COVID-19 Pandemic and Education: Nigerian Parents’ Approach and Perception of Children’s Education Based on Socioeconomic Indicators

Abstract

This paper, using 201 parents' reports collected via a descriptive survey, explores (a) how children from different socioeconomic backgrounds (education and income) engage in learning in the absence of inclusive actions to keep learning ongoing in educational contexts in Nigeria; and (b) the indirect impact of COVID-19 on children's education (children's access to learning materials, engagement in learning, and educational achievement). Findings showed that many Nigerian children engaged in learning and parents perceived educational achievement during pandemic school closure as worse. Socioeconomic backgrounds were associated with patterns of engagement. Children from highly educated and high-income backgrounds had significantly higher access to learning resources, which were also associated with their greater engagement in learning, than those children from low socioeconomic backgrounds. Children from low-income backgrounds were more engaged in face-to-face outside the home learning. This could have been due to the low access to online resources and low compliance to COVID-19 rules (driven by mistrust in government) of low-income and less educated families. Implications of findings for informing educational intervention programs, including post-COVID pedagogies, are discussed.

Keywords

COVID-19 pandemic educational achievement learning materials students’ engagement socioeconomic status

Introduction

Novel coronavirus or COVID-19, which broke out from Wuhan city in the Republic of China and has spread globally, was declared a pandemic after reaching the epidemiological criteria of having infected more than 100,000 people in at least 100 countries on March 11, 2020, by the World Health Organization (Cucinotta and Vanelli, 2020). Various countries took measures to reduce its negative impacts. African countries, including Nigeria, took proactive measures (including early lockdown policies) to prevent and manage the effect of the COVID-19 pandemic in their countries (Nyabola, 2020). Yet COVID-19 negatively impacted all spheres of life, including education, in varying degrees across nations. Schools, just like other interaction centers, were locked down to prevent more spread of the disease. Kuhfeld et al. (2020) noted that virtually all K−12 students in the United States missed face-to-face instruction due to COVID-19. Many educational disadvantages, such as lack of peer interaction and social-emotional activities, are associated with in-person school closures and online study from home. As rightly stated by Organization for Economic Cooperation and Development [OECD] (2020), expected implications of distance learning during crises are that children are less physically active, spend much longer time on-screen, experience more irregular sleep patterns, and follow less healthy diets. Many children in low socioeconomic backgrounds (measured based on parents’ educational and income levels) may face greater disadvantages than those in high socioeconomic backgrounds in times of uncertainties.

Pre-COVID-19 studies on the relationship between socioeconomic factors and educational achievements have consistently shown a wide gap existing between low- and high-income learners (Duncan and Brooks-Gunn, 2000; Engle and Black, 2008; Hair et al., 2015; Obiagu, 2020). However, pre-COVID-19 studies were conducted in cases where both groups accessed educational facilities (Duncan and Brooks-Gunn, 2000; Engle and Black, 2008; Hair et al., 2015). In the COVID-19 pandemic, many students in both low socioeconomic and high socioeconomic groups (measured by parents’ income and educational levels) were out of in-person school and may not have accessed online schooling in some contexts, especially resource-limited contexts. Hence, educational inequality may further increase and the educational gap between children from low and high socioeconomic backgrounds may change by either reducing (e.g., high SES children decreasing their engagement, thus decreasing the gap) or further increasing the gap. Concerns about rise in educational inequality were expressed by the public, and studies on COVID-19 and education surfaced.

A review showed that educational literature involved the implementation and effectiveness of e-learning and teachers’ preparedness to transition to online teaching, especially in developed contexts (Anwar and Adnan, 2020; Consortium for School Networking [COSN], 2020; Levinson et al., 2020; Rapanta et al., 2020). Other studies involved the implications for reopening of schools following clinical evidence that children’s (ages 1–8) reduced susceptibility to COVID-19 infection (Cevik et al., 2020); and the perception of teachers, students, and parents about e-learning, mainly in resource-limited countries (Abbasi et al., 2020). There is a need for studies on how parents and children of different socioeconomic backgrounds (e.g., educational and income backgrounds) engaged in educational activities during the COVID pandemic and the unprecedented school shutdowns. The findings are useful for preparing education measures in (post) health emergency contexts. Hence, this study investigated parents’ perception of their children’s access to learning materials, engagement in learning, and academic achievement before versus during COVID-19 pandemic school closures in the Nigerian context.

Theoretical Framework

To provide a clear understanding of the findings of this study, we used Ajzen’s theory of planned behavior (TPB) (Ajzen, 1991) and the diathesis-stress model that developed from health studies (Broerman, 2018; Kendler, 2020). TPB is used to understand why and how people make their behavioral decisions. For this study, TPB is helpful in making sense of the relationship or difference between socioeconomic backgrounds and children’s education, particularly (a) their engagement in learning and (b) parents’ perception of their level of academic achievement before and during COVID-19 school closures. TPB assumes that behavioral intentions and decisions are determined and influenced by three factors. The factors include (a) attitudes that involve beliefs toward certain behaviors, (b) subjective norms that involve the influence of social surroundings—including family and peers—on someone’s behavioral decisions or behavior, and (c) perceived control which involves someone’s or the actor’s perception of their ability to perform a task or fulfill an expected behavior. Based on these TPB’s assumptions, especially the subjective norms, we argue that socioeconomic backgrounds (defined by parents’ income and educational levels and associated with different norms and expectations) will predict children’s engagement in learning and parents’ perception of their children’s academic achievement before versus during COVID-19 pandemic school closures.

Results on children’s access to learning materials during the COVID-19 pandemic will better be explained from the lens of diathesis-stress model. This theory is mostly applied to psychopathological studies and assumes that environmental stressors affect individuals differently but have greater effect on individuals who are more inherently vulnerable (Broerman, 2018). We extend this theory to educational research, particularly on the availability and accessibility of learning materials across different socioeconomic backgrounds during the COVID-19 pandemic. We argue that the perceptions of parents about their children’s education during and after COVID-19 will indicate that COVID-19 affected more the education of children from low socioeconomic backgrounds.

Socioeconomic Factors and Children’s Education

The possibility of accessing learning opportunities while at home depend on the socioeconomic status of the parents of the child (Morales, 2014). Students’ learning habits are triggered also by socioeconomic factors and contribute to children’s intrinsic motivation (Mihret et al., 2019; Turner et al., 2009). Hence, under this section, we reviewed existing literature on the impact of socioeconomic factors on students’ access to learning materials, engagement in learning at home, and educational achievement which are examined in the current study.

Studies on learning educational materials showed that learning materials positively impact students’ learning and achievement (e.g., Olayinka, 2016; Tety, 2016). Learning materials are like instructional materials and represent important factors in children’s education; it includes both conventional and modern educational items that aid teaching and learning, promote comprehension of concrete and abstract contents, and foster meaningful learning. Tety (2016), in a study conducted in Tanzania, found that the use of instructional materials enhanced students’ academic performance and improved their knowledge and skills. Notably, learner’s access to important learning materials may be influenced by socioeconomic backgrounds of their parents. According to Reardon (2011), high-income families invest relatively more time and resources in their children’s cognitive development than lower income families. There is, however, worry that provided essential learning materials needed for learning are not adequately utilized by students because of partial distraction by their usage of social media networks and entertainment activities at home (Hemphill et al., 2017). Nevertheless, students in higher income families were more likely to buy books and receive them as gifts than students in poor families regardless of reading interest while students who do not come from wealthy backgrounds are more likely to borrow (Hemphill et al., 2017; Le et al., 2019). We extend this study to the Nigerian context by investigating the influence of parents’ education and income on children’s accessibility (ownership) to learning materials. Another factor in students’ education is students’ engagement in learning and this can be influenced by accessibility of learning materials.

