The Influence of Gender and Academic Achievement on University Students' Perceptions of LMS-Based Self-Regulated Learning in Korea

Yongjeong Kim ^1,†, Saera Kwak ^2,†, Taejin Koh ^3,†,* 

1. Institution of Indian Studies, Hankuk University of Foreign Studies, 107 Imunro, Seoul, Korea

2. Department of Persian and Iranian Studies, Hankuk University of Foreign Studies, 107 Imunro, Seoul, Korea

3. Department of Indian Studies, Hankuk University of Foreign Studies, 107 Imunro, Seoul, Korea

† These authors contributed equally to this work.

* Correspondence: Taejin Koh 

Academic Editor: Anton R. Kiselev

Special Issue: Metacognition and Learning: Ideas and Insights from Neuro

Received: April 28, 2025 | Accepted: September 25, 2025 | Published: October 09, 2025

OBM Neurobiology 2025, Volume 9, Issue 4, doi:10.21926/obm.neurobiol.2504304

Recommended citation: Kim Y, Kwak S, Koh T. The Influence of Gender and Academic Achievement on University Students' Perceptions of LMS-Based Self-Regulated Learning in Korea. OBM Neurobiology 2025; 9(4): 304; doi:10.21926/obm.neurobiol.2504304.

© 2025 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

1. Introduction

1.1 LMS Evolution and Self-Regulated Learning in Digital Education

The rapid development of information and communication technology has brought innovative changes to educational environments [1], with Learning Management Systems (LMS) establishing themselves as essential infrastructure in modern education [2,3]. The global expansion of digital learning platforms has been further accelerated by the emergence of Massive Open Online Courses (MOOCs), which have fundamentally transformed online education since 2012. A comprehensive overview of the development of MOOCs from 2012 to 2022 demonstrates how these platforms have evolved from simple content delivery systems to sophisticated learning environments that integrate advanced pedagogical approaches and technological innovations [4]. This evolution mirrors the emergence of institutional LMS systems, as both platforms increasingly prioritize learner engagement, personalized learning pathways, and data-driven educational decision-making.

In the early 2000s, as cyber universities emerged in Korea in response to the digital era, e-learning became increasingly widespread, and learning management systems developed in tandem with this trend [5]. LMS is an electronic support system that manages course content, learner progress, and instructional delivery through both asynchronous and synchronous formats, while integrating modular Learning Objects (LOs) to support flexible, goal-aligned learning [6]. Conventionally, the utilization of LMS has been predominantly confined to the domain of distance education and online learning. However, a recent paradigm shift has occurred, marked by an expansion in its role that surpasses its traditional function as a supplementary tool in face-to-face education. Instead, LMS has evolved into a pivotal mediator of the educational process [7]. LMS is a system that supports and manages learners' learning processes in web-based learning environments [8], enabling comprehensive monitoring and management of the overall teaching and learning process [9].

The intersection of cloud-based technologies and educational platforms has produced new possibilities for innovative learning experiences. Recent research on computer simulation and cloud-based innovative technologies reveals their potential to facilitate successful open learning environments [10]. The integration of cloud technologies with augmented reality demonstrates the synergistic power of combining multiple technological approaches in educational settings [11]. These technological convergences indicate that contemporary LMS platforms must be comprehended within the broader context of evolving digital learning ecosystems.

In particular, LMS is gaining attention as a tool that supports the development of learners' Self-Regulated Learning (SRL) abilities [12,13]. SRL is defined as an active and constructive process in which learners set their own learning goals, plan, monitor, and regulate their learning processes, and evaluate learning outcomes [14,15,16]. In LMS environments, learners have the capacity to monitor and modify their learning progress, task performance, and assessment results in real-time, thereby fostering the development of SRL abilities [17]. A salient feature of LMS-based learning pertains to its association with metacognitive development [18]. Metacognition is defined as the awareness of one's own cognitive processes and the ability to regulate them. It is considered a fundamental component of effective learning [19]. Metacognition is defined as comprising cognitive knowledge (i.e., knowledge about one's cognitive processes) and cognitive regulation (i.e., the ability to plan, monitor, and evaluate cognitive processes) [20]. In the context of LMS environments, the integration of functionalities that facilitate the recording and analysis of learning activities by learners has been demonstrated to support the development of cognitive knowledge and regulatory abilities [21,22].

The functions of LMS can be primarily classified into three categories: teaching activity support functions, learning support and management functions, and learning statistics functions. The third category is further subdivided into system support and content management functions [23]. The first category, teaching activity support functions, includes instructional design functions, learner information provision functions, content authoring functions, and interaction management functions [24]. Effective feedback is a pivotal component in metacognitive development [25], and immediate and specific feedback provided during the learning process can enhance the accuracy of learners' metacognitive monitoring [26]. LMS has the capacity to create an environment conducive to providing various forms of feedback, including automated feedback, instructor feedback, and peer feedback. The utilization of learning analytics features within LMS has been demonstrated to exert a favorable influence on learners' self-regulated learning abilities and academic achievement [27,28].

Contemporary learners, often termed 'digital natives', are well-versed in digital technology and demonstrate varied patterns in their approach to accessing and utilizing information, in contrast to earlier generations [29]. They proactively engage in the acquisition of knowledge, using a range of digital tools and demonstrating a propensity to manage their learning processes autonomously [30]. Digital native learners proactively engage in the learning process by utilizing a range of digital tools, thereby fostering the natural development of their SRL abilities [31]. Nevertheless, it is imperative to acknowledge the potential challenges associated with learner diversity and equity when implementing digital learning technologies. Research on gender representation in educational technology contexts reveals persistent stereotypes that can influence student engagement with digital learning platforms [32]. The findings of this study indicate that when devising implementation strategies for LMS, it is imperative to exercise caution and consideration with respect to the influence that disparate learner characteristics, including gender, may exert on technology adoption and the ensuing learning outcomes.

1.2 Contemporary Learning Challenges and Language Education Applications

In the context of digital learning environments, learners are afforded greater opportunities for visual confirmation and reflection on their learning processes, thereby exerting a positive influence on metacognitive SRL development [33,34,35]. In early 2020, in response to the global coronavirus pandemic, all Korean universities transitioned to remote instruction. During this period of transition, a variety of innovative teaching methods were adopted by many instructors, including real-time video remote lectures and video lectures uploaded to LMS. However, the preponderance of instructors expressed a preference for real-time video lectures, and the diverse functions of the LMS systems built by universities were still not being sufficiently utilized. In the context of the pandemic, the significance of SRL has been further accentuated. In non-face-to-face learning environments, learners' autonomy and responsibility have increased, thus making effective self-regulated learning a pivotal factor for academic success [36,37].

Even though LMS platforms furnish valuable tools for supporting learners' SRL and metacognitive development – such as planning aids, progress tracking, and self-assessment functions that help students adapt in non-face-to-face environments [13,17], and allow instructors to monitor learner activity and intervene when necessary [38] – many instructors were unfamiliar with these features and found it challenging enough to manage real-time online lectures alone. The data collected from the LMS has been shown to contribute to the adjustment of instructional approaches by being utilized in ongoing formative assessment [39]. Through the utilization of data stored in LMS, instructors can comprehend students' performance and learning levels about specific instructional treatments [40] and modify and supplement instructional design accordingly [41]. The learning analytics function of LMS can also be employed in the development of teaching strategies that support learners' metacognitive development [42].

