Influences of social media learning environments on the learning process among AEC university students during COVID-19 Pandemic: Moderating role of psychological capital

Abstract The outbreak of COVID-19 has adversely affected university students’ learning process, especially in developing countries like Nigeria. Consequently, many institutions have adopted social media for the learning process because of the COVID-19 lockdown. Hence, the current study aimed at assessing the influences of social media learning environments on the learning process among university Architecture, Engineering, and Construction (AEC) students during the COVID-19 Pandemic lockdown. Data was collected from the students of AEC-related courses using a structured questionnaire. Warp 7.0 PLS-SEM was used for the analysis of the collated data and model development. The study results indicated that the AEC students’ psychological capital (PsyCap) moderates the relationships between social media learning environments and the learning process among AEC university students in Nigeria. The results also showed significant influence change between the social media learning environments and the learning process among the AEC students in Nigeria. The study is limited to students in the AEC-related courses in Nigerian universities. Moreover, the results of this study could be helpful to other developing countries having issues with the use of social media learning environments. The study further recommends the training of AEC students on the use of resources in the social media learning environments for the positive developmental state of the student’s self-efficacy, hope, optimism, and resilience.


PUBLIC INTEREST STATEMENT
This research aims to assess the influences of social media learning environments on the learning process among university Architecture, Engineering, and Construction students during the COVID-19 pandemic lockdown. The purpose is to identify the major social media learning environments used among the university AEC students during the COVID-19 pandemic and also to assess the moderating roles of students' psychological capital on the influences of social media learning environments and the learning process. The research developed a model for the relationships between major social media learning environments, the learning process, and the moderating effect of psychological capital. The study would facilitate stakeholders in AEC education to identify the influences of social media learning environments on the learning process of AEC university students. Also, the stakeholders would evaluate the role of AEC students' psychological capital to enhance the process of learning AEC courses.

Introduction
The AEC-related courses have advanced into a sector of interdisciplinary knowledge that provides collaboration among all engineering and built environment courses over the last decades (BuHamdan et al., 2021;Gambo, Musa et al., 2021). The learning process in AEC-related subjects has been endorsed to enhance the quality of knowledge that engages students with materials, particularly the learning materials provided in the social media environments. Learning is the process students pass through to acquire new knowledge and skills that ultimately influence their decisions, attitudes, and actions . Studies in the past consider social media learning environments as the learning platforms that occur within an unorganized and unstructured context, mostly seen as self-learning platforms (Febriyantoro & Arisandi, 2019;Gambo, Musonda, et al., 2021). Therefore, social media learning environments are embedded in an unplanned activity that is not explicitly designated as a learning environment but contains a vital learning element (Gambo & Musonda, 2021a;Shi et al., 2020).
The outbreak of COVID-19 has affected the lives of all sections of society as all institutions of learnings were closed, and students were asked to self-quarantine in their homes to prevent the spread of the COVID-19 virus. The lockdown had severe implications on AEC university students' learning process that requires a more practical learning process than class teaching . Hence, globally resulting in psychological problems including frustration, stress, and depression. The problem of idleness among AEC university students, most especially in developing countries, forces the students to engage in social media learning environments for learning, chatting, and interactions among colleagues because the government restricted the face-to-face learning process, unlike in the developed nations where there are shifts from faceto-face to the online learning process (Galvin & Greenhow, 2020). Many public institutions, especially in developing countries, often do not have access to formal online learning systems that facilitate lectures and practical exhibitions. Therefore, AEC university students in developing countries engaged in social media learning environments that include YouTube (YoTub), Facebook (FacBok), Twitter (TwitTe) for learning; the students spent from 1-5 hours daily learning, chatting, and communicating with friends (2021bFebriyantoro & Arisandi, 2019; Gambo & Musonda, 2021b).
The social media learning environments integrate both the 'social components of daily activities and the components of active learning, such as the AEC teaching activities . This means that AEC students with low psychological capital engaging with uncensored social media materials were confused without proper guidance. Hence, this tends to disrupt the learning process of AEC university students (Febriyantoro & Arisandi, 2019;. Kemp (2017) designated that the most widely used social media platforms in developing countries are YoTub with 49% then followed by FacBok with 48%, TwitTe with 39%, then followed by Instagram (InsTag) with 38.9%, WhatsApp (WhatAp) with 37%, and Google + (GoGle + ) 36% occupy the next position. The rest are sequentially occupied by FB Messenger, Line, LinkedIn, BBM, Pinterest, and WeChat. The learning process among different categories of students is a difficult task; for example, in the Netherlands, Matzat, and Vrieling (2016) conducted a study on the use of social media learning environments as a means of instruction among 459 secondary school teachers in the area of teaching in humanities, the social, and the natural sciences. The study tests the concept that social media learning environments would be "naturally allied" with the psychological capital for self-regulated learning (SRL). The study results indicated that teachers apply social media learning platforms for information sharing with students outside the class and, more often, for teaching within the class. The results also indicated that students with high psychological capital showed more understanding of the circulated information on the social media learning environments. Psychological capital refers to a set of resources an AEC university student uses to help improve their learning process (Shi et al., 2020). Hence, psychological capital is a resource that includes self-efficacy, optimism, hope, resilience, that aids student learning, etc.
Similarly, in the United Kingdom (UK), Hennessy et al. (2016) conducted a study on social media learning environments in anatomy education, using TwitTe to enhance the students' learning process in the University of Southampton. The findings indicated that about 52% of the students used TwitTe for other non-academic activities. Based on the study's conclusions, the paper suggested learning on social media should be guided by the state of the students' PsyCap. Moreover, Poore (2015) stated that using social media learning environments for classroom instructions must be guided by the level of students' PsyCap and, Alizadehsalehi, Hadavi, and Huang (2020) added that many educational institutions usually adopt these developments into their learning systems. Therefore, this study aimed to assess the moderating role of AEC university students' PsyCap on the use of social media learning environments influencing the learning process of AEC students in Nigeria to enhance the learning of AEC-related subjects in Nigeria.
The objectives of this study are: • To identify the significant social media learning environments influencing the learning process of AEC university students in Nigeria during the COVID-19 Pandemic lockdown.
• To assess the influences of the social media learning environments on the learning process of AEC university students in Nigeria during COVID-19 Pandemic lockdown.
• To assess the moderating role of AEC students' PsyCap on social media learning environments influencing the AEC university students' learning process during COVID-19 Pandemic lockdown in Nigeria.