Existing studies showed that students’ engagement in learning, especially at home, can be influenced by some factors including motivation (intrinsic) and extrinsic factors, like availability of learning materials, parent’s education, income, and involvement in the child’s education (Barger et al., 2019; Lara and Saracostti, 2019). Le et al. (2019) found a positive correlation between socioeconomic status and students’ learning habits. In past decades, except for some resource-limited countries where some health epidemic (e.g., Ebola) and educators’ industrial actions affect in-person schooling and create uncertainty in the school calendar, there has not been a scenario where schools were indefinitely shut down with no clue of resumption time across the globe. Reasonably, parents will act differently to their children’s learning in this situation and the impact will likely vary across socioeconomic groups. Given that students are shown not to maintain the same learning habits during school (first term, second term, and third term) breaks, as they do during school hours, and that their commitment to learning declines during holidays even with availability of kits (Warner et al., 2008), it is logical to reason that students whose schools do not transition to remote teaching and learning would have their learning reduced than is the case during school breaks and holidays. Studies are limited regarding students’ study-at-home behavior during sudden school termination in which the students are required to stay at home and to adjust their living and learning habits. This study covered this gap by investigating the level of children’s engagement in learning during the COVID-19 school shutdown and the difference in learning across socioeconomic backgrounds. Another educational factor related to students’ learning habit or engagement in learning is students’ educational achievement.

Educational achievement, and its relationship with socioeconomic background, is one of the enduring issues in educational research. Academic achievement is a measurable index that depicts a student’s cognitive, affective, and psychomotor domains in an educational setting. Students’ academic achievement is ascertained by testing, which has and will continue to play significant role in the educational system of any country (Kpolovie et al., 2014). Socioeconomic inequalities can influence educational achievement. Extant literature showed that students from low-income families, regardless of ethnicity or race, consistently have low educational achievement compared to those from high income groups (Le et al., 2019; Obiagu, 2020; Smith et al., 1997). A study on fifteen-year-old students in 9th grade from five provinces in western China showed a positive correlation between the students’ family SES index and students’ academic performance (Liu and Lu, 2008). These findings are associated with the tendency of high-income families to invest relatively more time and resources in their children’s cognitive development than lower income families (Reardon, 2011). Parental involvement in children’s education also affects children’s cognitive competences and learning ability (Topor et al., 2010).

Parents’ education, another factor considered in this study and which often correlates with parents’ income, is also found to positively relate with students’ educational performance (Ensminger and Fothergill, 2002; Le et al., 2019). In fact, among the factors attributed to the consistent better educational achievement of students from high income backgrounds is parents’ education. As noted elsewhere, “…evidence suggest that low-income families often have limited education reducing the abilities to provide a responsive stimulating environment for their children” (Engle and Black, 2008, p. 245), and parents’ education sometimes moderate the effect of low income on children’s education, such that poor families who are better educated and have rational decision-making skills protect their children’s education from the effects of low income than poor families poorly educated with poor decision-making skills (Obiagu, 2020).

COVID-19 and Education in Relation to Learners’ Socioeconomic Factors

While reviewed studies on the relationship between socioeconomic factors and students’ education are informative, they are conducted in the pre-COVID era. COVID-19 pandemic can impact students’ health which in turn impact educational achievements. The COVID-19 pandemic may impact students’ mental and social health. Students may face mental issues such as stress, depression, fear, anxiety, worry and stigmatization if from largely affected neighborhoods, irrespective of socioeconomic background. Chiu et al. (2021) stated that added stress and anxiety from the pandemic may demotivate and disengage students from learning since prior studies found that negative emotions may inhibit learning. Similarly, Kuhfeld et al. (2020) projected that the school closures caused by COVID-19 have additional trauma effect on students, loss of resources, and loss of opportunity to learn that go well beyond a traditional summer break for many families. Speculatively, children from more affluent communities are more likely to come from families with financial resources, stable employment, and flexible work from home and childcare arrangements that allow them to weather this storm more easily than families who are renting their housing, working in low-pay fields that are hardest hit by the economic impacts, and experiencing higher rates of food insecurity, family instability, and other shocks from this disruption. Resource, health, and social effects of COVID-19 may likely disrupt previously found relationship between socioeconomic factors and children’s education. The gap between high- and low- income (or achieving) students may either increase or reduce. Kuhfeld et al. (2020) found that out-of-school time for US students may not on its own widen academic achievement between students from high-SES schools (low-poverty schools or <10 students eligible for free or reduced-price lunch) and low-SES schools (high-poverty schools or >90 students eligible for free or reduced-price lunch). However, they found that the likelihood of students from high-SES US schools receiving remote learning than students from low-SES US schools resulted in significantly larger achievement gaps.

The studies on COVID-19 and education were largely speculative: studying (a) the perceptions of parents and educators on the likely effect of the pandemic on educational inequalities and achievements and (b) perceptions about, and the efficiency of, remote learning. It is anticipated that loss of learning will occur across groups and children from disadvantaged backgrounds will be more affected, since they have fewer family resources and less access to online learning resources to offset lost instruction time, than others. Kuhfeld et al. (2020), in their study using data from prior out-of-school time (effect of summer loss and absenteeism on students’ achievement) literature to project the impact of COVID-19 on student achievement, found that under each projection (especially partial and full absenteeism caused by COVID-19) students’ learning gains will be lower at the end of 2019–2020 with full absenteeism likely leading to extreme loss of learning across all grades and subjects. Similarly, Eyles et al. (2020) stated that COVID-19 induced unexpected temporary school closures and reduced instruction time will reduce educational achievement, both in the short and long term.

Purpose of the Study

Remote learning, serving as an alternative to physical classroom with school shutdowns, requires more resources (such as e-learning platforms, video-conference tools, learning applications), which may further place low-income students at greater disadvantage than higher income students. However, it is a difficult task to introduce and to adapt to new learning methods. Many students, schools and teachers are not prepared, and e-learning systems might not be available or fully developed especially for children of low-income families, disadvantaged groups, and resource-limited countries, such as Nigeria. As Chiou and Tucker (2020) observed, even in countries that moved schooling online, not everyone had the same experience, and outcomes were mediated by inequality as more than 300,000 students in public schools lacked access to a personal computer or tablet to use for their schoolwork. Efforts to transit to remote learning in Nigeria, the context of this study, were challenging especially in public schools, when schools closed in March 2020. The difficulty of transitioning to e-learning implies that many students, out of the estimated 46 million students in the country (United Nations Educational, Scientific and Cultural Organization [UNESCO] cited in Nigeria Education in Emergencies Working Group, 2020), did not have access to remote learning during COVID-19 closure unless privately planned by their families; thus, students from low-income backgrounds might have been more affected.

We argue that children from high income and educated families were more likely engaged in learning and maintained their academic performance during the COVID-19 pandemic school shutdowns because they are mostly surrounded by facilities and people that could serve as sources of influence and pressure to their learning behavior. Parents with high education and economic means are consistently shown to be involved in their children’s education and to spend more resources on their children’s education (Engle and Black, 2008; Reardon, 2011). Also, a high socioeconomic environment is mostly filled with parents who seem to motivate their children to make wise learning decisions through providing them support (e.g., parental- or hired-tutor support). Children of high-income backgrounds are more likely to have greater access to learning materials necessary for engagement in learning and maintenance of educational achievements during the COVID-19 school closure than are more likely to be accessed by children from low-income and less educated background.

Hence, the aims of this study include the following:

1. To ascertain the learning materials accessible to children based on different socioeconomic backgrounds in Nigeria.

2. To examine how children from different socioeconomic backgrounds in Nigeria engage in their studies during unprecedented school closure.

3. To ascertain the perceptions of parents of different income and educational levels about their children’s educational achievement based on income and educational levels.

Specifically, the following research questions were considered:

1. What learning materials are accessible to children from different socioeconomic backgrounds in Nigeria?

2. How do children from different socioeconomic backgrounds in Nigeria engage in their study during unprecedented school closure?

3. Does parents’ perception of their children’s educational achievement vary based on income and educational levels?

Based on the research questions we hypothesized the following:

H₁1

Children from high income or high educated backgrounds would have access to learning materials than children from low income or less educated backgrounds.

H₁2

Children from high income or high educated backgrounds would engage in learning than children from low income or less educated backgrounds.

H₁3

Higher income or higher educated parents than low income or less educated parents would perceive their children’s achievement during the COVID-19 pandemic school closure as better compared to their educational achievement prior the COVID-19 pandemic.

The findings may provide insights into what post-COVID policies and practices, including pedagogy and educational interventions, are needed with the return of schools to normalcy. Although associated with the COVID-19 era, findings may reveal the impact of socioeconomic backgrounds in educational activities of Nigerian students in general.

Method

Sample

Participants were 201 parents, whose child(ren) were enrolled in pre-higher education levels in Nigeria. The initial sample was 209, but eight cases were deleted because of incomplete biodata section, thus resulting in difficulties with categorizing them. One hundred and fifty-eight parents resided in one of 12 states (Abia, Akwa Ibom, Ebonyi, Enugu, Kwara, Nasarawa, Niger, Ogun, Ondo, Plateau, Sokoto, and Yobe; some states had small sample of 3 respondents only). Parents were used because they are the sponsors of their child(ren)’s education and as well in the best position to access the achievement of their child(ren) in an uncertain time. Many sociodemographic variables were collected from participants, but only participant’s educational qualification and income level were used for this study. Educational levels were based on seven groups: (Group 1: Doctorate 6%; Group 2: Masters 22.9%; Group 3: First Degree 33.8%; Group 4: NCE/National Diploma 3.5%; Group 5: Secondary Education 25.9%; Group 6: Primary Education 5.5%; Group 7: No Education 2.5%). Due to subsample sizes, for education level, parents were re-categorized into two groups: Groups 1–3 were defined as or coded highly educated (126 parents) while parents in Groups 4−7 were coded low or less educated (75 parents). Participant’s family monthly income level also was based on 7 groups: (Group 1: <₦18,000 19.9%; Group 2: ₦18,000 – <₦30,000 13.9%; Group 3: ₦30,000 – <₦50,000 22.9%; and Group 4: ₦50,000 – <₦100,000 10.4%; Group 5: ₦100,000 – <₦150,000 10.4%; Group 6: ₦150,000 – <₦200,000 9.5%; Group 7: Above ₦200,000 12.9%; note ₦ stands for Naira, Nigerian currency = $0.0024 US dollar). For income, parents were re-categorized into two groups: Groups 1–4 were classified as low-income earners (135 parents) while parents in Groups 5–7 were classified as high-income earners (66 parents). This re-classification was informed by Trading Economics (2019) in that the estimated living wage for a family in Nigeria is ₦137 ($600 per month in US currency). See Table 1 for the profile of the participants’ demographic features.

Table 1.

Profile of Study Participants (N = 201).

		Frequency	Percentage
Gender	Male	77	38.3
Gender	Female	124	61.7
Age	20–30	45	22.4
	31–42	96	47.8
	41–50	52	25.9
	51–60	7	3.5
	Above 60	1	0.5
Education	Doctorate	12	6.0
	Master’s	46	22.9
	First degree	68	33.8
	NCE/National diploma	7	3.5
	Secondary education	52	25.9
	Primary education	11	5.5
	None	5	2.5
Monthly Income	Less than ₦18,000	40	19.9
	₦18,000 – <₦30,000	28	13.9
	₦30,000 – <₦50,000	46	22.9
	₦50,000 & <₦100,000	21	10.4
	₦100,000 & <₦150,000	21	10.4
	₦150,000 & <₦200,000	19	9.5
	₦200,000 & above	26	12.9

Profile of Study Participants (N = 201).

Note. ₦ = Nigerian currency.

Research Design

We used a descriptive survey design to test the hypotheses and answer the research questions (Yin, 2013). This design is considered appropriate because the study was set up to understand the educational status of Nigerian children across different socioeconomic backgrounds in Nigeria in COVID-19 era as reported by participants.

Measures

All measures were contained in one instrument titled “Impact of COVID-19 on Children’s Education Across Different Sociodemographic Groups in Nigeria,” which was developed by the authors. The instrument comprised of a biodata section and 17 items capturing three outcome variables: (a) accessibility of learning materials (10 items), (b) engagement in learning (5 items), and (c) educational achievement (2 items). All items were generated from existing literature and experience in schooling. Three experts in Social Science and Science Education validated the instrument and assessed the suitability of the items for the respondents, the comprehensiveness of the instrument, and the language clarity of the instrument developed in English language.

Accessibility to Learning Materials

Accessibility to learning materials at home by children was measured with an ordinal scale of 10 items, rated dichotomously – yes (1) and no (0). Parents were instructed to select all items listed that are accessible for children to use in their home for learning. These items included TV, educational games, textbooks, and laptop. These items were generated through literature reviews and observation of learning materials commonly used in homes. Blank spaces were available to note materials available at home but was not captured on the list. A score (continuous data) was created by summing up the number items selected. For example, if a respondent selected that five learning items were accessible out of the 10 items on the list, then that respondent’s total score was 5/10. Kuder-Richardson 20 (KR-20) was conducted on the 10 dichotomously scored items, using IBM SPSS statistics 20, to ascertain the reliability of the items and the result yielded a reliability coefficient of .880. Alpha coefficient of .50 to .70 are sufficient and acceptable level of internal consistency (Hinton et al., 2014).

Engagement in Learning

Engagement in learning is defined as the extent children was involved in learning during COVID-19 and was measured by five items. One item asked parents, do your child(ren) study at home during this lockdown? Another item was how many days do your child(ren) study at home in a week? The third item was how many hours does your child(ren) study in each day? And the final two items were as follows: (a) who teaches your child(ren) at home and (b) what online classes do your child(ren) utilize? Due to the setup of the questions, they were rated differently. For the first question, the possible responses were binary (yes/no; 1/0). For the second question measuring the number of days studied in a week (study frequency), there were five response options rated on nominal scale of daily (1), one day in a week (2), two days in week (3), three days in a week (4), and none (5). Regarding the number of cumulative hours children studied each day (length of study), there were also five response options rated on nominal scale: between 30-60 minutes (1), between 1 to 2 hours (2), between 2 to 3 hours (3), from 4 hours and above (4), and none (5). There were six response options for who taught the child at home (offline learning) rated on nominal scale: father (1), mother (2), employed teacher (3), self-study (4), others lesson center (5), and none (6). And there were five options for the type of online classes used (e-learning) rated on nominal scale: none (1), Nigerian Television Authority (NTA) (2), paid TV channels (3), radio programs (4), and internet enabled programs or learning online (5). Due to the setup of the questions to measure engagement, each item was independently scored and analyzed. The results were interpreted using percentage score: a score below 50% implied low engagement; a score between 50% and 69% implied moderate engagement; and a score of 70% to 100% implied high engagement. Kuder-Richardson 20 (KR-20) was conducted on the five items, after they were recoded into wrong and right answer items, to ascertain the reliability of the items. For all items other, the option “none” was recoded 0 while all other options were recoded 1. The result yielded a reliability coefficient of .772.

Educational Achievement

Two items measured parents’ assessment of their students’ educational achievement. One question was, do you access your child(ren)’s achievement? The response was either yes or no (1 or 0). And the other question, dependent on the response to the first question, was how is their current performance compared to pre-COVID-19 performance? This question was rated on nominal scale: better (1), same (2), worse (3), can’t say (4). Each response option to the item was independently scored and analyzed using percentage for each question. A score below 40% indicated rejection of the response option while a score of 40% indicated acceptance of a response option. Kuder-Richardson 20 (KR-20) was conducted on the two achievement items after recoding the second item into a wrong and right answer item, to ascertain the reliability of the items. For the achievement comparison item, the options “better and same” were recoded 1 while the options “worse and can’t say” were recoded 0. The result yielded a reliability coefficient of .373. The low coefficient could be explained by small number of items (two items).

Procedures

Data were collected both online and offline. The online and offline versions of the questionnaire are the same; only the mode of collection varied. Online version of the questionnaire was administered via Google Form while the print version of the questionnaire (offline collection) was administered by the first and second researchers, with COVID-19 guidelines observed, to parents who were residents in low socioeconomic quarters and otherwise mostly lacked access to an android mobile phone or any access to internet or could not afford to have or use one. For offline collection, parents who could not read, a researcher, who grew up and resided in the locality and hence was fluent in the local language, read the items to participants and translated them in their language (Igbo language; Nsukka dialect to be specific). Online respondents were recruited to respond to the questionnaire through messages sent to them with the survey link via WhatsApp, Facebook Messenger, or Emails between May and July 2020. WhatsApp platforms of parent associations were targeted and friends of the first researcher who belonged to any of such associations helped with sending the survey link with a message requesting participation and consent to their various social networking group platforms. Parent-colleague email addresses were also targeted. Convenience sampling was used to include all online respondents in the study.