Despite LMS offering a wide range of capabilities, including the provision of relevant information, competency analysis, learning management support, and facilitation of meaningful interaction among instructors, learners, and content [43], its implementation in actual educational settings has remained limited and passive. The evolution of LMS platforms towards more interactive and learner-centered designs reflects broader trends in the development of educational technology. Contemporary learning management systems are progressively incorporating features that not only facilitate content delivery but also sophisticated learning analytics, collaborative tools, and adaptive learning pathways. This evolution is consistent with research demonstrating the educational potential of integrated technological approaches, including the combination of simulation technologies, cloud computing, and immersive learning environments [9].

The Open Learning Model (OLM) has been demonstrated to facilitate metacognitive activities, such as self-checking, planning, and reflection, by providing a visual representation of learners' knowledge levels or learning processes [44]. Additionally, it assists in the exploration of learning paths and the determination of class sequences [45]. Similarly, LMS platforms have initially focused on delivering learning content, but are gradually evolving towards promoting interaction between learners and between instructors and learners [46]. The rapid advancements in technology and the evolution of LMS types and functions can be regarded as positive developments in terms of enhancing the connection between online and offline learning.

LMS has also been demonstrated to contribute to enhancing metacognitive development and educational effectiveness in the field of language education by enabling additional learning material provision, expansion of self-directed learning opportunities, immediate feedback, and collaborative interaction among peer learners [47,48]. Consequently, the utilization of LMS in face-to-face language classes, not only in non-face-to-face classes, has the potential to generate favorable outcomes in various domains, including learners' self-regulated learning abilities, metacognitive development, class satisfaction, participation, and academic achievement. In the domain of language learning, it is widely acknowledged that continuous practice and feedback are pivotal components of effective pedagogy. The establishment of an environment conducive to ongoing learning activities beyond the confines of the classroom is of paramount importance. It is evident that LMS has the capacity to augment the efficacy of language education by facilitating continuous learning experiences [49].

Research findings demonstrate the potential efficacy of LMS in English education at the university level. Specifically, the utilization of self-monitoring and reflection tools within LMS has been shown to contribute to the development of learners' metacognitive awareness and self-regulated learning abilities [50]. This suggests that LMS holds significant educational value. Research has been conducted on the impact of LMS use on learner satisfaction and learning motivation in language education [51,52]. In particular, the quality of instructors has been identified as an essential element in students' participation in online classes utilizing LMS [53]. Building on this knowledge, studies on the relationship between LMS use and learning satisfaction indicate that the ease of use of LMS, quality of learning materials, system stability, and instructors' utilization competence have a significant impact on learner satisfaction [54].

Research conducted on the subject of SRL in LMS environments has confirmed that SRL strategies such as time management, metacognition, effort regulation, and critical thinking have a significant relationship with academic achievement [12]. In particular, in the context of foreign language learning, the use of LMS to support SRL has been shown to be an effective method of improving learners' language abilities [55]. Moreover, the active utilization of LMS is of significant importance in terms of the implementation of educational equity and learner responsiveness. The concept of educational equity in higher education signifies the commitment to ensuring that all students have equal access to learning opportunities, resources, and support systems, irrespective of their background. The utilization of LMS has been identified as a potential mechanism for achieving these objectives [6,56,57].

1.3 Present Study and Objectives

From a global standpoint, the development of digital learning platforms is indicative of the necessity of a dual understanding: firstly, of local educational contexts, and secondly, of international best practices, in order to ensure successful LMS implementation. Whilst the present study concentrates on the Korean higher education context and the specific case of Hankuk University of Foreign Studies' e-class system, the findings contribute to a broader international discourse on how institutional learning management systems can be optimized to support diverse learner needs and educational objectives.

The present study focuses on the use of LMS in face-to-face language classes. Contrary to the majority of previous studies, which have focused on the effects of LMS in online or blended learning environments, this study is distinctive in its exploration of the educational that active utilization of LMS can bring in traditional face-to-face language education settings. In particular, by analyzing how perceptions and utilization patterns of LMS-based face-to-face language classes differ according to variables such as learners' gender and academic achievement, the objective is to seek more personalized LMS utilization strategies.

When LMS is employed in face-to-face language classes, it is expected to enhance the connection between in-class learning and out-of-class learning, thereby creating a learner-centered, self-directed learning environment. This is especially the case in the domain of language learning, which is characterized by the need for continuous input and output. The provision of additional learning materials, immediate feedback, and collaborative learning activities through LMS has been demonstrated to contribute to the enhancement of language abilities among learners. Furthermore, the self-regulated learning support functions of LMS have been shown to promote metacognitive development in learners, thereby enhancing learning efficiency.

In this study, a survey was conducted with 42 university students to examine their perceptions of LMS-based face-to-face language classes in terms of satisfaction, participation, learning improvement, and SRL. To ensure the reliability of the collected data, particular attention was paid to the methodological aspects of web-based survey implementation, especially regarding response rates and data quality. Recent research has emphasized that various factors—such as the timing of survey distribution, the credibility of the sender, and the use of reminders or incentives—can significantly influence participants' willingness to respond and the overall validity of the results [58]. The methodological considerations outlined above served as the foundation for the design of the present study's data collection procedures and analysis approach.

The present study will analyze the responses according to gender and academic achievement, with a view to identifying differences in LMS utilization across learner characteristics. The study will then propose practical strategies for integrating LMS into face-to-face language instruction, based on these insights. It is anticipated that this research will examine the role of LMS in enhancing the quality of traditional classroom education in the era of digital transformation. Furthermore, it is expected to contribute to the development of tailored LMS utilization strategies that are responsive to diverse learner needs.

In the ensuing sections, the research delineates the methodological framework and research questions, subsequently accompanied by a presentation of statistical findings on students' LMS perceptions as shaped by gender and academic achievement. The study utilized a two-way analysis of variance to elucidate the perceptions of students with lower academic performance regarding LMS-supported SRL. The findings offer insights into the potential for LMS to produce diversified educational benefits, contingent on learner profiles.

2. Research Context and Focus

2.1 Research Context: From Passive Adoption to Active Engagement in LMS-Based Higher Education

In the contemporary context of digital transformation in higher education, LMS/TMS has emerged as a pivotal element. A significant number of Korean universities have adopted LMS platforms to facilitate teaching and learning management. The implementation of such systems confers a number of advantages, including the provision of centralized access to learning and teaching materials, automated grading processes, real-time student monitoring, and the capacity for data-driven instructional adjustments. Furthermore, the integration of LMS facilitates enhanced tracking of learning progress for both instructors and students, thereby offering opportunities for personalized feedback and flexible learning pathways. Nevertheless, despite these advantages, the active utilization of LMS by instructors in many universities remains limited or passive. A significant proportion of faculty members employ LMS in order to execute fundamental functions, including the dissemination of announcements and the uploading of syllabi. Conversely, students access the system solely when it is deemed absolutely necessary. This limited use can be attributed to various factors, including the persistent value of face-to-face communication, the emotional and educational richness of in-person learning, and growing concerns regarding excessive digitalization and cognitive overload. Furthermore, confident learners and instructors have expressed concerns regarding the rigidity and burden of LMS features, particularly in instances where they do not align with their educational objectives. Consequently, there is an emergent need to transition from a passive adoption of LMS systems to active use in ways that reinforce, rather than supplant, human-centered pedagogy. This study, therefore, investigates how students perceive LMS-based education when LMS systems are actively used for weekly assessment, material delivery, real-time feedback, and systematic digital interaction. The investigation seeks to ascertain whether students' academic achievement and gender affect their participation and perception of the LMS-based learning environment. The present study analyses real classroom environments where LMS tools are systematically utilized in language education, such as intermediate Hindi grammar instruction, in order to provide practical insights into how LMS can function as an educational partner beyond a simple repository of learning materials. The objective of this study is to furnish information that will inform future educational design and to support educators in balancing the technological and human aspects of effective learning in the digital age.

2.2 Research Focus: Exploring Differential Perceptions of LMS-Based Instruction

The primary objective of this study is to examine how university students perceive LMS-based courses, with a particular focus on the impact of active LMS integration into the course. This study, conducted in an intermediate Hindi grammar course at Hankuk University of Foreign Studies, seeks to explore how factors such as academic achievement and gender affect students' satisfaction, engagement, perceived learning improvement, and perceptions of SRL in LMS within an LMS-integrated instructional environment. It is important to note that this study does not directly focus on test scores or academic achievement outcomes, but focuses on students’ subjective experiences and evaluations of the LMS-based learning process. As the use of LMS in higher education increases, it is necessary to understand how students with different academic backgrounds perceive and engage with LMS-based instruction, especially in terms of motivation and SRL. The present study aims to contribute to this field by conducting research based on the following primary research questions:

1) How do students with different academic achievements perceive and engage with LMS-based instruction?

2) How do male and female students experience LMS-integrated learning differently?

3) How do gender and academic level interact to shape students' experiences with and perceptions of LMS?

4) What are the learner characteristics that influence satisfaction and engagement with LMS-based courses?

5) How can students’ different experiences with LMS-supported self-regulated learning help guide improvements in LMS-based instructional practices?

Therefore, this study examines how students' gender and academic performance influence their perceptions of LMS regarding satisfaction, engagement, learning gains, and perceived SRL in LMS. By identifying these perceptual differences, instructors can determine which students thrive in LMS-based environments and which require additional support or differentiated approaches. Beyond analyzing these experiential differences, this research aims to provide practical insights for improving LMS implementation, such as e-class, in authentic classroom settings. The ultimate goal is to develop more adaptive, focused, and learner-centered digital environments that foster self-directed engagement and equitable participation across diverse learner profiles.

2.3 Theoretical Rationale for Examining Gender and Academic Achievement in LMS Contexts

The inclusion of gender and academic achievement as key variables in this study is grounded in a substantial body of theoretical and empirical research, which indicates that these factors systematically influence students' engagement with educational technology. One foundational framework emphasizes the role of self-efficacy beliefs in regulating learning behaviors and outcomes, particularly in autonomous environments where learners must take responsibility for their own progress and motivation [59]. Although gender-differentiated pathways are not explicitly addressed in this framework, subsequent research has shown that female students tend to employ more advanced metacognitive strategies and exhibit stronger self-monitoring behaviors [60]. These tendencies may be advantageous in LMS environments that require a high degree of self-regulated learning.

Learners' responsiveness to socially supportive learning environments also emerges as a relevant factor. Prior studies have found that students—particularly females—often show increased academic engagement in contexts characterized by peer collaboration and positive interpersonal dynamics [61]. While such evidence is derived from face-to-face classrooms, its implications may extend to LMS-based settings that incorporate interactive features such as discussion boards and peer feedback tools. Furthermore, gender-based differences have been observed in the domain of technology acceptance, with female learners more influenced by social and collaborative affordances, and male learners more focused on performance outcomes and task efficiency [62]. These patterns suggest that gender may shape differing perceptions of LMS utility and engagement.

From a cognitive perspective, effective engagement with LMS platforms necessitates the use of metacognitive strategies, including planning, monitoring, and evaluation. Although these cognitive processes were not examined within digital contexts, their relevance to online and self-directed learning has been widely acknowledged [63]. In parallel, academic achievement has been associated with enhanced digital competence and ICT self-efficacy, both of which are foundational for navigating complex LMS platforms [64]. Learners who possess higher levels of such competencies are more likely to utilize LMS features effectively and focus on instructional content, rather than being hindered by technological obstacles.

Another relevant framework highlights the importance of satisfying learners’ basic psychological needs—such as autonomy, competence, and relatedness—as a means of fostering motivation and engagement [65]. LMS environments, which often allow learners to control the pace, sequence, and structure of their learning, are well-positioned to meet these needs. However, this potential may be differentially realized depending on students’ academic readiness. Those with higher educational achievement are more likely to benefit from the autonomy provided by LMS platforms. In contrast, those with lower achievement levels may experience frustration or disengagement when faced with self-regulatory demands.

Taken together, these perspectives support the decision to examine gender and academic achievement in tandem. These two variables may interact in complex ways, producing distinct patterns of behavior and perception that cannot be fully understood when examined in isolation. Establishing a clear theoretical foundation is therefore critical for interpreting empirical results and for generating evidence-based strategies to enhance the design and implementation of LMS in diverse educational contexts.

3. Structure and Application of E-Class in LMS/TMS-Based Instruction

The utilization of learning and teaching management systems (LMS/TMS) has emerged as a pivotal component in contemporary higher education. As digital-based learning environments continue to proliferate, instructors are increasingly utilizing LMS/TMS to enhance student participation, facilitate effective communication between teaching and learning, and optimize the delivery of lecture content. In the context of blended or online learning, the LMS is a vital instrument in establishing a flexible, accessible, and customized educational environment for learners. Nevertheless, students' response to LMS-based classes varies depending on the characteristics of individual learners, such as gender and academic achievement. Consequently, a precise understanding of the effectiveness and acceptability of LMS-based education is a prerequisite for designing a learning environment that promotes educational equity and learner responsiveness [57].

3.1 Understanding LMS Functionality

Before examining the specific implementation of LMS in this study, it is essential to understand that an LMS serves broader educational functions that can be achieved through various means, both digital and non-digital. The core educational functions that LMS systems facilitate include frequent assessment and feedback, enhanced communication channels, transparency in learning progress, and support for student metacognition and self-regulated learning. While these functions are not exclusive to digital platforms, LMS systems offer unique advantages in terms of scalability, real-time data processing, and accessibility them particularly valuable in contemporary higher education contexts.

Frequent assessment—a critical component of effective learning—can be implemented through various approaches, including traditional paper-based quizzes with manual grading, verbal questioning during class, peer assessment activities, and digital quizzes with automated feedback. Each approach has distinct advantages and limitations. Paper-based assessments allow for complex, open-ended responses but are time-intensive to grade and provide delayed feedback. Verbal questioning enables immediate clarification but may not reach all students equally. Digital quizzes, while potentially limited in question complexity, offer immediate feedback, consistent scoring, and detailed analytics on student performance patterns. The implementation of frequent, low-stakes digital assessments through LMS platforms has been shown to enhance learning retention while providing ongoing diagnostic information about student understanding. This approach aligns with formative assessment principles that emphasize learning improvement rather than performance evaluation [66,67].

