The effects of Covid-19 pandemic on the education system in Nigeria: The role of competency-based education

Abstract

Covid-19 revealed the strengths and weaknesses in the global education atmosphere in both developed and developing countries. To that effect, this current study explored the impacts of the covid-19 pandemic on Nigeria's education system and in the process provided a distinctive solution to the challenges facing the sustainability of education in the country. However, the closure of schools for over six months at the onset of the covid -19 pandemic, and the inability of schools to engage learners in educational activities while at home also revealed the poor state of the education system in the country, which led to the discovery of the unavailability of distance online education, web-based learning system and ICT infrastructure in the Nigerian education environment. Covid-19 incidence impacted the stability of the academic calendar, caused teachers attrition, increased the rate of students dropout, and lack of interest in digital education. These outcomes resulted in the exploration of students' and teachers’ perceptions, attitudes, literacy, competency, and willingness to engage in distance online education. A cross-sectional approach was applied through an online survey to obtain data from n = 82 learners across the three levels of institutions. And SPSS was used to analyze the demography data, while SMART PLS was used for structural equation modeling (SEM). The study outcome satisfied the objectives of the study that the lack of student-teacher digital competencies influences their perception and acceptability of web-based learning approach and use of smart learning and teaching devices.

1. Introduction

Since the emergence of COVID-19 to date, Nigeria's education system has suffered unprecedented setbacks, and continuously experiencing the impact of Covid-19 after the long shutdown period of the schools as a result of the ravaging nature of the pandemic (Crawford et al., 2020). For almost three years, there is staggering growth in education development as other variants of the virus mutated such as the Delta variant, Omicron variant, etc. This has increased the fear and tension for the school leadership on the possibility of a second lockdown of the academic institutions (Samuel, 2020). The conditions of the first lockdown of schools that lasted for over six months have quadrupled the retrogressive situations of Nigeria's education, such as the high rate of teachers’ attrition (Alhat, 2020), school dropout percentage has risen dramatically as students do not fancy going to schools as to avoid the unknown outcome of the virus and its mutational potency in affecting their lives (Lee, Malcein, & Kim, 2021). The reports that the pandemic would still exist beyond its anticipated period attributed to the attritional and dropout rate in Nigerian schools, coupled with not being totally immune from getting infected after being vaccinated (UNESCO-IESALC, 2020). Hence, the fear of the unknown caused by the widespread of the virus and the mortality tendencies led to teachers' attritions and students' withdrawals from school (Hu et al., 2021). As of now, school administrators are finding it difficult to get the teachers and students back to school to continue teaching and learning as a result of long-term school closure (Samuel, 2020). These issues occurred due to the lack of implementing alternative means to engage students in educational activities during the lockdown period (Jowsey, Foster, Cooper-ioelu, & Jacobs, 2020). In addition, financial condition is perceived as a factor causing the hesitancy of students and teachers returning to school (Amir et al., 2020). The majority of the parents feel that the tuition paid before the lockdown was not adequately implemented in ensuring continuous learning activity at home, therefore need the assistance of their kids in making enough monies for their schooling (Crawford et al., 2020).

Evidently, the lack of implementing blended teaching and learning system before the emergence of the Covid-19 pandemic has creates a huge divide and irregularities in the Nigerian education systems (Adefuye, Adeola, & Busari, 2021). One crucial function of a blended or virtual learning system is its transformability tendency in increasing the competency base learning of the students and the teachers. Numerous studies underscored that distance education played a significant role in sustaining educational programmes during the covid-19 outbreak in some nations (Songsom, Nilsook, Wannapiroon, Fung, & Wong, 2019). Distance education or online education has become a sophisticated validated teaching and learning tool in this 21^st century to support pedagogical activities having the potency to build competent teachers and students (Khan & Abid, 2021). Countries with advanced distance education like the USA, Estonia, Canada, China, Germany, France, etc are testimonies to Khan and Abid's (2021) assertion on distance education is a tool to inculcate competencies and innovative mindset both the teachers and students teaching and learning abilities.

Another benefit of distance education is the tendency to enhance smart learning culture thereby exposing teachers and students to the understanding and use of intelligent learning devices efficacy (Lee et al., 2021). Also, the engagement of students in the blended learning atmosphere improves students' confidence and creates inner trust in believing in the learning behavior (King, Pegrum, & Forsey, 2018). Consequently, Chaichumpa et al. (2021) state that learning institutions with the incorporation of web-based tutoring and learning smart technologies help in elevating the positive learning behaviors of the students and improving their digital ability. Furthermore, distance online education enhances adequate personalized learning and strengthens users' confidence and competency. Similarly, teaching remote or online classes requires teachers to possess a considerable amount of digital skills with apt understanding to be able to influence learners with positive competency (Lee, 2020). For example, one of the salient skills teachers ought to have in digital education is the ability to issue personalized feedback through smart devices, prepare teaching content to meet learners learning capability, increase flexibility in learning for students and ability to use different smart devices, and learning APPS that elevate their cognitive ability and brain power (Farber, 2013).

In furtherance, this current study aims to give an in-depth overview of the struggles the education sector has witnessed especially the Nigerian schools during the covid-19 period, however, from the extensive explorations of literature on this discourse, the authors discovered strands of studies focused majorly on medical aspects of coronavirus on individual's health, such as the (i) influence of Covid-19 on mental health (Krohn et al., 2021), (ii) individual's breathing (Liu et al., 2021), (iii) psychological impacts on the medical practitioners and students (Martínez, Lázaro, Gómez, & Fernández, 2020). Meanwhile, amongst all the studies done on Covid-19 related phenomena, fewer studies have explored the impacts of covid-19 on the Nigerian education sector focusing on the need for distance education and web-based learning as the new trend (Oyebode, 2020).

Students from different Nigerian academic institutions revealed the reasons they couldn't engage in academic activities at home are due to lack of online learning apps for a synchronous mode of learning, poverty, lack of internet connectivity, lack of digital literacy, lack of ICT teaching and learning devices such as computer desktops, projectors, screens, to engage students for immersive learning at homes. In addition, many students lamented that not engaging in learning for over six months of lockdown affected their psychological, emotional, and mental breakdown, including the development of acute depression syndrome (Adefuye et al., 2021). In defense of the failure of school administrations in finding alternative learning means for the students, Oyebode (2020) opined that schools have no wherewithal to fund distance education and remote learning systems, and there reduce the competencies in education.

Interestingly, the study sets to examine the students' and teachers' competency, perception, internet/computer literacy or skill, attitude, and willingness to execute their classroom activities and learn remotely. the core focus of the paper is to understudy teacher-student competency, willingness, attitude, literacy or skill, and perception of distance education, web-based learning, remote learning, etc as it is predicted to be the new normal for education sustainability in post-covid-19 education (Dhawan, 2020). Sanches (2020) reinforces that the adoption of distance education can protect education from the brink of collapse in future pandemics or any unforeseeable social crises (Sanches, 2020).

In line with this purpose, the research questions that drive this study centered on;

RQ1: How feasible can distance online education be in Nigerian Schools for competency-based education?

RQ2: To what extent can teachers and students accept distance online education as an alternative means of learning and teaching for competency development?

In order to elevate competency-based education in the country's education through distance education, the article thereby suggests the need for professional training programs for educators and teachers with no ICT competency and computer knowledge for effective teaching. At the same time inform the education policymakers, stakeholders, and school leaders see the need of institutionalizing distance online education to protect learners' future.

2. Summary of literature review

Digital distance education is inevitably quintessential in this 21^st century, thus making teaching and learning more seamless. However, it makes education convenient and efficient for teachers and learners to teach and learn (Boca, 2021). Digital distance schooling transcends conventional and traditional modes of education into an electronic system of schooling (Alhat, 2020). Numerous researchers revealed the submissions of students from different levels of education on online distance education stating its acceptability, flexibility, and influencing power to search for more information (Naidu, 2014). Many students confirmed that learning online improves their attention rate to assimilate what has been taught and heightened their intentional learning behavior more than face-to-face classes. An effective virtual classroom is achieved through adaptative learning possibilities with virtual learning technologies (Boca, 2021). Evans and Moore (2012) opined that exposing students to ICT devices for their personalized learning provides an avenue for deep learning and in-depth learning behaviour for self-development.

Evidently, Covid-19 hastened the quest for internet-inclined schooling to be fostered in academic institutions across the globe (Alhat, 2020). The Covid-19 incident created universal awareness of education digitization globally. Additionally, the incidence of the pandemic awakened the consciousness of the African education system especially Nigeria in charting a new part for the unprecedented education evolution with technology (Singh & Thurman, 2019). This created a new pathway for educational reformulation and a total overhaul in knowledge delivery tactics (Parmigiani, Benigno, Giusto, Silvaggio, & Sperandio, 2021). Distance digital schooling illuminated plentiful benefits, as iterated above that the remote pedagogical approach encompasses synchronous and asynchronous characteristics that improve the learning behavior of the students and web-based education (Haghshenas, 2019). Student-teacher usability of ICT devices and educational applications (APPS) triggers some sort of responsibility to learn and revise the recorded classes with the playback function (for students), and teachers volitiously attempt significant ways to stimulate instructional activities in the virtual learning environment (Amir et al., 2020). The ability of distance online education learning devices to provide timely assessment and feedback encourages spontaneous readjustment in the student's academic performance and attitude (Cavalcanti et al., 2021). Also, the artificial intelligence and machine learning features in some teaching applications give high precisional grading scale feedback. Proper inclusive education has been realized from online instructional pedagogy (Tedre et al., 2021). Teachers' and students' skills are said to attain greater heights from their engagements in digital educational activities (Parmigiani et al., 2021).

Arguably, teaching APPS such as Tencent/Voov, DingTalk, Skype, etc is pivotal in content delivery in electronic education. An appropriate pedagogical teaching and learning APPS makes distance and electronic education enjoyable and convenient (Sayibu et al., 2021). The chatbox in the digital learning applications helps in smooth communication during online classes, this also motivated learners to ask questions without being intimidated. In relation to the covid-19 period, it is evidenced that distance online education was claimed to be a factor in the student-teacher insignificant mortality rate from being affected by the virus, thus bringing life security and reducing the human transferability potency of the virus (Parmigiani et al., 2021).

The first research question raises concern about the feasibility of distance online education in Nigerian schools for competency-based learning. Given the level of the digital divide, this questions the attainability of this purpose with the intense digital disparity across the country. For instance, the digital divide in the Nigerian education domain is extremely pronounced, invariably affecting the achievement of progressive distance online education integration in the educational atmosphere (Ajadi et al., 2008; Alkaria & Alhassan, 2017; Kpae, 2020). Hence, creating a lack of self-confidence and lack of trust in teachers and students (Jou, Tennyson, Wang, & Huang, 2016). The posing threat to the feasibility is the non-existential of the ICT framework in the schools, this is the result of the non-participation of the education stakeholders and administrative leadership in investing substantively in the education sector. The appropriated financial resources for the education sector are insignificant to achieving this aim due to the insufficiency of the allocated funds (Oboh Stephen & Oboh Omonyemen, 2020). The second challenges are the unawareness of the distance of education, poverty, and negative perception by the teachers, parents, and students on the use of digital devices for personalized and autonomous learning both in and outside the school vicinity (Reinhart et al., 2021). This necessitated the lack of digital competency, literacy, and skills for a great percentage of students and teachers. However, the political and economic factors are the causatives of the poor digital education application across the education system (eLearning Africa, 2020). Furthermore, factors such as unsteady power supply, unavailability of unlimited internet connectivity, shortage of ICT framework in an education environment, and high prevalence of poverty constitute the influencing factors to the unavailability of distance education infrastructure (De Giusti, 2020). The negligence of electronic learning implementation deprives about 25 million learners of education for almost six months (Agbele & Oyelade, 2020).

For absolute incorporation of distance and virtual learning, teachers' professional training is deemed critical and crucial for instructional efficiency and effectiveness. The rigorousness and technicalities of ICT and smart technological education devices ought to be taught to teachers and students for a complete understanding of the operationalities of the devices (Scull et al., 2020). The TPD (teachers' professional development and training) equips teachers adequately on how to deliver content, and stimulate learning, thereby training students on the usability and applicability of smart education devices for their autonomous learning (Amir et al., 2020). Instructors ' and teachers ' digital literacy is essential to achieve maximum efficiency of blended learning with the internet and cloud-based devices. Also, regular professional training programs for improvement are pivotal. Boling et al.'s (2012) study applied the cognitive apprentice model to expound on the educational benefits of computer-mediated learning for learners' effective learning. They further stated that the attainment of cognitive ability in a web-based environment lies in the teachers' teaching strategy to engage the learners. That achievement lies in the four tested CAM parameters, interactions, sociology, sequencing, content, and method. Additionally, Affouneh, Salha, and Khalaf (2020) postulated eight frameworks and dimensions of electronic education learning; "(1) institutional, (2) pedagogical, (3) technological, (4) interface design, (5) evaluation, (6) management, (7) resource support, and (8) ethics"(Affouneh, Salha, & Khlaif, 2020).

Nigerian education system yearns for a total overhaul and institutional restructuring including curriculum adjustment to accommodate the characteristics of online distance education. According to Boiling et al. (2012) postulation on the financial cost of online education delineated that remote education is relatively cheaper than the face-to-face approach (Boling, Hough, Krinsky, Saleem, & Stevens, 2012). The second research question tries to assess the readiness of the students and teachers to accept remote learning and distance online education. Numerous scholarships revealed that perception, and willingness to try new things bring acceptability, and a positive attitude to explore learning devices (Filho, Price, Wall, Shiel, & Azeiteiro, 2021). As distance online education or web-based learning system is a new concept in the Nigerian education environment, adequate sensitization is needed to enlighten the public on its benefits of it. To achieve that joint efforts by the school leaders and ministries of education would be effective in creating awareness to minimize the negative perceptions of web-based education. Nilsen, Almås, and Gram (2020) suggested the practicability of teaching and learning with smart devices and ICT should be adopted at the early stage of education for competency and raising of technocrats. In order to promote education sustainability, an adequate propagation of the essence of digital and distance education ought to be widely communicated to the schools' leaders, parents, students, and the general society for a complete understanding (Albrahim, 2020; Stewart & Lowenthal, 2021; UNESCO, 2020).

Regardless of the strengths of DOE (distance online education), some scholars (Distler, 2015; Ma et al., 2022; Rizun & Strzelecki, 2020) hold contrasting views on its tendency to influence progressive learning, some scholars argued that the removal of conventional teaching styles might reduce the learning and teaching interest of the students, thereby causing learners' poor performance and teachers quitting their jobs (Chen et al., 2020). The conventional teaching method of the face-to-face approach cannot automatically be displaced due to the nature of some courses like STEM programmes with laboratory and experimental components (Bacon & Peacock, 2021). The systematic approach that is designed to balance e-learning is the blended learning forum, this learning methodology entails face-to-face and web-based teaching and learning systems (Series, 2021). This satisfies the argument of (Bacon & Peacock, 2021) on the traditional way of teaching will not be annihilated in place of digital distance education. According to (Batdı, Doğan, & Talan, 2021) the combination of these two concepts of learning (i) physical learning mode and (ii) technology enhance learning strategically creates an intense competency level in teachers and students. The cointegration of these two modes of instruction and schooling sustains the looming pandemic that is still on the rise to avoid the reoccurrence of school closure during the initial lockdown. Conclusively, digital education, virtual learning, distributed learning, mobile learning, etc, have restored the hopes of education as a means to mitigate the unforeseeable incidence that might affect the bearing of education (Adefuye et al., 2021).

3. Methods and materials

3.1. Study design

The cross-sectional survey was structured to cover the three levels of education, primary, secondary and higher education. The questionnaire covered the main focus questions in conjunction with the reports on the effects of coronavirus on the Nigerian education system. The electronic questionnaire was shared to capture the perception of pupils and students in the above-mentioned education levels to get their responses on the acceptability of distance online education. Also, the distributed electronic questionnaire was created with a single link that validated the study with a pilot study, from which 82 responses were gotten were quantitatively analyzed. Thus, the hypotheses of the study were tested alongside the validated variables as structurally expressed in Table 1 . Further exploration and robust analysis, research parameters such as correlation, significance testing, and good model fitness were inductively reported.

Table 2.

Study hypotheses and dimensions, Fig. 1.

Hypotheses	Dimensions
H1	Competency is positively associated with the attitude of teachers and learners to digital distance learning.
H2	Attitude has a positive effect on willingness to accept distance learning.
H3	Competency is positively associated with perception.
H4	Perception has a positive influence on digital literacy and ICT skills.
H5	Perception has a positive effect on willingness.
H6	Competency has a positive on literacy and skill.
H7	Attitude influences literacy and skills
H8	Computer literacy and skill are positively associated with the willingness to accept online distance education.

Table 1.

Explanation of the variables and hypotheses predictors.

Variables	Descriptions	Authors
Competency	Possession of Pedagogical competencies and knowledge of remote learning skills with an understanding of synchronous and asynchronous dimensions of a digital classroom.	(Addleman et al., 2014; Alkaria & Alhassan, 2017; Stickler, Hampel, & Emke, 2020)
Perception	It is perceived feelings and beliefs of the assessment of an individual's technical know-how of web-based instruction and operation of ICT gadgets to accepting or rejecting remote learning.	(Awotokun, 2016; Bayram, 2013; Wang, 2014)
Attitude	Intentions and behaviour of accepting or rejecting virtual learning, and the knowledge of how to use digital devices.	(Addleman et al., 2014; Milovanović et al., 2020; Stickler et al., 2020; Wu, Hu, & Wang, 2019)
Literacy/skills	This entails the acquisition of computer literacy and skills in the operations of smart devices including learning software.	(Awotokun, 2016; Samuel, 2020; Wu et al., 2019)
Willingness	The total acceptance to opt for mobile learning as a means of schooling, simultaneously possessing a significant competency level to try a new phenomenon.	(Almaiah, Al-Khasawneh, & Althunibat, 2020; Cicha et al., 2021; Larreamendy-Joerns & Leinhardt, 2006)

3.2. Data collection approach

As stated earlier that data collection was targeted at three categories of education (primary, secondary, and higher education). However, it is important to state that no data was gathered from the primary school level. This was so due to the inability of learners at this level of education to comprehend the contents of the questionnaire, and the lack of digital devices to answer the questions. As a result, the study explored the responses obtained from secondary school and higher education.

Although the questionnaire was an electronic version, the age of the participant was also considered in responding to the questionnaire.

Given the outcome from the primary school level, the authors targeted secondary school students from the age of 18 upwards and higher education students that can understand the questions and provide reasonable answers to questions, and also must have smart devices to provide their responses electronically. Notably, the questionnaire link was shared with the teachers in these three levels of education using the requirements set by the authors to select eligible respondents for the study. Nonetheless, the data collected measured the competency, perception, attitude, literacy/skill, and willingness of the students to switch to web-based learning. Finally, the total respondents of (n = 82) were statistically logical to represent the study population.

3.3. Study model

$$\text{Ywi}=\alpha +{\beta }_1\text{comp}{\mathrm{t}}_1+{\beta }_2\text{percep}{\mathrm{t}}_2+{\beta }_3\text{att}{\mathrm{i}}_3+{\beta }_4\text{li}{\mathrm{t}}_{4+}\epsilon$$ (1)

The mathematical linear model equation explores the linearity of the estimated variables of the study. Wi = the independent variable, willingness, compt = competence, percept = perception, atti = attitude, lit = literacy, ε = error term

3.4. Procedure and measures

Distributed digital survey link shared has comprehensive six structured constructs with 34 items. The study established its independent or outcome variable to be willingness; this implies the willingness of the learners and teachers' acceptability determines the degree of effort the education policymakers and stakeholders to implement structures for digital learning (Crawford et al., 2020; Lee, 2020; Parmigiani et al., 2021; Wear & Levenson, 2004). The participants were questioned on "their willingness to accept online distance learning." The questions were streamlined to assess students' willingness and attitude in considering online distance education for their personal academic growth (Cicha, Rizun, Rutecka, & Strzelecki, 2021).

Dependent variables: The dependent variables that lead to willingness are competency, perception, attitude, and literacy/skill (mediator). The description of the variables is detailed in Table 1.

The itemized constructs explore the measurement of these variables.

3.5. Data analysis

3.5.1. Descriptive analysis

The descriptive analysis illustrates the demography variables of the study such as gender, age, education, school category, and schools as shown in Table 3 . However, Smartpls for structural equation modeling was used to explore the interconnectedness among variables (predictors and predicting variables). The adopted robust experimental parameters, Cronbach alpha, composite reliability, factor loading, and extraction of the variance average are fully illustrated in Table 4 using Smartpls software (SEM) to estimate the correlational strengths of the variables. The tested validity and reliability of the study items were examined with composite reliability parameters with the loading figure higher than 0.7 symmetrically align with the designed data construct (van der Linden, Klein Entink, & Fox, 2010). The descriptive analysis was executed with SPSS software.

Table 3.

Demographic descriptive results.

V1	V2	Frequency	Percent%	Mean	StdDeviation	Variance	N (Sample)	Skewness	Kurtosis
Gender	Female	47	57.3	1.43	.498	.248	82	.301	-1.958
	Male	35	42.7
Age	12-18	0	0	2.63	.824	.679	82	.778	-1.078
	18-27	48	58.5
	27-30	16	19.5
	30<	18	22.0
Education	Primary	0	0	3.00	.157	.025	82	.000	40.500
	school
	Secondary	1	1.2
	school
	Higher Edu.	80	97.6
	Dropout	1	1.2
School Type	Private	6	7.3	2.04	.429	.184	82	.219	2.661
	School
	Public	67	81.7
	School
	None	9	11.0
School Category	Polytechnic, College of Education University	11, 3, 68	13.4, 3.7, 82.9	2.70	.697	.486	82	-1.966	2.067

Table 4.

Factor analysis, factor loading, reliability, and validity.

Constructs	Loading of Variables	Average Variance Extracted	Composite Reliability
Competency	0.788 0.890 0.773	0.787	0.743
Perception	0.785 0.834 0.811	0.856	0.729
Attitude	0.688 0.794	0.741	0.782
Literacy/Skill	0.803 0.807 0.639	0.683	0.881
Willingness	0.777 0.776 0.882	0.5570	0.847
df		3.00
sig		.000
Overall, Cronbach Alpha Test		0.852

Note: Higher Edu in Table 3 means higher education, and dropouts are students who were in school before the emergence of coronavirus, then decided not to go back to school.

3.5.2. Result

The percentage of female to male participants in the study is 57.3% and 42.7%, represented in Table 3. The ages with distance education knowledge are between 18-27 years to 30 years and above. The 18-27 age range outweighs the rest of the population with 58.5%, 27-30 years 19.5%, 30 years and above has 22.0%, and the least of them all, 12-18 years recorded zero percent (note that there was zero response from the primary school pupils due to their lack of understanding of the topic, and not being exposed to any form of digital education). The demographic descriptive Table 3 highlighted the salient analyses of the constructs such as the standard deviation, variance, skewness, kurtosis, mean score, etc. The study also revealed that 97.6% of the respondents are aware of online distance education and had once indulged in one form of distance online education (either in self-developmental tutorials and classes electronically), which most are students from tertiary education. The responses led to questioning the school's alternative measures in maintaining continuous learning activity during the lockdown period during the heat of covid-19.

However, 63.4% confirmed there was no means of continuous learning initiated by the school, and this shows that many education leaders lack digital education competency. Schleicher (2020) stated that insufficient financial resources remain the numerating factor of schools not incorporating ICT structure to enhance distance online education. Furthermore, 75.6% of respondents narrated that their school was shut down for over six months during covid-19 and it affected their learning behavior after school was reopened.

The factor analysis of the conceptualized constructs (Competency, Perception, Attitude, Literacy/Skill, and Willingness). The loadings of the items of each construct were tested and above 0.6 standardized thresholds. The Cronbach alpha for validity and reliability is 0.852, Table 4. However, Cronbach's alpha output indicates the collinearity and satisfactory dimension of the variables as stipulated by Publications, Reserved, Pdf, and Datasets (2019). The tested Cronbach reliabilities are greater than the 0.6 approved acceptability level. Statistically, the CFA approach was employed in the assessment of variable loading. Precision moderations and augmentations of the low loading of items were carried out to improve model measurement. One of the benefits of the structure model with smart pls is the deletion of the inconsequential items for the improvement of the model. Note, items below 0.6 levels were accommodated in the process of structural equation modeling (Mohamad, Mohammad, Azman, & Ali, 2016) as shown in Table 4.

