The challenges encountered by these learners underscore the intricate balancing act required to create effective online language learning environments.
Key findings from this study center on the significant roles of technological self-efficacy, which was found to be a strong direct predictor of overall satisfaction, as well as the importance of supportive contextual factors such as clear learning objectives and opportunities for interaction. While intrinsic motivation proved to be a key predictor of e-learning strategy use, its direct influence on overall satisfaction was less pronounced, suggesting a complex indirect relationship with learning outcomes.
These findings emphasize the need for fostering a supportive online environment where learners’ confidence in their technological abilities is nurtured, and where the learning design integrates clear learning pathways, while also recognizing that learners must also develop a genuine desire to learn and have the capacity to utilize diverse learning strategies. The study also highlighted the unique perspectives of different learners, and the need for a more flexible and personalized approach to address individual preferences and prior experiences, all in support of successful e-learning journeys.
Furthermore, many open questions remain regarding how to most effectively tailor instruction, build lasting engagements, and reduce the challenges that learners face in navigating online learning environments. Further exploration of participatory platforms, user-friendly technologies, and strategies that foster learner autonomy are needed to better facilitate diverse learning styles, and to create learning opportunities that lead to successful and satisfying e-learning experiences.
Practical implications
The findings of this study hold several practical implications for e-learning design and implementation:
For course designers: The study underscores the need for well-structured curricula with clear learning objectives, engaging online materials, and interactive activities to facilitate learners’ positive perceptions of their e-learning environment. Course designers should prioritize activities that promote autonomy, and provide space for learners to have personalized learning pathways. Additionally, course designers should focus on creating opportunities for collaboration and feedback through peer interaction.
For language instructors: To foster positive e-learning experiences, instructors should focus on building students’ technological self-efficacy by providing training and support on how to effectively use online tools and platforms. Moreover, educators should prioritize activities that promote intrinsic motivation, by emphasizing the relevance of course content, and by giving learners more control over their learning process. Instructors should also aim to create a supportive online environment where learners feel comfortable seeking assistance.
For educational administrators: Administrators should ensure access to reliable technology and internet connectivity, while also investing in the professional development of educators to design and facilitate engaging online language learning experiences. Moreover, administrators need to support strategies that promote a balance of structure and learner autonomy, while ensuring that learners are equipped with the necessary technological skills to navigate the e-learning environment successfully, and that they have opportunities for meaningful interaction with peers and instructors. Furthermore, it is important that resources are available to foster both learner and teacher technological literacy.
Author contributions
MM conceived and designed the study, collected and analyzed the data, wrote the manuscript, and reviewed and approved the final version.
Funding
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availability
The data that support the findings of this study are available in a supplementary file attached to this submission.
Competing interests
The authors declare no competing interests.
Ethical approval
This study protocol was reviewed by the Beni Suef University - Faculty of Education Institutional Review Board (BSU-FoE-IRB). In accordance with the policy of the Supreme Council of Universities in Egypt (Article 25, dated 28/03/2023), which provides exemptions for non-interventional educational research involving standard educational practices and posing no more than minimal risk, the BSU-FoE-IRB confirmed that formal ethical approval was waived for this specific study on [27 October 2023]. The reference number associated with this confirmation is BSU-FoE-IRB-01-10-27-2023. All research activities were performed in accordance with the ethical principles outlined in the Declaration of Helsinki and its later amendments, as well as relevant institutional and national guidelines/regulations.
Informed consent
Written informed consent was obtained from all individual participants included in the study prior to their participation. Consent was secured by the researcher between (23rd October, 2023 and October 25th, 2023). Participants were provided with a detailed information sheet and consent form explaining the study’s purpose, procedures (including questionnaires and interviews), expected duration, potential benefits, the voluntary nature of participation, and their explicit right to withdraw at any stage without any penalty or negative consequences. Participants were fully informed that their anonymity and confidentiality would be strictly maintained through data anonymization and secure storage, how their data would be utilized solely for research purposes (including analysis and potential publication of anonymized findings or excerpts), and that there were no anticipated risks associated with participation beyond those encountered in everyday life. The obtained consent covered participation in the research activities and the use of their anonymized data for research analysis and publication.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1057/s41599-025-04947-0.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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