Similarly, communication between instructors and students can occur through face-to-face office hours, email correspondence, discussion forums, or real-time messaging systems. The effectiveness of each method depends on factors such as accessibility, response time, documentation capabilities, and the nature of the inquiry. LMS-integrated communication systems offer particular advantages in terms of centralized record-keeping, mobile accessibility, and the ability to track communication patterns as part of overall student engagement metrics. Research indicates that structured digital communication channels can reduce barriers to student-instructor interaction, particularly for students who may be hesitant to participate in traditional face-to-face settings [68,69]. The asynchronous nature of LMS communication enables more thoughtful exchanges while maintaining detailed records that inform instructional decisions.

One of the most significant advantages of implementing a comprehensive LMS is the enhancement of transparency in the learning process. Traditional classroom environments often limit students' ability to continuously monitor their own progress, whereas LMS platforms provide real-time access to grades, feedback, assignment status, and learning analytics. This transparency serves multiple educational functions: it enables timely intervention for struggling students, supports student metacognitive development through self-monitoring, and provides instructors with detailed data for instructional adjustment [70]. The support for self-regulated learning through LMS platforms represents a shift from instructor-controlled to learner-centered educational approaches. When students have access to comprehensive data about their learning progress, they are better positioned to make informed decisions about study strategies, time allocation, and help-seeking behaviors [3].

3.2 The E-Class System: Implementation and Active Integration Strategy

In response to the evolving educational landscape, the majority of universities in Korea have established their own LMS/TMS systems. Hankuk University of Foreign Studies utilizes an integrated system called e-class to manage learning activities systematically between professors and students. The e-class system is accessible through both web browsers and a dedicated mobile application, ensuring ubiquitous access to learning resources and communication tools across different devices and contexts.

The e-class platform incorporates a comprehensive range of functions that serve distinct educational purposes within an integrated digital learning environment. The system architecture is designed to support the full spectrum of academic activities, from content delivery to assessment and communication. Core learning management functions include syllabus management with dynamic updating capabilities, online lecture hosting with streaming and recording features, comprehensive learning material distribution with download tracking, and automated assignment submission management with deadline enforcement. These functions replace traditional paper-based systems while providing enhanced organizational capabilities and detailed usage analytics.

The platform features sophisticated online examination and quiz capabilities with randomized question ordering to ensure academic integrity, automatic grading with immediate feedback provision, and integrated grade management with real-time updates. Advanced quiz settings allow instructors to control viewing options, time limits, and attempt restrictions while providing students with immediate access to results and explanations. Beyond basic announcement posting, e-class provides real-time notification capabilities for mobile devices, ensuring timely communication between instructors and students. The integrated Q&A messaging system allows for documented exchanges that can be referenced throughout the semester, while discussion forums and voting tools facilitate peer interaction and collaborative learning activities. Team project coordination tools facilitate group work management through shared spaces and cooperative document editing.

The platform features comprehensive attendance tracking, sophisticated student monitoring functions that enable instructors to track engagement patterns and identify at-risk learners, as well as detailed self-monitoring tools that allow students to track their own progress, assignment completion rates, and grade trajectories. This dual monitoring capability supports both instructor responsiveness and student metacognitive development through data visualization and progress tracking dashboards.

Despite the LMS system's established presence in practical applications, there is a notable absence of active engagement with its functionalities by many professors. Due to factors such as the continuous uploading of content, the time burden associated with learning activities, concerns about real-time communication compared to offline classes, and insufficient preparation for the transition to online instruction, the LMS has been predominantly used as an auxiliary tool, limiting its ability to realize its educational potential [66,67,68,69] entirely. The academic impact of an LMS depends not merely on its availability but on how comprehensively and strategically its functions are integrated into the instructional design. This study distinguishes between passive and active LMS implementation based on the extent to which the system's capabilities are utilized to achieve specific educational objectives rather than simply digitizing existing practices.

Table 1 provides a detailed comparison between passive and active LMS implementation, illustrating how the same technological infrastructure can serve fundamentally different educational purposes depending on implementation strategy and pedagogical integration.

Table 1 Comparative Analysis of Passive and Active LMS Use.

3.3 Pedagogical Integration and Educational Rationale

The present study sought to analyze the effects of active LMS integration on student participation, awareness, and academic achievement. In the context of higher education, where instructors are responsible for managing large enrolment classes, the capacity to discern and address the learning progress of individual students is frequently constrained by practical limitations. However, the systematic use of LMS functions – including real-time assessment feedback, mobile-accessible communication, detailed engagement tracking, and student self-monitoring tools – can provide comprehensive insights into the learning process that would be difficult to achieve through traditional methods alone.

This approach signifies more than a mere digitization of prevailing practices; it denotes a substantial transition towards a pedagogy informed by data and responsive to it. The integration of frequent assessment through online quizzes, documented communication through messaging systems, and transparent progress tracking creates an educational environment where both instructors and students have unprecedented visibility into the learning process.

The Hindi language course under scrutiny in this study was meticulously crafted to illustrate how active LMS integration can facilitate SRL while upholding the academic rigor and cultural substance that are indispensable for language acquisition. The course design utilized the distinctive features of the e-class system to engineer learning experiences that would be challenging to replicate through non-digital means. In contrast to merely transferring conventional classroom activities to a digital platform, this approach employed the e-class system's unique capabilities to create authentic, immersive, and engaging learning experiences.

The weekly classes incorporated a variety of learning components, including interactive lecture slides, culturally relevant reference materials, targeted announcements, and scaffolded assignments. Students enrolled in the LMS needed to download the relevant materials each week for preparation and review purposes. Assignments, composed manually to preserve the kinesthetic learning benefits that are critical for language acquisition, were digitized through scanning and submitted via the LMS platform. The submission time and file details were automatically recorded, thereby enabling efficient verification of task completion while maintaining detailed records for progress tracking.

The comprehension of learning materials was assessed through brief online quizzes administered weekly via the e-class system. The assessments employed randomized question ordering and controlled viewing arrangements to ensure academic integrity while concurrently reducing test anxiety. It was observed that students completed quizzes using their preferred devices, which included smartphones, tablets, and laptops. This reflected the platform's mobile-responsive design. Upon completing the quiz, students could access their scores, along with the correct answers and detailed explanatory commentary for each question. This facilitated immediate error correction and reinforced learning objectives.

The LMS messaging function enabled students to submit inquiries related to assignment requirements or technical issues, with instructors providing real-time responses through mobile notification capabilities. This communication system maintained comprehensive documentation of all exchanges, creating a searchable archive of common questions and responses that benefited both current and future students.

It is evident that, according to the detailed activity logs maintained by the e-class system, the utilization of monitoring functions by instructors was instrumental in identifying students exhibiting low participation rates. The provision of individualized feedback and support then followed this identification process. This proactive approach to student support represents a significant advancement over traditional reactive methods, which often identify struggling students only after they have performed poorly on high-stakes assessments.

The present study's contribution does not lie in advocating for LMS adoption per se, but in demonstrating how strategic integration of digital tools can enhance specific educational functions while maintaining pedagogical quality and student engagement. The findings offer insights into how technology can serve educational goals rather than drive them, providing practical implications for instructors seeking to enhance learning outcomes through the thoughtful integration of available digital resources.

