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Article

Ergonomic Factors Affecting the Learning Motivation and Academic Attention of SHS Students in Distance Learning

by
Ma. Janice J. Gumasing
1,*,
Iris Samantha V. Dela Cruz
2,
Dean Angelo A. Piñon
2,
Hedy Nicolaison M. Rebong
2 and
Daniel Luis P. Sahagun
2
1
School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
2
Young Innovators Research Center, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(12), 9202; https://doi.org/10.3390/su15129202
Submission received: 25 April 2023 / Revised: 23 May 2023 / Accepted: 3 June 2023 / Published: 7 June 2023
(This article belongs to the Special Issue Sustainable Approaches for Online Education: Learning Technologies, Learner Interaction and Student Engagement)
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Review Reports Versions Notes

Abstract

:
Since the COVID-19 pandemic, the world has experienced a shift in education, forcing students to transition from traditional face-to-face classes to distance learning. Students found these adjustments challenging, thus affecting their academic performance. In order to address this issue, this study sought to identify the factors affecting students’ learning motivation and academic attention in distance learning using a novel framework of ergonomic domains. Using purposive sampling, 311 senior high school students who took part in online learning at selected schools in NCR Plus answered an online survey. Partial least square structural equation modeling (PLS-SEM) was utilized to investigate and assess the physical, cognitive, and macro-ergonomic factors influencing their learning motivation and academic attention. The study’s results revealed that cognitive and macro-ergonomic factors significantly influence students’ learning motivation, affecting students’ academic attention, while the physical ergonomic factors were found to be insignificant. The findings and analysis imply that academic professionals should be aware of and apply physical, cognitive, and macro-ergonomic concepts to positively influence students’ learning in an online setup. Furthermore, intrinsic motivation should be the main driving force behind learning because it is more successful and beneficial in the long term. The study concludes that as online learning has become more prevalent than ever, it is imperative that ergonomic factors are considered to establish an effective online education system and improve students’ learning motivation and academic attention in distance learning.

1. Introduction

Educational institutions play a vital role in an ever-changing world. To remain relevant and meet the demands of the current generation, these institutions need to continuously improve and evolve [1]. This can be accomplished using new technologies, designing customized learning programs, and focusing on skill development. It is widely accepted that the COVID-19 pandemic has disrupted education globally. According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO) [2], nationwide school closures occurred in 111 countries during the lockdown in 2020, affecting over 1.07 billion students, or approximately 61% of the global student population. Students from different schools showed notable differences in online learning approaches and learning media. However, the specific difficulties that distance learning presents to teachers and students should be discussed more. According to Manlapaz [1], there are four main challenges associated with distance learning: learning resources, the learning environment, teaching and learning capabilities, and psychological and physical effects.
The lack of learning resources is one of the most significant difficulties with online classes [1]. Students often need access to the same resources as in a traditional classroom setting. This includes textbooks, laboratory equipment, and internet access [3]. This lack of resources can severely inhibit a student’s ability to learn, as they cannot engage with the material in the same way as their peers. Another area for improvement is the learning environment itself. Students can regularly interact with their peers and teachers in a traditional classroom; this social interaction is crucial for effective learning, allowing students to ask questions, receive feedback, and collaborate on assignments [4]. However, this interaction is much more limited in an online setup, making the learning process more difficult. In addition, distance learning can also challenge a teacher’s ability to teach the material effectively. It relies heavily on technology; not all teachers are comfortable using it [5]. In addition, distance learning can be very time-consuming, as teachers must create and maintain online resources and provide regular feedback to students. This can be daunting, especially for teachers already overloaded with work [6]. Finally, distance learning can also adversely affect teachers and students both psychologically and physically [7]. The constant use of technology can lead to screen fatigue, and the lack of social interaction can cause isolation and loneliness. In addition, the increased workload can lead to stress and burnout. These effects can significantly impact a person’s ability to learn and succeed online [8]. Thus, by understanding the various challenges related to distance learning, institutions can address them more holistically and effectively.
In addressing the challenges associated with online learning and promoting an effective and healthy learning environment, ergonomics plays a vital role. As defined by the International Ergonomics Association (IEA), ergonomics (or human factors) is the scientific field that focuses on the study of interactions between individuals and other aspects of a system, as well as the profession that adheres to theory, principles, data, and methods of optimizing human well-being and overall system performance. Ergonomics is divided into three major categories: physical, cognitive, and organizational [9].
According to Singleton [10], physical ergonomics is concerned with the human body’s response to biological factors in the environment, such as light, sound, temperature, and air quality. It also includes designing tools, machines, and workstation layouts to minimize the risk of injuries and accidents. Cognitive ergonomics concerns the human mind’s ability to process information and make decisions. It includes studying how people perceive, remember, and acquire knowledge and how they make decisions [11]. The design of work systems such as job tasks, work schedules, and communication systems is the focus of organizational ergonomics. It also includes studying how the work environment and organizational structures can impact worker productivity, health, and safety. Ergonomics is a growing field that is constantly evolving as scientists learn more about how humans interact with their environment [12]. By addressing ergonomic considerations, distance learning institutions and individuals can help reduce physical discomfort, enhance concentration, and promote overall well-being. This, in turn, can lead to improved student engagement, learning outcomes, and overall satisfaction with the distance learning experience.

1.1. Research Objectives

The study aimed to identify and evaluate the ergonomic factors that affected the learning motivation and academic attention of students in distance learning. Specifically, it intended to identify the factors of physical, cognitive, and macro-ergonomics that affected the learning motivation of students in distance learning and determine how the identified physical, cognitive, and macro-ergonomic factors had a significant and positive effect on students’ learning motivation in an online setup. Furthermore, the study sought to analyze how learning motivation affected the academic attention of students.