Offline respondents were randomly recruited via convenience sampling from a low socioeconomic neighborhood in Nsukka Local Government Area, Enugu State. The neighborhood was close to the residence of the researchers. In August 2020, the first two named authors visited homes and street stores in the sampled neighborhood and administered the questionnaire to any parent who consented to filling the questionnaire and returning it on the spot. Participants were not remunerated for participating in this study.

After completing the appropriate informed consent, online or offline, a total of 158 parents filled out the questionnaire online and 51 parents filled out the questionnaire offline, bringing the total number of respondents to 209 parents. No incentive was given for participation. Offline participants completed the measure on the spot for an average of 25 minutes while online participants completed it for an average of 10 minutes.

Results

Preliminary Analyses

Missing data, which occurred in only eight cases, were respondents who did not enter some biodata, were managed by listwise deletion of the affected eight cases before any statistical analyses were ran. Shapiro-Wilk Test was used to establish if the data met normality, while Levene’s test was conducted to establish whether the data met homogeneity of variance and independence assumptions. Outliers were investigated using boxplots. Only achievement variable had outliers (14 extreme variables). To determine whether the outliers influenced the results for chi-square of independence test on achievement, statistical analyses were conducted on the variable two ways: one with the outliers included (i.e., with all 201 sample) and another with the outlier excluded (i.e., with 187 participants; Mertler and Vannatta, 2016). If the outliers did not affect the outcome of the statistical test, then the data with the outliers were used for analyses. All analyses were conducted using IBM SPSS version 20.

An Independent t-test was used to examine accessibility of learning materials variable between the specified groups, as the outcome variable was rated categorical, with adding up the number of responses changing it to continuous data, and the data were not normally distributed (Field, 2013). Two independent t-tests were run: one with education groups on mean difference of accessible learning and another with income level group on mean difference of accessible learning.

Chi-square tests for independence was conducted on academic achievement and learning engagement variables because these variables are nominal data and met the assumptions of chi-square test (McHugh, 2013). Chi-Square test makes it possible to measure each item of these variables separately. In all, 14 chi-square tests were run—three with income and education groups respectively on each of the five engagement items and two with income and education groups respectively on each of the two achievement items analyzed separately. The assumption of chi-square that there should be no case where more than 20% of the expected counts was less than five and any of the individual expected counts was less than one (McHugh, 2013) was met.

Accessibility of Learning Materials

In regard to the accessibility to learning materials available to the children, only 5% and 6% of parents reported the availability of a desktop or educational softwares, respectively. Over half of the parents reported that their children had an English (54%) or mathematics textbook (53%). Between 33 to 40% reported access to educational TV channels (33.8), Internet materials (37.9%), a laptop (39.7), and smartphone (42.4). Between a quarter to a third of the parents reported accessibility to all textbooks (28.3) or educational games (26). A summary of these descriptive statistics for accessibility to learning materials is presented in Table 2.

Table 2.

Accessibility of learning materials across socioeconomic backgrounds.

Number	parent
	Education				Income				All parents
	High		Low		High		Low		All parents
	126		75		66		135		201
Items	n	%	n	%	n	%	n	%	n	%
Desktop	6	4.8	4	5.3	6	9.1	4	3.0	10	5
Laptop	77	61.1	3	4.0	59	89.4	21	15.6	80	39.8
Smartphone	82	65.1	3	4.0	60	90.9	25	18.5	85	42.3
Educational TV channels	58	46.0	8	10.7	39	59.1	27	20	66	32.8
Educational games	51	40.5	1	1.3	48	77.7	4	3.0	52	25.9
Software	12	9.5	0	0	11	16.7	1	0.7	12	6.0
Internet materials	75	59.5	3	4.0	56	84.8	22	16.3	78	38.8
Mathematic textbooks	88	69.8	20	26.7	56	84.8	52	38.5	108	53.7
English textbooks	40	70.6	20	26.7	59	89.4	50	37.0	57	54.2
All textbooks	40	31.7	17	22.7	31	47.0	26	19.3	57	28

Two independent t-tests were conducted on the number of learning materials accessible to children based on the grouping variables of educational level of parents (low versus high) and income level (low versus high). In regard to educational level, the finding showed a statistically significant difference (t(193) −11.328; p _< .001; ƞ² = .65) in the mean score of learning materials accessible to children from high educated backgrounds (M = 4.63; SD = 2.98) and those from less educated backgrounds (M = 1.05; SD = 1.47). For income level, the finding showed a statistically significant difference (t(199) = −14.555; p _< .001; ƞ² = 1.00) in the mean score of learning materials accessible to children from high-income backgrounds (M = 6.43; SD = 2.22) and those from low-income backgrounds (M = 1.76; SD = 2.09).

Engagement in Learning

Generally, for item 11 (i.e., study at home [yes, no]), ninety-five (95%) of all surveyed parents reported that their children engage in learning during the school closure while 5% reported that their children do not engage in learning. A summary of parents’ responses, based on education and income levels, about their children’s engagement in learning (i.e., frequency of study per week, frequency of study per day, utilization of face-to-face learning, and utilization of online learning) during the COVID-19 school shutdown are presented in Table 3.

Table 3.

Frequency of engagement in learning.

	Number of days in a week
	Daily		1 Day per week		2 Days per week		3 Days per week		None
	n	%	n	%	n	%		%	N	%
Highly educated	87	69.0	15	11.9	23	18.3	0	<0.0	1	0.8
Less educated	29	38.7	13	17.3	19	25.3	3	4.0	11	14.7
High-income earners	52	78.8	6	27.3	8	12.1	0	0.0	0	.00
Low income earners	64	47.4	22	26.7	34	25.2	3	2.0	12	8.9
All parents	116	57.7	28	26.8	42	20.2	3	1.50	12	6.0

	Number of hours per day
	30 min to 1 hr		1–2 hrs		2–3 hrs		4 hrs and more		None
	n	%	n	%	n	%	n	%	N	%
Highly educated	19	15.1	35	27.8	54	42.9	16	12.7	2	1.6
Less educated	11	14.7	19	25.3	25	33.3	9	12.0	11	14.7
High-income earners	7	10.6	18	27.3	34	51.5	6	9.1	1	1.50
Low-income earners	23	17.0	36	26.7	45	33.3	19	14.1	12	8.9
	30	14.9	54	26.8	79	39.3	25	12.4	13	6.47

Note. Highly educated = undergraduate and postgraduate degree holders; less educated = qualifications below first or undergraduate degree or higher national diploma. Low-income = earns less than ₦100,000; High-income = earns ₦100,000 and above.

Table 4 presents the summary of responses of respondents based on the sources of educational engagement in both offline (home) and e-learning schooling (i.e., responses on item 15). Generally, most of the parents (92.54%) reported that their children engaged in offline learning. Many parents reported that their children were taught mostly by an employed home teacher (32.3%) or learnt on their own (self-study) (16.9%). Thirteen percent (13.44%) of the parents reported that their children used the offline learning services of private lesson centers despite the dangers posed by COVID-19 and the guidelines of shutdown that prohibited in-person gatherings.

Table 4.

Source of educational engagement (frequency and percentage) at home.

	Highly educated		Less educated		High income		Low income		All parents
Offline (in-person) Learning**
Source	N	%	n	%	N	%	n	%	n	%
Father	29	23.0	3	4.0	12	18.2	20	14.8	32	15.92
Mother	18	14.3	10	13.3	9	13.9	19	14.1	28	13.93
Employed teacher	41	32.5	24	32.0	22	33.3	43	31.9	65	32.34
Self-study	21	16.7	13	17.3	15	22.7	19	14.1	34	16.91
Others (lesson center)	14	11.1	13	17.1	6	9.1	21	15.6	27	13.40
None	3	2.4	12	16.0	2	3.0	13	9.6	15	7.50
E-learning**
NTA school*	8	6.3	2	2.7	6	9.1	4	3.0	10	5.00
Radio school *	16	12.7	7	9.3	8	12.1	15	11.1	23	11.44
Other paid TV school*	36	28.6	6	8.0	21	31.8	21	15.6	42	20.90
Internet programs	23	18.3	3	4.0	15	22.7	11	8.1	26	12.93
None	43	34.1	57	76.0	16	24.2	84	62.2	100	49.80

Note. *NTA (Nigerian Television Authority), Radio and other TV schools are governments’ (and other agencies) emergency e-learning response actions to keep Nigerian students educationally engaged during prolong school closure resulting from COVID-19 lockdown. They are freely provided. **Sum of father, mother, employed teacher, self-study, and lesion center is the total percentage (92.5%) engaged in offline learning. Sum of NTA school or programs, radio school or programs, other paid TV school or programs, and internet programs is the total percentage (50.20%) engaged in online learning.

Approximately 50.20% of surveyed parents reported that their children used e-learning versus 49.80% of the parents reported that their children did not use e-learning. Only 5% and 11% of parents reported that their children used the Nigerian Television Authority and Radio intervention programs of the government, respectively. Specifically, 18.3% of the highly educated parents and 4.0% of less educated parents reported that their children used internet programs during the school closure. Further, 22.7 of high-income parents and 8.1% of low-income parents reported that their children used Internet programs.

It was hypothesized that children’s engagement in learning (i.e., study at home (yes, no), frequency of study per week, frequency of study per day, utilization of in-person learning, and utilization of online learning) during COVID-19 school closures would vary based on parents’ educational level (less-educated versus high-educated) as well as vary based on income level (low-income versus high-income). Thus, 10 chi-square tests of independence were run on five outcome variables of engagement in learning, noted above, with five based on educational level and the other five based on income level.

Results showed a significant relationship between parents’ educational status and whether children studied or not at home during COVID-19 (X²(1, N = 201) = 12.49, p < .001; Phi Cramer’s V = −.249) as well as between parents’ income level and home study (X² (1, N = 201) = 5.12, p < .023; Phi Cramer’s V = .160). The effect size was moderate as indicated by Phi Cramer’s value. Further inspection indicated that children from high-income (100%) and highly educated (99.2%) backgrounds were more likely to study during school closure than children from low-income (92.6%) and less educated (88%) background.

Similarly, chi-square test of independence for frequency of children’s studying in a week (engagement in learning) during COVID-19 was significant based on parents’ educational status (X² (4, N = 201) = 29.84, p < .001; Phi Cramer’s V = .385) as well as between parents’ income level and frequency of study per week (X² (4, N = 201) = 20.17, p < .001; Phi Cramer’s V = .317). The effect size was moderate as indicated by Phi Cramer’s value. Children from high-income (78.8%) and highly educated (69.0%) backgrounds were more likely to study on daily basis during school closure than children from low-income (47.4%) and less educated (38.7%) backgrounds who were more likely to study one day out of a week during COVID-19 school lock down.

A significant relationship was found between parents’ educational status and frequency of children’s study per day (engagement in learning) during COVID-19 (X² (4, N = 201) = 13.65, p = .009; Phi Cramer’s V = .261) as well as between parents’ income level and frequency of children studying per day (X² (4, N = 201) = 9.57, p = .048; Phi Cramer’s V = .218). The observed relationship was moderate as indicated by Phi Cramer’s value. Children from high-income (51.5%) and highly educated (42.9%) backgrounds were more likely to study for a longer period (i.e., number of hours), specifically 2–3 hours per day during COVID-19 school lockdown than children from low-income (33.3%) and less educated (33.3%) backgrounds who were more likely to study only for 30–60 minutes per study day.

Chi-square test of independence for children’s utilization of offline learning (father, mother, and/or employed teacher face-to-face or self-study offline) during COVID-19 was significant based on parents’ educational status (X² (1) = 12.62; N = 201; p < .001; Phi Cramer’s V = .251) but not significant between parents’ income level and children’s utilization of offline learning (X² (1) = 2.79; N = 201; p = .09; Phi Cramer’s V = .118). The effect size was moderate as indicated by Phi Cramer’s value. Children from high-income (97%) and highly educated (97.6%) backgrounds were more likely to utilize offline learning than children from low-income (90.4%) and less educated (84%) backgrounds. Children from low-income (15.6%) and less educated (17.1%) backgrounds were more likely to use commercial lesson centers (i.e., in-person outside the home learning) than children from high-income (9.1%) and highly educated (11.1%) backgrounds. These lesson centers do not comply with COVID-19 protocols.

A significant relationship was found between parents’ educational status and children’s utilization of online learning (television, radio, and internet programs) during COVID-19 (X² (1) = 32.97; N = 201; p < .001; Phi Cramer’s V = .405) as well as between parents’ income level and children’s utilization of online learning (X² (1) = 25.57; N = 201; p < .001; Phi Cramer’s V = .357). The observed relationship was moderate as indicated by Phi Cramer’s value. Children from high-income (75.8%) and highly educated (65.9%) backgrounds were more likely to utilize online learning than children from low-income (37.8%) and less educated (24.0%) backgrounds.

Educational Achievement of Children

Parents’ responses on the assessment of their children’s learning and educational achievement during the COVID-19 are presented in Table 5. Approximately 81.1% of the surveyed parents reported assessing the achievement of their children based on several elements: the employed teacher’s report, parents’ asking questions of their child(ren), and parents’ interpretation of their child(ren)’s scores on self-assessment. Approximately 7% of parents reported that their child(ren)’s achievement during COVID-19 lockdown period was better than their achievement pre-COVID-19 era. About 11.4% of the parents reported that the achievement of their child(ren) was the same as that of pre-COVID-19. Close to half of the respondents (46.8%) reported that the achievement of their children during COVID-19 lockdown was worse while 34.8% of the parents reported that they could not say whether there was a difference in the educational performance of their children before and during-COVID-19.

Table 5.

Children’s educational achievement based on parents’ reported assessment.

	Assess child(ren)’s performance		Comparison of performance pre- and in-COVID
	No n (%)	Yes n (%)	Better n (%)	Same n (%)	Worse n (%)	Can’t say n (%)
Highly educated	14 (11.1)	112 (88.9)	11 (8.7)	18 (143)	55 (43.7)	42 (33.3)
Less educated	24 (32.0)	51 (68.0)	3 (4.0)	5 (6.7)	39 (59.0)	28 (37.3)
High income	4 (6.1)	62 (93.9)	7 (10.6)	9 (13.6)	26 (39.4)	24 (36.4)
Low income	34 (25.2)	101 (74.8)	7 (5.2)	14 (10.4)	68 (50.4)	46 (34.1)
All parents	38 (18.9)	163 (82.5)	14 (7.0)	23 (11.4)	94 (46.8)	70 (34.8)

A significant relationship was found between parents’ educational status and parents’ assessment of their children’s educational outcomes (during COVID-19 (X² (1) = 13.38; N = 201; p < .001; Phi Cramer’s V = .258) as well as between parents’ income level and parents’ assessment of their children’s educational achievements (X² (1) = 10.57; N = 201; p = .001; Phi Cramer’s V = .229). The observed relationship was moderate as indicated by Phi Cramer’s value. Children from high-income (93.9%) and highly educated (88.9%) backgrounds were more likely to have their educational performance assessed by their parents than children from low-income (74.8%) and less educated (68%) backgrounds.

Chi-square test of independence was run on the item 17 regarding parents’ perception of their children’s level of educational achievement prior and during COVID-19. There were 14 extreme outliers on parents’ perception of change in their children’s educational achievement pre- and in-COVID. Thus, four chi-square tests of independence were conducted on educational achievement (with and without outliers) based on parents’ educational level as well as their income level. The findings did not change without the outliers; thus, findings including the outliers are only reported for both educational level and income level, respectively: X² (3, N = 201) = 4.81, p = .186; X² (3, N = 201) = 3.49, p = .322.

