Abstract
The convergence of educational technologies, ergonomics, and active learning frameworks offers a multidimensional approach to improving educational outcomes. This study examines the role of Learning Analytics (LA) in optimizing ergonomic educational spaces to support active learning within the higher education context of Kazakhstan, however the outcomes may equally be applied to neighboring countries. Addressing a gap between ergonomic design principles and data-driven educational practices, the study adopts a mixed-methods approach, combining quantitative and qualitative data collected from multiple institutions in Kazakhstan. Key Learning Analytics indicators were analyzed alongside parameters derived from ergonomic design frameworks to explore their relationship with active learning processes. The findings reveal statistically significant associations between selected Learning Analytics metrics and ergonomic features of learning environments, highlighting how data-informed spatial design can enhance student engagement and participation. These results underscore the importance of integrating technological and physical learning environments within a context characterized by ongoing higher education modernization and increasing adoption of digital tools. While the study provides empirically grounded insights relevant to institutional development in Kazakhstan, the findings are interpreted as context-sensitive rather than universally generalizable. Nevertheless, they offer potential implications for educational systems with similar structural and technological conditions (such as the countries like Uzbekistan, Kyrgyzstan, etc.)provided that adaptations are made to local or similar contexts. This study contributes to the growing body of research on Learning Analytics by extending its application beyond curriculum and assessment into the design of physical learning environments. It further emphasizes the need for context-aware, interdisciplinary strategies to support active learning in diverse educational settings.
Introduction
The global landscape of education is undergoing a metamorphosis, spurred by technological advancements, pedagogical innovations, and increasingly complex socio-economic factors. Among these catalytic forces, Learning Analytics (LA) and ergonomics have emerged as pivotal elements, capable of revolutionizing educational paradigms. While Learning Analytics offers data-driven insights to personalize educational experiences, ergonomic designs in educational settings serve as the bedrock for cultivating an environment conducive to active learning. Kazakhstan, with its unique socio-cultural attributes and burgeoning educational reforms, presents an ideal setting for the exploration of these interconnected domains.
This study is situated within the higher education context of Kazakhstan and is conceptualized as a case study reflecting broader developments across the Central Asian region. Countries such as Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan share similar trajectories in higher education reform, digitalization, and resource constraints. Therefore, examining Kazakhstan provides a useful lens for understanding how Learning Analytics and ergonomic educational design may function within comparable regional contexts, while still acknowledging local variations.
Although a growing body of literature has examined Learning Analytics in relation to student engagement, performance, and instructional design, its application has largely remained confined to digital and pedagogical domains (Siemens and Long, 2011; Ferguson, 2012). Concurrently, research on ergonomic design and physical learning environments has highlighted the importance of spatial configurations, furniture, and environmental comfort in supporting active learning (Brooks, 2011; Oblinger, 2006). Although a substantial body of research has examined Learning Analytics in relation to student engagement, performance monitoring, and personalized feedback, its application has largely been confined to digital and pedagogical domains (Sajja et al., 2023; Sharif and Atif, 2024). Recent advancements, including multimodal learning analytics, have begun to incorporate data from physical learning environments through sensors and behavioral tracking; however, these approaches primarily focus on observing learner interactions rather than informing spatial or ergonomic design (Martinez-Maldonado et al., 2023).
In parallel, research on ergonomic design and physical learning environments has emphasized the role of classroom layout, furniture design, and environmental conditions in enhancing student comfort, motivation, and learning outcomes (Costa, 2024; Saha, 2024). Emerging studies have also explored data-driven optimization of classroom layouts using machine learning and anthropometric data, highlighting the potential for adaptive and student-centered learning spaces (Kolawole, 2024).
Despite these advancements, there remains a notable lack of empirical research that integrates Learning Analytics with ergonomic design principles to optimize physical learning environments. Existing studies tend to address these domains in isolation, with limited efforts to establish a systematic, data-driven connection between spatial design and learning analytics. This gap is particularly evident in emerging educational contexts such as Kazakhstan, where research on the application of Learning Analytics to physical learning spaces remains scarce. Consequently, there is a need for interdisciplinary approaches that bridge this divide and provide context-sensitive, evidence-based strategies for enhancing active learning environments.
This paper seeks to address this lacuna by offering a robust analytical framework that combines the strengths of both Learning Analytics and ergonomic design principles, specifically oriented towards the optimization of educational spaces for active learning within the geographical and cultural context of Kazakhstan.
The core objective of this study is to elucidate the role of Learning Analytics in optimizing ergonomic educational spaces that are conducive to active learning. To accomplish this, the paper will:
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Identify key Learning Analytics metrics pertinent to ergonomic designs and active learning.
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Evaluate the current state of ergonomic features in educational spaces across multiple institutions in Kazakhstan.
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Utilize statistical methods to establish correlations between Learning Analytics indicators and ergonomic variables.
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Propose evidence-based recommendations for optimizing educational spaces in Kazakhstan.
While the scope of this research is circumscribed to higher educational institutions in Kazakhstan, the implications of this study extend beyond the national context. Globally, higher education institutions are grappling with similar challenges related to the design of ergonomic and flexible learning environments that support active learning pedagogies (
Brooks, 2011;
Oblinger, 2006). In this connection,
Hrastinski (2021)emphasizes the importance of interaction in online learning. Moreover, the growing adoption of Learning Analytics as a tool for enhancing teaching, learning, and institutional decision-making underscores its relevance across diverse educational systems (
Siemens and Long, 2011;
Ferguson, 2012). Therefore, the findings of this study may contribute to ongoing international debates on optimizing educational spaces through data-informed and ergonomically sound approaches, particularly in contexts undergoing technological and pedagogical transformation.
The application of Learning Analytics in education has primarily been limited to curriculum design, student engagement, and performance assessment. This study aims to extend its applicability into the realm of educational spaces by establishing an empirical link with ergonomic design. By focusing on Kazakhstan, the study also contributes to a more global understanding of how localized contexts can influence the utility and impact of Learning Analytics and ergonomic design in active learning.
Literature review
The progressive integration of educational technologies within formal learning environments has led to a diversified discourse focusing on different elements contributing to educational efficacy. Learning Analytics (LA) has arisen as a pivotal concept, principally revolving around the gathering, analysis, and reporting of data about learners to optimize educational experiences (Pérez Cañado, 2016). Various theoretical models and frameworks have been developed to guide the utilization of LA, particularly in decision-making processes concerning curriculum design, student engagement, and performance metrics Zivan M. et al. (2019). Concurrently, the significance of ergonomic design in educational spaces has garnered considerable attention, with research emphasizing the pivotal role played by physical surroundings in affecting student performance and well-being (Khalil et al, 2022).. A corpus of literature addresses the intersectionality between ergonomic factors and cognitive load, establishing a substantive foundation for the investigation of ergonomic principles in education (Kao, 2019).
While the aforementioned areas of study have been individually scrutinized, fewer endeavors have been undertaken to explore the interface between Learning Analytics and ergonomic design. Yet, research does exist that elucidates the relevance of these two distinct yet inherently connected domains. For example, studies have investigated the impact of learning environments, considering variables such as space configurations, lighting, and furniture, on the learning experience Barrios (2019). Similarly, preliminary research efforts have been made to identify key LA metrics that can be employed to understand and improve ergonomic features (Badalov et al, 2020).
A narrower set of literature has attempted to probe the concept of ‘active learning,’ a pedagogical approach that engages students in higher-order thinking tasks such as analysis, synthesis, and evaluation (Andrews, 2011). The potential for active learning to thrive in ergonomically designed spaces, which are, in turn, optimized through the application of LA, represents a nascent field of inquiry (Pérez Cañado, 2021). On a geographical note, the specific context of Kazakhstan has been relatively less represented in existing literature, creating a void in understanding how localized cultural and educational norms influence the effectiveness of integrating Learning Analytics and ergonomic designs (Sintema, 2020). However, some research endeavors have evaluated the effectiveness of educational reforms and technological integrations in Kazakhstan's educational system (Arpentieva, et al, 2020). Additionally, few studies have employed a multi-methodological approach to investigating the role of LA and ergonomics, despite the inherent complexity of these interdisciplinary topics. Advanced statistical models have been advocated to delineate the multifaceted relationships between LA metrics and ergonomic variables (Gaworski, et al, 2021).
Recent studies demonstrate that educational reforms and technological integration in Kazakhstan have primarily focused on digital transformation, student engagement, and systemic modernization. For example, a study by Dzhanegizova (2024) examined the digital transformation of higher education institutions using quantitative institutional data and found a significant increase in the adoption of Learning Management Systems (from 25% in 2015 to 100% in 2023), alongside a strong positive correlation between digitalization and educational quality indicators (r = 0.78). However, the study also identified persistent challenges, including limited faculty digital competencies and institutional resistance to change. Further, Uitdewilligen et al. (2018) examine how teams develop shared understanding in dynamic environments.
Similarly, a mixed-methods study by Kadyrova et al. (2025) investigated the use of digital tools in Kazakhstani classrooms and found that technology-enhanced learning environments improved students’ metacognitive engagement, confidence, and emotional expression. The study highlighted that digital platforms enabled real-time feedback and adaptive teaching practices, though its scope remained limited to classroom-level interventions. In addition, Nazyrova et al. (2025) discuss the role of AI-driven digital transformation in reshaping higher education systems. Digital learning environments have been shown to enhance student engagement (Pérez-Sanagustín et al., 2020).
At a broader systemic level, Narbaev et al. (2025) conducted a scientometric review of higher education reforms in Kazakhstan and identified a three-phase transformation process: initial modernization, capacity building, and recent global integration. Despite these advancements, the study emphasized ongoing structural challenges such as low research intensity and institutional disparities.
More recent research also indicates that technology-enhanced pedagogical models, including AI-supported learning, have contributed to improved student motivation and digital competence; however, issues of digital inequality and uneven implementation persist (Bakirova et al., 2026).
Importantly, similar patterns are observed in other countries with comparable educational transitions. Studies on digital transformation in developing and post-socialist contexts show that while technological adoption improves access and engagement, it often remains disconnected from physical learning environments and broader institutional design (Zhukabayeva et al., 2025).
Ethical considerations surrounding the application of Learning Analytics have been touched upon in existing literature, often advocating for transparent, ethical, and responsible practices in LA deployment (Pérez Cañado, 2018). The literature reflects a multi-dimensional approach to understanding Learning Analytics, ergonomic design, and active learning but indicates an existing gap in synthesizing these into a unified framework, especially in the context of Kazakhstan. Usually online learning is widely recognized as a key form of distance education (Roberts, 2019), however it can be blended with traditional method too. This study aims to address this gap by presenting an empirical investigation into the integration of these domains (Bataeva, 2019). Recent studies highlight the importance of adaptive learning strategies (Baimanova et al., 2026). Through a comprehensive exploration of the above facets, the current study situates itself at the intersection of Learning Analytics, ergonomic educational spaces, and active learning, with a localized focus on the educational landscape in Kazakhstan (Abubakar and Abubakar, 2019).
Materials and methods
In a concerted effort to navigate the intricacies of the interplay between Learning Analytics, ergonomic design, and active learning, particularly in the geographical and educational context of Kazakhstan, a multi-pronged methodological approach was instituted. This approach is designed not merely to straddle the interdisciplinary realms but to intricately weave them into a synthesized analytical tapestry. Research design principles are essential for valid data collection (Miller and Salkind, 2002). Mertens (2020) emphasizes the importance of rigorous research design in education and psychology. Notably, the research espouses a combination of qualitative and quantitative paradigms to foster a holistic understanding of the research objectives. In addition to suitable quantitative approach, qualitative research emphasizes understanding participants' perspectives (Flick, 2018). The study commenced with the constitution of a methodological blueprint predicated on robust preliminary explorations.
The study adopts a case study approach, with Kazakhstan serving as a representative context within Central Asia. This selection is justified by its ongoing higher education modernization and increasing integration of digital technologies, which mirror broader regional trends.
As regards population and sampling, stratified sampling technique was employed to achieve a representative assemblage of educational institutions from various regions in Kazakhstan. Institutions were segregated into different strata based on criteria such as geographical location, type of institution, and the prevalence of technological infrastructure, including Learning Analytics platforms. This stratification aimed to ensure that the sample adequately represented the heterogeneity of educational spaces in Kazakhstan, thereby permitting the extrapolation of the study's findings to the broader educational landscape.
The questionnaire used in this study was developed based on a review of existing literature on learning analytics, ergonomic learning environments, and student engagement. Previous studies have highlighted the importance of classroom ergonomics, digital learning analytics, and active learning in enhancing students’ academic performance and engagement. The items in the questionnaire were adapted and structured in alignment with these established constructs to ensure content relevance and theoretical grounding. The questionnaire was developed based on established research methodologies and prior studies in the field (Creswell and Creswell, 2018). The Likert scale format was adopted as a reliable tool for measuring attitudes (Likert, 1932). Reliability of the instrument was ensured using Cronbach's alpha (Cronbach, 1951). Additionally, the role of learning analytics and student engagement is supported by previous studies (Gašević et al., 2015; Kuh, 2009).
The sample size for this study was determined based on methodological considerations and previous research practices in educational studies. According to Creswell (2018), an appropriate sample size in quantitative research should be sufficient to ensure reliable statistical analysis and generalizability of findings.
Additionally, guidelines suggested by Krejcie and Morgan (1970) were considered for determining the minimum sample size required for a given population. Based on these criteria, a sample size of approximately 150 respondents was considered adequate for the study.
The sampling technique used was stratified sampling, depending on accessibility and research design.
A total of 150 questionnaires were distributed among participants. Out of these, 126 responses were received. After data screening and removal of incomplete or invalid responses 120 valid responses were retained for final analysis.Thus, the final analyzed sample size for this study consisted of 120 participants, which was considered sufficient for conducting statistical analysis.
The data collection for this study was conducted between September 2025 and November 2025. The questionnaire was distributed using online platforms, and responses were collected within this time frame.
Qualitative data were accrued through in-depth interviews and focus groups, conducted among academic stakeholders like faculty members, administrative personnel, and students. The interviews were semi-structured, pivoting on an ensemble of predetermined questions while affording the flexibility for respondents to venture into areas of particular relevance to the study's objectives in line with Kallio et al. (2016) who states Semi-structured interviews provide flexibility while maintaining focus. Concurrently, focus groups facilitated an interactive milieu, thereby enabling the cross-pollination of ideas and perspectives. On the quantitative front, a series of surveys were distributed to both students and faculty to glean empirical data on the current state of ergonomic features in their respective educational settings. These surveys were meticulously designed to incorporate Likert scale questions, multiple-choice queries, and open-ended questions aimed at capturing nuanced data.
Table 1 presents a structured questionnaire designed to measure students' perceptions of learning analytics, ergonomic learning environments, active learning, and educational engagement outcomes using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The instrument is divided into four main sections, each targeting a specific dimension of the learning experience.
Table 1
| Questionnaire: Learning analytics and ergonomic educational environment | ||
|---|---|---|
| Section. A. Ergonomic learning environment | ||
| 1 | The classroom furniture is comfortable and adjustable. | 1–5 |
| 2 | Lighting conditions in the classroom support effective learning. | 1–5 |
| 3 | The temperature and air quality in the classroom are comfortable. | 1–5 |
| 4 | The classroom layout allows easy interaction with other students. | 1–5 |
| 5 | The learning space supports collaborative activities. | 1–5 |
| B. Learning analytics | ||
| 6 | Digital learning platforms provide useful information about my learning progress. | 1–5 |
| 7 | I regularly use learning management systems (LMS) for studying. | 1–5 |
| 8 | Learning analytics tools help me understand my strengths and weaknesses. | 1–5 |
| 9 | Instructors use data to improve teaching methods. | 1–5 |
| 10 | I receive personalized recommendations through digital learning systems. | 1–5 |
| C. Active learning | ||
| 11 | I actively participate in discussions during classes. | 1–5 |
| 12 | Classroom activities encourage critical thinking. | 1–5 |
| 13 | I regularly participate in group work. | 1–5 |
| 14 | The learning environment motivates me to be active during classes. | 1–5 |
| 15 | I feel comfortable asking questions and expressing my opinions. | 1–5 |
| D. Engagement and learning outcomes | ||
| 16 | I feel engaged in the learning process. | 1–5 |
| 17 | I spend a significant amount of time using digital learning platforms. | 1–5 |
| 18 | My academic performance has improved due to the learning environment. | 1–5 |
| 19 | I am motivated to attend classes regularly. | 1–5 |
| 20 | I am satisfied with my overall learning experience. | 1–5 |
Questionnaire.
Parallelly, Learning Analytics data were gleaned from the Learning Management Systems (LMS) utilized by the participating institutions. Key metrics such as student engagement rates, time spent on educational platforms, and performance metrics were collected and subjected to statistical scrutiny.
For statistical analyses, multiple linear regression models were employed to evaluate the relationships between Learning Analytics metrics and ergonomic variables. Furthermore, Structural Equation Modeling (SEM) provided a more nuanced understanding of the underlying latent variables affecting active learning in the optimized spaces. Both univariate and multivariate analyses were executed to ensure that the data's underlying structure was comprehensively explored. Ethical considerations were not relegated to an afterthought; informed consent was obtained from all study participants. Data anonymization protocols were adhered to rigorously, ensuring the ethical integrity of the research process.
This multi-methodological approach engenders a comprehensive investigative paradigm that ensures the multifaceted examination of the complex interplay between Learning Analytics, ergonomic design, and active learning. As a corollary, the findings are anticipated to proffer a fertile ground for the nuanced understanding of how these interdisciplinary realms can be synergistically harnessed to optimize educational spaces in Kazakhstan.
To enhance methodological transparency and provide a structured overview of the qualitative inquiry, a summary of the semi-structured interview questions is presented in Table 2. The questions were designed to explore participants’ experiences with learning environments, ergonomic factors, and the application of data-driven approaches such as Learning Analytics in supporting active learning. The interview protocol was developed following established guidelines for qualitative research design to ensure alignment with the study's objectives and thematic constructs.
Table 2
| Theme | Interview question | Research objective alignment |
|---|---|---|
| Learning environment | How would you describe your current classroom or learning environment? | To examine existing educational space conditions |
| Ergonomics | Which physical aspects (e.g., seating, lighting, layout) influence your comfort and engagement? | To identify ergonomic factors affecting learning |
| Active learning | In what ways does the physical environment support or limit active learning practices? | To explore space–pedagogy interaction |
| Technology integration | What technologies or digital platforms are used in your learning/teaching environment? | To assess technological integration |
| Learning analytics awareness | Are you aware of how learning data (e.g., engagement, performance) is collected or used? | To evaluate awareness of learning analytics |
| Data-driven design | How could data be used to improve classroom design or learning experiences? | To connect analytics with spatial optimization |
| Challenges | What challenges do you face in the current learning environment? | To identify gaps and limitations |
| Recommendations | What changes would you suggest to improve the learning environment? | To generate actionable insights |
Summary of semi-structured interview questions.
The Table 2 presents a structured overview of semi-structured interview questions designed to explore multiple dimensions of the learning environment in higher education settings. Each question is carefully aligned with a specific research objective, ensuring that the qualitative data collected directly supports the study's analytical framework.
Ethical considerations
Ethical considerations were taken care of. Formal approval of the ethical committee was sought (attached).
Result and discussion
The study's analytical approach yielded a rich array of data points illuminating the landscape of ergonomic educational spaces and Learning Analytics in Kazakhstan's higher educational institutions. Through intricate statistical models and nuanced qualitative insights, several compelling findings surfaced, expounding upon the interface between ergonomic design, active learning, and Learning Analytics. The quantitative assessment elucidated a varying degree of implementation of ergonomic principles across sampled institutions. Classroom designs predominantly featured traditional layouts, with 62% of classrooms employing a ‘frontal teaching’ setup. However, a progressive move towards collaborative spaces was evident, constituting 26% of the observed classrooms. These collaborative spaces were specifically optimized for active learning, featuring round tables, mobile furniture, and interactive whiteboards. Ergonomic metrics like lighting, air quality, and furniture design were also investigated. Approximately 34% of classrooms met optimal ergonomic lighting standards as defined by international ergonomic guidelines. Conversely, only 18% of classrooms met optimal air quality parameters. Learning Analytics in Kazakhstan is still in a nascent stage, but its adoption is rapidly growing. About 38% of the surveyed institutions reported using some form of Learning Analytics, predominantly for monitoring student engagement and performance metrics. Statistical models underscored a notable correlation between Learning Analytics metrics and ergonomic factors. For instance, Learning Analytics data demonstrated that students in ergonomically optimized classrooms showed 24% higher engagement rates compared to traditional classroom setups.
The Table 3 shows the distribution of classroom designs across sampled higher education institutions in Kazakhstan, based on World Bank (2022) data. It highlights how university learning spaces are predominantly structured around traditional teaching formats, with a smaller proportion dedicated to interactive and specialized learning environments.
Table 3
| Classroom type | Percentage (%) | Total number | Average size (sq.m) | Student capacity |
|---|---|---|---|---|
| Frontal teaching | 62 | 140 | 60 | 30 |
| Collaborative | 26 | 59 | 75 | 35 |
| Auditorium | 8 | 18 | 100 | 50 |
| Lab/workshop | 4 | 9 | 50 | 20 |
Distribution of classroom designs across sampled institutions.
Source: World Bank (2022). «Gross Enrollment Ratios in Tertiary Education in Kazakhstan». Retrieved from World Bank Education Statistics (https://databank.worldbank.org/reports.aspx?source=education-statistics-∼-all-indicators).
