Volume 12, Issue 1
AI-Driven Personalized Learning Tools as Catalysts of Digital Transformation in TVET education: Exploring Digital Natives' Experiences
Asogwa Emmanuel C., Akubuko Phoebe A., Eze Benedict C. & Ogbuabor Priscilla N.
Abstract
Artificial Intelligence (AI) is reshaping the educational landscape by enabling tools that
personalize learning experiences to meet individual learner needs. Despite its global
advancement, the integration of AI-powered personalized learning tools among digital natives in
learning, particularly within the TVET institution in developing regions among digital native's
remains underexplored. This study therefore, investigates digital natives' experiences with AIdriven
personalized learning tools and their influence on digital transformation A descriptive
survey design was adopted, and data were collected using a structured questionnaire
administered via Google Forms. The instrument was validated by experts and recorded a
reliability coefficient of 0.81 using Cronbach's alpha. Data were analyzed using mean and
standard deviation with a cut-off mean of 2.5, while factor loading was used to assess how well
each item measured its intended construct. Findings indicate that AI-based personalized
learning promotes engagement in learning, improves interaction, and motivation among digital
natives by offering adaptive content and instant feedback. However, the study highlights digital
literacy gap, high implementation costs, limited institutional funding, and reluctance among
educators to integrate AI tools in teaching as the key challenges hindering its effective adoption.
It therefore recommended that educators should encourage the responsible use of AI-powered
tools. Both staff and students should also receive regular digital literacy training to foster
inclusive and technology-driven learning environments.
Keywords: AI-Driven Learning; Digital Natives; Adaptive Learning Systems; Networking
Education; Digital Inclusion. TVET
Leveraging Robotics for Skill and Competence Development in Computer Education Students in Universities in Enugu State
Ude Esther Uju, Uzo Nnenna Judith, Dr. Obichukwu, Uzochukwu Peter and Dr. Chijioke Jonathan Olelewe
Abstract
This study explore examines how using robots in university computer education courses can help students
in Enugu State learn more than just theory but build real, useful skills. The aim of this study is to provide
insight into how robotics can be used in universities in Enugu State and beyond not just to teach computer
education courses, but to prepare students for jobs and for a future where technology matters more and
more. The research design of the study is a descriptive survey. The population of the study comprise of
one hundred and twenty (120) undergraduate Students of the Department of Computer and Robotics,
Education, Faculty of Vocational and Technical Education, University of Nigeria, Nsukka; and Enugu
State University of Science and Technology, Agbani. The sample size is fifty respondents. A structured
questionnaire titled “Leveraging Robotics Skills and Competence Development Questionnaire
(LRSCDQ)” was designed to obtain information from respondents. Data was collected through physical
contact. The data collected from the respondents were analyzed using mean and standard deviation. The
mean cut off was 3.00 points for the decision rule. Therefore, items with a mean score of 3.00 or above
were accepted while items with a mean score below 3.00 were rejected. The findings of the study showed
that robotics based-learning improve computer education skills however, universities in Enugu State lack
some of the physical infrastructure needed for meaningful robotics teaching. The recommendations
include the fact that government and NGOs should provide funds and resources to help effective roboticbased
learning
Key Words: Robotics; Skill; Competence; Development; Universities; Enugu
Leveraging Modern Digital Learning Tools for Computer Science Education Delivery at Colleges of Education: Opportunities and Challenges
Olabimitan, Emmanuel Oludare, Haruna Aminu Enefola, Aishatu Abubakar Muhammad
Abstract
The rapid advancement of digital technologies has transformed educational practices globally, creating
new opportunities for improving teaching and learning processes in teacher education programmes,
including the Nigeria Certificate in Education Computer Science education programme responsible for
training future computer studies teachers. This discussion paper examines the opportunities and
challenges of leveraging modern digital learning tools for computer science education delivery in
Colleges of Education. It identifies key digital tools including online learning platforms (Massive Open
Online Courses (MOOCs), flipped classrooms), collaborative tools (GitHub, Google Workspace), gamebased
learning (coding games, gamified quizzes), multimedia simulations, cloud-based IDEs, and mobile
applications. The paper highlights significant opportunities such as enhanced teaching effectiveness,
increased flexibility and accessibility, promotion of collaboration, development of practical digital skills,
personalised instruction, improved assessment, and reduced administrative workload. However, major
challenges include limited ICT skills and digital literacy among educators and students, poor internet
connectivity and unstable power supply, financial constraints, inadequate infrastructure, resistance to
change, and the limited suitability of fully online environments for hands-on practical components. The
paper concludes that effective integration of digital learning tools is essential for producing digitally
competent computer science educators capable of functioning effectively in modern classrooms. It
therefore recommends increased investment in ICT infrastructure, continuous digital literacy training,
curriculum review, institutional support systems, and collaborative partnerships to enhance digital
transformation in Colleges of Education.
Keywords: Digital learning tools, computer science education, colleges of education, Nigeria, technology
integration, pedagogical transformation
Students' Perceptions of Humanoid Robots as Tools for Enhancing Teaching Efficiency in Private Secondary Schools
Anih Anselem Anayochukwu (PhD)
Abstract
Teacher shortages in Nigerian private schools have increased the need for innovative technologies that
can supplement instructional delivery. This study investigated students' perceptions of humanoid robots as
instructional tools in private schools in Nigeria, with particular attention to their perceived usefulness
and gender differences. A descriptive survey design was adopted. The study involved 187 students
selected from private schools, and data were collected using a structured questionnaire. Mean and
standard deviation were used to answer the first research question, while an independent-samples t-test
was used to examine gender differences at the 0.05 significance level. Findings indicated a high level of
students' general perception of humanoid robots as instructional tools (cluster mean = 3.34). Students
also reported favourable perceptions of their usefulness for enhancing teaching efficiency. However, the
independent-samples t-test revealed no significant difference between male and female students'
perceptions, t(185) = 0.98, p = .329. The study concludes that students demonstrate broad acceptance of
humanoid robots as potential supplementary instructional tools, irrespective of gender. The study
recommends their phased adoption, supported by adequate technical infrastructure and maintenance,
particularly in private schools experiencing teacher shortages.
Keywords: Humanoid Robots; Instructional Technology; Students' Perception; Teacher Shortage., private
secondary schools,
Extent of the Use of Mobile Learning in Teaching Computer Graphics in Tertiary Institutions in Enugu State, Nigeria.
Akubuko Phoebe Amarachi, Eze Angela Ogechi (PhD) and Isiguzo Goodness Chinyere
Abstract
The study aimed at investigating the extent of the use of m-learning in teaching computer
graphics in universities in Enugu state. Four research objectives, four research questions and
two null hypotheses were formulated to guide the study. The design adopted for the study was
descriptive survey design. The population for the study is forty-four (44) lecturers in computer
education departments in public universities in Enugu state. A structured questionnaire titled
extent of the use of m-learning in teaching computer graphics questionnaire. (EUMLTCG) which
was developed by the researcher and validated by experts. The reliability of the instruments was
established using Cronbach Alpha yielding a coefficient of .75 showing that the instrument is
reliable. The data collected was analyzed using the mean and standard deviation while t-test and
ANOVA Statistics were used to test the hypothesis at 0.05 level of significance. The findings
reveal that: there is a low extent of m-learning in teaching image creation in computer graphics
in universities in Enugu State, there is low extent of the use of m-learning in teaching image
editing in computer graphics in universities in Enugu state. This study further reveals that there
is no significant difference in the mean responses of male and female computer education
lecturers on the extent of the use of mobile learning to teach computer graphics in tertiary
institutions in Enugu state. The analysis of the study also revealed that there is significant
difference in the mean responses of universities and colleges of education lecturers on the extent
of the use of mobile learning in teaching computer graphics in Enugu state. Based on the
findings, the study recommended that tertiary institution lecturers in Nigeria should strive to
adapt the use of mobile learning tools and its platform in teaching computer graphics.
