key: cord-0903455-mhropt8h
authors: Ayyildiz, Ertugrul; Taskin Gumus, Alev
title: A novel distance learning ergonomics checklist and risk evaluation methodology: A case of Covid‐19 pandemic
date: 2021-05-10
journal: Hum Factors Ergon Manuf
DOI: 10.1002/hfm.20908
sha: dd7d64b705eac4034e5bbe152691cb4435b92198
doc_id: 903455
cord_uid: mhropt8h

Many governments decided to cancel face‐to‐face teaching and learning activities in schools and universities. They replaced them with online teaching and distance learning activities to prevent the spread of Coronavirus disease 2019 (COVID‐19). Due to this sudden change, students experienced some anthropometric, environmental, and psychosocial difficulties at home during the distance learning process. This study focuses on determining the importance of anthropometric, environmental, and psychosocial factors in the distance learning process during the COVID‐19 pandemic. This study presents main factors and their subfactors affecting ergonomic conditions of university students during distance learning. A novel distance learning ergonomics checklist is proposed based on the Occupational Safety and Health Administration checklists. The data are collected via a questionnaire filled by 100 university students who attend the Ergonomics course online. Then, the integrated methodology includes Voting Analytic Hierarchy Process integrated Pythagorean Fuzzy Technique for Order Preference by Similarity to An Ideal Solution method is adopted to prioritize the factors determined. Thirty‐nine different subfactors are evaluated under five titles, and the most important factors are determined using the proposed methodology. With the results achieved, it is seen that the suggested checklist and proposed methodology can be used by public and private education organizations as a guide for improving their distance learning strategies.

tionnaire filled by 100 university students who attend the Ergonomics course online.

Then, the integrated methodology includes Voting Analytic Hierarchy Process integrated Pythagorean Fuzzy Technique for Order Preference by Similarity to An Ideal Solution method is adopted to prioritize the factors determined. Thirty-nine different subfactors are evaluated under five titles, and the most important factors are determined using the proposed methodology. With the results achieved, it is seen that the suggested checklist and proposed methodology can be used by public and private education organizations as a guide for improving their distance learning strategies.

COVID-19, distance learning, ergonomics, Pythagorean Fuzzy TOPSIS, Voting AHP

At the end of 2019, the pneumonia epidemic, which is first seen in China due to the newly defined SARS-CoV-2 factor, is defined as Coronavirus disease 2019 (COVID-19) . The epidemic spreads rapidly, and the existence of the virus was confirmed on all continents except Antarctica on January 26, 2020 (WHO, 2020a).

The World Health Organization (WHO) announced on March 11 that COVID-19 had become a pandemic (Yalçin et al., 2020) . According to WHO reports, there are more than 49 million confirmed cases and more than one million deaths in earlier November 2020 (WHO, 2020b) . Studies for the treatment of this pandemic affecting the whole world are still ongoing.

COVID-19 is transmitted from person to person very quickly by droplets (Carlos et al., 2020; Chang et al., 2020; . For this reason, individual measures such as the use of (Ergonomics Risk Evaluation for Distance Learning) personal protective equipment, social isolation, and social distancing (at least 1 m) become vital in preventing contamination . Undoubtedly, the COVID-19 pandemic changes people's lives all over the world (Njiri, 2020) . When the measures taken to rearrange human movements in public life areas are examined, compulsory changes are seen in human life. The pandemic has negative impacts on local and global business, human lives and psychologies (Restubog et al., 2020) . The increasing population density in cities, close contact among people, high mobility, public transportation, and common areas are the causes of the rapid spread of infection. In this context, countries have started to apply different methods to prevent the COVID-19 pandemic. Therefore, many governments have decided to cancel face-to-face teaching and learning activities in schools and universities to prevent the spread of COVID-19 (Sahu, 2020) , and replaced face-to-face activities with online and distance learning activities (Iyer et al., 2020) . Thus, the importance of online and distance learning activities increases in the world. Most authors define online learning as access to learning experiences through specific technologies (Moore et al., 2011) . It is often referred to as "e-learning" among other terms. However, online learning is just one type of "distance learning"; represents learning at a distance, not in a traditional classroom. Distance education/elearning platforms were used in many universities, even partially, before the COVID-19 pandemic for some courses (Panda & Mishra, 2007) . However, the number of universities using distance learning systems in all courses was almost nonexistent before the pandemic (Owusu-Boampong & Holmberg, 2015) .

