Aim/Purpose: This study is based on the DeLone and McLean’s Information Systems Success (D&M ISS) model, which was modified to determine the success factors responsible for the acceptance of an e-learning system called Canvas by students of a Nigerian University. Background: The adoption of eLearning has been under studied within the context of developing countries. There have been calls in the literature for further research from a developing country perspective. This study attempts to fill this knowledge gap by investigating the factors responsible for students’ adoption of eLearning in a Nigerian University. Methodology: The study was built on the premise that system quality (SQ), service quality (ServQ) and information quality (IQ) are determinants of behavioral intention to use Canvas and user satisfaction of Canvas, both of which in turn influence the actual usage of Canvas. Responses from 366 students were analyzed with AMOS 22 using structural equation model (SEM) to test the relationships between the constructs of the proposed model. Contribution: The study contributes to the research gap about the paucity of studies in the adoption of eLearning in developing countries that have placed emphasis on the use of eLearning systems or on the software quality attributes of the systems under investigation. Findings: The results partially supported the effect of the quality antecedents on behavioral intention and user satisfaction of students. There was full support for the relationship between behavioral intention and user satisfaction of students on their actual usage of Canvas. Recommendations for Practitioners: The level of significance of the constructs identified in the study will guide the formulation of strategies and methods that could further enhance the adoption of eLearning systems in developing countries, specifically Nigeria. Recommendation for Researchers: Theoretically, the study contributes to the body of knowledge on eLearning adoption by empirically validating the DeLone and McLean model in a different context, specifically sub-Saharan Africa. Future Research: Future research could investigate the factors that influence instructors’ behavioral intentions to use eLearning applications and also the effect that the instructors have on students’ adoption of eLearning.

The aim of the study is to investigate factors that influence the adoption and use of educational technology by students of a higher education institution in developing countries. The study employed the unified theory of acceptance and use of technology (UTAUT). The online survey method was used to collect data from 286 students of a higher education institution in Nigeria. The maximum likelihood method based on structural equation modelling (SEM) using IBM Amos 22.0 application was used to analyse the data. The study determined that performance expectancy and effort expectancy (p < 0.001) were significant factors in influencing the behavioural intention to use Canvas. Social Influence was deemed to be a statistically non-significant factor in influencing behavioural intentions as (p > 0.05). Facilitating conditions and behavioural intentions were determined to be salient factors that positively influence the actual usage of Canvas by the students. The results from the data obtained partially support the UTAUT’s ability to explain the factors responsible for the acceptance of educational technology in developing countries, in Nigeria to be specific. Furthermore the study contributes to the formulation of approaches and guidelines to enhance the adoption of educational technologies in developing countries.

Research has shown that effective and efficient learning management systems (LMS) were the main reasons for sustainable education in developed nations during COVID-19 pandemic. However, due to slow take-up of LMS many schools in developing countries, especially Africa were completely shut down due to COVID-19 pandemic. To fill this gap, 4 AI-based models; Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT) were developed for prediction of LMS determinants. Nonlinear sensitivity analysis was employed to select the key parameters of the LMS determinants data obtained from 1244 schools’ students. Five statistical indices were used to validate the models. The performance results of the four developed AI models discovered facilitating conditions, attitude towards LMS, perceived enjoyment, users’ satisfaction, perceived usefulness, and ease of use to be the most significant factors that affect educational sustainability in Nigeria during COVID-19. Further, single model’s performance results comparison proved that SVM has the highest prediction ability compared to GPR, ANN, and BRT due to its robustness in handling data uncertainties. The study results identified the factors responsible for total schools’ closure during COVID-19. Future studies should examine the application of other linear and other nonlinear AI techniques.

Research has shown that technology, when used prudently, has the potential to improve instruction and learning both in and out of the classroom. Only a handful of African tertiary institutions have fully deployed learning management systems (LMS) and the literature is devoid of research examining the factors that foster the adoption of LMS. To fill this void, the present research investigates the factors contributing to students' acceptance of LMS. Survey data were obtained from registered students in four Nigerian universities (n=1,116); the responses were analyzed using artificial neural network (ANN) and structural equation modeling (SEM) techniques. The results show that social influence, facilitating conditions, system quality, perceived ease of use, and perceived usefulness are important predictors for students' behavioral intention to use LMS. Students' behavioral intention to use LMS also functions as a predictor for actual usage of LMS. Implications for practice and theory are discussed.

Purpose Academic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students’ academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation. Design/methodology/approach A machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university. Findings Results revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA. Originality/value This predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions.