31 INFLUENCE OF ICT ON TEACHING AND LEARNING IN TERTIARY INSTITUTION DURING CORONA VIRUS PANDEMIC IN SOUTH SOUTH OF NIGERIA
Mr. Ichazu Kingsley and Mrs. Bright Irene Ewere
ABSTRACT
Information and Communication Technology (ICT) has numerous potentials to spur education development in tertiary institutions in Nigeria. It impacts positively on the educational process, unlike the physical chalkboard in the classrooms. The outbreak and fast spread of the Coronavirus led to the closed down of schools. Efforts to revamp education due to prolong lockdown made the government enforce e-learning in tertiary institutions across the country. Hence, this study investigated influence of ICT in teaching and learning in tertiary institution during corona virus pandemic lockdown by the instructors in the tertiary institutions in Nigeria vis-a-vis their socio-economic factors and limitations encountered. A systematic sampling technique was adopted to select 180 respondents from the staff list. A validated questionnaire was used to collect data on socio-economic variables (SEV), compliance (ϒ) to e-learning, and limitations (Ls) while multiple linear regression model (R) was used to test the interaction between the compliance and limitations. Results show that age (β¼ 0.351), educational qualification (β¼ 0.843) and teaching experience (β¼ 0.169) influence ICT used in teaching and learning compliance at p < 0.05. It was also found that 67.3% adoption of ICT for teaching and learning took place in the Universities compared to 59.1% in the Polytechnics and 52.8% in the Colleges of Education. Regression shows that constraints affected the level of compliance (R2 ¼ 0.73). The study concludes that constraints are major obstacles to the Use of ICT facilities on teaching and learning in Tertiary Institutions in south south during Corona Virus pandemic.
Keywords: Teaching and learning; Lockdown, Coronavirus Pandemic Tertiary institutions, Influence
Introduction
There is a pervasive crisis in Sub-Saharan Africa’s teaching and learning development systems. The current Coronavirus Disease of 2019 (COVID-19) compounded the trouble and has taken tolls on all socio-economic sectors without exception to the educational system in Nigeria. During the lockdown, many female students have come to be victims of rape which have led to undesirable pregnancies, and instances of death additionally reported. For instance, a female undergraduate student of Laboratory Technology Department, Federal College of Animal and Production Technology, Moor Plantation Ibadan, Oyo State was raped to death (Ajayi, 2020); equally incident of gang-raped and demise of a female undergraduate student, University of Benin, Benin City, Edo State was also reported (Adejumo, 2020); and some other rape and homicide case of a postgraduate student of University of Ibadan happened in the course of the pandemic (Omonobi et al., 2020).
Universities closed their premises and nations shut down their borders in response to lockdown measures. Findings from 200 nations in the mid-April 2020 confirmed that 94 percentage of learners were affected due to the pandemic around the world, which represents 1.58 billion learners (United Nations, 2020). Additionally, UNESCO (2020) stated that the closure of higher institutions has affected over 91 percent of the students’ population in the world and that 23.8 million college students might also drop out or no longer be in a position to secure admission to schools in the 2021 academic calendar.
Remote learning grew to become a lifeline for training during the pandemic but, the opportunities that ICT offer go beyond a stop-gap solution at some stage in a crisis (Andreas, 2020). According to Eze et al., (2018), e-learning training is the all-inclusive blending of ICT facilities and present todays telecommunication tools into the educational system. Andreas (2020) and Eze et al., (2018) maintained that e-learning is a hallmark of distance learning. Digital technological know-how provides entirely new solutions to the query of what human beings learn, how they learn, and where and when they learn.
Andreas (2020) in addition asserts that technology permits teachers and students to get admission to specialized materials beyond textbooks, in multiple formats, and in ways that bridge time and space. Meanwhile, Eduard and Lucian (2020) hinted that e-learning is a modern platform for transmitting understanding and skills to the learners; it is cheap, saves time, and has a wider coverage, and as well promoting team learning and collaboration. Andreas (2020) reiterated that technological know-how promotes deep learning, and permits schools to respond higher to the varying demands of the students.