Discussion

This study investigated parents’ approach and perception of their children’s education (access to learning educational materials, engagement in learning, and educational achievement) based on varying socioeconomic backgrounds (educational and income levels) during the COVID-19 pandemic school closure. Overall, our study’s assumptions that children from high-income or high educated backgrounds would have more access to learning materials and engage more in learning were met while our assumption that high-income and highly educated parents are more likely to perceive their children’s educational achievement as better during the COVID-19 pandemic than low income and high educated parents was not met. Our findings showed that many children were engaged in learning during the COVID-19 school closure and that parents’ income and education influenced students’ engagement in learning. Results of this study showed that children from high-income and highly educated backgrounds were more likely to access learning materials and engage in learning in this period than children from low-income and less educated backgrounds. It further showed that parents’ educational and income levels influenced the learning materials accessible to children; the COVID-19 pandemic may have had an indirect impact on the accessibility of learning materials in various socioeconomic backgrounds. A detailed discussion is presented below.

Children’s Access to Learning Materials Based on Socioeconomic Factors

The results showed that students’ access to certain learning materials was associated with their socioeconomic backgrounds in Nigeria. However, education and income variables have a significant moderate positive correlation (e.g., Rho = .50 range); thus, the results need to be interpreted with caution. A significant difference was also found between access to learning educational materials of children from high-income backgrounds and those from low-income backgrounds as well as between children from highly educated families and less educated families. Children from high-income and highly educated backgrounds had access to more materials based on parents’ reports. This finding is consistent with findings in other countries (Hemphill et al., 2017; Le et al., 2019; Morales, 2014). Notably, the study revealed that English textbook, Mathematics textbook, smartphone, laptops, internet materials, educational TV programs, all textbooks, educational games, educational software, and desktop computer, in that order of magnitude, were the educational materials owned and/or used by parents to teach their children at home during the COVID-19 with in-person school closure. Over 50% of parents, regardless of their socioeconomic and educational status, had mathematics and English textbooks for their children’s education, which is quite commendable. This pattern portrays the emphasis and importance given to the two subjects in Nigeria. The popularity of mathematics and the English language in Nigeria is because they are major prerequisites for admission into any University in Nigeria.

The study also showed that besides English and mathematics textbooks, mobile devices (smartphone and laptops) were next among educational materials used by parents to teach their children during the COVID-19 school closure. The usage of mobile devices for mobile learning has become increasingly popular. However, income and educational levels of parents have a significant influence on the availability and usage of these technological or electronic learning devices, and all required textbooks for teaching and learning from home. The disparity, in the ownership of technological devices and “all textbooks” for children’s education, between the highly educated/high-income parents and the less educated/low-income parents can be explained from the perspective of poverty and growing inequality. Poverty prevents individuals from meeting their needs. The lack of learning materials is partly due to the lack of finance to acquire them. A greater number of low-income parents do not have television or other technological devices, such as laptops, which are necessary for accessing television educational programs and e-learning COVID-19 response actions. As rightly observed by Reardon (2011), high-income families invest relatively more time and resources in their children’s cognitive development than the lower income. Availability of learning materials has implications for engagement in learning as is shown in next paragraph. It further widens the gap between education performance of children from the different socioeconomic groups.

Children’s Engagement in Learning Based on Socioeconomic Factors

Generally, findings showed that Nigerian children were actively engaged in learning in the era of unprecedented COVID-19 school closure. However, majority of the parents indicated that their children studied between 2–3 hours, followed by 1–2 hours, 4 hours and above, and between 30 minutes to 1 hour any time they studied. These hours of engagement of the children in learning, though highly commendable, are not commensurate to the number of hours they take lessons at normal school. This finding highlights the impact of the COVID-19 pandemic on the child's education in Africa.

More specifically, statistically significant relationships between parents’ educational and income levels and engagement in learning were found indicating that children from highly educated families were more engaged in both offline and online learning as well as committed more time to studying than their counterparts from less educated and low-income backgrounds. This finding is not surprising; it is consistent with the findings of similar studies it is consistent with the findings of similar studies conducted in the pre-COVID-19 era (Barger et al., 2019; Lara and Saracostti, 2019). The higher engagement in learning, of children whose parents are highly educated, could be associated with their higher income since education correlated with income or their higher possession of or access to learning materials. Relying of Ajzen’s (1991) theory, the finding that children from high-income and highly educated backgrounds engage more in learning than their counterparts from low-income and less educated backgrounds can be explained by their social environment that is surrounded by educated people and possibly peers who are more likely to influence their attitudes and behaviors towards learning in times of uncertainties or school closure leading to their greater engagement in learning while children from low socioeconomic backgrounds lack or have limited access to social influence that could positively impact their attitudes and behaviors toward learning. Also drawing on the diathesis-stress model, the low utilization of online learning by children from low-income and less-educated backgrounds could be explained by the additional stress, occasioned by the COVID-19 pandemic, on the resources available to low-income parents for the provision of technological devices, data subscriptions, and electricity for using e-learning.

Surprisingly, our finding showed that children from low-income backgrounds engaged in outside the home learning (extramural learning centers schooling) than those from high-income backgrounds during the COVID-19 pandemic. The finding can be explained by socioeconomic disruptions created by the COVID-19 pandemic. It could be that low-income parents could not afford online education resources or offline employed home teachers as shown by their low access to learning materials and hence, they relied more on commercial face-to-face learning centers. Health reasons could be a further explanation why children from low-income backgrounds are more engaged in learning at lesson centers. Probably, high-income parents complied more with COVID-19 prevention instructions and relied less on face-to-face outside the home schooling for their children as shown in Table 4 while low-income parents cared “less” about COVID-19 guidelines and hence, heavily utilized the services of lesson centers (albeit unqualified teachers) which were in violation of COVID-19 guidelines. This is supported by United Nations Development Program [UNDP] Nigeria’s (2020) findings that salary earners and highly educated people reported higher proportions of practicing social distancing than non-salary earners and lowly educated persons. Interviewed parents in low-income neighborhoods, however, noted that they resorted to using lesson centers after unfruitful months of waiting for governments’ inclusive interventions. This further complicated their reason for violating COVID-19 guidelines in search of their children’s education as a lack of trust in the government. This highlights the serious implication of the COVID-19 pandemic on children’s educational engagement as a result of school closure.

Parents’ Perception of Children’s Academic Achievement

The result on parents’ perception of their children’s educational achievement during COVID-19 compared to pre-COVID-19 is received with caution since parents’ reports could be influenced by social desirability bias and they may also not have adequate or real conception of their children’s educational achievement. The finding, however, cannot be overlooked given that parents mostly have first-hand information about their children. The perception of high-income and low-income parents about changes in their children’s educational achievement prior and during the COVID-19 school closure did not significantly differ. Similarly, highly educated and less educated parents’ perceptions about changes in their children’s educational achievement prior and during the COVID-19 school closure did not significantly differ. However, the percentage scores suggest that more highly educated and high-income parents compared to low-income and less educated parents perceived their children’s academic achievement during the pandemic school closure as better than their achievements prior the school closure. This result appears realistic given previous findings that children from high-income and highly educated backgrounds consistently perform academically better than their counterparts all things not being equal (Engle and Black, 2008; Ensminger and Fothergill, 2002; Kuhfeld et al., 2020; Le et al., 2019; Liu and Lu, 2008; Smith, et al., 1997). More specifically, previous learning experiences and academic advantages of children from high income and highly educated backgrounds must have impacted the perceived less negative effect of prolonged school closure on them. This finding that COVID-19 did not negatively affect the educational achievement of some students further lends credence to a general finding in existing literature that parents’ involvement in child’s education positively contribute to academic achievement of students (Topor, 2010).

The results also showed that more highly educated parents than less educated parents reported that their children’s achievement was worse during COVID-19. This shift from what is known, from existing literature before COVID-19 school closures, about the effect of SES on academic achievements could be explained by highly educated parents’ probable stronger conception of their children’s pre-COVID-19 and present achievement. A larger percentage (46.77%) of the parent-respondents believed that their children had a decline in their academic achievement during the closure. This finding generally denotes an indirect negative impact of the COVID-19 school closure on students’ academic achievement in Nigeria.