Availability and Utilization of Innovative Technologies in Teaching and Learning Computer Hardware Maintenance in Federal Colleges of Education in Enugu State, Nigeria
Obichukwu Uzochukwu P. & Ede F.O.
Abstract
This study investigates the availability and utilization of innovative technologies in the teaching and
learning of computer hardware maintenance in Federal Colleges of Education in Enugu State, Nigeria.
The population of the study was 60 third year students from the Department of Computer science
Education in Federal College of Education, Eha-Amufu. The research adopted a descriptive survey
design with participants from lecturers and students in computer education departments across Federal
Colleges of Education in Enugu State. Data were collected using structured questionnaires and analyzed
using mean and frequency distribution. Findings indicated that innovative technologies such as virtual
laboratories, simulation software, interactive learning platforms, and multimedia instructional tools
enhance practical understanding and troubleshooting competence among Computer Science Education
students. The study concludes that innovative technologies significantly improve the teaching and
learning of computer hardware maintenance when effectively implemented. It was recommended among
others that increased investment in digital infrastructure, continuous professional development for
lecturers, and policy reforms promoted technology- driven instruction in teacher education institutions.
Keywords: Innovative technologies, computer hardware maintenance, troubleshooting, technology
integration, teacher education, Nigeria.
Awareness and Utilization of Artificial Intelligence in Teaching Computer Hardware Maintenance in Universities in Enugu State, Nigeria
Eneovo, John Ifeanyi and Ezema, Gloria Ogechukwu
Abstract
This study investigated the awareness and utilization of artificial intelligence (AI) in teaching
computer hardware maintenance in universities in Enugu State, Nigeria. The study had two
specific purposes and two research questions. A descriptive survey research design was adopted
for the study. A sample of 27 respondents comprised of lecturers from University of Nigeria,
Nsukka, and Enugu State University of Science and Technology obtained through total
population sampling given that the population was small and manageable. Instrument for data
collection was a structured questionnaire entitled “Awareness and Utilization of Artificial
Intelligence in Teaching Computer Hardware Maintenance Questionnaire (AUAITCHMQ)”
developed by the researchers. The instrument was face validated by three experts; and the
reliability was tested using Cronbach's alpha. Data collection was done through personal
contact. Data were analyzed using mean and standard deviation, with a cut-off point of 2.50 used
to interpret responses. The null hypotheses were tested at 0.05 levels of significance using
independent sample t-test. The results of the study revealed that lecturers were aware of artificial
intelligence integration in teaching computer hardware maintenance. The findings further
revealed that the extent of utilization of artificial intelligence in teaching computer hardware
maintenance in the universities was low. It was recommended among others that Universities
administration should collaborate with AI experts to train lecturers on the utilization of AI
technologies in teaching.
Key words: Artificial Intelligence; Awareness; Utilization; Teaching; Computer Hardware
Maintenance
Evaluation of the Impact of Machine Learning-Based Predictive Analytics on Early Intervention for At-Risk Learners in Secondary Schools in South-South Nigeria
Dr. Patrick B. Asu-Nandi and Dr. Eteli Harold Imbasi
Abstract
This study explores the impact of machine learning-based predictive analytics on facilitating early intervention for at-risk learners in Nigerian secondary schools. The research aimed to determine the effectiveness of predictive models in identifying students at risk of academic failure and initiating timely support strategies within resource-constrained educational settings. A mixed-methods approach was adopted, combining quantitative data from academic records, attendance logs, and behavioural assessments of 500 students across five secondary schools in southern Nigeria, with qualitative interviews involving teachers and administrators. A supervised machine learning model was developed using logistic regression and decision tree algorithms to predict at-risk students based on identified indicators. Findings revealed that the predictive model achieved an accuracy of 87%, successfully identifying students who required early academic or psychological intervention. Implementation of targeted support strategies - such as peer mentoring, counselling, and remedial sessions - led to measurable improvements in the academic performance and classroom participation of identified learners. Teachers also reported enhanced decision-making in student management based on data-driven insights. This research provides new evidence on the applicability of machine learning tools in improving educational outcomes in developing countries. It offers a scalable framework for early intervention that can be adapted across varying educational contexts globally. The study’s unique contribution lies in bridging AI-powered analytics with human-centred educational support, making it highly relevant for policymakers, EdTech developers, and educators seeking innovative solutions to persistent learning challenges.