The AEC university students' learning process
The AEC university students' learning process is where the students acquire new knowledge, skills, and attitudes that ultimately transform their decisions and actions. Konrad et al. (2021) stated that one of the essential parts of the student's learning process is given attention, which means listening to the learned instructions; this is the most critical process of acquiring knowledge and skills. Attention is the notice or takes of something or judgment that is vital to the learning process (Engeness, 2021). But Malva et al. (2021) argued that the most crucial factors of the learning process are memorizing learned subjects and understanding the language. Memory is a complex process that involves receiving, using, storing, and retrieving information. The main memory systems are short-term, working, and long-term (Rönnberg et al., 2021). Pires Pereira et al. (2021) viewed understanding the instructions' language as an essential component of the learning process. Language is the medium of communication that comprises words in structured ways and conventions conveyed by speech or gesture (McNeill, 2000). In addition, Moran and Volkwein (1992) added that learning organization is another factor influencing the learning process and said that learning organization is an act of establishing something or an orderly putting different parts of the learning process together. While Thomas and Thorne (2009) viewed that graphomotor and higher-order Thinking (HOT) are the most critical aspects of the learning process. Graphomotor skills are the students' artistry necessary for writing down the learned instructions successfully. There are five individual skills associated with graphomotor: visual and perceptual, orthographic coding, motor planning and execution, kinesthetic feedback, and visual-motor coordination (Alamargot & Morin, 2015). While HOT is more than memorizing information but the sense of expression, i.e., making individuals think about a particular matter. Higher-Order Thinking is a factual assessment of information, e.g., understanding and manipulation of information. HOT includes concept formation, concept connection, problem-solving through grasping the "big picture"; visualizing; creativity, questioning; inferring, creative, analytical, and practical thinking about the subject matter. Then metacognition is regarded as thinking about something, thinking, and knowing how students think about a particular activity, process information, and learn (Alamargot & Morin, 2015). But McNeill (2000) argued that emotions and motivations are the most critical components that support the learning process among AEC university students. Emotion is the feeling that a student derives when he receives information, while motivation is a process that initiates, guides, and maintains goal-oriented behaviors among the AEC students.

The social media learning environments
Social media learning environments are the online learning platforms, or destinations were both censored, and uncensored educational materials and information are shared for consumption. Therefore, social media learning environments refer to online spaces where individuals establish and maintain virtual social interactions with others (Narayanan et al., 2012). The purpose of carrying out such virtual social interactions may vary among participants. AEC students in Nigeria and other developing countries usually use the platforms to interact, co-create content, share knowledge, and learn from one another. It combines social elements like networking, tagging, file sharing, and microblogging to create working spaces and learn collaboratively (Struck et al., 2018). Social media learning environments refer to the diverse physical locations, contexts, and cultures students interact with (Narayanan et al., 2012). Therefore, social media platforms create a partial environment for learning that influences the ability of students to learn. Struck et al. (2018) described the leading social media learning environments are the YoTub, the FacBok, and the TwitTe. These platforms endorsed self-directed learning, which helps AEC university students to learn various aspects of courses that require physical class teachings and practices.

Youtube learning environment
YoTub is one of Nigeria's most commonly used social media learning environments among AEC university students (Gambo et al., 2017). AEC university students in Nigeria and other developing countries used the platform to obtain vital information regarding the practical aspects of AEC courses. The YoTub platform serves as a virtual library for learning (Sepasgozar, 2020). This provides students with access to video illustrations of complex concepts beyond the students' understanding of procedures, and also, the platforms offer some concepts relating to the AEC subjects. The platform also provides some multimedia, such as videos, that support the learning process (Love et al., 2019). The YoTub platform commonly allows students to access e-materials (Helenowska-Peschke, 2017). Love et al. (2019) noted that one of the most critical factors influencing AEC university students in Nigeria to use the YoTub learning environment is the variety of the learning contents. The platform influences students to review the taught subjects. While Sepasgozar (2020) argued that the most important factor influencing student use of the YoTub learning environment is the alternative contents of the materials therein. Apart from the timely display of materials on a particular topic that influences the learning process of AEC university students, there are other varieties of materials.
Moreover, the level of the friendliness of some content attracts the students to use the platform as a learning environment. Also, understanding subject matter among the student is another important factor influencing students' use of the YoTub learning environment (Love et al., 2019). This is in line with the findings of (Helenowska-Peschke, 2017) and added that students are highly using the platform for socializing. Sepasgozar (2020) viewed that the YoTub platform influences the learning process by transferring complex learning materials.

Facebook learning environment
Facebook is currently considered the most popular social media platform for online social networking among university students in developing countries (Kabilan et al., 2010). Facebook is a website that allows users to sign-up for free profiles and connect with friends, colleagues, or people they do not even know online. It will enable users to share different contents like articles, pictures, videos, etc. Facebook was founded in 2004 to give people the power to build community and bring the world closer together. AEC students in Nigeria mostly use the FacBok platform during the COVID-19 lockdown to connect with teachers, friends, and family, and also to discover what's going on in the world, and share and express what matters to them (Cheung et al., 2011).
Even though many use Facebook now, university students are still the "biggest fans" of the platform. A recent paper by Dessie and Adane (2019) reported that 94% of AEC university students in developing countries are active Facebook users, spending 5-6 hours online each day communicating with friends lists between 150-200 people. Similarly, Hamdani and Babu (2015) conducted an extensive survey on university students from universities in developing countries indicated that about 94% of students are using Facebook daily. Similarly, in Malaysia, Kabilan et al. (2010) surveyed three hundred (300) undergraduate students of the Universiti Sains Malaysia and found that they believed that Facebook takes much of their time away from learning essential topics.
The factors influencing the use of Facebook as a learning environment are the simplicity of the platform to the AEC students' mental capacity development. The platform influences physical collaboration, discussion among peers, and AEC university students used the platform to obtain social or various information. AEC university students form groups on Facebook and post both social and academic information to get feedback from peers (Dessie & Adane, 2019). Facebook influences the convenience and quality-oriented supplement to their traditional oncampus courses (Cheung et al., 2011). However, Kabilan et al. (2010) viewed that Facebook is not a completely comfortable learning platform for students with low PsyCap. Hamdani and Babu (2015) described that the Facebook platform provides a direct means of communication between a student and other people, which would be counterproductive to the learning process.

Twitter learning environment
TwitTe is a microblogging system that allows users to send and receive short posts called tweets. Tweets are usually up to 140 characters long and include links to relevant websites and resources (Small, 2011). AEC university students use TwitTe to follow other users on any subject matter or topic hence influencing the learning process of AEC students. Tweets from TwitTe are used to stay up to date on issues of interest; this influences AEC university students' learning process (Carpenter et al., 2016). AEC students in Nigeria used to discuss various topics that influence their learning process, for example, through thematically relevant hashtags. The students usually use research hashtags on a topic or use Apps like Tweet deck to subscribe to them, which influence their learning process (Chamberlin & Lehmann, 2011). TwitTe provides an information network that mainly disconnects from AEC experts' opinions and interests.
Marketers of social programs use TwitTe as an alternative communication tool for viral marketing (Carpenter et al., 2016), a popular topic in the service industry. Small (2011) described that TwitTe promotes research among AEC university students by linking blog stories, journal articles, and news items. This is, however tending to confuse the students and usually influences the students' learning process. But, Chamberlin and Lehmann (2011) stated that the most critical factor influencing the use of TwitTe among AEC university students in developing countries is its accessibility to many people quickly through tweets and retweets and ease of following the work of other people in a specific field. Carpenter et al. (2016) argued that the most critical factor influencing the use of TwitTe among the students is that it builds social relationships with different people and other followers and keeps up to date with the latest news and developments through sharing with others instantly thus influencing the learning process of students. TwitTe easily reaches out to new audiences and seeks feedback about information, and gives feedback to others.