The competency construct has the highest factor loading of 0.890, and 0.639 for literacy/skill is the least. Convergent validity testing with the experimentation of average variance extract (AVE) and the measurement of a correlation matrix. The average variance extract (AVE) value < 0.5 falls within the approval mark of convergent validity (Sayibu et al., 2021). The output of the average variance extract shows the justification of latent model measurement for the constructs, thus validating the correlation between high values and discrepancies. This shows that the square root value of AVE is greater than the constructs correlations outcomes. The detailed analysis of convergent validity is shown in Table 4.

3.5.3. Structural model results analysis

In the measurement of the model fitness index, the cutoff fit models have a high susceptibility to the quality of the measurement (Mcneish et al., 2017). The parsimonious fit, absolute fit, and incremental fit are categorized as fit model characteristics. The study considers reporting some of the salient models fit, with the estimated significance level, noting that the cutoff points vary in different contexts and studies (Mcneish et al., 2017). For testing the model fitness and its dimensions, TLI (Tucker-Lewis Index), RMSEA (Root Mean Square of Error Approximation), CHSQ/DF (ChiSquare/ Degree of Freedom), and Chi-Square Discrepancy, CFI (Comparative Fit Index) were all recorded (Awang, Wan Afthanorhan, & Asri, 2015). Table 6 displays a good fitness measurement of TLI, RMSEA, and CFI (Ene, 2020). The comparative fit index (CFI) of 0.915 shows a perfect fitness model and reliability. And it becomes proof that the experimented variables satisfy the standards of the model fits and quality of the measurements. The root means square of error approximation (RMSEA) acceptable benchmark is 0.06 to evaluate multiple fit parametric tests of latent variables. This demonstrates that competency, perception, attitude, literacy/skill, and willingness are highly justified as better constructs.

Table 6.

Model fit index.

Good fitness	Values
CFI	.915>0.95
TLI	.920>0.95
RMSEA	.063<0.08
IFI	.930>0.90
Chi-Square/df	2.400<3.0
GFI	.937>0.90
AGFI	.864>0.95

Chi-square/degree of freedom(Chsq/df), *P<.01, **P<.001, and **P<.005, Root mean Square of error of approximation=RMSEA, Normed Fit Index= IFI.

4. Hypotheses analysis and discussion

The standardized testing model with constant latent variables was tested with path regression coefficients as illustrated in Table 6. Table 7, elaborated the outcomes of the study established hypotheses and their significance and non-significance dimensions. Hypothesis 1 (H1) (β = 0.230, t = 3.241, p < 0.003), the result shows a significant relationship between competency and attitude, which implies that competency can trigger an attitude to accept distance online education. This result outcome corroborates what numerous existing studies affirmed that teachers' and learners' ICT competency encourage them to adopt digital learning and teaching approach without self-doubt in their ability to learn and teach effectively (Crawford et al., 2020). Hypothesis 2 (H2) (β = 0.314, t = 4.043, p < 0.000), indicated that attitude is significantly related to the willingness of remote learning acceptability. This implies that learning attitude determines the online distance education mindset. Learners' background in ICT is vital for their willingness to opt for a digital learning approach (Boca, 2021). Family financial status can determine the learners’ knowledge of ICT and online education willingness, given that some students are from a poor background and have not heard about computer or owned any digital device (Okagbue, Wang, & Ezeachikulo, 2022). This also extends to the attitude of school administrators and the level of digital competence they have can determine their conscious efforts in integrating web-based learning in the learning environment. The third hypothesis (H3) (β = 0.163, t = 1.325, p < 0.124), there is no significant relationship between competency and perception. Although statistically, the result shows a parallel relationship between competency and perception, Dhawan (2020) holds a contrary view that digital competency and efficacy influence teachers' and learners' perceptions of online distance education. In justification of his statement, possession of firsthand knowledge of the benefits of digital learning increases the chance of learning online(Almaiah et al., 2020).

Table 7.

Regression path coefficients (β)weights and hypotheses.

hypotheses	tested Items		β	SE	t-value	p-value	Condition
H1	Comptency	Attitude	0.230	0.061	3.241	0.003	Accepted
H2	Attitude	Willingness	0.314	0.054	4.043	0.000	Accepted
H3	Competency	Perception	0.163	0.050	1.325	0.124	Not
H4	Perception	Literacy	0.042	0.084	0.678	0.426	Not
H5	Perception	Willingness	0.281	0.059	3.535	0.001	Accepted
H6	Competency	Literacy/Skill	0.135	0.073	1.486	0.133	Not
H7	Attitude	Literacy	0.221	0.067	2.864	0.004	Accepted
H8	Literacy	Willingness	0.026	0.082	0.652	0.311	Not

Also, hypothesis 4, perception and literacy/skill, H4 (β = 0.042, t = 0.678, p < 0.426), perception and willingness, H5 (β = 0.281, t = 3.535, p < 0.001), competency and literacy, H6 (β = 0.135, t = 1.486, p < 0.133), attitude and literacy H7 ((β = 0.221, t = 2.864, p < 0.004), literacy and willingness, H8 (β = 0.026, t = 0.652, p < 0.311). The H4 shows no positive association between perception and literacy and skill. To justify this outcome Almaiah et al. (2020) opined that negative perception toward electronic education can be caused by a lack of ICT basic skills, and also supported their claims that digital illiteracy and technical know-how become contributory factors to the preference for electronic education or non- electronic learning approach. In order to lay more emphasis on the H4 outcome, Alkaria and Alhasan (2017) affirmed that a negative perception of web-based class platforms diminishes the motivation to accept online education. Hypothesis 5 explored the association between willingness and perception, and the study showed that there is a significant relationship between these two variables, H5 (β = 0.281, t = 3.535, p < 0.001), the relationship of these two variables confirms the assertions, those positive and negative perceptions towards distance education influence the attitude of students and teachers in their decisions to adopt technology in their learning and teaching activities (Lee, 2020).

Hypothesis 6, for competency and literacy, (β = 0.135, t = 1.486, p < 0.133), there is a non-statistical significant relationship with the variables, this simply means there is no positive relationship between competency and literacy. Although the study indicated no relationship between competency and literacy, some scholars narrated that digital competency and literacy influence students' decisions in trusting distance and online education. This outcome answers the question of the feasibility of distance online education in Nigerian schools, supporting the responses given by the respondents that school leaders and managers have no digital competency and literacy to provide a learning platform during the lockdown period. Hypothesis 7 result revealed the statistical significance of attitude having a positive association with the literacy H7 (β = 0.221, t = 2.864, p < 0.004), Milovanović et al. (2020) agreed that students and individuals with enthusiastic learning attitudes tend to invest more time in seeking more skills in pursuance to academic excellence, also equip themselves by engaging in some digital self-learning programmes. The final hypothesis, (H8) on the association between literacy and willingness having the output (β = 0.026, t = 0.652, p < 0.311). This result shows no significant association between digital literacy/skill and willingness for online education. The indicator that illustrates P < 0.311 which implies the parallel and non-statistical linearity of these two variables. On this result, the study by Jou et al. (2016) evinced that digital literacy and skills can affect the intention of learners to indulge in digital classes, they further narrated that having ICT skills and knowledge boost learners' motivation and satisfaction. Juniu (2019) supported the statement of Jou et al. (2016) against the H8 result, narrating that technological literacy by the teachers aids in stimulating learning activities outcome and trigger learners' interest to see distance online education as a viable schooling option. In support of the H8 validated statement, Vanderlinde, Aesaert, and Van Braak (2014) espoused that learners can still have the willingness to accept distance online education without possessing digital skills and literacy. Many students with no digital knowledge can develop an interest in distance online education depending on the teachers' competency and engaging style (Vanderlinde, Aesaert, & Van Braak, 2014), which can foster students' decision and mindset to perfect their skills in ICT (Baek & Touati, 2017). From the survey, participants were asked whether they "prefer online distance education to in-class learning," shockingly, 63.4% answered 'NO,' 28% said 'YES, and '8.5% ticked 'MAYBE'; this result justifies the H5 result with a p-value of 0.001 on perception with a positive association to willingness. On the other hand, the H7 result proves the responses from participants which 32.9% of them affirmatively stated that their lack of computer literacy influences their judgment and perception of not subscribing to online distance education.

These responses validate that Nigerian schools lack proper computer learning courses and information and communication technology in the school environment. Zhang and Kenny (2010) delineated that the digital ecosystem in schools breeds a positive perception that will convince learners to acquire digital education. More so, practical digital education courses should be added to the schools’ learning curriculum to create willingness for the students. To adequately sustain distance education, teachers’ professional training on the use of ICT devices and teaching APPS is highly needed to elevate and instill digital skills and mindset in the learner (Hatzigianni, Gregoriadis, & Fleer, 2016). Especially, training teachers to master the synchronous and asynchronous dynamics of electronic education for efficient learning (Alhih et al., 2017). In responding to the research questions on the feasibility and sustaining possibility of online distance education in Nigerian education and, the readiness and acceptance of ODE by the teachers and students (online distance education), determined by the attitude and efforts of the school leaders, managers, policymakers and education stakeholders in providing the needed resources to develop online and distance education in schools in the schools (Hatzigianni et al., 2016; Vanderlinde et al., 2014). It is also important to note that the correlations of the experimented variables showed moderate and robust relationships, as displayed in Table 5 .

Table 5.

Fornell-Larcker discriminant validity results.