Keywords: Predictive analytics, machine learning, early intervention, at-risk learners, Nigerian education.
Effect of Humanoid Robot-Assisted Instruction on Classroom Engagement and Learning Motivation among Upper Basic School Learners In Nigeria
Dr Emmanuel Bala, Dr Patrick B. Asu-Nandi, and Sunday Dugga
Abstract
This study examined the effect robot-assisted instruction on classroom engagement and learning motivation among Upper Basic school learners in Cross River State, Nigeria. A quasi-experimental, non-equivalent control group design was adopted. The study involved 120 Upper Basic learners from four public schools, with 60 learners assigned to the humanoid robot-assisted instruction group and 60 to the conventional instruction group. The intervention was implemented over six weeks, while classroom engagement and learning motivation were measured before and after the intervention. Data were analysed using descriptive statistics and Analysis of Covariance (ANCOVA), with pre-test scores controlled at the 0.05 level of significance. For classroom engagement, the ANCOVA revealed a statistically significant effect of instructional group, F(1, 117) = 19.62, p < .001, partial η² = 0.144. The adjusted post-test Mean for the humanoid robot-assisted instruction group was 78.46 (SE = 1.20), compared with 69.17 (SE = 1.19) for the conventional instruction group, representing an adjusted mean difference of 9.29 points. For learning motivation, the effect of instructional group was also statistically significant, F(1, 117) = 22.14, p < .001, partial η² = 0.159. The adjusted post-test mean was 76.83 (SE = 1.16) for the humanoid robot-assisted instruction group and 67.21 (SE = 1.15) for the conventional instruction group, yielding an adjusted mean difference of 9.62 points. These findings indicate that, after controlling for pre-test scores, learners who received humanoid robot-assisted instruction had higher adjusted post-test scores for both classroom engagement and learning motivation than those who received conventional instruction. The findings provide context-specific evidence on the potential of purposefully integrated humanoid robot-assisted instruction as a complementary instructional approach in Nigerian Upper Basic classrooms.
Keywords:
humanoid robot-assisted instruction, classroom engagement, learning motivation, Upper Basic learners, educational robotics, Nigeria
Cybersecurity Threats Experienced by Computer and Business Education Students and their Efforts to Solve them
Uba, Mabel Obianuju and Dr. Chinenye Clara Akpa
Abstract
Internet usage has grown exponentially, with individuals and companies performing multiple
daily transactions in the Cyberspace rather than in the real world. This study investigated the
Cybersecurity threats experienced by Computer and Business Education Students and their
efforts to solve them. Two specific purposes and two research questions were raised to guide the
study. The study adopted a descriptive survey research design in Cybersecurity threats
experienced by Computer and Business Education Students and their subsequent mitigation
efforts. The population for the study was 250 Undergraduate Students from Computer and
Business Education second year students in Federal Universities in South East Nigeria, the
results of the study identified a high prevalence of social engineering attacks, particularly
phishing (78%) and malware incidents (62%), often linked to software privacy and vulnerable
personal projects. Based on these results, it was recommended among others that Universities
should ensure that Students have affordable or free access to necessary commercial software
through campus-wide licenses or academic partnerships, reducing the incentive for piracy.