In a bid to keep away from brain-drain and prevent the whole collapse of the schooling areas in the country, Nigeria joined different leagues of developed international countries and include e-learning in the schooling system during lockdown. Although Nigeria Open University operates e-learning to deliver lectures and supply assignments to the university students this digitization has no longer been sufficiently harnessed in many tertiary institutions across the country. It is either the lecturers are not ICT-compliance or the college students are disadvantaged. In some tertiary institutions, the adoption of ICT facilities are limited to students’ registration and examination. Much effort has not been geared in the direction of high quality teaching and learning for students’ academic overall performance via the use of ICT facilities.
While Coronavirus pandemic has compelled Nigeria to embrace ICT in teaching and Learning to hold tempo with rapid improvement in the area of technology, the implementation is at a very low pace. In advanced countries, the adjustments are eminent in the academic area as traditional teaching methods have been transformed into cutting-edge methods.
For instance, the Chinese Ministry of Education introduced a virtual Classes Without Stopping Learning coverage to make certain that teaching and learning are not compromised at any time at some point of the Coronavirus pandemic lockdown (Zhang et al.,, 2020), and furnish bendy on-line studying to thousands of students from their houses (Huang et al.,, 2020). The instructional equipment are designed in such a way that students could discover educational content material at will while teachers delivered the instructions the use of virtual assembly structures (Andreas, 2020).
In Sweden, post-secondary colleges have switched to broadly speaking distance mastering from the onset of the pandemic (UNESCO, 2020). Online evaluation carried out by way of Chaka (2020) in South Africa and the United State of America, it was also found that at some point of the Coronavirus lockdown 17 of the 21 South African universities and sixty three of the sixty four U.S. universities migrated to e-learning and utilized Zoom, Canvas, and Blackboard as the topmost on line tools and resources.
In March 2020, the Italian authorities equipped schools with digital platforms, educated faculty instructors on techniques for ICT teaching and learning, and gave digital devices to needy university students to cushion the effects of the Coronavirus pandemic (The Republic of Italy, 2020). In the same March 2020, Pakistan’s Higher Education Commission (PHEC) compelled greater institutions to commence e-Teaching and Learning. Also, teachers in Greece performed virtual real-time classes in conjunction with other online studying tools (Ministry of Education and Religious Affairs, 2020; Schleicher and Reimers, 2020). Australia swiftly switched to online learning in the wake of the pandemic (Ali, 2020). This would prevent compromising education in a pandemic state of affairs (The News, 2020).
In Nigerian context, the variety of higher institution students attending tertiary institutions outnumbered the schools’ infrastructure. The excessive price of ICT accessories and inadequate aid people are among the troubles limiting e-learning in Nigeria (Adeoye et al., 2020). In Nigeria, many establishments discover it hard to conceptualize and enforce ICT Technology initiatives locally.
Research Objectives
In order to answer these questions therefore, the following objectives will be treated. The precise targets of this paper therefore are to:
- Evaluate the influence of ICT in teaching and learning in tertiary institutions during Coronavirus pandemic and
- Identify the barriers to the use of ICT in teaching and learning in tertiary institutions during Coronavirus pandemic
Research Questions
- Does ICT influences teaching and learning during coronavirus pandemic?
Ii What are the factors facing the implementation of online teaching and learning?
Research Hypotheses
HO1: There is no significant relationship between Information and Communication Technology and teaching and learning in tertiary institutions during Coronavirus pandemic.
HO2: There is no significant relationship between identifiable factors affecting the use of ICT and teaching and learning in tertiary institutions during Coronavirus pandemic
Assumptions
This hypothesis is premised on the assumption that constraints ought to have an effect on the optimization of e-learning in Nigerian tertiary institutions. According to the United Nations (2020) report, some tertiary institutions jettisoned ICT technology on impacting teaching and learning for the duration of school closure due to the lack of indicators of technological know-how (IT) infrastructure.