1.2. Significance of the Study

Society, academic institutions, e-learning infrastructure, teachers, parents, students, and future researchers would specifically benefit from this study. The findings of this research spread awareness in society by recognizing ergonomic factors that can be focused on improving one’s academic attention by boosting learning motivation. Academic institutions and administrations may advocate initiatives and campaigns addressing ergonomic factors that can help students improve their academic attention. In addition, educational institutions could be informed that macro-ergonomics, such as the e-learning infrastructure they provide to students, impact the pupils. E-learning infrastructure developers perceive that their products and services are aligned with macro-ergonomics principles, which significantly impact students’ learning motivation, resulting in affected academic attention; hence, it may encourage them to improve their products and services. This research would also help instructors perceive, understand, and consider their students’ ergonomic factors that could affect their academic attention and remind them to contemplate it. It may encourage parents to look at their children’s ergonomics and take action, which may help them raise their learning motivation and enhance their academic attention. The students would benefit directly from this research as its findings may stimulate them to consider their ergonomic factors in distance learning, which could ameliorate their learning motivation and boost academic attention. Future researchers may learn necessary information to expand their knowledge, such as new ideas, significant findings, or a background source, and be of great use as related literature.

2. Review of Related Literature

Physical, cognitive, and macro-ergonomic factors play important roles in aiding students with online learning [13]. Several studies have looked at how physical ergonomics can help students learn better. In India, the significance of physical ergonomics in the design of the work environment for online learning was examined [14]. According to the research, poor workstation design may cause musculoskeletal problems that have a negative impact on student attention and learning. Moreover, previous studies have revealed the fact that pupils who are educated in an environment that promotes learning are more driven, interested, and demonstrate increased fundamental acquiring knowledge abilities [15,16]. Based on Soltaninejad et al. [17], learning motivation among students is increased when they are exposed to an educational environment. Lighting, noise, temperature, ventilation, air quality, educational equipment, and other factors have an impact on educational growth and are useful in generating interest in and motivation for learning in pupils [18]. The research results by Adewale et al. [19] also indicate that how well students perform in school is influenced by how comfortable the surroundings and facilities are.
Studies have also examined how cognitive ergonomics affects how students perceive and learn. Ananthi and Eagavalli [20] found a strong link between a student’s academic success in India and their preference for learning through sight, sound, or touch. Obralić and Akbarov [21] conducted a study in Bosnia and Herzegovina that showed how perceptual learning styles are linked to academic success. The results demonstrated a relationship between perceptual modality choice and student academic progress.
Furthermore, in terms of macro-ergonomics, access to technology, the way students are taught, and learning management systems (LMSs) all play vital roles in channeling student motivation and academic success [22,23,24]. Research in the Philippines showed how important it is for students’ academic success that information and communication technologies and the use of LMSs work together [25]. Similar findings were published regarding how students’ use of LMSs during COVID-19 in Saudi Arabia impacts their participation in online learning. Students claim that LMSs are a reliable and successful method of education that enables efficient management and usefulness of distant education as a sustained engagement with less time and fewer resources needed [26]. There is proof to back up the idea that learners who devote more of their time to pursuing their online education are more likely to engage in active learning and benefit from it.