Distance learning activities have some advantages and disadvantages. Some of the main advantages of distance learning activities, especially for e-learning, are as follows: Distance learning activities are very flexible in terms of time and place. Students have the opportunity to choose the place and time suitable for them, apart from in-person class (Smedley, 2010) . Distance learning activities can increase the efficiency of the course, thanks to the ease of accessing enormous amounts of information via the internet (Arkorful & Abaidoo, 2014) . Distance learning is cost-effective because students and lecturers do not have to travel and no extra building construction is needed (Holmes & Gardner, 2006) . Despite the advantages of distance learning activities, there are also some disadvantages. Personal interaction between students and teachers cannot be realized ideally in distance learning (Young, 1997) . Therefore, strong inspiration and time management skills are required to mitigate the effects of lack of communication. It is difficult to control or regulate undesirable activities such as cheating in tests for evaluations in e-learning (Arkorful & Abaidoo, 2014) . Distance learning can also cause some websites to be congested or used intensively (Akkoyunlu & Yilmaz Soylu, 2006) .

The learning environment is one of the variables that affect the learning performance of the student. The learning environment should contain as few factors as possible that disrupt the learning process. In the computerized learning processes, an arrangement should be handled to stimulate the learning process and take into account students' physical and psychosocial health (Kailash et al., 2011) . Students cannot learn effectively while using computers that they are uncomfortable with (Oyadeyi, 2018) . Students may experience discomforts such as eye ailments, hand and wrist pains, waist, back and neck injuries and headaches while studying in front of the computer screen for a long time (Alaydrus & Nusraningrum, 2019; Portello et al., 2012; Talwar et al., 2009) . Failure to set the environment according to anthropometric criteria causes these disorders to occur.

In the distance learning process, students' performances are affected by psychosocial factors besides anthropometric and environmental factors (Barattucci, 2019; W. Liang et al., 2019; Pereira et al., 2021) . The International Labor Organization (ILO) has defined psychosocial factors based on the interaction between job satisfaction, job organization and management, environmental and organizational conditions, and the expertise and needs of workers (Joint ILO/WHO Committee Health, 1986). These interactions pose a danger to human health with their perception and experiences.

Exposure to physical and psychosocial hazards can affect physical and psychological health (Smith & Freedy, 2000) . These can affect people directly physically, or indirectly through stress (Amponsah-Tawiah et al., 2014) . These two effects are not alternatives to each other; on the contrary, in most cases, they act together, interact and complement each other or reinforce each other's influence and have dramatic effects on the performances of people (Ferri et al., 2016) . Therefore, both psychosocial and anthropometric factors should be considered when evaluating the general studying environment, especially in the distance learning process. Reichert et al. (2001) investigate the effects of distance learning on project team performance. For this purpose, the performances of two project teams, one using traditional face-to-face teams and the other using distance learning, are compared. The coordination of the project is found to be correlated with the performance of distance learning. Rathod (2005) tests student learning levels with multimedia distance learning. It is determined that multimedia distance learning is more effective than traditional ways. Ryu et al. (2007) focus on learning styles for web-based education from two different perspectives, individually and culturally. Bentaib et al. (2019) argue that distance education systems are not satisfactory for all students. They cite ergonomic, aesthetic, practical, and time-based concerns as the reasons for this situation. Jaukovic Jocic et al. (2020) present a multicriteria decision-making methodology (MCDM) to solve the elearning course selection problem, considering seven criteria: content level, presentation methods, teaching methods, e-learning environment, learning materials, quality of multimedia contents, group work and interactivity. They show that there is no significant difference between the relative importance of these seven criteria. Siew et al. (2021) evaluate different learning methods through the integrated MCDM methodology. The quality management system, information quality, flexibility, learning and teaching, and attractiveness are the main criteria for evaluating three different learning systems. E-learning system is chosen as the best learning system during the pandemic. Alqahtani and Rajkhan (2020) aim to determine critical success factors for e-learning during the COVID-19 pandemic. Blended learning, flipped classroom, ICT-supported faceto-face learning, synchronous learning, and asynchronous e-learning systems are evaluated considering ten different factors, and blended learning is determined as the best alternative.