Moderating role of psychological capital
The studies in the past described the effect of social media learning platforms such as YoTub, FacBok, and TwitTe on the learning process of AEC students in Nigeria (Al-Rahmi et al., 2018;Balakrishnan & Gan, 2016;Gambo, Musonda et al., 2021;Kaya & Bicen, 2016;Lau, 2017). However, some studies concluded that students with high PsyCap utilize social media learning environments to moderate their learning process (Aziz et al., 2018;Jiang, 2021). Psychological capital refers to a student's set of resources to facilitate his learning process (Tsaur et al., 2019). According to (Darvishmotevali & Ali, 2020), PsyCap is influenced by several factors such as self-efficacy, optimism, hope, and resilience. PsyCap has gained prominence as an essential construct in education and learning research, which is a vital factor for leadership development and learning development. Therefore, PsyCap is an individual's positive psychological state of development and is characterized by having confidence (self-efficacy) to take on and put in the necessary effort to succeed at the learning stage, making a positive attribution (optimism) about succeeding in learning; persevering toward goals and, when necessary, redirecting paths to goals (hope) to succeed in learning; and, when beset by problems and adversity, sustaining and bouncing back and even beyond (resilience) to attain success (Tsaur et al., 2019). These attributes of PsyCap constructs facilitate the learning process (Darvishmotevali & Ali, 2020).
The positive psychological features of the PsyCap have been established as a moderator of the learning process, the attitudes, and behaviors of students towards their learning of AEC subjects (Avey et al., 2011). Thus, it may be seen as having an even more significant impact on productivity, institutional development (Lee et al., 2017). Furthermore, PsyCap is generally viewed as a state-like (changeable) construct that can be measured by scientific tools (Darvishmotevali & Ali, 2020) and improved by systematic developmental efforts (Lee et al., 2017); and thus, investing in the development of PsyCap of students have substantial benefits for an institutional learning process (Tsaur et al., 2019).

AEC nigerian university students and the COVID-19 pandemic
The COVID-19 Pandemic has an unusual dissipating influence on AEC students learning process in Nigeria other developing countries through schools' closures with resulted in the broadened existing divide in learning access and outcomes and increased school dropouts (Greig et al., 2021). The main experiences of Nigerian students about the COVID-19 Pandemic are the threats posed to the learning process, which were compounded because of peculiar vulnerabilities, including poor health systems, poverty and inequality, hunger, internally displaced populations, high population densities, urban-rural divide, and out-of-school population (Eze et al., 2021). Before the COVID-19 Pandemic, Nigeria accounts for one in every five of the world's out-of-school children. About 10.5 million children in Nigeria were out of school, and only about 61 % of the students accessed basic education (Abari & Orunbon, 2020). This was further aggravated by the coming of the COVID-19 Pandemic and subsequent lockdown. Hence, Nigerian students are battling the underlying challenges of learning infrastructure facilities, e-learning facilities which kept the country behind in learning during the lockdown Kpae, 2020).
The COVID-19 Pandemic affects the mental health and well-being in addition to the physical health of people (Abrantes et al., 2007;Sa'eed et al., 2020). AEC University students in Nigeria are more affected psychologically by the Pandemic due to school closure and lockdown. The pandemic outbreak leads to unavoidable stress, fear, and anxiety among the students (Al-Jibouri, 2003). The mental well-being of AEC university students is negatively affected by shifting face-to-face classes to poorly and inorganized online, suspension of the semesterend final examinations, and unavailability of books, computers, and high-speed internet connection at home (Diab & Elgahsh, 2020).

Research methodology
This study is quantitative in design, and a questionnaire survey was administered to 600 AEC university students at Abubakar Tafawa Balewa University (ATBU) Bauchi, Nigeria. The questionnaires were distributed to the selected students willing and volunteered to participate in the study through self-administration. The population of the study comprises all the AEC students in Nigeria, and the sample size for this study is determined using the Cochran (1977) formula for determining the sample size of unknown population: ¼ margin of error i:e:; 5%; and q ¼ 1 À p The calculated sample size for this study is 385 AEC students of the ATBU in Nigeria. A total of 415 questionnaires were returned, and 385 were selected and used for further analysis. Thirty (30) questionnaires were rejected, i.e., not included in the analysis because of discrepancies in the responses, and or most of the items in the questionnaire were left unattended/unanswered. A Warp 7.0 PLS-SEM regression algorithm was used in the data analysis. The data were bootstrapped to 999 times from the original samples with replacement. The bootstrapping approach generated an empirical representation of the sampling distribution of the effect by treating the actual sample size as a representation of the population in the miniature: this is repeatedly resampled during analysis to copy the original sampling process (Hayes, 2009). A proportionate random sampling method was used for this study. Proportionate random sampling involves dividing the entire population into homogeneous groups called proportion. A random sample from each proportion is taken in a number proportional to the group size compared to the people. These group subsets are then pooled to form a random sample (Sekaran & Bougie, 2011). Therefore, AEC university students in ATBU were proportionately selected according to the AEC courses offered in the University, i.e., Architecture, Building Technology, Engineering (Civil), and Quantity Surveying. This study recorded the overall return rate of 69% and response rate of 64% as against the research of Gerber et al. (2015) on surveying the evolution of computing in architecture, engineering, and construction education with 51%, and that of Mandhar and Mandhar (2013) which studies BIMing the architectural curricula: integrating Building Information Modelling (BIM) in architectural education with only 25% valid response rates.