Limitations of the Study

This study’s findings are limited by several issues, which provide directions for future research. One was the instrument for data collection. There was non-uniform scaling and limited number of items in some subscales; there was also the sourcing of information about students’ pre- and in-COVID-19 education achievement from parents’ reports. Also, no assessment was made in differentiating the effects of the pandemic on the education based on the gender or educational level of the children. Parental status was also not surveyed for type of family unit: a one-parent or two-parent family.

Implications for Future Studies

Future studies should consider adopting more robust instruments for data collection that address the limitations with the instrument used for this study. It is also important for future studies to consider the differential effect of COVID-19 or other crises on girl and boy child’s education and children’s education level in their work. Given that in-person schooling has resumed, exploring the difference between pre-COVID-19 and post-COVID-19 in-person schooling academic achievement may be possible. Limitations notwithstanding the study’s findings offer some knowledge about the state of affairs of children’s education during the COVID-19 pandemic’s abrupt and indefinite school closure and insights into what post-COVID-19 pandemic educational measures to adopt for improving students’ educational outcome after return to school. This study is the first of its kind in Nigeria to explore an important issue of how children from different socioeconomic backgrounds engage in education activities in emergencies, and has some policy and practice implications as subsequently discussed.

Policy and Practice Implications

These findings suggest policy and practice implications beyond emphasis on promoting safety measures in school after reopening as is the case with Nigeria. Nigeria reopened schools in October 2020; this was a welcomed development considering that majority of citizens did not comply with the COVID-19 guidelines, but engaged in face-to-face learning, making school closure an ineffective response. In its published guidelines for school reopening, safety measures such as outdoor learning, alternate attendance, staggered attendance, platooning, decreased interaction, procurement of WASH facilities, and sensitization of teachers and students on COVID-19 and its preventive measures among others are emphasized (Federal Ministry of Education, 2020). Just like most other countries, less attention is paid to what intervention measures or post-COVID pedagogy is needed to ameliorate the negative impacts (e.g., loss of learning, loss of interest in schooling, and mislearning) of COVID-19 emergency school closure on learners’ education. This situation is, as explained by Murphy (2020), a result of the difficulty to “predict what educational landscape will look like after COVID-19 passes” and “the emphasis on normalizing emergency e-learning which has robbed the education sector of the opportunity for open discourse on how the sector can be emancipatory of all students (p. 501).” The stated difficulty notwithstanding, findings of our study implicate some post-COVID-19 pedagogic practices and intervention measures.

Educational interventions and pedagogical practices for post-COVID-19 instructions should be more targeted at children from low socioeconomic backgrounds as our study showed their education to be more negatively impacted by the COVID-19 pandemic. Teachers serving low-income students should be supported by relevant stakeholders in their post-COVID-19 classroom practices since they are more likely going to encounter more students’ challenges in relation to learning loss, mislearning, preparedness, and assimilation.

Post-COVID or post-crisis pedagogy should first examine learners’ prior knowledge on different subjects to understand the level of loss of learning among students prior to resuming post-COVID-19 instructions. Given the high number of lessons delivered by unqualified teachers, particular attention should be paid to understanding what the learners learned under the care of unqualified teachers or self-study during school closure. This approach is important considering that there may be a probability of mislearning that may need to be unlearned. Misconceptions flowing from mislearning can inhibit meaningful learning (Obiagu, 2020). Awareness of the knowledge gained by students during COVID will best position the teachers to adopt inclusive practices and effectively manage their classes.

The government should not assume that learners accessed its (emergency) e-learning programs as our study showed that most students did not have access to them. Hence, topics covered via e-learning should be repeated for in-person learning after school reopening, through intervention school programs administered outside normal school hours. This implies that school hours in post-COVID pandemic will have to be extended for a period of one or two hours with additional measures (e.g., free school lunch) to keep learners motivated to learn.

There may also be a need for socioemotional intervention programs to remotivate students who might have lost interest in schooling, following the prolonged school closure, to continue with schooling and launch back better. This suggestion also applies to all emergency situations affecting schooling. Finally, there is a need for tighter measures to ensure that the Government's free textbook program for students is justly implemented to prevent the case of having most low-income learners not accessing all required textbooks for learning.

Conclusion

The closure of schools by the authorities of different countries, including Nigeria, due to the outbreak of COVID-19, as one of the measures of preventing further spread of the virus, seems to have widened the educational gaps between students from low and high socioeconomic backgrounds in Nigeria. This worry necessitated the present study. Our findings showed disparity between studied groups in the ratio of ownership of educational (learning) materials that are readily available for child education, utilization of the federal and state governments launched educational TV programs, and access to internet materials. The findings showed that the government's emergency e-learning response action were poorly utilized by students and their parents in Nigeria for economic reasons, ranging from a lack of electronic devices to limited reach of the intervention programs. It was observed, in this study, that highly educated/high-income children had more access to educational resources than the lower educated/low-income children. Children from high-income and highly educated backgrounds seemed to be more engaged in learning. A contrasting finding with what is found in the literature is that children from low-income backgrounds are more engaged in offline outside the home learning. This contradiction is explained from the perspective of the disruption caused by COVID-19 and the low compliance with COVID-19 guidelines by low-income earners. It is recommended, among other factors, that post-COVID-19 pedagogies consider psychosocial interventions that will help keep students motivated to continue learning and relate peacefully with others after a long period of school closure.

Footnotes

Acknowledgments

We sincerely thank the participants of our study for making this study possible. We are very grateful to the editor of this journal, Prof. Beverly Vandiver, the Editor’s Assistant, and the reviewers of our manuscript for their time, commitment, and suggestions that sharpened our work overall.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Adaobiagu N. Obiagu

References

Abbasi

Ayoob

Malik

Memon

S. I.

(2020). Perception of students regarding E-learning COVID-19 at a private medical college. Pakistan Journal of Medical Sciences Online, 36(S4), S57–S61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Anwar

Adnan

(2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://www.j-psp.com/article/online-learning-amid-the-covid-19-pandemic-students-perspectives-8355

Barger

M. M.

Kim

E. M.

Kuncel

N. R.

Pomerantz

E. M.

(2019). The relation between parents’ involvement in children’s schooling and children’s adjustment: A meta-analysis. Psychological Bulletin, 145(9), 855–890. https://doi.org/10.1037/bul0000201

Broerman

(2018). Diathesis-stress model. In Zeigler-Hill

Shackelford

(Eds), Encyclopedia of personality and individual differences (pp. 1107–1109). Springer. https://doi.org/10.1007/978-3-319-28099-8_891-1

Cevik

Bamford

(2020). COVID-19 pandemic – a focused review for clinicians. Clinical Microbiology and Infection, 26(7), 842–847. https://doi.org/10.1016/j.cmi.2020.04.023

Chiou

Tucker

(2020) Social distancing, internet access and inequality (Working Paper No. 26982) (1–23). National Bureau of Economic Research. https://doi.org/10.3386/w26982

Chiu

T. K.

Lin

Lonka

(2021). Motivating online learning: The challenges of COVID-19 and beyond. The Asia-Pacific Education Researcher, 30(3), 187–190. https://doi.org/10.1007/s40299-021-00566-w

Consortium for School Networking [COSN] . (2020, March). COVID-19 responses: Preparing to take school online. COSN. https://dpi.wi.gov/sites/default/files/imce/broadband/COVID-19_Member_CoSN.pdf

10.

Cucinotta

Vanelli

(2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157–160. https://doi.org/10.23750/abm.v91i1.9397

11.

Duncan

G. J.

Brooks-Gunn

(2000). Family poverty, welfare reform, and child development. Child Development, 71(1), 188–196. https://doi.org/10.1111/1467-8624.00133

12.

Engle

P. L.

Black

M. M.

(2008). The effect of poverty on child development and educational outcomes. Annals of the New York Academy of Sciences, 1136(1), 243–256. https://doi.org/10.1196/annals.1425.023

13.

Ensminger

M. E.

Fothergill

(2002). A decade of measuring SES: What it tells us and where to go from here. In Bornstein

M. H.

Bradley

R. H.

(Eds), Socioeconomic status, parenting, and child development (pp. 13–28). Routledge. https://doi.org/10.4324/9781410607027

14.