Variables	1	2	3	4	5	Mean
Competency	0.834					3.54 (1.22)
Perception	0.660⁎⁎	0.808				3.89 (1.05)
Attitude	0.620⁎⁎	0.550⁎⁎	0.745			1.90 (2.02)
Literacy/Skill	0.520⁎⁎	0.600⁎⁎	0.500⁎⁎	0.720		4.00 (1.60)
Willingness	0.518⁎⁎	0.629⁎⁎	0.475⁎⁎	0.510⁎⁎	0.712	4.11 (1.10)

^⁎⁎P < 0.005, SD = standard deviation

4.1. Structural equation model

The Fig. 2 below is the path diagram of the SEM and the connections of the latent variables. However, the predicting endogenous variables are Competency, Attitude, Perception, and Literacy/Skill. And the scales of observed indicators measure the structured latent variables. In turn, each of these latent variables is measured by sets of indicators or observed variables. Thus, the latent variables establishment aims to illustrate its relationship with the hypotheses as predicted. As willingness is the exogenous variable, the four endogenous variables are presumed to exert their effects in the study. More so, the study indicators are presented as reflective indicators with latent variables (Wahbeh, Yount, Vieten, Radin, & Delorme, 2021).

Fig. 1.

Conceptual framework.

Fig. 2.

Latent variables.

5. Conclusion

Our study highlighted the importance of institutionalizing distance online education or a web-based learning system in the Nigerian education system as the new normal in post-covid-19 education. The study also pinpointed that the engagement of students in electronic education increases their competency and confidence to acquire digital knowledge and skills and master how to apply them in their learning activities (Scull et al., 2020). On the teachers' side, teaching with smart devices fosters creativity and immense digital literacy to instruct learners effectively and enhance competency-based learning (Batdı, Doğan, & Talan, 2021). The suggestions this article raised to the education policymakers, school administrators, and stakeholders were from the complaints of the students during the shutdown of the schools for over six months at the emergence of coronavirus, due to schools leadership provided no continuous learning platforms for them while at home. For that reason, this current research aimed to explore and understand learners' competency, perception, attitude, literacy/skill, and willingness on distance online education, and their intention to accept electronic learning as a viable learning approach. Furthermore, given the incidences of Covid-19 and its impacts on education globally, numerous efforts have been made to blend web-based learning with distance education to mitigate the threats of the pandemic on learners' life (Zhang, & Kenny, 2010) and health, and thereby encourage schools to adopt and adapt to digital, and distance learning approach, as a new normal of schooling, along the line normalizing the culture of remote learning and distance education. Concerning the research questions of the study, the H7 validates the concern of the questions with 0.001 significant statistical relationships between perception and willingness, this proves that teacher's and student's feelings, and views towards digital learning approach and distance education determine influence learners' behaviour and the decision to accepting them as means of learning (Cabero-Almenara & Meza-Cano, 2019).

Finally, this research aims to suggest to the ministry of education and appropriate stakeholders to consider making digital distance education inducted as an acceptable schooling approach to encourage students and teachers to become tech-savvy, simultaneously enhancing their cognitive and efficacy abilities (Milovanović et al., 2020). Most importantly, creating a digital environment and culture in the education environment that sustains educational growth against future pandemics or threats that will affect education in the country (Lee et al., 2021).

6. The limitation and future direction

The study shows good model fitness, and hypotheses were cross-sectionally tested. However, the study explored the ICT-related variables to evaluate why many Nigerian students prefer face-to-face learning to online distance education, at the same time brings new insights to properly understand the plight of the students and their perceptions of learning with ICT devices and provide accurate approaches to solve the issue of digital and distance education in school. Additionally, the study tested small-scale participants but suggested that future studies on this discourse should consider larger participants for holistic views. Statistically, out of the eight hypotheses proposed for the study, four were not significant, and four were confirmed significant. We, therefore, suggested that further studies should consider exploring those not significant variables in a wider context for further justifications.

More so, as this article summarized the few experiences Nigeria students and Nigerian education systems witnessed during the school lockdown, the authors advised that future studies should apply a qualitative research approach with an in-depth interview to get extensive information from the students, teachers, and school leaders on their experiences during the lockdown, and the challenges Nigerian's education system are still encountering the after the covid-19 school lockdown was lifted. The information gathered from future studies can equally consult our findings in this study to adequately advise the administrative leaders and education policymakers on the dire need to integrate distance online education and virtual learning approach in the country's education system for its sustainability.

Funding

This study received no funding.

CRediT authorship contribution statement

Ekene Francis Okagbue: Project administration, Writing – review & editing, Visualization, Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft. Ujunwa Perpetua Ezeachikulo: Project administration, Writing – review & editing, Visualization, Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft. Ilokanulo Samuel Nchekwubemchukwu: Visualization, Writing – review & editing. Ilodibe Emeka Chidiebere: Visualization, Writing – review & editing. Obisoanya Kosiso: Visualization, Writing – review & editing. Cheick Amadou Tidiane Ouattaraa: Visualization, Writing – review & editing. Esther Onyinye Nwigwe: Visualization, Writing – review & editing.

Declaration of Competing Interest

The authors declare no conflict of interest in this current study.