Questionnaire development
Partial least squares-structural equation modeling (PLS-SEM), using WarpPLS-SEM 7.0 (Kock, 2016), was used to analyze the data obtained and examine the relationships among the constructs by developing a conceptual framework into a model. PLS-SEM facilitates theory building in studies that seek to explore causal relationships between latent variables . Moreover, PLS-SEM was employed for the analysis because of its high predictive ability and examining the validity of measured, reflective constructs (Hair J. F. et al., 2014;Hazen et al., 2014).
Sekaran and Bougie (2011) described validity for any questionnaire as the extent to which the measure "behaves" in a way consistent with theoretical hypotheses and represents how well scores on the instrument indicate the theoretical construct. The validity is used in research to validate the research instruments used for the study. Content validity is used to assess how well an idea or concept is represented by the items/indicators. The content validity for this study was conducted by requesting experts in AEC education research and academics on the suitability of the items in the questionnaire. A simple random sampling method was used to select the experts for the questionnaire validity. Sekaran and Bougie (2011) described that a simple random sampling technique provides bases for unbiased judgment among experts. After thorough discussions, the experts verified and validated thirteen (13) items under LeaPro constructs, eleven (11) items under YoTub, ten (10) items under FacBok constructs, eight (8) items under TwitTe construct, and five (5) items under PsyCap (moderating variable).The factors in the questionnaire were adapted from past literature, as shown in Table 1.

Knowledge connection
Todor and Pitică (2013) The questionnaire used 5-point Likert scales that rated the responses on LeaPro, YoTub FacBok, TwitTe, and PsyCap. The response scales were based on 1 to 5 scales that measure the influences of the social media learning environments on the construct LeaPro, i.e., from the very low-very high effect for the independent variable. Similarly, the moderating variable is also on a 5-points Likert scale, i.e., from a very low-very high impact on the social media platforms and the AEC learning process.
In this study, two significant theories were adopted to examine the influences of social media learning environments on AEC university students' learning process in Nigeria: The moderating role of students' PsyCap.
• Connectivism learning theory and, • Social learning theory The theory of connectivism by Siemens (2005) is characterized as the learning theory of the digital age. One underlying assumption in this theory is that knowledge is distributed and "can reside outside of ourselves". Downes (2007) contends that "knowledge is distributed across a network of connections, and therefore learning process consists of the ability to construct and traverse those networks". Therefore, Connectivism Theory explains the connection between the various e-learning platforms and a child's learning process, which depends on his psychological capital. This demonstrates that learning is a process of connecting specialized nodes or information sources and psychological reasonings.
Similarly, the social learning theory, According to Bandura (1977), posits that the outcome of a student's learning process is highly influenced by the student's own choice of a particular social media platform, participation, and peers, i.e., friendship networks that work within both cognitive and behavioral frameworks which embrace psychology, attention, memory, and motivation. The theory argues that children learn from observing others and "model" behavior, which involves attention, retention, reproduction, and motivation. Thus, this study adopted both the Connectivism and Social Learning Theories to assess the influences of the social media learning environments on AEC university students' learning process in Nigeria during the COVID-19 Pandemic and the moderating role of PsyCap.

Figure 1. Measurement Model.
Based on the measurement model in Figure 1, the following hypotheses were developed H A 1: There is a significant negative relationship between YoTub and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic.
H A 2: There is a significant negative relationship between FacBok and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic.
H A 3: There is a significant negative relationship between TwitTe and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic.
H A 4: The AEC university students' PsyCap significantly moderates the relationship between YoTub and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic.
H A 5: The AEC university student's PsyCap significantly moderates the relationship between FacBok and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic.
H A 6: The AEC university students' PsyCap significantly moderates the relationship between TwitTe and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. Table 2 indicates the assessment of the model by Warp 7.0 PLS-SEM analysis, which typically follows two steps: the evaluation of the measurement model and structural model (Chin, 2010;. The assessment of the measurement model examines the validity and reliability of the measurement instrument and the relationship among the constructs. The model for this study has five reflective constructs: the LeaPro, YoTub, FacBok, TwitTe, and the PsyCap.

Model assessment using warp 7.0 PLS-SEM
All the five constructs were first-order constructs. The reflective measurement model evaluates the reliability and validity of the model. The two criteria are composite reliability (CR) and the average variance extracted (AVE) (Chin, 2010;. On the other hand, Kock (2018) stated that the construct validity using Warp PLS-SEM is assessed to evaluate the validity of the reflective measurement model for SEM. The indicator validity was evaluated by cross-checking the loading of each construct on its associated latent indicator, and the loading should be higher than 0.70 before accepting the validity of the indicators Hulland, 1999). Therefore, Table 2 indicated that all the factor loadings for the five (5) constructs are well above the threshold of 0.7 and hence shows that the items of the constructs are valid measures of the individual construct. For the assessment of construct reliability, two coefficients are considered, i.e., CR and Cronbach's alpha (α; Bagozzi & Yi, 19881988;Chin, 2010;Cohen, 1988). Hair J. F. et al. (2014) recommended CR for PLS-SEM. Table 2 shows this study's measurement model results, which indicated an adequate internal consistency and reliability. The indicators loadings were above 0.70, and both the CR and α ranged from 0.928-0.957 and 0.908-0.944, respectively. This demonstrates that all the indicators and constructs' reliabilities are acceptable.
The convergent and discriminant validity are also considered invalidating the reflective measurement model . The constructs' average variance extracted (AVE) values must be greater than 0.50 for an accepted convergent validity (Bagozzi & Yi, 1988;. The AVE is only applicable for models with reflective indicators. AVE measures the total variance of a construct through its indicators (Chin, 2010). The AVE values for this study ranged between 0.583-0.818 and the loadings of the indicators. Therefore, the convergent validity of the measurement model is highly acceptable (Davcik, 2014;Hair J. F. et al., 2014).  F. et al., 2014). This is achieved through checking of the AVE of each construct and must be higher than the highest squared correlation of the construct of any other construct in the model, or the loading of an indicator with its associated construct must be higher than that with other constructs (Chin, 2010;Fornell & Larcker, 1981;. The results indicated that the square root of AVE for each construct correlated to another construct is acceptable discriminant validity of the measurement model. Based on the measurement model results, the questionnaires were acknowledged to be reliable and valid for assessing the five AEC learning study constructs. Table 4 indicated that 29.35% of the respondents (students) were from the Department of Architecture, while 18.96% were from the Department of Building Technology. About 25.19% of the students were from the Department of Civil Engineering, while 26.50% of the respondents were from the Department of Quantity Surveying of ATBU. The average ages of the respondents were 23.95 years. All the respondents were undergraduates of ATBU.

Model Fit Indices
Studies in the past provided basic sets of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis as the primary statistical analysis techniques, and such indices include Chi-square χ2, Alaike Information Criteria AIC, Comparative fit, Parsimonious fit, Goodness-of-fit index, etc. (Davcik, 2014;Hair J. F. et al., 2014;Hazen et al., 2014;Schreiber et al., 2006;Xiong et al., 2015). However, Kock (2012) stated that there is a straightforward philosophical distinction between CB-SEM and PLS-SEM. If the research objective is theory testing and confirmation, then the appropriate method is CB-SEM. In contrast, if the research objective is prediction and theory development, then the suitable method in PLS-SEM. Conceptually and practically, PLS-SEM is like using multiple regression analysis. On the interpretation of the model fit, if the goal is to only test hypotheses, where each arrow represents a hypothesis, then the model fit indices are of little importance. However, suppose the goal is to determine whether one model fits the original data better than another. In that case, the model fit indices are useful sets of measures related to model quality (Kock, 2012). However, PLS-SEM software algorithms reported the following indices: The fit indices are used to compare the indicator correlation matrices such as the standardized root mean squared residual (SRMR), standardized mean absolute residual (SMAR), standardized Chi-squared (SChS), standardized threshold difference count ratio (STDCR), and standardized threshold difference sum ratio (STDSR). As with the classic model fit and quality indices, the interpretation of these indices depends on the goal of the SEM analysis. Since these indices refer to the fit between the model-implied and empirical indicator correlation matrices, they become more meaningful when the goal is to determine whether one model has a better fit with the original data than another, particularly when used in conjunction with the traditional indices Kock (2012). When assessing the model fit with the data, several criteria are recommended as follows: Table 5 indicates average path coefficient (APC) = 1.038, with a P −value <0.001, Average R-squared (ARS) = 0.317, with a P −value < 0.001, then Average adjusted R-squared (AARS) = 0.306, with a P −value < 0.001, The Average block VIF (AVIF) = 2.271, acceptable if ≤ 5, ideally ≤ 3.3, regarded as ideally. The average full collinearity VIF (AFVIF) = 2.913, acceptable if ≤5, ideally ≤ 3.3, regarded as ideally. The VIF is used when indicators are formative. Tenenhaus GoF (GoF) = 0.497, small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36 then GoF is regarded as large, GoF is the geometric mean of the average communality (outer measurement model), and the average R 2 of endogenous latent variables, represents an index for validating the PLS model globally, as looking for a compromise between the process of the measurement and the structural model, respectively. The Sympson's paradox ratio (SPR) = 1.000, acceptable if ≥ 0.7, ideally = 1. Therefore, it is regarded as an ideal in this study. The R-squared contribution ratio (RSCR) = 1.000, acceptable if ≥ 0.9, ideally = 1 it is regarded as ideal in this study. The Statistical suppression ratio (SSR) = 1.000, acceptable if ≥ 0.7 and ideally if = 1, so it is regarded as an ideal in this study. Nonlinear bivariate causality direction ratio (NLBCDR) = 1.000, acceptable if ≥ 0.7 which is regarded as acceptable in this study. Therefore, this model for the influences of social media learning environment on the learning process of AEC university students: Moderating role of psychological capital has good fits indices (Kock, 2012).  Average age of respondents (students) = Σfx/Σf = 9220/385 = 23.95 years coefficient must be significant for a good relationship and is the coefficient of determination, i.e. highly dependent on the research area. However, Chin (1998) suggested 0.67, 0.33, and 0.19 are substantial, moderate, and weak measures for R 2 . The R 2 for this study was 0.317, indicating a moderate relationship exists between criterion and predictor variables with a β value and a P −value between YoTub and the LeaPro was 2.144 and <0.001, regarded as significant. The path coefficient between the construct FacBok and LeaPro had a β value of −2.349 with a P −value of <0.001; this is also regarded as significant at a P −value of ≤ 0.05 level of significance. The path coefficient between TwitTe and LeaPro had a β value of 0.304 and a P −value <0.001, regarded as significant at a P −value ≤ 0.05 level of significance. The path coefficient between the moderator PsyCap and the path between YoTub and LeaPro had a β value of 0.476 and a P −value <0.001, as indicated in the model. Also, the β value of the moderator PsyCap on the path between FacBok and LeaPro was 0.602 with a P −value <0.001. The path coefficient between the moderator PsyCap had a β value of −0.355 with a P −value <0.001 all regarded as significant at a P −value ≤ 0.05 level of significance. Therefore, substantial path coefficients exist between all the independents, moderating the model's dependent variables. Table 5 indicates the influence (effect) size (f 2 ) is a measure that verifies whether the effects demonstrated by the path coefficient are low, moderate, or high for the values of f 2 0.02, 0.15, and 0.35, respectively (Cohen, 1988). Influence size (f 2 ) indicates the impact of a particular variable on the latent dependent variable is substantial (Chin, 2010). The f 2 between YoTub and LeaPro influenced 0.368, indicating a high influence change exists between the two constructs. The f 2 between FacBok and LeaPro influenced 0.355, also showing a high influence change between the two variables. The f 2 between TwitTe and the LeaPro was 0.045, indicating a low influence change exists between the two constructs. The f 2 between the moderator variable PsyCap and the path between the YoTub and LeaPro influenced 0.239, indicating moderate influence change between PsyCap and the path between the YoTub and the LeaPro. Hence, PsyCap moderates the relationship between the YoTub and the LeaPro. While the f 2 between the moderator variable PsyCap and the path between FacBok and LeaPro was influencing 0.308, indicating a moderate influence change between the moderator variable PsyCap and the path between FacBok and the LeaPro.