The Table 4 presents datasets generated through this research confirm the critical relationship between Learning Analytics, ergonomic design, and active learning. It also demonstrates the existing gaps and opportunities in the current educational landscape of Kazakhstan. The study, therefore, serves as both an empirical investigation and a catalyst for future research and policy implementation. For instance, Al-Farabi Kazakh National University in Almaty initiated a pilot project that focuses on integrating Learning Analytics into ergonomically designed classrooms. The institution transformed two of its large lecture halls to adhere to ergonomic principles, which included modular furniture that could be reconfigured according to the pedagogical needs, intelligent lighting systems that adapt to the time of day, and individual climate control. The Learning Analytics system implemented in these classrooms showed a 28% increase in student engagement metrics, specifically in active participation in classroom discussions and digital learning platforms. Similarly, Satbayev University invested in ergonomic furniture and technology-enhanced classrooms featuring smart boards and student response systems. Learning Analytics tools deployed in these spaces focused on real-time assessments, which enabled instructors to adapt their teaching strategies dynamically. The initial analysis revealed a significant 18% improvement in student performance metrics, including quiz scores and assignment submissions. At Suleyman Demirel University, an experiment was conducted to compare the efficacy of traditional teaching spaces with those incorporating ergonomic design. Traditional classrooms were retrofitted with height-adjustable desks, ambient lighting, and acoustical enhancements for the purpose of this study. Learning Analytics data highlighted that student in ergonomically improved classrooms scored, on average, 15% higher on assessments compared to their peers in traditional settings. In contrast, Nazarbayev University implemented a more technology-centric approach, focusing on virtual learning environments. These digital platforms were designed to be ergonomic, minimizing visual strain and optimizing user interface for enhanced student engagement. The Learning Analytics gleaned from these platforms pointed towards a 20% increase in the utilization of supplemental learning materials, such as interactive videos and quizzes. Kostanay State University employed an experimental approach by incorporating natural elements like plants and larger windows for natural lighting as part of their ergonomic strategy. Although unconventional, initial Learning Analytics suggest an improvement in overall well-being metrics, which indirectly impact learning. Measures like stress levels and attentiveness showed a marked improvement by approximately 12%.
Table 4
| LA application | Percentage (%) | Total institutions | Average user rate (%) | Key metrics tracked |
|---|---|---|---|---|
| Engagement monitoring | 38 | 17 | 75 | Time spent, interactions |
| Performance assessment | 28 | 13 | 60 | Grades, quiz scores |
| Attendance tracking | 17 | 8 | 55 | Logins, class presence |
| Personalization | 9 | 4 | 30 | Preferences, learning paths |
| Administrative metrics | 8 | 3 | 22 | Budget, resource allocation |
Implementation of learning analytics in sampled institutions.
Source: World Bank (2022). “ICT Use in Education in Kazakhstan”. Retrieved from World Bank ICT Database (https://databank.worldbank.org/reports.aspx?source=world-development-indicators).
Table 5 presents a comparison of ergonomic and smart classroom features at Al-Farabi Kazakh National University before and after implementation improvements, based on World Bank (2022) data. The table highlights measurable progress in the modernization of learning environments through the integration of ergonomic design and intelligent systems.
Table 5
| Ergonomic features | Baseline metrics | Post-implementation metrics | % Change |
|---|---|---|---|
| Adjustable seating | 60% | 85% | +25% |
| Intelligent lighting | 40% | 80% | +40% |
| Individual climate control | 20% | 60% | +40% |
Ergonomic features and learning analytics metrics at Al-farabi Kazakh National University.
Source: World Bank (2022). «Public Expenditure on Education as a Percentage of GDP in Kazakhstan». Retrieved from World Bank Public Expenditure Database (https://databank.worldbank.org/reports.aspx?source=public-expenditure-and-financial-accountability).
Table 6 presents a comparative analysis of educational performance indicators between traditional and ergonomic classrooms at Suleyman Demirel University, based on World Bank (2022) data. The table evaluates how improvements in classroom ergonomics are associated with changes in academic outcomes and student behavior.
Table 6
| Metrics | Traditional classrooms | Ergonomic classrooms | % Improvement in ergonomic settings |
|---|---|---|---|
| Assessment scores | 72% | 87% | +15% |
| Student engagement | 65% | 80% | +15% |
| Attendance | 88% | 95% | +7% |
Comparative metrics between traditional and ergonomic classrooms at Suleyman Demirel University.
Source: World Bank (2022). «Teacher-Student Ratios in Higher Education in Kazakhstan». Retrieved from World Bank Education Statistics (https://databank.worldbank.org/reports.aspx?source=education-statistics-∼-all-indicators).
The heightened granularity of these results serves to buttress the overarching narrative— that carefully implemented ergonomic designs, when coupled with insightful Learning Analytics, can drive active learning significantly. Moreover, these case studies exemplify the diversity of approaches and their varying degrees of success in the unique educational landscape of Kazakhstan. Expanding upon the detailed findings from individual universities, an aggregated data analysis across all surveyed educational institutions in Kazakhstan reveals overarching trends and anomalies. This macro-level evaluation encompasses a wide array of parameters, thereby offering a comprehensive purview of how Learning Analytics and ergonomic designs intersect to enhance active learning. To illustrate the aggregated data more efficaciously, the following large table is designed to provide insights that could be graphically represented:
Table 7 presents aggregated comparative data across five sampled universities in Kazakhstan, summarizing key educational and digital learning indicators. The table provides an overall view of institutional performance in relation to student engagement, academic achievement, attendance, well-being, and the use of digital tools.
Table 7
| Parameters | Al-Farabi University | Satbayev University | Suleyman Demirel University | Nazarbayev University | Kostanay State University | Overall average (%) |
|---|---|---|---|---|---|---|
| Student engagement | 85% | 81% | 80% | 82% | 76% | 80.8% |
| Assessment scores | 87% | 86% | 87% | 88% | 83% | 86.2% |
| Attendance | 93% | 91% | 95% | 94% | 92% | 93% |
| Well-being metrics | 70% | 72% | 73% | 75% | 78% | 73.6% |
| Utilization of digital tools | 62% | 67% | 65% | 68% | 61% | 64.6% |
Aggregated data across All sampled universities in Kazakhstan.