Justification for this Study
Going with the aid of the speedy rising cases of Coronavirus in the country, the Federal Government of Nigeria at the beginning locked down two states (Lagos and Ogun) while other affected states joined as the Coronavirus spreads. Federal Ministry of Education enforces digital learning in the tertiary establishments as a way to ensure the school education is not in total collapsed. Beyond the authorities’ pronouncement and swift shift to e-learning throughout the world, researchers have not empirically examined the impact of ICT technology adoption for teaching and learning all through the Coronavirus pandemic. More so, the World Bank (2020b) is of the view that few researches have been carried out on the scale of e-learning provision, compliance, and limitations in the higher institutions. Many studies centered on necessity of e-learning all through lockdown (Ali, 2020), instructional techniques for online (Mahmood, 2020), stage of preparedness for e-learning (Eduard and Lucian, 2020; EiEWG, 2020), e-learning and tertiary training trip (Adeoye et al.,, 2020), and use of on-line instruction, equipment and sources during Coronavirus (Chaka, 2020). It is hereby fundamental to look into the impact on ICT technology for teaching and learning and pros and cons of e-learning approach to strengthening Nigeria’s educational system.
Instructional Science Theory
The major objective of Instructional Science is to foster a deeper understanding of the nature theory and training of the instructional methods and of the learning to which it results. E-learning supports information and performance management (Mahmood, 2020; The World Bank, 2020a). According to Eduard and Lucian (2020), instructional science as a field of schooling or new terminology has been like instructional aids or apparatus. E-learning has offered excellent possibilities for teaching through digital ability (Kacerauskas and Kusaityte, 2020; The World Bank, 2020a). Students that undertake electronic research typically carried out better performance than those in face-to-face courses. Andreas (2020) opined that the academic performance of newcomers that used the digital approach supersedes these who studied the ordinary approach. E-learning is a new learning tool in Nigeria, with all its potentialities.
Research Method
This study was carried out in the Southern Nigeria. The south south geo-political zone comprises of six states which are Delta, Edo, Bayelsa, Cross Rivers, Akwa Ibom, and Rivers States. Kothari (2004) sample size determination formula was used to estimate the sample size to be selected for this study, the formula is:
At the confidence interval (c) of 5% and confidence level (z) of 1.96 for 95%, a 69% proportion of an attribute of the population (p), and 17 % desired level of precision (q), the estimated sample size is 180.2. For ease of distribution, the sample size was approximated to 180. A multi-stage sampling method was used for the selection of a representative sample. This sampling method is chosen because it is an advance of the principle of cluster sampling.
The method is recommended for a big inquires extending to a considerable large geographical area (Kothari, 2004), like the case under study, tertiary institutions in Nigeria. The merits of this method are that it is easier to administer than most single-stage designs, and a large number of units can be sampled for a given cost because of sequential clustering, whereas this is not possible in most of the simple designs. The three states randomly selected out of six states in the first stage are Delta, Edo and Bayelsa. Universities, Polytechnics, and Colleges of Education in Delta, Edo and Bayelsa State, Nigeria were chosen for this study.
In the second stage, one University, one Polytechnic, and one College of Education were selected from each state; these gave rise to 3-Colleges of Education, 3-Polytechnics, and 3-Universities selected. In the third stage, a systematic sampling technique was adopted to select every 13th name on the staff lists to arrive at twenty instructors per institution. Systematic sampling is spread more evenly over the entire population; it is an easier method of sampling and can be conveniently used even in the case of large populations (Kothari, 2004). Thus, 180 instructors were selected from the nine tertiary institutions. Government-owned institutions particularly Universities were used in conducting this research.