Coefficient of determination (R 2) measures and path coefficients of the model
Lastly, the f 2 between the moderator variable and the path between the TwitTe and the LeaPro was influencing 0.185, indicating a low influence change between the moderator variable and the path between the TwitTe and the LeaPro. The predictive competency of each endogenous construct in the model was determined by Stone-Geisser's (cross-validated redundancy; Q 2 ; Hair J. F. et al., 2014). The predictive skill of this model was 0.311, and Warp PLS-SEM automatically generates Q 2 (Kock, 2012). Hazen et al. (2014) reported that the Q 2 value indicates the predictive relevance as either weak (0.02), moderate (0.15), or strong (0.35). Therefore, this model exhibits a predictive relevance because the Q 2 > 0 and hence the prediction capability of the model is moderate (0.311) (Chin, 2010;Hair J. F. et al., 2014;Hazen et al., 2014). The coefficient of determination for this model was 0.317, indicating that 32% of the variances were explained by the model (Hair J. F. et al., 2014;Hazen et al., 2014).

Discussions
This study assessed the effect of the social media learning environments on the learning process of AEC university students during the COVID-19 Pandemic: the moderating role of students' psychological capital. The study constructs were the AEC learning process, and three (3) independent variables, and one (1) moderating variable comprising of LeaPro, YoTub, FacBok, TwitTe, and PsyCap, respectively (Kemp, 2017). The study is quantitative. The findings indicate that the YoTub and FacBok relate negatively with LeaPro, indicating that learning through these social media platforms affects the learning performance of AEC students during the COVID-19 Pandemic. This is possible because fear, depression, and anxiety were common among the AEC university students during the pandemic lockdown in Nigeria. In contrast, TwitTe had a positive influence on the AEC students learning process; this is because the teachers shared their opinions, personal experience, and emotions with students, and also the platform provides speedy information between teachers and the AEC students during the pandemic lockdown where everyone resorts to social media platforms for learning and information dissemination.
The first hypothesis stated that there is a significant negative relationship between the YoTub and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic lockdown. The results indicated a significant negative relationship exists between YoTub and the LeaPro of AEC university students. This implied that the YoTub influences the LeaPro of AEC university students in a negative way. This is due to the frequency of the use of YoTub during the COVID-19 pandemic lockdown among the AEC university students. The finding is in line with the study of Klobas et al. (2018) that researched and found compulsive use of YoTub among university students negatively influences their academic process because most students use YoTub for entertainment purposes only. On the other hand, this study contradicts that of Buzzetto-More's (2014) research that examines the relationship between undergraduate students' perceptions and preferences of the use of YoTub in the teaching and learning process. The study found that the YoTub learning environment positively influences the teaching and learning process. This is due to the levels of controls and guides given during the learning process.
The second hypothesis was also supported by the collated data based on the analysis. The hypothesis stated that there is a significant negative relationship between the FacBok and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic lockdown. Also, the results indicated a significant negative relationship exists between FacBok and the LeaPro of AEC university students in Nigeria. The finding supports that of Gray et al. (2010), which found that FacBok as part of learning and teaching is a challenge for many students as it may be for most educators. This is a result obtained from a study conducted among medical students in the university that requires more physical contact during practical hours. Contrarily, the study opposes that of Selvarajah and Ali (2021), which found that postgraduate students used FacBok for knowledge sharing. There is a positive relationship between learning on FacBok and the process among postgraduate students in Malaysia. This result may be explained because postgraduate students were surveyed and are expected to have high PsyCap.
The third hypothesis was not supported by the collated data, which stated that there is a significant negative relationship between the TwitTe and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. This study found a significant positive relationship between the TwitTe and LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. The results indicated that TwitTe had a positive influence on the LeaPro of AEC university students in Nigeria. The study supports Thoms and Eryilmaz (2015) research that found Twitter discussion board supports learning in an online and blended learning environment. The findings imply that the selective impact of TwitTe usage suggests how the medium might best be exploited to increase connections between students themselves, students and their tutors, and students and educational resources. In particular, it appears that using TwitTe is an effective way to engage the student. But the study contradicts that of O'dea et al. (2015), which found that TwitTe is not a fully matured platform for learning and does not directly relate to the learning process. This could be because the study focuses on and used classification learner to classify and detect suicidality on the TwitTe platform.
The collected data supported the fourth hypothesis, which stated that AEC university students' PsyCap significantly moderates the relationship between YoTub and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. The results indicated that the student's PsyCap moderates an AEC university student's LeaPro. This supported the findings from the study of Nielsen et al. (2017), which found that PsyCap directly had a positive moderating role in the instructor support, family support, and the well-being of postgraduate students. Contrarily, Liu et al. (2015) found that PsyCaP could only mediate the relationship between adverse life events and school adjustment among Chinese nursing students. This is because the study considered the construct as mediating variable, not a moderating variable.
The fifth hypothesis was also supported by the collected data, which stated that AEC university students' PsyCap significantly moderates the relationship between the FacBok and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. The results indicated that the student's PsyCap moderates an AEC university student's LeaPro. This finding supports the previous finding from You (2016) study, which found that the college students' PsyCap had a significant positive relationship with learning empowerment, and learning empowerment fully moderated the relationship between PsyCap and engagement. On the other hand, Siu et al. (2014) found weak and negative relationships between PsyCap and engagement and intrinsic motivation among university students. This is because the study considered all the university students regardless of their courses.
The sixth hypothesis was not supported by the collected data, which stated that AEC university students' PsyCap significantly moderates the relationship between the TwitTe and the LeaPro of AEC university students in Nigeria during the COVID-19 Pandemic. The results indicated that an AEC university student's LeaPro is improved with the regular use of Twitter during the COVID-19 Pandemic. This is because the model showed a positive relationship between TwitTe and the LeaPro, as discussed earlier.

Conclusions
Learning is the acquisition of knowledge or skill through study, experience, or teaching. This study assessed the influences of social media learning environments on the LeaPro of AEC university students during the COVID-19 Pandemic in Nigeria. The students' struggles to learn the technical aspects of AEC subjects remain a recurring subject of debate among the professionals and educators in both developing and developed countries. The assessments of the influence of social media learning environments are valuable for future improvement in the education system of developing countries. This also serves as an awareness and wake-up call for all governments in developing countries, especially Nigeria, to provide adequate infrastructure for e-learning. This would enhance learning in AEC-related subjects. The findings of this study indicated that the social media learning environments such as YoTub and FacBok influence the LeaPro of AEC university students during the COVID-19 Pandemic lockdown in Nigeria. Also, the findings from the collected data further confirmed the two adopted theories in this study, i.e., the theory of Connectivism and the Social learning theory.
Similarly, the results indicated strong influence changes between the YoTub, FacBok learning environments, and the LeaPro of AEC students during the COVID-19 pandemic lockdown in Nigeria. Therefore, YoTub and FacBok platforms affect the LeaPro of AEC university students in Nigeria. Therefore, this study proposed adequate funding for e-learning infrastructure and engaging AEC teachers and students for annual appraisal of the learning process through setting objectives, selfappraisal, and the preparation of individual portfolios. Similarly, the policymakers in AEC education should focus on the past success and experiences of developed countries that adopted learning through social media platforms; this would provide a robust way to increase the students' level of self-efficacies and social modeling. Also, the stakeholders in AEC education should reframe the negative experiences students have through social media platforms (psychological responses). This study is limited to only students of AEC-related courses in Abubakar Tafawa Balewa University Bauchi, Nigeria. Moreover, the results of this study could be helpful to other developing countries having issues with the learning process among students of AEC-related courses, most especially during the waves of COVID-19. The study recommends the regulation of e-learning in social media environments and guides the students on the use of social media platforms for learning.