4. Research Methods and Instrumentation

4.1 Data Collection and Analysis Methods

This study was applied to the 'Intermediate Hindi Grammar I' course offered by the Department of Indian Studies at Hankuk University of Foreign Studies in the first semester of 2024. The course was designed to explore how LMS-based classes can improve learning participation and awareness across various types of learners by linking with e-class, the university's learning and teaching management system (LMS/TMS). The course was scheduled to run for a total duration of 16 weeks, with two-hour classes held every week. This provided students with consistent exposure to and practice with intermediate Hindi grammar concepts throughout the semester.

E-class comprehensively supported primary functions related to classes, such as providing weekly class materials, operating simple online quizzes, assignment submission and feedback, and communication through notices and messages. Through this system, instructors could monitor learning participation, sincerity, and class immersion in real-time based on individual student access records, file downloads, quiz attendance status, and message interactions. This served as a systematic learning management function that was difficult to provide in a traditional offline class environment.

Consequently, a structured survey was developed and administered at the conclusion of the semester to evaluate students' overall perception of LMS-based classes. The survey consisted of 21 questions, categorized into four core areas: satisfaction, participation, learning effectiveness, usability, and necessity. Each item was evaluated using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The instrument was designed to capture both emotional and behavioral aspects of student-LMS interactions. The survey received responses from a total of 42 undergraduate students enrolled in the course, grouped by academic achievement and gender as shown in Table 2.

Table 2 Demographic Characteristics of Participants.

This study employs a 5 (Academic Achievement: excellent, good, average, not bad, and poor) × 2 (Gender: male and female) between-subjects design to examine how student characteristics influence perceptions of LMS-based instruction. Academic Achievement represents students' final grades in the intermediate Hindi grammar course, measured at the end of the semester. Students' course performance was self-reported on the questionnaire using the following categories: A (Excellent), B (Good), C (Average), D (Not Bad), and E (Poor). It should be noted that while Academic Achievement serves as an independent variable in this analysis, it is technically a measured variable rather than a manipulated variable, as it represents actual course performance rather than a pre-determined grouping. Gender was also self-reported by participants on the questionnaire, with students selecting either male or female. Both variables were collected through self-report measures included in the survey questionnaire administered to students.

Consequently, a structured survey was developed and administered at the conclusion of the semester to evaluate students' overall perception of LMS-based classes. The survey comprised 21 questions, which were categorized into four core areas: satisfaction, participation, learning effectiveness, and self-regulated learning (SRL) in LMS. Each item was evaluated using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The instrument was designed to capture both emotional and behavioral aspects of student-LMS interactions, while also collecting demographic information and academic performance data necessary for the factorial analysis.

The survey received responses from a total of 42 undergraduate students enrolled in the course, grouped by academic achievement and gender as shown in Table 2. The final dataset was organized according to a between-subjects design, with participants classified by both their self-reported academic achievement levels and gender, allowing for a comprehensive analysis of main effects and interaction effects between these two independent variables on students' LMS perceptions across the four measured domains.

The collected survey responses were analyzed using SPSS 21 and Jamovi 2.3 statistical software, with the analysis methods used including Cronbach's α reliability analysis, descriptive statistics, one-way and two-way ANOVA, post-hoc tests (Tukey HSD), and t-tests.

4.2 Questionnaire and Reliability

The questionnaire employed in this study comprises a total of 21 questions, which have been categorized into four primary domains to capture students' perceptions of LMS-based classes from multiple perspectives. Each area is carefully designed to offer a comprehensive overview of the learning experience in an LMS environment from an emotional, behavioral, and cognitive perspective. The fundamental meaning of each domain is outlined below:

Firstly, the 'Satisfaction' domain sought to measure the emotional response of learners to the LMS-based classes as a whole. It comprised questions designed to evaluate the extent to which students found their learning experience enjoyable and beneficial through the LMS environment. The questions addressed aspects such as the way the class was conducted, the clarity of the content, and overall satisfaction with the learning process. This domain is instrumental in elucidating the emotional underpinnings of learner motivation.

Secondly, the 'Participation' domain evaluated the extent to which learners engaged with various functions within the LMS system. For instance, it focused on actual learning behaviors such as undertaking online tests, reading materials, sending and receiving messages, and checking announcements. The area under discussion measured whether learners functioned as active participants rather than passive recipients. The 'Participation' domain is designed to gauge the level of learner-driven activities through the LMS.

Thirdly, the 'Learning Improvement' domain diagnoses how classes that utilize the LMS have affected the cognitive achievement of learners. Thematic analysis of the survey responses explores the extent to which students comprehend lesson content, their ability to recall and apply knowledge, and the impact of the LMS on their academic performance. This area enables an indirect measurement of the educational effectiveness of the platform based on the students' self-perceived academic achievement.

Finally, the ‘SRL in LMS’ domain focuses on how learners engage in SRL through their use of the LMS. This includes the ability to plan, monitor, and evaluate learning processes using LMS features such as managing assignments, accessing learning materials, taking quizzes, and reviewing feedback. Students consider how the LMS supports goal setting, time management, and learning strategies in an autonomous and structured manner. This area also examines how the LMS environment fosters learner responsibility and self-directed engagement, offering insight into the platform's role in promoting independent and ongoing learning behaviors.

The domains are specifically designed to provide feedback on LMS-based classes at varying levels, thereby functioning as an integrated and structured perception assessment tool rather than a fragmented satisfaction survey. The assessment of each domain for internal consistency employed Cronbach's alpha, a statistical technique used to evaluate the reliability of a set of items, with all subscales demonstrating high reliability.

As demonstrated in Table 3, the survey instrument exhibited high internal consistency across all domains. The Cronbach's alpha coefficient was 0.862 for the four subscales: satisfaction (0.862), participation (0.809), learning improvement (0.906), and SRL in LMS (0.848). The range was from 0.809 to 0.906, and the overall reliability for all 21 items was 0.954. This study’s findings indicate that the instrument is both reliable and appropriate for assessing students' perceptions of LMS-based instruction, incorporating both the affective aspects of engagement and the cognitive behavioral dimensions of SRL.

Table 3 Reliability of survey.

5. Survey Results and Discussion

The final dataset comprises 42 undergraduate students enrolled in the intermediate Hindi grammar course, which actively utilizes the e-class platform. This study employs a 5 (Academic Achievement: excellent, good, average, not bad, and poor) × 2 (Gender: male and female) between-subjects design. The analysis was conducted using SPSS version 21, employing two-way ANOVA as the primary analytical approach, followed by post-hoc comparisons to examine how students' perceptions of LMS-based instruction vary according to gender and academic achievement.

5.1 Two-Way ANOVA Results

A series of two-way ANOVAs was conducted to examine the main effects of gender and academic achievement, as well as their interaction effects, on each of the four dependent variables: Satisfaction, Participation, Learning Improvement, and SRL in LMS (see Table 4).

Table 4 Two-Way ANOVA Results: Gender and Achievement Effects on LMS Perceptions.

The two-way ANOVA analysis revealed differential patterns for the main effects of gender and academic achievement across the four domains of LMS perception. When examining gender as an isolated factor, the results indicated no statistically significant differences between male and female students across any of the measured domains, with all F-statistics yielding identical values of F(1, 34) = 0.00, p = 1.000. This finding suggests that gender, when considered independently of other factors, does not serve as a reliable predictor of how students perceive various aspects of LMS-based instruction, including their satisfaction, participation levels, perceived learning improvements, or experiences with self-regulated learning features.