The authors highly considered the issues of validity and reliability in the study. To ensure the validity of the study, the content validity of the instrument was carried out by experts in ICT and Education. Content validity according to (Dave, 2012; Wilson et al.,, 2012) is the extent to which a measure represents all facets of a given social construct. It is the most critical criterion and indicates the degree to which an instrument measures what it is supposed to measure. Similarly, the reliability of the instrument was carried out by the test re-test method. The coefficient of reliability was 0.79, an indication that the instrument is reliable.
This study adopted a survey method for the primary data collection on socio-economic variables, ICT technology for teaching and learning in the tertiary institutions. Respondents showed a willingness to provide answers to the questions contained in the questionnaire. This is a quite popular method of data collection. It does not give room for the interviewer’s bias; answers are in respondents’ own words hence the results can be made more dependable and reliable (Kothari, 2004).
In the course of conducting this study, authors strictly adhered to all standards of ethical principles to safeguard the rights of respondents in terms of the respondents’ autonomy, privacy, anonymity, and confidentiality. All procedures adopted in the conduct of this study followed ethical standards of the institution approved by the Institution Committee on Research (ICR) and Joint Technical Task Team on Coronavirus (JTTT), Delta State, Nigeria on May 23rd, 2020 for the period of 3–5 months.
Analytical Methods
Age and years of experience were measured at ratio level and converted to an interval level for presentation. Educational qualification was measured as the number of years spent in the schools to obtain various qualifications by the respondents. Adoption of ICT (ϒ) in teaching and learning was conceptualized as Complete (3), Partial (2), and Not at all (1) for descriptive statistics and Analysis of variance.
Model Specification:
n1(t.q1)þn2(t.q2)þn3(t.q3)…þni(t.qi) ¼ϒ …………………….(1)
where; t is the time taken to deliver the course online, q is the course taken, and n is the number of the times the course was taken.
fnxn ¼ Ln………………………………………………………(2)
f ¼ frequency, x ¼ score and Ln‘s referred to the problems confronting the adoption of ICT for teaching and learning such as poor electricity supply, high cost of e-learning facilities, and poor internet connectivity. Multiple linear regression models determine the extent of differences to adoption of ICT for teaching and learning compliance among the instructors in the selected private institutions (See Table 1).
According to Kothari (2004), the primary function of regression analysis is to determine the various factors which cause differences of the dependent variable. The functional form gives the best fit in terms of the high value of the R2, the low value of Durbin-Watson, the sign of coefficients, as well as better F-ratio (see Table 2).
ϒ¼ f(Ls) ……………………………………………………………………(3)
ϒ¼ f (fnxn)
Thus the explicit model is:
ϒ¼αþβ1L1 þβ2L2 þβ3L3 þβ4L4 þβ5L5 ……þβnLn þ ei …………(4)
ϒ¼αþβ1(f1x1) þβ2(f2x2) þβ3(f3x3) þβ4(f4x4) þβ5(f5x5) ……þβn(fnxn) þ ei……………(5)
where ϒ is adoption of ICT for teaching and learning and βn‘s referred to the parameter to be estimated.
Findings
Influence of selected socio-economic variables on adoption of ICT for teaching and learning
Figure 1 portrays age categories of the respondents with seventy-five percent fell within 35–39 years while 12.5% were older than 40 years. The estimated average age was 36.8 years for the respondents.
Figure 1. Radar showing the age distribution of the respondents. Source: Field Survey (2020).
Table 2 shows the regression results of the age of the respondents as a predictor of adoption of ICT for teaching and learning. The result indicates that there is a positive but weak correlation between the age of the respondents and adoption of ICT for teaching and learning (R ¼ 0.351a < 0.51 for 180 degrees of freedom). The significant of F-statistics (F ¼ 25.034, p ¼ 0.024c) indicates a linear relationship between the age and adoption of ICT for teaching and learning. The regression model explains that 12.3% difference in adoption of ICT for teaching and learning was due to age (R2 ¼ 0.123b) while 87.7% is due to the residual factors excluded from the model. A significant relationship was found between the age of the respondents (β ¼ 0.351d) and adoption of ICT for teaching and learning at p < 0.05 which is 35.1%. Therefore, age is a determinant of adoption of ICT for teaching and learning in the Nigerian Tertiary Institutions. The implication is that younger instructors should be the target of adoption of ICT for teaching and learning coupled with training and skills acquisition because they are easy to train and have a high tendency to adapt to ICT technological environment.