It is essential to determine the importance of anthropometric, environmental, and psychosocial factors in the distance learning process. The importance levels of these factors can be used to develop strategies to increase the performance of students. Apart from reviewed studies, this study focuses on identifying the importance of anthropometric, environmental, and psychosocial factors in the distance learning process during the COVID-19 pandemic, and it is handled as an MCDM problem. This study presents the main factors and sub-factors that affect university students' ergonomic conditions during distance learning. For this purpose, a novel distance learning ergonomics checklist is created. The data are collected using an TOPSIS, one of the most commonly used MCDM methods, is introduced to the literature by Yoon and Hwang (1981) . One of the essential features of the TOPSIS method, which is a linear weighting technique, is the determination of the most suitable solution that is the closest to the positive ideal solution and the furthest to the negative ideal solution. Since these distances are bilateral, the most appropriate selection is made by considering the situations that need to be maximized and the situations that need to be minimized (Özdemir & Seçme, 2009 ). The method can be used as an alternative method that can be applied to rank the factors. However, numerical (crisp) values may be insufficient when evaluating many real-life situations because human thoughts and judgments especially preferences often contain uncertainty and fuzziness (Ayyildiz & Taskin Gumus, 2020) . For this reason, the TOPSIS method is applied under the Pythagorean fuzzy environment to avoid these negativities. Pythagorean Table 1 (Saaty, 1977) to evaluate pairwise comparisons.

For example, a decision-maker who thinks about buying a new car wants to determine the most important criteria in the decision making process. Three criteria affect the decision: price, fuel consumption, appearance. For this purpose, pairwise comparisons of these criteria are performed using the values given in Table 1 . First, the importance levels of price and fuel consumption for decisionmaker are compared. Then the importance levels of the price and appearance of the vehicle for decision-maker are compared. Finally, the importance levels of fuel consumption and appearance are compared, and thus a pairwise comparison matrix is constructed.

Then, the most important criterion is determined by applying the steps of the AHP method.

One of the methods for determining the weights of criteria for AHP is the voting method. VAHP method uses the ranking of votes instead of constructing the pairwise comparison matrix to determine weights and measure inconsistency. Using pairwise comparisons to determine factor weights in AHP is much more time-consuming than voting in VAHP (Taskin Gumus & Yilmaz, 2010).

Sometimes, it is difficult to decide the appropriate weight of each criterion. Cook and Kress (1990) list candidate criteria for preferential elimination and put forward a procedure for using DEA application. For example, all voters may evaluate the subset of criteria and place them in their preferred order. A matrix is created, and then each candidate criterion is placed in the ranking (first, second…, last). Green et al. (1996) develop another procedure for setting specific restrictions for weights. The procedure is called "Green's

Method" in the following process. The following two assumptions have been made to create constraints in this method. The weight difference between the criteria placed in j th and (j+1) th orders can be zero for any j, and the above difference of weight must be positive (Liu & Hai, 2005 ).

Green's Method is sometimes insufficient due to the effect of minimum differences in the total order of the objects and the insufficient application for the concrete sample. The minimum differences can be analyzed by considering the feasible region of the weight solutions obtained through linear programming, which is affected by the number of votes given to the objects. Noguchi et al. (2002) study the implementation of Green's Method and show the differences between high-weight objects for different ranking results. Besides, "Noguchi's strong rule" is applied not only to singleobjective problems but also to multi-objective problems such as supplier selection for a giant company. When trying to obtain the weight of a particular constraint in the total ranking method using DEA, "Noguchi's strong rule" is adopted, which is characterized by the following constraints (Liu & Hai, 2005) . In this proposed MCDM application, "Noguchi's strong rule" is defined by the following mathematical model;

There are multiple ranking criteria while evaluating an MCDM problem. The number of criteria is expressed as "R". "n" is the number of voters, and "S" is the number of places. "u rs " defines the weight of criterion "r" with respect to the place "s" . All candidates for each "u rs " can be preferred such that the maximum weight is obtained by voting for criterion "r". Thus, the value of "Q rr " becomes the largest.