In contrast, academic achievement demonstrated a more complex pattern of influence across the four domains. The analysis revealed statistically significant main effects of academic achievement on three of the four dependent variables. Specifically, academic achievement emerged as an important predictor of students' satisfaction with LMS-based instruction, F(4, 34) = 2.70, p = 0.048, indicating that students' course performance levels systematically influenced their emotional responses to the learning management system. Similarly, academic achievement significantly predicted students' participation in LMS activities, F(4, 34) = 3.32, p = 0.021, suggesting that performance levels were associated with varying degrees of behavioral engagement with platform features such as assignment submissions, discussion participation, and resource utilization. Additionally, academic achievement showed a significant relationship with students' perceptions of SRL support provided by the LMS environment, F(4, 34) = 2.98, p = 0.033, indicating that students at different performance levels experienced varying degrees of benefit from self-regulation tools and features. However, the effect of academic achievement on perceived learning improvement, while showing a trend in the expected direction, did not reach conventional levels of statistical significance, F(4, 34) = 2.21, p = 0.090.

The examination of interaction effects between gender and academic achievement revealed a more nuanced pattern of relationships across the four domains. A statistically significant interaction effect was observed exclusively in the SRL in the LMS domain, F(4, 34) = 2.80, p = 0.040, indicating that the relationship between academic achievement and perceptions of self-regulated learning support varies systematically between male and female students. This interaction suggests that the influence of academic performance on students' evaluations of LMS self-regulation features operates differently for male and female students, with the pattern of differences across achievement levels being moderated by gender. Conversely, no statistically significant interactions between gender and academic achievement were detected in the remaining three domains, with the satisfaction domain showing F(4, 34) = 1.92, p = 0.126, the participation domain yielding F(4, 34) = 1.25, p = 0.310, and the learning improvement domain producing F(4, 34) = 1.91, p = 0.129.

5.2 Descriptive Statistics by Academic Achievement Group

To understand the patterns underlying the significant ANOVA results, descriptive statistics were examined for each academic achievement group across the four domains. The descriptive analysis provides essential context for interpreting the statistical significance tests and reveals the magnitude and direction of differences between achievement groups (see Table 5).

Table 5 Descriptive Statistics by Academic Achievement Group.

Examination of the descriptive statistics reveals systematic variation in LMS perceptions across academic achievement levels. In the satisfaction domain, students with excellent achievement (M = 4.27, SD = 0.81) and average achievement (M = 4.27, SD = 0.41) reported the highest levels of satisfaction. In contrast, students with poor achievement demonstrated substantially lower satisfaction levels (M = 3.00, SD = 0.71). The participation domain exhibited a clear hierarchical pattern, with excellent achievers showing the highest engagement (M = 4.40, SD = 0.60), followed by average achievers (M = 4.34, SD = 0.50), and poor achievers demonstrating the lowest participation levels (M = 3.05, SD = 0.77). A similar pattern emerged in the learning improvement domain, where average achievers reported the highest perceived gains (M = 4.14, SD = 0.62) and poor achievers indicated the least improvement (M = 2.90, SD = 0.66). The SRL in the LMS domain showed comparable trends, with average achievers rating self-regulated learning support most favorably (M = 4.33, SD = 0.27) and poor achievers providing the lowest ratings (M = 3.08, SD = 0.80). These findings suggest that students with higher academic achievement are more likely to view the LMS as a valuable tool for supporting self-regulated learning, such as planning, monitoring, and managing their academic activities [9]. By contrast, students with lower performance may find it more challenging to utilize LMS features for autonomous learning, possibly due to differences in digital fluency, learning motivation, or metacognitive awareness [71,72].

These descriptive patterns are consistent with the statistically significant differences identified in the ANOVA analyses and provide the empirical foundation for understanding how academic performance influences students' LMS experiences.

5.3 Post-Hoc Analysis

Following the identification of significant main effects in the two-way ANOVA, Tukey HSD post-hoc comparisons were conducted to determine the specific nature of differences between academic achievement groups (see Table 6). This analysis is essential for identifying which particular achievement levels differ significantly from one another and for understanding the practical implications of the observed statistical effects.

Table 6 One-Way ANOVA Results by Academic Achievement Group.

The post-hoc analyses using Tukey HSD revealed consistent patterns of significant differences between academic achievement groups, with poor-performing students systematically rating their LMS experiences less favorably than their higher-achieving counterparts. In the satisfaction domain, the analysis identified significant differences, indicating that both students with average achievement (Group C) and those with excellent achievement (Group A) reported significantly higher satisfaction levels compared to students with poor achievement (Group E). These findings suggest that students at the lowest performance level experience fundamentally different emotional responses to LMS-based instruction compared to their more successful peers.

The participation domain demonstrated the most extensive pattern of significant differences among achievement groups. Students with excellent (Group A), good (Group B), and average (Group C) achievement all showed significantly higher levels of engagement with LMS features compared to poor achievers (Group E). Additionally, average achievers (Group C) demonstrated substantially higher participation than students with "not bad" achievement (Group D), indicating a clear hierarchical relationship between academic performance and behavioral engagement with the learning management system. However, it is essential to note that this finding may reflect different types of engagement patterns. This finding suggests that students with lower academic achievement may have exhibited higher levels of behaviorally driven engagement with LMS tools, such as quizzes, messages, and assignment submissions, perhaps because the LMS provided a more accessible and less threatening way to interact in the classroom [73,74]. The LMS may also function as an equalizer, giving alternative avenues for learning and engagement that better meet the needs of academically disadvantaged students [75,76]. This pattern suggests that LMS participation may be both a cause and consequence of academic success, with higher-achieving students more actively utilizing platform resources while also benefiting more from their engagement.

In the learning improvement domain, the post-hoc analysis revealed a more focused pattern of differences, with average achievers (Group C) reporting significantly greater perceived learning gains compared to poor achievers (Group E). This finding suggests that students in the middle range of academic performance may be particularly well-positioned to benefit from LMS-based instructional approaches. In contrast, those with the lowest performance levels may struggle to perceive meaningful learning benefits from integrating digital platforms. The SRL in the LMS domain exhibited the most comprehensive pattern of significant differences, with poor achievers (Group E) rating self-regulated learning support significantly lower than all other achievement groups (A, B, C, and D). Furthermore, average achievers (Group C) demonstrated substantially higher ratings than students with "not bad" achievement (Group D). These extensive differences suggest that self-regulated learning features within LMS environments may be susceptible to students' academic performance levels, with lower-achieving students potentially requiring additional support or alternative approaches to utilize self-regulation tools and strategies effectively.

5.4 Gender-Based Analysis of LMS Perceptions

Although the two-way ANOVA revealed no significant main effect of gender when controlling for academic achievement, independent samples t-tests were conducted to examine gender differences when academic achievement groups were collapsed, providing additional context for understanding the role of gender in LMS perceptions (see Table 7).

Table 7 T-test Results by Gender.

When academic achievement is not controlled for, significant gender differences emerge across all domains, with female students reporting significantly higher satisfaction (t = -3.33, p = 0.002), participation (t = -3.64, p = 0.001), learning improvement (t = -3.67, p = 0.001), and SRL in LMS perceptions (t = -2.78, p = 0.012) compared to male students. The overall difference across all domains was also statistically significant (t = -3.72, p = 0.001). However, these differences become non-significant when academic achievement is considered as a covariate in the two-way ANOVA, suggesting that the apparent gender differences may be confounded with achievement level differences rather than representing actual gender-based differences in LMS engagement patterns. This finding indicates that the distribution of male and female students across achievement categories in this sample may account for the observed gender differences and that academic performance serves as a more reliable predictor of LMS perceptions than gender alone. Nevertheless, these findings highlight that understanding gender differences in learning management system (LMS) perceptions can help to develop more inclusive and adaptive teaching strategies [77,78].