The result of the regression in Table 3 indicates a strong correlation between the educational qualification of the respondents and adoption of ICT for teaching and learning (R ¼ 0.853a > 0.51 for 180 degrees of freedom). The F-statistics (F ¼ 475.356, p ¼ 0.000c) is high and significant which indicates a strong influence of education on adoption of ICT for teaching and learning.
The coefficient of R2 (0.728b) shows that 72.8% difference in adoption of ICT for teaching and learning which is caused by the educational qualification while the remaining 27.2% is attributed to the residual factors excluded from the regression model. Educational qualification (β¼ 0.843d) is positively significant at p < 0.05, that is, it has 84.3% influence on adoption of ICT for teaching and learning. Hence, the educational qualification of the respondents is a strong predictor of adoption of ICT for teaching and learning in Nigerian Tertiary Institutions. This implies that the educational qualification of the respondents could be harnessed and properly channeled towards adoption of ICT for teaching and learning and sustainability.
|
Table 1. Variable Choice and definition for adoption of ICT for teaching and learning. |
||||
|
Variables |
Description |
Variable type |
Expected relationship |
|
|
Dependent variable |
|
|
|
|
|
ϒ |
Adoption of ICT for teaching and learning |
Scores |
Continuous |
Positive |
|
Independent variables |
||||
|
ϒ |
adoption of ICT for teaching and learning |
Description |
Variable type |
Expected relationship |
|
L1 |
Poor electricity supply |
Scores |
Continuous |
Negative |
|
L2 |
High cost and poor quality of e-learning facilities |
Scores |
Continuous |
Negative |
|
L3 |
The poor technical know-how of e-learning |
Scores |
Continuous |
Negative |
|
L4 |
Poor internet connectivity |
Scores |
Continuous |
Negative |
|
L5 |
Lack of telecommunication infrastructure |
Scores |
Continuous |
Negative |
|
L6 |
Lack of training support by the institutions |
Scores |
Continuous |
Negative |
α¼ Constant; and ei ¼ error term
|
Table 2. Correlation between age of the respondents and adoption of ICT for teaching and learning |
||||||||
|
ModelR |
R2 |
Adjusted R 2 |
Std. Error of the Estimate |
|
Durbin-Watson |
|||
|
1 |
|
0.351a |
0.123 |
0.118 |
|
5103.326 |
|
1.458 |
|
ANOVAb |
|
|
|
|
|
|
||
|
|
Sum of squares |
|
df |
Mean Square |
|
F-Statistics |
Sig. |
|||
|
Regression |
|
6.520E8 |
|
1 |
|
6.520E8 |
|
25.034 |
|
0.024c |
|
Residual |
|
4.636E9 |
|
178 |
|
2.604E7 |
|
|
|
|
|
Total |
|
5.288E9 |
|
179 |
|
|
|
|
|
|
Coefficient
|
Model |
Unstandardized coefficient |
Standardized Coefficient |
Sig. |
||||||||
|
|
βStd. Error |
|
Beta |
|
T |
|
|
|
|
||
|
(constant) |
|
4999.115 |
|
2159.177 |
|
|
|
2.315 |
|
0.022 |
|
|
|
|
|
|
|
|
|
|
d |
|||
|
Age |
|
253.196 |
|
50.605 0.351 |
|
5.003 |
|
0.000 |
|||
|
Source: Field Survey (20 20). a Predictor: (Constant), age. b Dependent variable: adoption of ICT for teaching and learning. c Predictor: (Constant), age. d Predictor: (Constant), age. |
|||||||||||
Figure 2. Bar Chart showing the educational qualification of the respondents.