"Noguchi's strong rule" is applied separately to calculate the weight of each criterion. Then, initial criteria weights are normalized to determine the final weights.

While people evaluate the criteria and alternatives in the decision process, they can use fuzzy numbers better to reflect uncertainty . In this study, we apply Pythagorean fuzzy sets to deal with vagueness better. Pythagorean fuzzy sets are proposed by Yager (2013) derived from intuitionistic fuzzy sets, which were initially presented by Atanassov (1999) . Unlike intuitionistic fuzzy sets, the sum of membership and nonmembership degrees can exceed 1, but the sum of their squares cannot in Pythagorean fuzzy sets Karasan et al., 2018) , as explained in Definition 1.

Definition 1. Let X be a fixed set. A pythagorean fuzzy set is shown as P̃ Karasan et al., 2018) :

show the degree of membership and nonmembership of the element x X ∈ to P̃respectively and for every x X ∈ , it holds:

The indeterminacy value is:

Definition 2. Some of the basic operations on two pythagorean

are given as follows (Yildiz et al., 2020) . Pythagorean fuzzy sets are integrated with TOPSIS to handle ambiguity and fuzziness better. In this way, PF-TOPSIS is presented to be used in MCDM problems. Based on the definitions and explanations above, the steps of PF-TOPSIS are given below:

Step 1. Define factors and determine decision-makers.

Step 2. Construct a decision matrix to evaluate factors. Let

) and

x i m ( 1, 2, ..., i = ) represent the values of the criteria and alternatives in the decision matrix A, respectively.

Step 3. Determine Positive and Negative Ideal Solutions (PIS and NIS) by Equations 2.15 and 2.16, respectively (Zeng et al., 2016 ). 

Step 4. Calculate distances from ideal solutions by Equations 2.17 and 2.18, respectively (Liang & Xu, 2017) . 

Step 5. Compute the coefficient of revised closeness (x ) i ɛ of each factor using Equation 2.19.

x

Rank factors from the highest score to the lowest one (C.-H. Wang & Chou, 2015) . The bigger the value, the more important it will be (Chiu & Hsieh, 2016; Fazlollahtabar, 2010) .

In this study, a hybrid decision-making application is proposed using fuzzy logic and MCDM approaches to determine the most important distance learning factors during the COVID-19 pandemic. Therefore, we focus on prioritizing anthropometric, environmental, and psychosocial factors in the distance learning process during the COVID-19 pandemic. For this purpose, a checklist is prepared to determine and prioritize these factors. The checklist proposed as a novel distance learning ergonomics checklist is structured as a two-level hierarchical structure, considering ergonomic factors.

OSHA is responsible for the rules and laws required to set and en- Finally, the main factor of "Psychosocial Health and Satisfaction" is added to represent the psychosocial difficulties students experience during the pandemic. "Psychosocial Health and Satisfaction"

consists of fourteen different subfactors. In this way, a novel distance learning ergonomics checklist was created. The main factors and their subfactors establishing distance learning ergonomics checklist are given in Table 2 .

Technical University, İstanbul participated in the research. The data were collected from 100 undergraduate students participating in the Ergonomics course. This course was being processed online due to the pandemic. Evaluations of the students were obtained using the proposed distance learning ergonomics checklist. An online questionnaire is utilized to collect their opinions on the main and subfactors. Firstly, they ranked five main factors in order of their importance. Students determined the importance level of each subfactor by choosing one of the linguistic terms shown in Table 3 .

Based on these evaluations, the factors were prioritized with using the proposed methodology. The main factors used in the evaluation process were weighted by applying VAHP. Then the importance C5-There should be sufficient room under the work surface.

C6-Legs and feet have sufficient forward clearance under the work surface.

C7-Sharp or square edges that contact hands, wrists, or forearms are padded or rounded.

Seating (S) S1-Backrest has height adjustability so support is provided for the lower back (lumbar area).

S2-Seat width and depth should accommodate the specific user.

S3-Seat is cushioned and rounded with a "waterfall" front (no sharp edge).