3. Conceptual Framework

This study is the first to investigate the relationship between ergonomic factors and students’ perceived learning motivation and academic attention. By addressing the interconnectedness of ergonomics’ physical, cognitive, and macro-ergonomic components during student learning, the establishment of an effective education learning system was noticeably beneficial. The conceptual framework proposed in this study, illustrated in Figure 1, showed how the ergonomic domains of physical, cognitive, and macro-ergonomics could be used to assess the learning motivation and academic attention of SHS students.
Physical ergonomics is concerned with issues relating to human anatomy, such as workplace design, working posture, safety, and health concerns [27] with the aim of reducing physical strain on a person by designing the physical work environment around the way the human body functions. Since online learning promotes the use of atypical learning places such as the kitchen table, the sofa, or in bed because of its “anytime, anywhere” flexibility, it is often disregarded how not ergonomically sound these spaces are for learning since they are deemed convenient [28]. With that, desk and chair designs that are suitable based on students’ anthropometric variables should be taken into account in people’s study stations [17]. This is because musculoskeletal problems can be brought on by a variety of factors, such as poor working posture and non-ergonomic chair seating, as per Büker et al. [29]. Additionally, non-ergonomic working conditions may negatively impact students’ psychological well-being, lowering their motivation and productivity. Porto and Thompson [28] even mentioned preventative strategies to avoid these health problems, such as using ergonomic equipment such as chairs, headsets, footrests, and special keyboards. With this data, it can be concluded that having a workstation aligned with ergonomic design principles would positively affect a student’s motivation to learn.
A study by Cheryan et al. [30] also suggests that a space to study with adequate lighting, controlled noise, good air quality, and a regulated temperature is needed so students can learn to their fullest potential. Ragpala [31] mentions that noise negatively affects children’s academic performance, resulting in decreased memory, motivation, and reading competence. Overly loud noises are a significant barrier to learning since they make it difficult for someone to concentrate and complete complex activities. In addition, the study by Cui et al. [32] proved that motivation improves when people are more comfortable with the temperature. It was also noted that warm environments had a negative effect on a person’s motivation. Lighting also plays a critical role in studying [33]. According to Soltaninejad et al. [17], good lighting enhances children’s capacity to perceive and comprehend visual information. Samani and Samani [34] found that high-quality lighting improved people’s motivation and well-being. Students are more relaxed and not sleepy in environments with excellent lighting, which may encourage and motivate them to learn more effectively there.
Overall, physical ergonomics in learning environments play a vital role in supporting learners’ motivation by providing comfort, promoting positive psychological states, and enhancing learners’ concentration. Thus, it was hypothesized that:
Hypothesis 1 (H1).
Physical ergonomic factors have a significant and positive effect on the learning motivation of students.
Cognitive ergonomics is the process of designing and organizing learning environments to optimize cognitive processes and facilitate effective learning [35]. It plays a crucial role in shaping learning motivation among students. According to de Naeghel et al. [36], well-structured and -communicated instructions are essential for promoting motivation. Cognitive ergonomics focuses on providing students with explicit instructions, well-defined goals, and clear expectations. When students understand what is expected of them and how to accomplish tasks, they feel more confident, competent, and motivated to engage in learning activities [37]. Studies have also shown that cognitive ergonomics promotes active learning strategies, encouraging student engagement and participation. This includes incorporating interactive activities, collaborative learning opportunities, problem-solving tasks, and hands-on experiences [38]. According to a prior study [39], cognitive ergonomics enhances motivation by actively involving students in learning and creating a sense of involvement, autonomy, and relevance. Active learning approaches allow students to see the practical application of their knowledge, which can inspire them to remain motivated and enthusiastic [40].
Educators can optimize cognitive processes, personalize learning experiences, and promote active engagement by incorporating cognitive ergonomics principles into learning environments. Thus, it was hypothesized that:
Hypothesis 2 (H2).
Cognitive ergonomics factors have a significant and positive effect on the learning motivation of students.
Macro-ergonomics focuses on the broader system-level aspects of ergonomics, including the organizational and socio-technical factors that influence work systems [41]. Regarding learning management systems (LMSs) and learning infrastructure, macro-ergonomics is vital in shaping their effectiveness and impact on learning motivation [42]. Studies have shown that LMSs impact student motivation in higher education. Results indicated that using LMSs positively influenced student motivation, particularly in increasing intrinsic motivation and engagement. LMS features such as interactive content, collaboration tools, and immediate feedback were found to enhance motivation levels [43,44]. Another study on the impact of infrastructure, including computer availability and accessibility, on students’ attitudes and motivation to use computers revealed that a well-developed infrastructure with adequate computer resources positively influenced students’ motivation to utilize computer-based systems for learning and professional development [45]. Additionally, a study that explored the impact of infrastructure and LMSs on learning outcomes and motivation in a blended learning environment also found that when students perceived the infrastructure as reliable, user-friendly, and supportive of their learning needs, it positively influenced their motivation to engage with the course materials and activities offered through the LMS.
Overall, prior studies suggest that well-designed and accessible infrastructure and LMS features promote student engagement and increase motivation and students’ learning effectiveness. Thus, it was hypothesized that:
Hypothesis 3 (H3).
Macro-ergonomic factors have a significant and positive effect on the learning motivation of students.
Learning motivation is a subjective and internal psychological drive that encourages people to pursue the knowledge they believe is valuable and pushes them to accomplish their objectives, such as improved academic attention [46]. It is one of the elements affecting students’ learning outcomes. Motivated students are more likely to choose academic pursuits that they feel are rewarding and meaningful [47]. Afzal et al. [48] state that student academic attention and motivation are positively correlated and causally linked. When students are more motivated, they perform better; when they perform better, they become more motivated. According to Amrai et al. [49], students who feel their work is worthwhile and meaningful are more interested in cognitive activities, employ more cognitive and monitoring skills, and generally perform better academically. Alhadi and Saputra [47] also found that learning outcomes correlate with learning motivation. When motivated to learn, students are more likely to pay attention to the subject matter, engage actively in the learning process, and persevere through challenges. Motivated students tend to exhibit higher concentration levels and sustained attention during academic tasks. They are likelier to block distractions and stay engaged with the material [50].
Overall, motivation provides the drive to maintain focus and acts as a driving force that enhances academic attention. Thus, it was hypothesized that:
Hypothesis 4 (H4).
Learning motivation has a significant and positive effect on the academic attention of students.

4. Materials and Methods

4.1. Setting

The study’s goal was to determine the ergonomic factors that impact the learning motivation and academic attention of students in higher educational institutions, specifically students from selected schools in NCR Plus in the Philippines. The schools were Augustinian Abbey School, Colegio San Agustin–Biñan, De La Salle Medical and Health Sciences Institute, De La Salle University, De La Salle University–Dasmariñas, Don Bosco School-Manila, Lyceum of the Philippines University, Mapúa University, and the University of Sto. Tomas. These schools were selected due to convenience and for easier data gathering. However, there is a wide range of students in the mentioned institutions. Considering this, the subjects may have different thoughts, ideas, and experiences about how they work and study. To address this issue, the researchers focused on SHS students to collect consistent data that can be processed reliably. SHS students have a great deal of experience with different online learning modalities; thus, this demographic group is an ideal fit for this study. The target respondents of the paper were students who only study or have studied from home in the previously mentioned schools. Also, the participants in this study had online classes with synchronous and asynchronous sessions since this research was conducted during the pandemic, which is limited by protocols and rules set by the government.

4.2. Participants and Sampling Technique

This study employed a non-probability sampling technique. In particular, purposive sampling through an online survey was utilized. The target respondents were SHS students at selected schools in NCR Plus. As suggested by Yamane’s [51] study, which established a level margin of error of 10, the minimum number of respondents expected in this paper is 300.