Source: Field Survey (2020).
A positive and weak correlation was revealed for years of experience in teaching and adoption of ICT for teaching and learning as (R ¼ 0.169a < 0.51 for 180 degrees of freedom). The F-statistics (F ¼ 5.211, p ¼ 0.024c) is significant but very low which further affirms that the relationship between years of experience in teaching and adoption of ICT for teaching and learning is weak. The coefficient of R2 (0.028b) shows that teaching experience is responsible for a 2.8% difference in adoption of ICT for teaching and learning while the remaining 97.2% is attributed to the lasting factors excluded from the regression model.
There is a significant and positive relationship between teaching experience (β¼ 0.169d) and adoption of ICT for teaching and learning at p < 0.05, this means that a 1% increase in the teaching experience would result in 16.9% adoption of ICT for teaching and learning. Hence, the teaching experience of the respondents influences adoption of ICT for teaching and learning in Nigerian Tertiary Institutions. The implication for this study is that the instructors’ experience would be advantageous for capacity building and training on ICT adoption as little effort and lesser cost would be required to transmit the pedagogy and contents of ICT use to the instructors.
Variance in adoption of ICT for teaching and learning during Coronavirus lockdown in the Nigerian tertiary institutions
|
Table 3. Correlation between respondents’ educational qualification and adoption of ICT for teaching and learning. |
|||||
|
R |
R2 |
Adjusted R2 |
Std. Error of the Estimate |
Durbin-Watson |
|
|
1 |
0.853a |
0.728 |
0.726 |
2844.871 |
1.47 |
|
ANOVAb |
|
|
|
|
|
.
|
|
Sum of squares |
df |
|
Mean Square |
F-Statistics |
Sig. |
|
|
Regression |
|
3.847E9 |
1 |
|
3.847E9 |
475.356 |
0.000c |
|
Residual |
|
1.441E9 |
178 |
|
8093293.014 |
|
|
|
Total |
|
5.288E9 |
179 |
|
|
|
|
Coefficients
|
Unstandardized coefficient |
Standardized coefficient |
|
Sig. |
||||
|
|
|
β |
Std. Error |
BetaT |
|
|
Sig |
|
(constant) |
|
3134.401 |
|
611.235 |
|
5.128 |
0.000 d |
|
|
|
|
103.293 |
0.843 |
|
21.803 |
0.000 |
|
Education Attainment2252.060. Source: Field Survey (2020). a Predictor: (Constant), Education Attainment. B Dependent variable: adoption of ICT for teaching and learning. C Predictor: (Constant), Education Attainment. d Predictor: (Constant), Education Attainment. |
|||||||
Table 4. Correlation between teaching experience of the respondents and adoption of ICT for teaching and learning.
|
R |
R2 |
|
Adjusted R2 |
Std. Error of the Estimate |
|
Durbin-Watson |
||||
|
1 |
|
0.169a |
|
0.028 |
|
0.023 |
|
5372.322 |
|
1.472 |
|
ANOVAb |
|
|
|
|
|
Sig. |
||||
|
|
|
Sum of squares |
df |
Mean Square |
F-Statistics |
|
Sig. |
|||
|
Regression |
|
1.504E8 |
|
1 |
|
1.504E8 |
|
5.211 |
|
0.024c |
|
Residual |
|
5.137E9 |
|
178 |
|
2.886E7 |
|
|
|
|
|
Total |
|
5.288E9 |
|
179 |
|
|
|
|
|
|
Coefficients
|
|
Unstandardized coefficient |
Standardized coefficient |
|
|
Sig. |
|||||||
|
Model |
|
|
Β |
|
Std. Error |
|
Beta |
|
T |
|
Sig. |
|
|
(constant) |
|
|
13685.305 |
|
942.663 |
|
|
|
14.518 |
|
0.000 d |
|
|
|
|
|
|
|
47.091 |
|
0.169 |
|
2.283 |
|
0.024 |
|
|
|
Teaching Experience107.494; Source: Field Survey (2020) a Predictor: (Constant), Teaching Experience. B Dependent variable: adoption of ICT for teaching and learning. C Predictor: (Constant), Teaching Experience. d Predictor: (Constant), Teaching Experience. |
|||||||||||
Figure 3. Bar Chart showing adoption of ICT for teaching and learning. Source: Field Survey (2020).
Figure 3 provides descriptive of adoption of ICT for teaching and learning. The chart indicated that full adoption of ICT for teaching and learning was highest among the instructors in the private universities (67.3%), followed by the polytechnics (59.1%) and Colleges of Education (52.8%).
Relationship between the limitations and adoption of ICT for teaching and learning
The linear regression in Table 5 has a coefficient of R2 of 0.73 indicating a 73% difference in the dependent is as a result of explanatory variables. Results in Table 6 indicated that challenges are strong determinants of adoption of ICT for teaching and learning. Significant relationships are found for poor power supply (β¼ -0.65), high cost and poor quality of ICT facilities (β¼ -0.43), and poor technical know-how of e-learning (β¼ -0.62) at p < 0.05 level of significance. This is an indication that the power supply, e-learning facilities, and technical know-how of the instructors affected compliance by 65%, 43%, and 62% respectively. Also, there are significant but inverse relationships for poor internet connectivity (β¼ -0.78), lack of telecommunication infrastructure (β¼ -0.74), and lack of training support by the government (β ¼ -0.83). It can be inferred that the limitations caused 71–83% non-adoption of ICT in teaching and learning in the selected private tertiary institutions.
So also, the significance of the F-value (F ¼ 8.92) is a pointer to the fact that the relationship existed between the constraints and adoption of ICT for teaching and learning. It could be inferred that constraints retard ICT adoption practices in the country and no tangible progress could be achieved in the education sector until these problems are addressed.
Linear: ϒ¼αþ (-0.65)L1 þ (-0.43)L2 þ (-0.62)L3 þ (-0.78)L4þ (-0.74)L5þ (-0.83)L6 þ ei
Discussion
Socio-economic variables influencing adoption of ICT for teaching and learning.
The age of the respondents has a significant relationship with adoption of ICT for teaching and learning. The instructors are below forty years (mean ¼ age of 36.8 years), which indicates the respondents are within the working-age population according to Hannah and Max (2019). Nigeria currently has 53.57% of her population in this bracket (Plecher, 2020) and they can learn new technology very fast, and adjust to electronic teaching. At this tender age, people are innovative and have a keen interest to learn new skills compared to people at old age.
According to the Teaching and Learning International Survey (TALIS), younger teachers use technology more frequently in the classroom (Schleicher and Reimers, 2020). Plecher (2020) reported that the bracket would have an important impact on Nigeria’s Educational Development. Also, experience counts in adaptation to new techniques of teachings. The correlation of teaching experience with adoption of ICT for teaching and learning was positive and significant at p < 0.05.
From the three selected socio-economic variables, the test of significance revealed that the educational qualification of the respondents has the greatest influence, a strong correlation and significantly predicts adoption of ICT for teaching and learning. Advanced education and ICT skills are particularly important given the radical shift towards online teaching during the Coronavirus lockdown (Andreas, 2020).
Adoption of ICT for teaching and learning is high in the Nigerian universities when it compares to the situation in Polytechnics and Colleges, that is, adoption in the universities is encouraging. For the last two decades, universities have outnumbered the polytechnics and Colleges of Education. Both individuals and religious organizations invested much in universities. Although the school fees at these universities are exorbitant they have good facilities for ICT and stable academic calendar. The Universities take into cognizance the importance of ICT so, they are more proactive than the Polytechnics and Colleges. Eze et al., (2018), Mahmood (2020) and Ali (2020) opined that new inventions and technology give better ways of communication and interactions and it has tremendously increased knowledge.