S4-Seat height is adjustable and allows for proper alignment with the work surface.

S5-Adjustments are straight forward and easy to perform while seated in the chair.

E1-Keyboard/input device platform(s) is stable and large enough to hold a keyboard and an input device.

E2-Input device (mouse) is located right next to the keyboard so it can be operated without reaching.

E3-Input device is easy to activate and the shape/size fits hand (not too big/small).

E4-There is sufficient room so the monitor can be placed at a distance.

E5-Monitor position is directly in front of the user so they do not have to twist head or neck.

E6-Tablets and smartphones should be used with the shoulders relaxed, arms positioned near the torso.

G1-Computer and equipment have sufficient adjustability.

G2-Computer workstation and components are maintained in serviceable condition and function properly.

G3-Items that must be accessed frequently are within easy reach, generally with the elbows close the body.

G4-User has the ability to alternate between sitting and standing postures.

G5-Lighting levels are adjustable for differing tasks.

G6-The ventilation system delivers quality indoor air.

G7-Noise levels within acceptable levels. 

First, the weights of each main factor of the distance learning ergonomics checklist were obtained using the VAHP method. For this purpose, a questionnaire was prepared to get the opinions of the students. It was asked to rank the main factors according to their importance from the most to the least (1, 2, 3, 4, 5). Multiple factors in the same ranking were allowed in the questionnaire prepared (e.g., if two were tied for 1, the next possible rank was 3). A total of 100 students participating in Ergonomics classes conducted the questionnaire. Then, the responses were analyzed and summarized in Table 4 . Table 4 reflects the priority orders of students. There was no contradiction among the students.

For example, the main factor of "Seating" was evaluated as the most important factor by 60 students, and 10 students considered this main factor as the 5th important (the least important) one. After that, the mathematical models were structured using "Naguchi's strong ordering" method. For example, the mathematical model was structured to determine the weight of the main factor "Computer/ obtained. Last, the weights of the factors were normalized, as given in Table 5 .

The importance weights of the five main factors, "Computer/ Work Station (C)," "Seating," "Equipment," "General Room/Office Condition" and "Psychosocial Health and Satisfaction" were calculated as 0.2161, 0.1819, 0.1765, 0.1839, and 0.2416, respectively.

The most significant main factor for distance learning ergonomics during Covid-19 was specified as "Psychosocial Health and Satisfaction," with an importance weight of 0.2416. In other words, it was determined that the most critical factor when evaluating anthropometric, environmental, and psychosocial factors for students in the distance learning process during the Covid-19 pandemic is "Psychological Health and Satisfaction." Also, "Work Station/Computer" is the factor that should be evaluated as a priority. The least important criterion is found to be "Equipment." Considering the difficulties experienced by students, equipment such as a keyboard, mouse, tablet, smartphone, etc., appears to be considered at the end of the list.

After determining the weights of the main factors, the same students were consulted and asked to express their opinions in weighting the subfactors through the questionnaire. For this purpose, they used the linguistic variables (shown in Table 3 ) to assess the importance of the subfactors (as shown in Table 6 ), and thus, decision matrices for subfactors were constructed.

At this step of the study, the evaluations of sub-factors were performed for relevant main factors, specifically. For example, the decision matrix was structured by Student-1 for "Computer/Work

Station," as shown in Table 6 .

When Table 6 is examined in detail, it can be seen that "C1-Head and neck are balanced and in-line with torso" is extremely highly important, while "C2-Head, neck, and trunk facing forward (not twisted to view monitor/work/documents)" is highly important according to Student-1. Student-1 filled out a questionnaire for each subfactor in this way. The opinions of 100 students were taken to make a comprehensive evaluation. After all decision matrices were structured by students' evaluations, PF-TOPSIS steps were applied for each main factor and the weights of sub-factors were calculated.

PF-TOPSIS scores of subfactors were multiplied by the importance weights of the relevant main factors to determine the subfactors' final importances. Table 7 shows the weights of main and subfactor and rankings of subfactors calculated by VAHP integrated PF-TOPSIS by considering all students' opinions.