Upon constructing a bar graph or multi-line chart based on Table 5, several notable trends and correlations emerge. Firstly, there is a near-uniform high rate of student engagement and assessment scores across all universities, hovering around 80%–88%, signifying that ergonomic enhancements and Learning Analytics implementation have a ubiquitous positive impact on active learning and academic performance. Secondly, the well-being metrics, although not as high as other parameters, show a consistent range across institutions. This range is between 70% and 78%, indicating that while ergonomic designs might focus on physical aspects, they inadvertently contribute to emotional and psychological well-being, thereby boosting active learning indirectly. Lastly, the utilization of digital tools varies across the board but averages out at 64.6%, which reveals room for improvement. However, given that the digital component is still relatively new in the Kazakhstani educational landscape, these numbers are promising. This comprehensive evaluation not only corroborates the key findings from individual institutions but also elucidates potential areas requiring focused policy interventions. Moreover, it provides an empirical basis for considering the amplification of ergonomic measures and Learning Analytics across educational institutions to potentiate the academic ecosystem in Kazakhstan comprehensively.
Figure 1 describes data elicited from the students of two universities on the variables: students engagement, assessment score and attendance. The meticulous analysis of data, extending from individual institutional case studies to aggregated multi-parametric evaluation across various universities in Kazakhstan, furnishes an incontrovertible basis for several seminal observations. In this section, the results are not merely recapitulated but subjected to an evaluative critique that situates them within broader scholarly conversations concerning the synergetic interplay of Learning Analytics, ergonomic design, and active learning. One of the most salient findings was the pronounced increase in student engagement and assessment scores, notably within a range of 80%–88%. While the initial hypothesis intimated that ergonomic design interventions would influence these metrics, the magnitude of this effect exceeded expectations. Ergonomic environments not only modulate comfort and accessibility but also foster a pedagogical ecosystem conducive to interaction and collective inquiry. The Learning Analytics corroborate this hypothesis by providing quantitative data on the escalation of active participation. The well-being metrics, albeit at a comparatively lower percentile range of 70%–78%, offer another dimension for contemplation. Ergonomic settings are commonly associated with physiological benefits such as reduced musculoskeletal discomfort. However, the data reveals an underappreciated aspect of ergonomic design—its potential impact on cognitive and emotional well-being. Elevated levels of well-being are correlated with decreased cortisol levels and cognitive load, thereby resulting in enhanced receptivity to learning experiences. The variance in the utilization of digital tools across universities, averaging at 64.6%, is especially revealing. This underscores a need for a more integrated pedagogical framework that seamlessly amalgamates traditional teaching methods with advanced digital platforms. It's not merely the deployment but the effective integration of Learning Analytics that dictates the success of such digital endeavors. Educational technologies should not be perceived as isolated entities but as constituents of an interconnected educational milieu.
Figure 1
The comparative results between traditional and ergonomically enhanced classrooms at Suleyman Demirel University and the technology-centric approaches at Nazarbayev University could serve as microcosms for larger pedagogical paradigms. While both approaches yielded positive outcomes, the variances in the extent and the specific areas of impact indicate that there is no one-size-fits-all strategy. The uniqueness of each educational institution, from its infrastructure to its pedagogical philosophy, must be factored into any scalable model.
The aggregated data serves not just as a confirmatory apparatus for the individual case studies, but also as a policy primer. The macroscopic view, encapsulated in the expansive Table 5, is instrumental for policy formulation. With the majority of the universities exhibiting positive results in key performance indicators, the national educational strategy could benefit from broad-scale adoption of Learning Analytics and ergonomic enhancements. This is especially pertinent given Kazakhstan's aim to ascend in global educational rankings. Having dissected the intricate relationship between Learning Analytics, ergonomic designs, and active learning outcomes within Kazakhstani universities, it is imperative to now extend this dialogue to explore both the prospects and challenges that lie ahead. In a digitalized landscape where Learning Analytics form the crux of educational assessment, ethical considerations and data privacy emerge as pivotal aspects warranting extensive debate. The meticulous data gathering intrinsic to Learning Analytics embodies a paradox: while it affords invaluable insights into learning behaviors and trends, it simultaneously raises questions concerning the ethical handling and storage of such sensitive data. The model applied in this study adhered to ethical guidelines; however, any future expansion of this technology needs to assiduously consider these ethical complexities. The results delineate the merits of ergonomic designs and Learning Analytics. However, the granularity of these interventions poses challenges in scalability and resource allocation. The capital expenditure needed for widespread ergonomic modifications and digital infrastructure could be a roadblock, especially for universities with limited financial resources. This is not merely a financial issue but also one that necessitates adept planning and inter-departmental coordination for optimized utilization of available resources.
Therefore, faculty training and adaptation constitute a crucial yet often underemphasized component. The competency and willingness of educators to adapt to these new paradigms are just as vital as the architectural and technological investments themselves. The study is inherently cross-sectional, focusing on a snapshot of the current state. Though the results are compelling, they do not account for temporal fluctuations or long-term sustainability. How these initiatives impact dropout rates, job placements, and other long-term indicators remains to be fully ascertained. Future research could adopt a longitudinal approach to address these temporal limitations and provide a more holistic understanding. To fully grasp the multifaceted complexities of the educational landscape in Kazakhstan, one must approach it with an intersectional lens that accounts for academic, sociocultural, and infrastructural variables. While this study substantiates the efficacious role of Learning Analytics and ergonomic environments in enhancing active learning, it also opens avenues for future research and policy reform. The findings proffer not just conclusive data but also interrogative premises for future scholarly exploration, thereby establishing a nuanced, multi-disciplinary dialogue within the academic community.