4.3. Research Survey and Procedure

The self-administered online survey was disseminated via Google Forms. For two months, the survey link was delivered to the desired respondents and was distributed in various cross-sectional designs. The language medium of the survey was English.
The survey included 31 questions. In the first component, respondents’ demographics were determined using 6-item questions covering age, gender, grade level, program, the most recent school they attended where they studied in an online setting, and place of residence. Furthermore, the LMS they used in the school they stated was requested for the macro-ergonomics section of the survey.
The second component of the survey includes indications based on physical, cognitive, and macro-ergonomic factors. This assesses students’ perceived enthusiasm for learning and academic attention. The survey was composed of item questions, with answers on a 5-point Likert scale ranging from strongly disagree to strongly agree. The survey employed five (5) latent variables: physical ergonomics, cognitive ergonomics, macro-ergonomics, learning motivation, and academic attention. Table 1 provides a summary of the measures and constructs. The construct items were adapted from current studies [52,53,54,55].
After validating the study survey and respondents, the researchers first asked permission from the Mapúa University Research Ethics Committee before collecting data. Once allowed, the online survey was distributed to the respondents as a Google Forms link sent through either email or social networking services (SNSs) such as Facebook Messenger. The respondents’ consent to participate in the study was acquired prior to completing the form. When the given time frame had passed and the expected minimum number of respondents had been reached, the data gathered were analyzed using the PLS-SEM tool.

4.4. Data Analysis

Multivariate analysis was used to examine the data that were gathered from the survey. A variance-based partial least squares structural equation model (PLS-SEM) with maximum likelihood estimation was used in this study. PLS-SEM is a device for analyzing the connections between abstract concepts [66]. It is concerned with and handles complicated constructs at higher abstraction levels; as a result, higher construct reliability and validity are produced, making it excellent for prediction [67]. Its primary objective is to provide the most comprehensive account of the variation in the dependent constructs. The measurement model’s characteristics are also used to evaluate the data quality. Ouellette and Wood [68] state that PLS-SEM, which is more frequently used in scientific investigations and studies, differs from previous modeling approaches because it considers both direct and indirect impacts on presumptive causal linkages. Also, PLS-SEM is the most suitable approach for developing new theories and making predictions, while covariance-based SEM (CB-SEM) is superior for assessing and validating preexisting theories [66].
In this study employing PLS-SEM, some fit indices were used, such as standardized root mean square residual (SRMR), normal fit index (NFI), and chi-square, to verify the model fit. A good fit for SRMS is deemed to be a value of less than 0.08 [69]. On the other hand, an appropriate fit for NFI is 0.80 and higher, while for chi-square, lower than 5.0 implies a model that fits the data well [70].

5. Results

5.1. Demographic Profile

Table 2 presents a breakdown of the characteristics of 311 individuals who participated in this study. A majority of the participants (54.98%) were male. The most common ages were 17 and 18 years old, with percentages of 30.87% and 57.23%, respectively. The highest representation in terms of year level was Grade 12, with 282 participants making up 90.68%. The survey covered nine schools from NCR Plus, with the highest number of respondents coming from Mapúa University, accounting for 60.45%. The Science, Technology, Engineering, and Mathematics (STEM) strand had the highest number of participants, with 254 individuals making up 81.67%, followed by Accountancy and Business Management (ABM) and Health Allied, which had almost equal rates of 5.79% and 5.47%, respectively. Blackboard was the most used LMS, with 67.58% of respondents reporting its use. Most of the participants (79.10%) lived in urban areas.

5.2. Results of SEM

The initial SEM employed in the study to evaluate students’ learning motivation and academic engagement is illustrated in Figure 2. Each measured variable is indicated by its indicators, which serve as the basis for establishing the affinity between the observed data and the construct. Students used a survey, including ergonomic assessments, to assess their learning environment. The model examines whether such assessments influence a student’s motivation to study. All constructs that affect learning motivation that were considered valid were included in the final SEM, as illustrated in Figure 2.
The reliability and validity values for the final model are shown in Table 3. Certain factor loadings fall short of capturing the variability of the latent variable. Items with initial loading values lower than 0.7 were not included in the final loading, such as PE4, CE3, and AA4. The internal consistency, reliability, and validity are then evaluated using Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE). Additionally, the cutoff value for the AVE’s convergent validity should be greater than 0.5 [71]. There is adequate internal consistency and reliability throughout the sample of test items because almost all values are higher than necessary. This suggests that most of the model’s constructs are reliable and valid [72].
The structural model is assessed through the Fornell–Larcker criterion and the Heterotrait–Monotrait correlation ratio developed by Henseler et al. [73], establishing a significant association between each latent variable. Kline [74] suggests that discriminant validity is confirmed when the value between two reflective constructs falls short of 0.85 for the Heterotrait–Monotrait ratio and when the assigned constructs have a more excellent value than all loadings of other constructs for Fornell–Larcker. Except between the reflective AA constructs, as indicated in Table 4 and Table 5, the reliability and convergent validity are acceptable, and the values fall within the intended range. The majority of the constructs are approved based on the findings. The conventional metric suggested by Fornell and Larcker [75] relates the squared AVE of each latent variable to all other latent variables in the structural model that were reflectively evaluated. All model constructs’ shared variances should not exceed their AVEs squared. According to Table 4’s findings, practically, most latent variables have higher squared AVEs than their corresponding latent variable’s correlation values. This shows that the model has a high level of convergent, credible, and discriminant validity.
The Heterotrait–Monotrait method correlations (HTMT) refer to the average variable correlations between constructs in proportion to the average correlations among items measuring the same construct. Henseler et al. [73] propose a threshold HTMT score of less than 1, indicating a difference in correlation between the two latent variables. If the HTMT value exceeds this threshold, it indicates a lack of discriminant validity. To avoid overlap between closely related latent variables, the authors recommend using an HTMT value of less than 0.85 or 0.90 for distinct constructs.