5.5 Discussion

The results of this study reveal complex relationships between student characteristics and perceptions of LMS-based instruction. The primary finding is that academic achievement serves as the most robust predictor of LMS perceptions, with statistically significant effects demonstrated across three of the four examined domains: satisfaction (F(4, 34) = 2.70, p = 0.048), participation (F(4, 34) = 3.32, p = 0.021), and SRL in LMS (F(4, 34) = 2.98, p = 0.033). Students with higher academic achievement generally report more positive experiences with LMS-based instruction, while those with poor academic performance consistently rate their LMS experience less favorably across these domains. Notably, the effect of educational achievement on perceived learning improvement, while showing a trend in the expected direction, did not reach conventional levels of statistical significance (F(4, 34) = 2.21, p = 0.090), suggesting that perceptions of learning gains may be less directly tied to academic performance levels than other aspects of LMS experience.

The post-hoc analyses using Tukey HSD revealed that poor-achieving students (Group E) were systematically disadvantaged across multiple domains, reporting significantly lower satisfaction, participation, and SRL perceptions compared to their higher-achieving peers. Notably, a comprehensive pattern was observed in the SRL domain, where poor achievers rated self-regulated learning support significantly lower than all other achievement groups, indicating that students with lower academic performance may struggle most with the autonomous demands of LMS-based learning environments.

The apparent contradiction between the independent samples t-test results (showing significant gender differences across all domains) and the two-way ANOVA results (showing no considerable gender main effects) can be explained by the confounding relationship between gender and academic achievement in this sample. When academic achievement is statistically controlled through the factorial design, gender differences disappear completely (F(1, 34) = 0.00, p = 1.000 across all domains), suggesting that the initial gender differences observed in the t-tests were artifacts of uneven distribution of academic achievement between male and female participants rather than representing genuine gender-based differences in LMS engagement patterns.

The significant interaction effect observed exclusively in the SRL in the LMS domain (F(4, 34) = 2.80, p = 0.040) indicates that the relationship between academic achievement and perceptions of LMS support for self-regulated learning varies systematically between male and female students. This interaction suggests that the influence of academic performance on students' evaluations of self-regulation features operates through different mechanisms for male and female learners, potentially reflecting differential strategies for utilizing LMS tools or varying responses to autonomous learning demands based on the intersection of gender and achievement level.

These findings have important theoretical and practical implications for LMS design and implementation. The consistent and strong effects of academic achievement across multiple domains suggest that LMS platforms should be designed with particular attention to supporting lower-achieving students, who may require more structured guidance, scaffolding, and support to utilize LMS features effectively. The significant interaction effect in the SRL domain indicates that gender-specific considerations may be necessary when designing self-regulation support features, as the effectiveness of these tools appears to vary based on the complex interplay between students' gender and academic performance levels.

From a pedagogical perspective, these results suggest that instructors implementing LMS-based instruction should be particularly attentive to the needs of students with lower academic achievement, who may experience frustration or reduced satisfaction with digital learning environments. The finding that gender differences are primarily explained by achievement-level differences, rather than representing inherent gender-based preferences, highlights the importance of focusing on academic support rather than assuming differential technological preferences solely based on gender.

6. Conclusions and Research Limitations

This study empirically analyzes how LMS-based instruction influences learners' perceptions of SRL in the context of higher education, where digital transformation is accelerating. In particular, it examines how demographic variables, including gender and academic achievement, influence students' LMS experiences. To this end, LMS was actively integrated into an intermediate Hindi grammar course, and surveys were collected in four domains: satisfaction, participation, perceived learning improvement, and perceived SRL. The data were analyzed using SPSS 21 and Jamovi 2.3 through descriptive statistics, t-tests, and one-way and two-way ANOVA.

The independent samples t-test revealed that female students showed significantly higher mean scores than males in all domains: satisfaction (M = 4.19 vs. 3.55, p = 0.002), participation (M = 4.32 vs. 3.64, p = 0.001), perceived learning improvement (M = 4.12 vs. 3.39, p = 0.001), and LMS-based SRL perception (M = 4.22 vs. 3.73, p = 0.012). This suggests that female students generally have more favorable perceptions of LMS-based instruction and demonstrate more consistent and stable SRL experiences in digital learning environments compared to their male counterparts. However, the results of the two-way ANOVA revealed a more complex pattern that partially contradicts these simple gender differences. When controlling for academic achievement, gender as a main effect showed no significant influence on any dimension (all F(1, 34) = 0.00, p = 1.000), suggesting that the gender differences observed in the t-test might be related to the distribution of academic achievement within gender groups.

The two-way ANOVA did reveal significant main effects of academic achievement on satisfaction (F(4, 34) = 2.70, p = 0.048), participation (F(4, 34) = 3.32, p = 0.021), and SRL perception (F(4, 34) = 2.98, p = 0.033), while its effect on learning improvement approached but did not reach significance (F(4, 34) = 2.21, p = 0.090). Additionally, a significant interaction between gender and achievement was found only for SRL in LMS (F(4, 34) = 2.80, p = 0.040). Male students showed marked variation in LMS perception depending on their academic achievement. For instance, low-achieving males (Group E) reported the lowest scores across all domains—satisfaction (F = 2.97, p = 0.033), participation (F = 3.51, p = 0.017), learning improvement (F = 2.40, p = 0.069), and SRL perception (F = 4.24, p = 0.007)—indicating negative LMS experiences. In contrast, high-achieving males evaluated LMS positively, similar to female students, while female learners generally maintained consistently high perceptions regardless of their achievement level.

These findings support the study's aim to demonstrate that LMS is perceived differently based on learner characteristics. LMS functions such as task submission, progress tracking, and achievement monitoring can enhance learners' self-monitoring and metacognitive regulation, which are closely tied to SRL. However, digital literacy, motivation, and academic level interact in complex ways to determine whether learners can effectively utilize these features. The study confirmed that low-achieving male students tend to experience lower satisfaction and self-efficacy in LMS-based instruction, forming more negative perceptions. This suggests that uniformly using LMS may marginalize specific learner groups.

The contradictory findings between the t-test (showing significant gender differences) and the two-way ANOVA (showing no main effect of gender) highlight the importance of considering multiple factors simultaneously when analyzing educational technology perceptions. While gender differences appear significant when examined in isolation, these differences seem to be better explained by the interaction between gender and academic achievement, particularly in contexts that involve self-regulated learning. Therefore, instructional design using LMS should be adjusted to ensure that all learners can achieve both autonomy and efficiency. In particular, LMS should serve as a compensatory environment for lower-achieving students, and instructors must make active use of tools such as LMS-based visualizations, personalized feedback, and progress monitoring functions [69,70]. The study underscores that the educational value of LMS depends not merely on its adoption but also on how it is utilized and aligned with diverse learner needs. By shedding light on the multilayered effects of LMS integration, the study provides an empirical foundation for developing learner-centered LMS strategies and designing inclusive educational environments.