Eyles

Gibbons

Montebruno

(2020). COVID-19 school shutdowns: What will they do to our children's education? Working Paper No. 001). Center for Economic Performance, London School of Economics and Political Science. http://eprints.lse.ac.uk/104675/3/Eyles_covid_19_school_shutdowns_published.pdf

15.

Federal Ministry of Education [FME] . (2020). Guidelines for schools and learning facilities reopening after COVID-19 pandemic closures: Taking responsibility for safe schools and quality learning. FME. https://education.gov.ng/wp-content/uploads/2020/07/COVID-19-GUIDELINES-FOR-SAFE-REOPENING-OF-SCHOOLS-LEARNING-FACILITIES.pdf

16.

Field

(2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Sage. http://www.uk.sagepub.com/field4e/default.htm

17.

Hair

Hanson

Wolfe

Pollak

(2015). Association of child poverty, brain development, and academic achievement. JAMA Pediatrics, 169(9), 822–829. https://doi.org/10.1001/jamapediatrics.2015.1475

18.

Hemphill

Broderick

Heerde

(2017). Positive associations between school suspension and student problem behavior: Recent Australian findings. In Trends and Issues in Crime and Criminal Justice. Australian Institute of Criminology. https://www.aic.gov.au/publications/tandi/tandi531

19.

Hinton

P. R.

McMurray

Brownlow

(2014). SPSS explained (2nd ed.). Routledge. https://www.routledge.com/SPSS-Explained/Hinton-McMurray-Brownlow/p/book/9780415616027

20.

Kendler

(2020). A prehistory of the diathesis-stress model: Predisposing and exciting causes of insanity in the 19th century. The American Journal of Psychiatry, 177(7), 576–588. https://doi.org/10.1176/appi.ajp.2020.19111213

21.

Kpolovie

P. J.

Joe

A. I.

Okoto

(2014). Academic achievement prediction: Role of interest in learning and attitude towards school. International Journal of Humanities Social Sciences and Education, 1(11), 73–100. http://www.arcjournals.org/pdfs/ijhsse/v1-i11/10.pdf

22.

Kuhfeld

Soland

Tarasawa

Johnson

Ruzek

Liu

(2020). Projecting the potential impacts of COVID-19 school closures on academic achievement. Educational Researcher, 49(8), 549–565. https://doi.org/10.3102/0013189X20965918

23.

Lara

Saracostti

(2019). Effect of parental involvement on children’s academic achievement in Chile. Frontiers in Psychology, 10, 1–5. https://doi.org/10.3389/fpsyg.2019.01464

24.

Tran

Trinh

Nguyen

Vuong

Bui

Vuong

Hoang

Nguyen

Vuong

(2019). Reading habits, socioeconomic conditions, occupational aspiration and academic achievement in Vietnamese junior high school students. Sustainability, 11(2), 51–63. https://doi.org/10.3390/su11185113

25.

Levinson

Cevik

Lipsitch

(2020). Reopening primary schools during the pandemic. The New England Journal of Medicine, 383, 981–985. https://www.nejm.org/doi/full/10.1056/NEJMms2024920

26.

Liu

(2008). Student performance and family socioeconomic status: Results from a survey of compulsory education in Western China. Chinese Education and Society, 41(5), 70–83. https://doi.org/10.2753/CED1061-1932410505

27.

McHugh

M. L.

(2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143–149. https://doi.org/10.11613/BM.2013.018

28.

Mertler

C. A.

Vannatta

R. A.

(2016). Advanced and multivariate statistical methods: Practical application and interpretation (6th ed.). Taylor & Francis Group. https://doi.org/10.4324/9781315266978

29.

Mihret

M. A.

Dilgasa

G. S.

Mamo

T. H.

(2019). Parenting style as correlates of adolescents’ academic achievement motivation of bate secondary school, Haramaya, Ethiopia. International Journal of Education and Literacy Studies, 7(2), 172–176. https://doi.org/10.7575/aiac.ijels.v.7n.2p.172

30.

Morales

E. E.

(2014). Learning from success: How original research on academic resilience informs what college faculty can do to increase the retention of low socioeconomic status students. International Journal of Higher Education, 3(3), 67–79. https://doi.org/10.5430/ijhe.v3n3p92

31.

Murphy

M. P.

(2020). COVID-19 and emergency e-learning: Consequences of the securitization of higher education for post-pandemic pedagogy. Contemporary Security Policy, 41(3), 492–505. https://doi.org/10.1080/13523260.2020.1761749

32.

Nigeria Education in Emergencies Working Group . (2020). Nigeria education sector COVID-19 response strategy in North East. https://resourcecentre.savethechildren.net/library/nigeria-education-sector-covid-19-response-strategy-north-east?embed=1

33.

Nyabola

(2020). Global justice: How to talk about COVID-19 in Africa. Boston Review. https://bostonreview.net/global-justice/nanjala-nyabola-how-talk-about-covid-19-africa

34.

Obiagu

A. N.

(2020). Unemployment and poverty rise in Nigeria: Implications for increasing learners’ social-emotional and cognitive development through inclusive critical storytelling pedagogy (TRIP working paper No. 001). https://doi.org/10.2139/ssrn.3548642

35.

Olayinka

A. B.

(2016). Effects of instructional materials on secondary schools students’ academic achievements in Social Studies in Ekiti State, Nigeria. World Journal of Education, 6(1), 32–38. https://doi.org/10.5430/wje.v6n1p32

36.

Organization for Economic Cooperation and Development [OECD] . (2020). Education responses to COVID-19: An implementation strategy toolkit. OECD. https://doi.org/10.1787/81209b82-en

37.

Rapanta

Botturi

Goodyear

Guardia

Koole

(2020). Online university teaching during and after COVID-19 crisis: Refocusing teacher presence and learning activity. Post Digital Science and Education, 2, 923–945. https://doi.org/10.1007/s42438-020-00155-y

38.

Reardon

S. F.

(2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In Duncan

Murnane

(Eds), Whither opportunity the rising inequality, schools, and children’s life chances (pp. 91–115). Russell Sage Foundation. https://www-jstor-org-443.web.bisu.edu.cn/stable/10.7758/9781610447515

39.

Smith

J. R.

Brooks-Gunn

Klebanov

P. K.

(1997). Consequences of living in poverty for young children's cognitive and verbal ability and early school achievement. In Duncan

Brooks-Gunn

(Eds), Consequences of growing up poor (pp. 132–189). Russell Sage Foundation. https://www-jstor-org-443.web.bisu.edu.cn/stable/10.7758/9781610448260

40.

Tety

J. L.

(2016). Role of instructional materials in academic performance of students in community secondary schools in Rombo District of Tanzania, [Master’s Thesis. The Open University of Tanzania]. The Open University of Tanzania Institutional Repository. https://repository.out.ac.tz/id/eprint/1829

41.

Topor

Keane

Shelton

Calkins

(2010). Parent involvement and student academic performance: A multiple mediational analysis. Journal of Prevention & Intervention in the Community, 38(3), 183–197. https://doi.org/10.1080/10852352.2010.486297

42.

Trading Economics . (2019). Nigeria living wage individual. www.tradingeconomics.com/Nigeria/living-wage-individual

43.

Turner

E. A.

Chandler

Heffer

R. W.

(2009). The influence of parenting styles, achievement motivation, and self-efficacy on academic performance in college students. Journal of College Student Development, 50(6), 337–346. https://psycnet.apa.org/doi/10.1353/csd.0.0073

44.

United Nations Development Program [UNDP] Nigeria . (2020). The COVID-19 pandemic in Nigeria: Citizen perceptions and the secondary impacts of COVID-19. https://www.ng.undp.org/content/nigeria/en/home/library/mdg/the-covid-19-pandemic-in-nigeria--citizen-perceptions-and-the-se.html

45.

Warner

Murray

Meyer

(2008). Holiday and school-term sleep patterns of Australian adolescents. Journal of Adolescence, 31(5), 595–608. https://doi.org/10.1016/j.adolescence.2007.10.005

46.

Yin

R. K.

(2013). Validity and generalization in future case study evaluations. Evaluation, 19(3), 321–332. https://doi.org/10.1177/1356389013497081