5.3. Model Fit Analysis

The suitability and reliability of the proposed model were verified through a model fit analysis. All parameter estimates, as displayed in Table 6, exceeded the minimum threshold value, confirming the adequacy of the suggested model. In addition, bootstrap samples were generated from modified sample data using the model-implied correlation matrix after orthogonalization or standardization of all variables. According to Dijkstra and Henseler [76], if the number of bootstrap samples producing discrepancy values higher than the actual model exceeds 5%, it is possible that the sample data was obtained from a population using the proposed model.

5.4. Results of Final SEM

The introduced hypothesis for the final SEM in the structural model was examined using the PLS-SEM. The results of the test are displayed in Table 7 and Table 8. As can be observed, the two constructs, specifically, cognitive ergonomics (CE) (β = 0.306, p = 0.010) and macro-ergonomics (ME) (β = 0.459, p = 0.001), have a significant and positive influence on a student’s learning motivation, while learning motivation (LM) (β = 0.442, p = 0.001) has a significant and positive effect on students’ academic attention (AA). On the other hand, physical ergonomics (PE) (β = 0.102, p = 0.451) did not significantly influence students’ learning motivation. Therefore, hypotheses H2, H3, and H4 are accepted, whereas hypothesis H1 is rejected.
The final SEM model can be seen in Figure 3. The beta coefficients and R2 values were calculated to assess the hypothesis model. For the variation of a student’s academic attention, 39.5% is designated, while the model apportions 63.8% to the variation in students’ learning motivation. With this data, the model is sufficient to interpret students’ learning motivation and academic attention because, as per Henseler et al. [73], for studies on behavioral intention, an R2 score of 20% and above is deemed high.