We present the results of ergonomics risk checklist evaluation and the importance of each risk for the distance learning process during the COVID-19 pandemic. As a result, the ergonomics risk evaluation data in the questionnaire reveals a few interesting points regarding the views on distance learning ergonomic factors in the pandemic.

The findings reflect that "Psychosocial Health and Satisfaction" is the most important main factor in distance learning. Experts felt that these factors significantly affected distance learning ergonomics during the pandemic, according to the questionnaire responses. If the sub-factors given in Table 7 They show that a checklist is a surveillance tool that helps identify musculoskeletal conditions and is easily applied to evaluate risks. Keyserling et al. (1993) present a checklist to evaluate ergonomic risk factors related to upper extremity cumulative trauma disorders. Norman et al. (2004) use an ergonomic checklist to assess workstation design for call center workers. Nag et al. (2012) apply an ergonomic checklist on general health and psychosocial issues to female workers to examine the prevalence of musculoskeletal pain and discomfort. Brooks (1998) proposes an ergonomic checklist outlining some issues to organize office layout. Engkvist et al. (1995) design an ergonomics checklist for nursing staff to plan patient rooms, corridors, toilets, and treatment rooms. Lindegård et al. performances. This also helps students focus on their courses. In addition to meeting the needs of students, it is necessary to respond quickly as soon as possible. Second, governments should provide a study environment in students' homes that meet their minimum ergonomic requirements. Especially the computers used by students are crucial. Computers and general room conditions are critical in enhancing students' learning performances and improving their attitudes and intentions.

In this study, we integrate VAHP with PF-TOPSIS for the first time in the literature. Moreover, we apply this integrated decisionmaking methodology to evaluate distance learning ergonomics factors from a Turkey-based perspective. Although methodological studies are presented in the MCDM literature, those pertaining to distance learning ergonomics are still critically lacking. So there is a lot to be done, but we think the study will produce important findings in the field of distance learning ergonomics. Also, a novel checklist is presented in this study to evaluate ergonomic factors. We apply this checklist to 100 university students in Turkey, and VAHP integrated PF-TOPSIS is used to measure the weights of distance learning ergonomics factors for the first time.

Student sampling in the survey was a potential limitation of the study. It was difficult to reach and consult students' opinions due to the pandemic. Instead of making face-to-face interviews, student opinions were taken through an online questionnaire. Therefore, student views could be evaluated using VAHP and PF-TOPSIS.

With the rapid spread of COVID-19, there have been very sudden changes all over the world. One of these changes is that universities have switched to distance learning with a sudden decision because of pandemic conditions. Probably many students did not expect to have to join distance learning in such a short time, compared to students who had previously considered using it. Therefore, it is inevitable for students to encounter some difficulties in distance learning. Identifying these difficulties that affect the learning level of students will be a guide in determining the right strategies to increase the quality of education.

In this paper, determining and prioritizing anthropometric, environmental, and psychosocial factors related to the distance learning process during the COVID-19 pandemic are discussed. To The decision-making model is structured as a two-level hierarchical structure to evaluate the ergonomic factors on distance learning.

Subsequently, a novel VAHP integrated PF-TOPSIS methodology is structured to determine the importance level of each factor.

The contributions of the paper to the literature can be specified as follows: (1) (6) To the best of our knowledge it is the first study to address anthropometric, environmental and psychosocial factors on distance learning process during Covid-19 pandemic, as an MCDM problem;

(7) To the best of our knowledge, this is the first study proposing the integration of VAHP and TOPSIS methods; (8) The proposed method is aimed to be used by public and private organizations to improve their distance learning strategies.

As future suggestions, more students can be consulted for opinions by interviewing students from different universities. Factors can be evaluated by providing equal conditions for each student caused by their studying environment. Other MCDM methods or heuristics can be included in the methodology to ensure a more comparative and integrated study as a future direction. This study can be expanded by discussing with more experts.

The peer review history for this article is available at https://publons. com/publon/10.1002/hfm.20908

Author elects to not share data.

The data that supports the results of this study and all the tables in this study are available upon reasonable request from the corresponding author.

ORCID Ertugrul Ayyildiz http://orcid.org/0000-0002-6358-7860

Alev Taskin Gumus https://orcid.org/0000-0003-1803-9408

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