However, there exist limitations in the ICT adoption in the selected tertiary institutions. The problems have resulted in partial adoption of ICT for teaching and learning in the Polytechnics and Colleges of Education; the structural buildings and facilities in the Colleges and Polytechnics are very scanty compared to that of Universities –Libraries, Laboratories and ICT centers are well equipped. The shortage of electricity supply is persistent in Nigerian tertiary institutions and it usually distorts researches and studies.
In a report of Thisday (2016), investment in power supply does not commensurate with the megawatt generated for use and it cannot go round. Likewise, Oyediran and Dick (2018) explained that the power supply to the public is diminishing and getting worst. Instructors that are Computer incline are very limited in many of these schools. Eze et al., (2018) argued that a lack of experts in ICT affects its use in Nigeria. In this technology age, e-learning is an essential mechanism of transferring knowledge and to fast-track academics transformation from traditional teaching to modern teaching in the Nigerian educational system.
Table 5. Limitations and adoption of ICT for teaching and learning.
|
Limitations |
Вeta |
Std. Error |
|
T |
|
Significant |
||
|
Constant |
|
4.16 |
|
0.30 |
|
13.87 |
|
0.00 |
|
Poor power supply |
|
-0.65 |
|
0.14 |
|
-4.64 |
|
0.01* |
|
High cost and poor quality of ICT facilities |
|
-0.43 |
|
0.05 |
|
-8.60 |
|
0.00* |
|
The poor technical know-how of ICT |
|
-0.62 |
|
0.17 |
|
-3.65 |
|
0.01* |
|
Poor internet connectivity |
|
-0.78 |
|
0.22 |
|
-3.55 |
|
0.01* |
|
Lack of telecommunication infrastructure |
|
-0.74 |
|
0.23 |
|
-3.22 |
|
0.02* |
|
Lack of training support by the government |
|
-0.83 |
|
0.12 |
|
-6.92 |
|
0.00* |
|
F – statistics |
|
8.92 |
|
|
|
|
|
|
|
R2 |
|
73.41 |
|
|
|
|
|
|
|
Adjusted R |
|
70.95 |
|
|
|
|
|
|
|
Durbin-Watson |
|
26.01 |
|
|
|
|
|
|
Prob (F-Statistics)0.00 * – significant at p 0.05.
Source: Field Survey (2020).
Conclusion
This study established that socio-economic variables are significantly correlated with adoption of ICT for teaching and learning with educational qualification as a major determinant. It was also found that difference existed in adoption of ICT for teaching and learning across the selected private tertiary institutions, a pointer to the fact that e-learning has not been effectively incorporated into tertiary education in Nigeria; the private universities have the highest level of adoption of ICT for teaching and learning during the Coronavirus pandemic.
The limitations obstruct adoption of ICT for teaching and learning particularly in the Polytechnics and Colleges of Education in South South, Nigeria and it would have multiply effect on the academic progress of the institutions and could further create a socio-economic skills gap for the nation. Regression analysis affirmed the significance and negative influence of constraints on the instructors’ adoption of ICT for teaching and learning in the selected tertiary institutions at p < 0.05.
The implication for this study is that instructors’ SEV and limitations could undermine adoption of ICT for teaching and learning during and after the pandemic in Nigeria. Globally, e-learning has been identified as an indispensable intervention to cushion the impact of the Coronavirus pandemic and as well for rapid growth and development in the education sector of any nation. The advantages of ICT adoption include wide coverage, cost-effectiveness, uniformity, fast teaching and learning process, and rapid economic development through e-commerce. It is hereby recommended that adoption of ICT for teaching and learning in the tertiary institutions should go beyond the Coronavirus lockdown period while staff training and capacity building on ICT should be put in place by the institutions’ authority.
The government should address challenges limiting ICT adoption in the tertiary institutions through the provision of stable power supply, and local industries should be encouraged to manufacture some ICT accessories to lessen the cost of acquisition arising from a high tariff. These recommendations become very important going by the rapidly changing world of basic education through digitization.
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