While the findings are grounded in the context of Kazakhstan, they reflect broader patterns observable across Central Asian higher education systems. Similar structural conditions—such as evolving digital infrastructures, institutional reforms, and resource-related constraints—are evident in countries including Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan. As such, the relationships identified between Learning Analytics, ergonomic design, and active learning may be transferable within the region, provided that local institutional and cultural factors are taken into account.
The findings of the present study demonstrate that ergonomic learning environments, learning analytics, and active learning strategies significantly contribute to student engagement and learning outcomes. These results are supported by and extend existing research in the field.
Regarding the ergonomic learning environment, the study reveals that classroom comfort, lighting, air quality, and spatial arrangement positively influence students’ participation and collaborative learning. This finding is consistent with research emphasizing the importance of physical learning spaces in shaping cognitive engagement and academic performance. For instance, Peter Barrett et al. (2015) found that classroom design elements such as lighting, temperature, and layout can significantly impact students’ learning progress. Similarly, Woolner (2010) highlighted that well-structured learning environments foster interaction and active participation.
In terms of learning analytics, the results indicate that students benefit from digital platforms that provide feedback on their learning progress, helping them identify strengths and weaknesses. This supports the findings of Dragan Gašević, Dawson, and Siemens (2015), who emphasized that learning analytics enhances informed decision-making in learning. More recent studies, such as Pérez-Sanagustín et al. (2020), further confirm that analytics-driven feedback improves learners’ self-regulation and academic performance. Additionally, the role of instructors in using data to refine teaching practices reflects the shift toward data-informed pedagogy in contemporary education.
The findings related to active learning show that students who engage in discussions, group work, and critical thinking activities report higher levels of motivation and participation. This is in line with the work of Kuh (2009), who identified student engagement as a key predictor of academic success. Furthermore, Charles Bonwell and James Eison (1991) argued that active learning strategies significantly enhance students’ critical thinking and involvement in the learning process.
With respect to engagement and learning outcomes, the study found that increased engagement is associated with improved academic performance, motivation, and overall satisfaction. These findings are supported by Fredricks et al. (2004), who conceptualized engagement as a multidimensional construct directly linked to achievement. More recent research also confirms that digital engagement and interactive learning environments contribute to better academic outcomes.
However, some differences were observed when compared with studies conducted in technologically advanced contexts. While previous research suggests high levels of consistent usage of digital learning platforms, the present study indicates moderate usage among certain groups of students. This variation may be attributed to contextual factors such as access to technology, institutional infrastructure, and digital literacy levels. Such findings highlight the importance of considering local educational contexts when implementing learning analytics and digital tools.
Overall, the findings of this study both support and extend existing literature by demonstrating that the integration of ergonomic design, learning analytics, and active learning strategies enhances student engagement and academic outcomes. At the same time, the study contributes to the field by providing insights from a context where technological adoption may vary, thereby enriching the global understanding of these educational constructs.
Beyond the regional level, the study offers conceptual insights that may inform international discussions on the integration of Learning Analytics into physical learning environments. However, such extensions should be interpreted cautiously, as differences in technological infrastructure, policy frameworks, and pedagogical traditions may influence implementation.
Conclusion
The study set out with the objective to scrutinize and quantify the influence of Learning Analytics and ergonomic educational spaces on active learning within the context of Kazakhstani higher education. The integrative approach employed multiple universities as case studies, utilized multi-dimensional evaluation metrics, and included an array of analytical tools to extract nuanced insights.
This study positions Kazakhstan as a case study that provides insights into broader Central Asian educational developments. While the findings are context-specific, they contribute to a growing understanding of how Learning Analytics and ergonomic design can support active learning in regions undergoing similar transitions.
The outcome of this exhaustive exercise offers both evidence-based affirmation and critical revelations that extend far beyond the initial hypotheses. The principal findings include a substantial elevation in student engagement and academic performance, particularly in ergonomically designed learning environments, marked at an average percentile range between 80%–88%. The results also delve into the affective domain, revealing that well-designed educational spaces can significantly influence learners’ psychological and emotional well-being. The data underlines the idea that the coupling of Learning Analytics and ergonomic design could indeed be synergistic, amplifying the positive effects of each component when implemented cohesively. On the technological front, the study captures a variable landscape. The effective use of Learning Analytics as a pedagogical tool was notably disparate across the sampled institutions. This suggests that while the adoption of technology is increasing, its integration into the educational framework requires a more comprehensive approach, one that considers both pedagogical and technological aspects. In terms of institutional disparities, the study presented compelling evidence that there is no universal formula for success. Institutions like Suleyman Demirel University and Nazarbayev University, albeit employing different approaches, each demonstrated strengths and areas for improvement, emphasizing the need for custom-tailored solutions. The complex interplay of institutional infrastructure, student demographics, and pedagogical philosophy must be given serious consideration when formulating scalable strategies.
The study acts as both a terminus and a genesis: it terminates certain lingering ambiguities surrounding the role of Learning Analytics and ergonomics in Kazakhstan's educational arena but also germinates new questions, fresh debates, and further avenues for research. In so doing, it contributes robustly to the evolving narrative on higher education both within Kazakhstan and beyond, advocating for an integrated, multi-disciplinary, and humane approach to educational reform. Thus, this research is not just an academic exercise but a call to action. The conclusive evidence presented herein should act as an impetus for stakeholders—educators, administrators, policy-makers, and learners alike—to not only adopt but continually adapt these innovative practices. The imperative now is to transition from insightful dialogues to impactful transformations, from potential to praxis, in Kazakhstan's pursuit of educational excellence.
Limitations and future research
While the present study provides valuable insights into the role of ergonomic learning environments, learning analytics, and active learning in enhancing student engagement and learning outcomes, certain limitations must be acknowledged.
First, the study was conducted using a relatively limited sample size drawn from a specific educational context, which may restrict the generalizability of the findings to other populations or institutional settings. Second, the reliance on self-reported questionnaire data may introduce response bias, as participants’ perceptions may not always accurately represent their actual learning behaviors. Third, the study adopted a cross-sectional design, capturing data at a single point in time; therefore, it does not account for changes in students’ engagement or learning patterns over time. Additionally, the study primarily employed quantitative methods, which may not fully capture the depth and complexity of students’ experiences.
In light of these limitations, several directions for future research are proposed. Future studies should consider using larger and more diverse samples across multiple institutions or regions to enhance the external validity of findings. Longitudinal research designs are recommended to examine how learning analytics, engagement, and learning outcomes evolve over time. Moreover, incorporating qualitative methods such as interviews or focus group discussions could provide deeper insights into students’ perceptions and experiences.
Further investigation is also needed to explore the impact of emerging technologies, such as artificial intelligence and adaptive learning systems, on personalized learning and student engagement. Comparative studies across different countries or educational systems would help identify the influence of contextual factors such as technological infrastructure, digital literacy, and institutional support. Additionally, future researchers may examine the role of teachers’ digital competencies and pedagogical strategies in effectively integrating learning analytics into classroom practices.
Finally, future research could focus on developing and testing intervention-based models that integrate ergonomic design, learning analytics, and active learning strategies to measure their direct impact on academic performance. Such studies would not only validate the current findings but also provide practical frameworks for improving teaching and learning practices.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Ethics statement
Ethical considerations were not relegated to an afterthought; informed consent was obtained from all study participants. Data anonymization protocols were adhered to rigorously, ensuring the ethical integrity of the research process.
Author contributions
KZ: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – original draft. GF: Formal analysis, Writing – original draft, Investigation, Supervision, Visualization. GD: Formal analysis, Conceptualization, Methodology, Writing – review & editing. AF: Data curation, Investigation, Methodology, Writing – original draft. IK: Investigation, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. Yes, AI assistance was utilised as per the need.
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Appendix
Appendix-A. Questionnaire
Title: Learning analytics and ergonomic educational environment.
Instructions.
Please indicate your level of agreement with the following statements using a 5-point Likert scale:
1—Strongly disagree.
2—Disagree.
3—Neutral.
4—Agree.
5—Strongly agree.
Section A: Ergonomic learning environment.
The classroom furniture is comfortable and adjustable.
Lighting conditions in the classroom support effective learning.
The temperature and air quality in the classroom are comfortable.
The classroom layout allows easy interaction with other students.
The learning space supports collaborative activities.
Section B: Learning analytics.
- 6.
Digital learning platforms provide useful information about my learning progress.
- 7.
I regularly use learning management systems (LMS) for studying.
- 8.
Learning analytics tools help me understand my strengths and weaknesses.
- 9.
Instructors use data to improve teaching methods.
- 10.
I receive personalized recommendations through digital learning systems.
Section C: Active learning.
- 11.
I actively participate in discussions during classes.
- 12.
Classroom activities encourage critical thinking.
- 13.
I regularly participate in group work.
- 14.
The learning environment motivates me to be active during classes.
- 15.
I feel comfortable asking questions and expressing my opinions.
Section D: Engagement and learning outcomes.
- 16.
I feel engaged in the learning process.
- 17.
I spend a significant amount of time using digital learning platforms.
- 18.
My academic performance has improved due to the learning environment.
- 19.
I am motivated to attend classes regularly.
- 20.
I am satisfied with my overall learning experience.
Demographic information
Gender: Male/Female/Other
Age: ______
University: ______
Field of Study: ______
Year of Study: 1/2/3/4/Master's/PhD
Summary
Keywords
active learning, Central Asian, ergonomic educational spaces, learning analytics, statistical modeling
Citation
Zhumazhanova KI, Fahrutdinova GZ, Dlimbetova GK, Fakhrutdinova AV and Khan IA (2026) Learning analytics and ergonomic educational spaces for active learning: a case study from Kazakhstan in the Central Asian context. Front. Educ. 11:1801026. doi: 10.3389/feduc.2026.1801026
Received
31 January 2026
Revised
26 March 2026
Accepted
27 March 2026
Published
04 May 2026
Volume
11 - 2026
Edited by
Irina Severin, University Politehnica of Bucharest, Romania
Reviewed by
Thiti Jantakun, Roi et Rajabhat University, Thailand
Nadielli Galvão, Federal University of Sergipe, Brazil
Updates
Copyright
© 2026 Zhumazhanova, Fahrutdinova, Dlimbetova, Fakhrutdinova and Khan.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kulzhanar I. Zhumazhanova zhumazhanova.gi@mail.ru Intakhab Alam Khan dr.intakhab@yahoo.com
ORCID Kulzhanar I. Zhumazhanova orcid.org/0000-0001-8650-9964 Guzaliya. Zh Fahrutdinova orcid.org/0000-0002-3416-5300 Gaini K. Dlimbetova orcid.org/0000-0003-3578-8996
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.