Keywords: Cybersecurity, Phishing, Malware, Student Security, Self-Efficacy, Threat Mitigation
Awareness, Use, and Perceived Educational Value of Arduino-Based Interactive Learning for Digital Pedagogy in STEM Education in Southeast Nigeria
Emmanuel Chekwube Eze, Eucharia E. Onuoha, Kalu Emeka Ukoji
Abstract
The increasing use of technology-mediated instruction has created interest in low-cost,
interactive tools that can connect abstract STEM concepts with practical activities. Arduinobased
learning is particularly relevant to digital electronics because it can support
experimentation with circuits, programming, and logic operations. This study examined students'
awareness and reported use of Arduino-based interactive learning, educators' perceptions of its
educational value, and barriers to adoption in selected federal universities in Southeast Nigeria. A
descriptive survey design was used because the study sought to describe existing awareness,
reported practices, perceptions, and institutional conditions without manipulating an instructional
intervention. The study involved 75 respondents comprising 25 lecturers and 50 final-year
students from five federal universities. Data were collected in March 2025 using a structured
questionnaire administered through Google Forms. The questionnaire contained demographic
items and substantive clusters addressing awareness/use, perceived effectiveness, and barriers to
adoption. Responses were analysed using means and standard deviations, with decision intervals
of 1.00–1.74 = very low, 1.75–2.49 = low, 2.50–3.24 = moderate, and 3.25–4.00 = high. The
reported overall internal-consistency reliability was Cronbach's alpha = 0.87. Students showed
moderate awareness of Arduino as a concept but low levels of reported practical use and
confidence. Educators perceived Arduino-based activities as useful for classroom engagement
and understanding of logic-gate concepts, while cost, limited training, and weak institutional
support were identified as adoption barriers. The findings indicate a gap between general
awareness and practical integration and support the need for targeted capacity development,
appropriate laboratory provision, and institutional mechanisms for sustained technology
integration.
Keywords: Arduino; Digital pedagogy; STEM education; Digital electronics; Interactive
learning; Higher education; Nigeria
Effect of Learning Analytics–Driven Feedback on Computer Science Education Students' Academic Achievement in Data Structures in Public Universities in South-South, Nigeria
Ukeh, Bartholomew Oluchi Ph.D
Abstract
This study investigated the effect of learning analytics-driven feedback on Computer Science
Education students' academic achievement in Data Structures in public universities in South-
South, Nigeria. It was guided by two research questions and two null hypotheses tested at 0.05
level of significance. It adopted a quasi-experimental pre-test, post-test non-equivalent group
research design. The population comprised 791 Computer Science Education students, while a
sample of 186 CSE students was selected from two public universities using purposive sampling
techniques. Data were collected using Data Structures Achievement Test (DSAT) and it was
subjected to face and content validation, while its reliability was established through a trial test
involving 20 CSE students from a public university in South-South. The trial test yielded a
reliability coefficient of 0.83, which was determined using the Kuder–Richardson 20 (KR-20)
formula. The DSAT was administered to the experimental and control groups before and after the
treatment. Mean and standard deviation were used to answer the research questions, while
Analysis of Covariance (ANCOVA) was used to test the null hypotheses. The findings revealed
that students taught Data Structures using learning analytics-driven feedback achieved a higher
mean score than those taught using the conventional lecture method. The findings also showed a
difference in the mean achievement scores of male and female students in the experimental group,
with male students recording a higher mean achievement score than their female counterparts.
In line with the findings, it was recommended that Computer Science Education lecturers should
adopt learning analytics-driven feedback to enhance students' achievement in Data Structures.
Keywords: Learning Analytics, academic achievement, Computer Science Education, Data
Structures
Extent of Utilization of Online Learning Platforms among Lecturers in the Faculty of Education, Enugu State University of Science and Eechnology (ESUT)
Onovo. Nonso Emmanuel, Agbo, Peace Chinenye & Okani, Chinecherem Glory