6. Discussion

This paper examined the effects of physical, cognitive, and macro-ergonomic factors on students’ learning motivation and academic attention in distance learning using partial least square structural equation modeling (PLS-SEM). The findings show that learning motivation (LM) has the strongest, most significant, and most favorable impact on students’ academic attention and is the only construct that directly affects it (β = 0.442, p ≤ 0.001). This implies that the students who are inspired to outperform their peers in terms of grades (LM1), expect to perform well in class (LM2), learn the material by properly studying (LM3), prefer course materials that spark their curiosity (LM4), and go above and beyond expectations to comprehend the material (LM5) have a significant impact on their academic attention (AA). This remained consistent with previous studies. As a result, to perform well in school, a student needs goals and objectives. This then comes with motivation, which positively affects a student’s study routines, academic attention, ability to change, and general well-being [78]. As per Çetin [79], goal setting is crucial in determining a student’s academic achievement.
An effective study plan and increased effort improve academic success, which is impacted by mostly self-determined motivation [80]. According to Afzal et al. [48], students affected by this or who are intrinsically driven take on activities and perform well in class because they are interested in learning. They continue by saying that because their performance will be consistent, intrinsically motivated pupils outperform extrinsically motivated pupils in the classroom. Based on peer interactions, topic interest, and self-regulatory behaviors (i.e., a person’s coping reactions and behavioral actions toward situations), students’ motivational drives are connected to academic success [81]. Also, as stated in the study by Yousefy et al. [82], there is a significant and favorable relationship between motivation indicators (i.e., praise, ability, competitiveness, objectives, interest in education, and pleasing others) and academic attention. Students are encouraged to employ boundary management, such as giving themselves prizes for completing assignments, paying attention to lectures for a specific amount of time, and completing all their classes. Students may have small rewards, such as watching movies or playing specific video games after finishing their academic work, to boost their motivation [83].
In light of these, it may be inferred that while both intrinsic and extrinsic motivation types lead to better academic attention, the former should be the primary motivation for learning as it is proven more effective and is better in the long run. The researchers, as well as several previous papers, recommend that school administrators and instructors should manage and pay greater attention to students’ learning motivation since it plays a critical role in academic attention [47,82,84].
Macro-ergonomics has been shown to have the most significant impact on the respondents’ learning motivation amongst the other ergonomic factors based on the beta coefficient and p-value (β = 0.459, p < 0.001), proving the hypothesis that macro-ergonomic factors have significant and positive effects on the learning motivation of students. This implies that the conditions for good academic attention concerning macro-ergonomics, such as a student’s use of their school’s LMS and its course format (ME1, ME2), access to technology and stable internet connection for online classes (ME3), instructors conducting clear, practical demonstrations and explanation as well as stimulating students through interesting materials and techniques (ME4, ME5), have to be met.
According to Wichadee’s [85] study, a positive correlation exists between students’ motivation and their level of participation in the LMS. In other words, the higher their learning motivation, the more they participated in the LMS. Ahmad et al. [86] assert that LMSs are crucial tools for teaching and learning in technical and vocational institutions, as they enable students, lecturers, and administrators to monitor learning outcomes when used consistently. D’Angelo’s [87] research supports these findings, suggesting that technology positively impacts students by facilitating communication with peers, fostering creativity through technological features, engaging higher-order cognitive processes, participating in inquiry-based learning, synthesizing information from multiple sources, and developing an online social presence. Additionally, Affum’s [88] study suggests that the internet is essential for student learning outcomes, although it can also have adverse effects that may impede student progress. Finally, Ekmekci and Serrano’s [89] study found that teacher quality significantly impacts student motivation in STEM fields, with students who receive instruction from educators who prioritize unique teaching methods and emphasize subject specific skills, such as mathematics, demonstrating higher levels of motivation towards the subject.
In the recently mentioned related literature, most favor macro-ergonomics having some, if not a significant, impact on a student’s learning motivation. Thus, this paper’s researchers, supported by several previously mentioned studies, recommend that future researchers and educators consider the effects of the previously stated macro-ergonomic factors on students’ learning motivation.
Cognitive ergonomics has been demonstrated to have a substantial impact on improving learning outcomes and motivation. The results show that there is a significant and positive relationship between cognitive ergonomics (CE) and students’ learning motivation (LM) (p = 0.010, β = 0.306). The outcome is positive, indicating that an increase in CE is associated with an increase in LM.
Based on these results, it could be implied that students learn better when the teacher writes on the board (CE1). This received the highest overall effect, implying that it benefited students’ cognitive ability and, as a result, their learning motivation. However, when students are asked to create a model (CE3), it is believed to be less successful in helping students’ cognitive processes because it has the lowest overall rate. Similarly, some of the ratings across the different indicators may provide further insights into the impact of cognitive ergonomics on students’ learning motivation. Notably, when instructions are given verbally, students understand better and (CE2) has a relatively high rating. Likewise, CE5 has the highest ratings in the first and third categories, which could suggest that students learn better when the teacher presents a lecture.
Previous studies have also shown the importance of cognitive ergonomics in various educational situations [90,91]. For instance, Rodrigues et al. [92] analyzed the cognitive-ergonomic aspects of e-learning courses. The findings demonstrated that students who received cognitive ergonomics-based training outperformed those who received standard instruction. Similarly, in the study by Wu et al. [93], they examined the effect of cognitive ergonomics on medical students’ academic performance. The findings revealed that cognitive ergonomics concepts, including clear and succinct instructions, reduced cognitive load and engaging multimedia use substantially influenced students’ academic performance [94]. Moreover, Kalakoski et al. [95] looked into the impact of cognitive ergonomics on online learners’ motivation and engagement. The results showed that cognitive ergonomics approaches to online course design significantly improved student motivation and engagement.
As a result, extensive evidence in the literature supports the favorable association between cognitive ergonomics and learning outcomes and motivation. Researchers and educators should use cognitive ergonomics concepts to enhance learning outcomes and motivation when creating instructional materials and educational situations.
Contrary to the two previously discussed constructs, physical ergonomics (PE) (β = 0.102, p = 0.451) was found to have no significant influence on students’ learning motivation. This finding suggests that students have an adjustable work chair and table for their work area (PE1, PE2). Moreover, this result could imply that light is properly distributed and that a good source of ventilation exists in their learning space (PE3, PE4). Background noise may also not be present or severe in the learners’ environment; thus, it does not undermine their comprehension of the lessons discussed during class (PE5). According to a study by Zhao et al. [96], it may also be possible that the students are in a somewhat separate or isolated setting, limiting the amount of noise that reaches and influences them when learning.
In relation to workspace lighting or illumination, the results of a study by Mott et al. [97] support this conclusion, as no effects of lighting on student motivation were found in the article. This, however, is opposed to a study’s findings that highlight that lighting quality enhances student motivation [34]. Additionally, differing from the results of this paper, prior research acknowledges that environmental factors in the pupil’s physical space, such as lighting, ventilation, acoustics, and furnishings, directly impact a learner’s motivation and concentration [98].
Per Choi et al. [99], a favorable physical learning environment positively influences students’ willingness to exert effort to learn, whereas a poor one could result in the opposite. This is supported by Williams and Williams’s study [100], which lists the environment as the fifth fundamental component of student motivation. The findings of a study by Keser Aschenberger et al. [101] also back this statement up, indicating that motivation strongly correlates to the quality of a student’s learning environment. Therefore, students’ learning motivation at home is significantly influenced by the availability of a high-quality study area which includes furniture that follows ergonomic principles and a peaceful learning environment [102].
However, to cite a statement from Baticulon et al. [103] in their sample, “having a quiet study area, with the same comfort provided by a classroom or library, was a privilege not available to all”. Therefore, it can be concluded that the intensity of how a student’s environmental setting influences their learning motivation also somewhat depends on their socio-economic living conditions. This idea could be potential ground to be covered more in future research. Nevertheless, the researchers recommend exerting effort to provide students with an optimal environment and workspace conducive to learning to boost their willingness and motivation to learn.
As a conclusion to this chapter, it was discussed and validated that researchers have put out and shown several techniques for improving students’ learning motivation. The purpose of testing these hypotheses was to validate concepts and presumptions. The findings had a rejected hypothesis (PE→LM), later known as purely hypothetical, despite the suitable model fit, validity, and reliability. More specifically, many other researchers claimed that physical ergonomics positively influenced learning motivation. The results of this paper declare otherwise, therefore contradicting claims made in several studies.

7. Conclusions

This paper used a unique ergonomic domains assessment framework to examine the effects of physical, cognitive, and macro-ergonomic factors on students’ learning motivation and academic attention in distance learning. An online survey was created and disseminated using purposive sampling to 311 SHS students who took part in online learning at selected schools in NCR Plus. The partial least square structural equation modeling (PLS-SEM) was utilized to investigate and assess the variables influencing their learning motivation and academic attention.
The data revealed that cognitive and macro-ergonomics significantly influence students’ learning motivation, which in turn affects the students’ academic attention; thus, all three hypotheses were accepted. In contrast, physical ergonomics did not significantly influence students’ learning motivation; hence, it rejected the hypothesis. The model designated 39.5% for a student’s academic attention variation and 63.8% for a student’s learning motivation.
The findings imply that students’ socioeconomic living conditions may have some bearing on how their environment affects their drive to learn. Also, teachers should be aware of and consider the effects of physical, cognitive, and macro-ergonomic factors to boost students’ learning motivation, which enhances learning outcomes. Lastly, intrinsic motivation should be the main driving force behind learning because it is more successful and beneficial in the long term.

7.1. Practical Implications

The study’s noteworthy findings shed light on the importance of ergonomic factors that impact a student’s learning motivation and academic attention. Academic institutions and e-learning infrastructure may use the results of this study to build initiatives and campaigns that address these aspects, and refine school LMSs, to improve the learning environment for students. Teachers can also use the outcomes of this study to improve their teaching practices and help boost their students’ learning motivation. Furthermore, students may benefit from this research as they will be aware of their ergonomic variables in distance learning, potentially improving these, thus enhancing their learning motivation and academic attention. Lastly, future researchers might utilize this work as a foundation for new ideas, significant results, and related material to broaden their understanding.

7.2. Theoretical Implications

Understanding the elements and factors affecting student motivation and academic attention is essential, especially now since the world has experienced a sudden shift in the mode of education from traditional face-to-face classes to online classes. This study is one of the first to investigate the relationship between ergonomic factors such as physical, cognitive, and macro-ergonomics on a student’s learning motivation and, ultimately, their academic attention in distance learning. This study also showed that its findings align with previous related pieces of literature regarding the significance of ergonomic aspects for students’ learning motivation; thus, this study may contribute to the development of knowledge in higher education and the research community. As a result, it is imperative that those who utilize a distance learning mode be mindful of the factors that affect student motivation and academic attention. Since online learning is more practical and necessary than ever, it is essential to properly plan a course that will be and remain practical as well as engage with students effectively.

7.3. Limitations and Future Research

Like numerous studies, this paper is also subject to limitations that can be investigated further in future research. Since the data gathering period was limited to two months, the study could only collect a few responses over the minimum number expected. Therefore, acquiring a more significant number of participants is encouraged for future studies as it would enable a more detailed and precise evaluation of how ergonomic factors affect the learning motivation and academic attention of students in online learning.
The scope of the respondents of the study was also limited to SHS students who have experienced studying in an online set-up in one or more of nine (9) specified schools in areas under NCR Plus in the Philippines. Thus, the researchers urge that in future research, the scope of the respondents in the study should be widened outside SHS students from the mentioned academic institutions. It is also possible to narrow down the investigation of a sample to a specific academic field as the requirements and tasks completed in each course in an online set-up might differ, making it of interest for subsequent studies.
Future research could also expand or focus the range of their sample on non-academic sectors such as office workers. Moreover, this study did not consider the participants’ socio-demographic variables such as gender, age, year level, program, LMS, area of residence, family income, social class, and household members. For that reason, future researchers could recreate this paper and consider these variables. Lastly, this study did not discuss how ergonomic factors affect the learning satisfaction and academic performance of students learning online; hence, future studies could be conducted to examine these as latent variables.

Author Contributions

Conceptualization, M.J.J.G.; methodology, M.J.J.G., I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; software, M.J.J.G.; validation, M.J.J.G.; formal analysis, I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; investigation, I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; resources, M.J.J.G., I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; data curation, I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; writing—original draft preparation, I.S.V.D.C., D.A.A.P., H.N.M.R. and D.L.P.S.; writing—review and editing, M.J.J.G.; supervision, M.J.J.G.; project administration, I.S.V.D.C. and D.L.P.S.; funding acquisition, M.J.J.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Mapúa University Directed Research for Innovation and Value Enhancement (DRIVE) (Funding No. FM-RC-21-89).

Institutional Review Board Statement

The study was approved by Mapúa University Research Ethics Committees (FM-RC-21-89).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study (FM-RC-21-89).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The researchers would like to express their appreciation of the participants’ responses to the questionnaire.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 09202 g001 550
Figure 1. Conceptual Framework.
Figure 1. Conceptual Framework.
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Figure 2. Initial SEM Model.
Figure 2. Initial SEM Model.
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Figure 3. Final SEM Model.
Figure 3. Final SEM Model.
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Table
Table 1. Construct and Measurement Items.
Table 1. Construct and Measurement Items.
ItemsMeasureSupporting References
Physical Ergonomics
PE1I have an adjustable work chair that is suitable for my area[51,52,53,54,56]
PE2I have a working table that is suitable for my work area
PE3I have proper lighting distribution in my work area
PE4I have a sufficient source of ventilation in my work area
PE5My background noise does not interfere with my understanding of what is being discussed during class
Cognitive Ergonomics
CE1I learn better by reading what the teacher writes on the board[55,57,58]
CE2When the teacher tells me the instructions, I understand better
CE3I learn more when I can make a model of something
CE4I learn better by reading than by listening to someone
CE5I learn better in class when the teacher gives a lecture
Macro-Ergonomics
ME1The use of LMS helps me comprehend the course materials[59,60,61]
ME2The course format in LMS makes it easier for me to meet my learning needs
ME3I have access to technology and the internet for the online class
ME4Our instructors conduct clear, practical demonstrations and explanation
ME5Our instructor stimulates students through interesting materials and techniques
Learning Motivation
LM1I want to get better grades than other students[62,63]
LM2I expect to do well in class
LM3By studying appropriately, I am sure that I can learn the material
LM4I prefer course material that arouses my curiosity
LM5I am satisfied with trying to understand the content
Academic Attention
AP1I made myself ready in all my subjects during online class[64,65]
AP2I pay attention and listen during online class discussion
AP3I actively participate in every online class discussion
AP4I enjoy homework and activities because they help me improve my skills in every subject
AP5There has been an improvement in my academic performance since the online class started
Table
Table 2. Summary statistics of demographic profile.
Table 2. Summary statistics of demographic profile.
Respondent’s ProfileCategoryN%
GenderMale17154.98%
Female13744.05%
Others30.96%
Age16175.47%
179630.87%
1817857.23%
19196.11%
2010.32%
Year LevelGrade 11299.32%
Grade 1228290.68%
ProgramABM185.79%
GAS30.96%
Health Allied175.47%
HUMSS165.14%
ICT30.96%
STEM25481.67%
SchoolAugustinian Abbey School175.47%
Colegio San Agustin—Biñan134.18%
De La Salle Medical and Health Sciences Institute134.18%
De La Salle University30.96%
De La Salle University—Dasmariñas51.61%
Don Bosco School—Manila289.00%
Lyceum of the Philippines University247.72%
Mapúa University18860.45%
University of Sto. Tomas206.43%
LMSAralinks51.61%
Blackboard21167.85%
Canvas175.47%
Mrooms227.07%
NEO LMS185.79%
SOLAR NGS237.40%
V-Smart154.82%
Area of ResidenceCity24679.10%
Province5016.08%
Town154.82%
Table
Table 3. Reliability and convergent validity result.
Table 3. Reliability and convergent validity result.
ConstructItemsMeanS.D.FL (≥0.7)α (≥0.7)CR (≥0.7)AVE (≥0.5)
Physical
Ergonomics		PE13.421.370.8350.8090.8180.757
PE24.021.000.752
PE33.801.040.708
PE44.210.870.654
PE53.081.330.762
Cognitive
Ergonomics		CE14.150.980.7630.8060.8690.760
CE24.370.760.756
CE33.690.970.543
CE43.331.160.753
CE54.290.870.763
Macro-
ergonomics		ME14.010.950.7730.7490.7780.675
ME23.911.010.771
ME34.640.630.730
ME43.820.890.739
ME53.791.040.779
Learning
Motivation		LM14.000.890.7770.8770.7520.744
LM24.060.850.852
LM34.350.770.756
LM44.490.940.794
LM54.101.140.725
Academic
Attention		AA13.421.130.8620.8000.8370.656
AA23.221.170.770
AA33.121.200.756
AA43.131.360.698
AA53.081.120.722
Table
Table 4. Discriminant Validity: Fornell–Larcker Criterion.
Table 4. Discriminant Validity: Fornell–Larcker Criterion.
AACELMMEPE
AA0.908
CE0.6500.770
LM0.6260.5600.798
ME0.6220.6480.6380.766
PE0.5920.6740.5940.6750.810
Table
Table 5. Discriminant Validity: Heterotrait–Monotrait Ratio.
Table 5. Discriminant Validity: Heterotrait–Monotrait Ratio.
AACELMMEPE
AA
CE0.781
LM0.7900.780
ME0.8270.7090.798
PE0.7900.6590.7280.671
Table
Table 6. Model fit.
Table 6. Model fit.
Model Fit for SEMParameter EstimatesMinimum Cut-OffRecommended by
SRMR0.063<0.08Hu and Bentler [69]
(Adjusted) Chi-square/dF3.17<5.0Hooper [77]
Normal Fit Index (NFI)0.921>0.90Baumgartner [70]
Table
Table 7. Total direct, indirect, and total effects.
Table 7. Total direct, indirect, and total effects.
NoRelationshipDirect Effectp-ValueIndirect Effectp-ValueTotal Effectp-Value
1PE→LM0.1020.451--0.1020.451
2CE→LM0.3060.010--0.3060.010
3ME→LM0.459<0.001--0.459<0.001
4LM→AA0.442<0.001--0.442<0.001
5PE→AA--0.0790.6330.0790.633
6CE→AA--0.3350.0190.3350.019
7ME→AA--0.2020.0020.2020.002
Table
Table 8. Hypothesis Test.
Table 8. Hypothesis Test.
NoRelationshipBeta Coefficientp-ValueResultSignificanceHypothesis
1PE→LM0.1020.451PositiveNot significantReject
2CE→LM0.3060.010PositiveSignificantAccept
3ME→LM0.459<0.001PositiveSignificantAccept
4LM→AA0.442<0.001PositiveSignificantAccept
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MDPI and ACS Style

Gumasing, M.J.J.; Cruz, I.S.V.D.; Piñon, D.A.A.; Rebong, H.N.M.; Sahagun, D.L.P. Ergonomic Factors Affecting the Learning Motivation and Academic Attention of SHS Students in Distance Learning. Sustainability 2023, 15, 9202. https://doi.org/10.3390/su15129202

AMA Style

Gumasing MJJ, Cruz ISVD, Piñon DAA, Rebong HNM, Sahagun DLP. Ergonomic Factors Affecting the Learning Motivation and Academic Attention of SHS Students in Distance Learning. Sustainability. 2023; 15(12):9202. https://doi.org/10.3390/su15129202

Chicago/Turabian Style

Gumasing, Ma. Janice J., Iris Samantha V. Dela Cruz, Dean Angelo A. Piñon, Hedy Nicolaison M. Rebong, and Daniel Luis P. Sahagun. 2023. "Ergonomic Factors Affecting the Learning Motivation and Academic Attention of SHS Students in Distance Learning" Sustainability 15, no. 12: 9202. https://doi.org/10.3390/su15129202

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