Despite its contributions, this study acknowledges several methodological and conceptual limitations that warrant careful consideration and suggest directions for future research. The most significant limitation is the restricted sample size (n = 42), which is drawn from a single course at a single institution, which affects both the statistical power of the analyses and the generalizability of the findings. While the observed effect sizes were sufficient to detect significant differences in several domains, the small sample size may have limited the ability to identify more subtle interaction effects or to provide stable estimates of population parameters. The constraint of working within a single course context, while giving ecological validity for understanding real classroom implementation, necessarily limits the external validity of the findings. Future research should prioritize multi-site studies with larger, more diverse samples to strengthen the robustness of statistical inferences and enhance generalizability across different institutional contexts, academic disciplines, and student populations.

This study relied exclusively on self-reported survey data, which, while valuable for understanding subjective perceptions, provides only one perspective on LMS experiences. The absence of behavioral LMS log data represents a significant limitation, as actual usage patterns, time spent on various LMS functions, and engagement sequences could provide crucial insights into the relationship between perceptions and behaviors. Additionally, the lack of qualitative data collection, such as interviews, focus groups, or open-ended survey responses, limits the depth of understanding regarding why specific patterns emerge and how students experience LMS integration in their own words. Future studies should implement mixed-methods approaches that combine survey data with behavioral analytics and qualitative insights to provide a more comprehensive understanding of LMS experiences.

While ANOVA and t-tests were appropriately selected for the research questions, this study did not explicitly report verification of statistical assumptions, including tests for homogeneity of variance, normality of distributions, and independence of observations. Although the analyses were conducted using established statistical software (SPSS and Jamovi), the presentation of assumption checking would strengthen the methodological rigor and reader confidence in the results. Future research should include explicit reporting of assumption testing and, where necessary, employ alternative analytical approaches such as non-parametric tests or robust statistical methods when assumptions are violated.

The interpretation of interaction effects, particularly the conclusion that LMS serves a compensatory function for low-achieving students, requires acknowledgment of the inferential limitations inherent in the study design. While the statistical patterns suggest differential benefits, the mechanisms underlying these effects remain largely speculative without additional empirical support. The cross-sectional design prevents causal inferences, and the specific context of intermediate Hindi grammar instruction may not generalize to other subject areas or instructional contexts. Future research should employ longitudinal designs and investigate LMS effects across multiple disciplines to strengthen causal understanding and broaden the applicability of these findings.

Although this study references inclusive and adaptive learning principles, the research design did not systematically operationalize these concepts or explicitly examine their implications for students with disabilities or those from underrepresented backgrounds. While the instructor maintained awareness of individual student needs through regular consultation sessions and personal interaction, this information was not systematically integrated into the research design or analysis. This represents a missed opportunity to examine how LMS implementation affects educational equity across diverse learning needs and backgrounds. The study acknowledges that students with learning disabilities, economic disadvantages, or other accessibility needs may experience LMS differently, but these considerations were not explicitly examined. Future research should prioritize inclusive design principles from the outset, incorporating accessibility considerations into both LMS implementation and research methodology.

The study was conducted within the specific context of Korean higher education and the particular LMS platform (e-class) used at Hankuk University of Foreign Studies. While this specificity provides valuable insights into LMS implementation in this context, it also limits the transferability of findings to other educational systems, LMS platforms, or cultural contexts. The interaction between cultural factors, academic traditions, and the adoption of technology may influence the patterns observed in this study. Future research should examine LMS perceptions across diverse cultural and institutional contexts to understand the role of contextual factors in shaping technology-mediated learning experiences.

This study examined student perceptions within an actively integrated LMS environment but did not include a comparison with traditional instruction or passive LMS use. While the research provides insights into how students experience comprehensive LMS integration, the absence of a control or comparison group limits the ability to attribute observed perceptions specifically to the LMS implementation strategy. Future studies should employ comparative designs that contrast active and passive LMS integration or compare LMS-enhanced instruction with traditional approaches to isolate the effects of different implementation strategies.

These limitations suggest several important directions for future investigation. Longitudinal studies tracking student perceptions and behaviors over extended periods could provide insights into how LMS experiences evolve and affect learning outcomes over time. Multi-institutional collaborations could address sample size concerns while examining how different institutional contexts impact the effectiveness of LMS implementation. Integration of learning analytics data with perception measures could reveal the relationship between subjective experiences and objective engagement patterns. In addition, the present study did not directly measure students’ digital literacy or LMS-specific capability, nor did it test whether effects vary by these factors; future work should incorporate direct indicators of LMS proficiency and digital access (e.g., behavioral logs, validated literacy scales) to assess potential heterogeneous effects. Finally, participatory research approaches that involve students as co-researchers could provide deeper insights into how LMS features can be designed and implemented to support diverse learning needs and preferences.

Overall, this research suggests that LMS can function not merely as a content delivery platform, but as a learning companion that helps students self-regulate their learning processes. LMS features such as quizzes, assignments, feedback, and progress tracking can promote metacognitive insight and self-monitoring, particularly for low-achieving students, by serving as a compensatory environment that mitigates learning gaps. However, because students may differ in their capability to use the LMS effectively, these conclusions should be interpreted as contingent on access and proficiency; targeted supports and inclusive design may be required for students with lower capability. Therefore, instructors should not use the LMS merely as a supplementary tool, but rather integrate its features as core design elements for promoting SRL from the beginning of course planning. Despite acknowledged limitations, this research offers valuable preliminary insights into how learner characteristics influence LMS perceptions and underscores the importance of differentiated approaches to educational technology implementation in higher education contexts.

Appendix

Acknowledgments

The authors would like to express our sincere gratitude to Dr. Lee (Department of Psychology, Dankook University) and Dr. Ahn (Department of Korean as a Foreign Language, Hankuk University of Foreign Studies) for their invaluable assistance in validating the statistical analyses, particularly the two-way ANOVA. Their expertise greatly contributed to enhancing the rigor and reliability of this study. Moreover, the authors are also grateful to the anonymous reviewers for their insightful comments and suggestions.

Author Contributions

Conceptualization, T.K., S.K.; Methodology, T.K., S.K.; Data curation, S.K., Y.K.; Writing – original draft, T.K., Y.K., S.K.; Writing – review & editing, T.K., Y.K., S.K.; Funding acquisition, S.K., Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2024S1A5A2A03030330). This work was also supported by the Hankuk University of Foreign Studies in 2025. And this work was also supported by the critical foreign language center of Hankuk University of Foreign Studies in 2025.

Competing Interests

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the article, or in the decision to publish the results.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy restrictions. Classes and surveys were conducted with student consent according to the research protocols approved by the university IRB. As this research involved educational methodology within a regularly offered university course, prior informed consent was obtained from all students enrolled in the course regarding their participation in the LMS-based learning approach. Additional informed consent was obtained from all participants before completing the survey questionnaire, with clear explanations provided regarding the voluntary nature of participation, data confidentiality, and the right to withdraw at any time without academic penalty.

AI-Assisted Technologies Statement

We acknowledge that the translation from Korean to English and refinement of academic expressions, were supported by AI-based language tools such as DeepL, Google Translate, and ChatGPT. These tools were employed strictly for basic language enhancement and expression refinement. All AI-assisted sections were carefully reviewed and edited by the authors to ensure accuracy and appropriateness, and the authors take full responsibility for the content of the manuscript.