Mekelle Institute of Technology

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    INVESTIGATING TEACHERS’ ACCEPTANCE OF TECHNOLOGY: A CASE STUDY OF HIGH SCHOOLS IN ADIGRAT CITY, TIGRAY, ETHIOPIA
    (Mekelle University, 2025-09-24) GEBREYOHANNES GEBRESLASSIE
    This study investigates technology acceptance among high school teachers in Adigrat, Ethiopia, where infrastructure gaps, inadequate training, and cultural resistance emerge as critical barriers to digital integration. Building on this context, the research seeks to extend the Technology Acceptance Model (TAM) by incorporating two context-specific constructs: Infrastructure Reliability and Community Validation. The literature review establishes that while global studies emphasize perceived usefulness and ease of use as primary adoption drivers, Adigrat-Tigray-Ethiopia's unique ICT4D challenges - particularly unreliable internet (with 77.3% of teachers lacking access) and strong collectivist cultural norms - necessitate localized adaptations. This study specifically addresses the research gap in understanding teacher acceptance within post-conflict Tigray's educational landscape. To comprehensively examine these issues, the study employs a rigorous mixed-methods approach, combining quantitative surveys (N=71) with qualitative interviews and focus group discussions (N=20), supplemented by observations in six classrooms. The methodology features stratified sampling across four public high schools, with triangulation ensuring data validity and strict ethical protocols protecting participant confidentiality. The findings reveal a significant paradox: while 80.3% of teachers express intention to adopt technology, actual implementation faces multiple obstacles. Infrastructure limitations (β=0.79), time constraints (reported by 40% of teachers), and privacy concerns (55.4%) emerge as primary barriers. Additionally, the study uncovers notable gender disparities (with only 26.8% female participation) and subject-specific adoption patterns, where STEM teachers utilized technology tools 2.5 times more frequently than their counterparts in other disciplines. These findings lead to important theoretical and practical implications. The study makes a substantial contribution by demonstrating how Infrastructure Reliability and Community Validation surpass traditional TAM constructs in predicting technology adoption within low-resource contexts. Building on these insights, the discussion proposes actionable policy recommendations, including offline-first solutions, gender-inclusive professional development programs, and the innovative Tech Ambassadors initiative to address cultural resistance. The practical application of these findings materializes in the Adigrat High Schools Educational Technology Platform (AHSETP), which achieved 91% offline functionality and an 89% exam creation success rate during pilot testing. This platform stands as a scalable model for technology integration in resource-constrained educational environments, demonstrating the study's potential for real-world impact.
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    Assessing Cloud Computing Awareness in Schools Administration and Learning under Low Resource Constraints of selected schools in Mekelle Zone
    (Mekelle University, 2025-09-24) Dejen Zeru
    This study investigates the level of cloud computing awareness in school administration and learning across selected schools in the Mekelle Zone, where resource limitations significantly impact technology adoption. Despite the global advancement of digital technologies in education, many schools in developing regions, including the Mekelle Zone, remain unfamiliar with cloud-based platforms and their potential benefits. The primary objectives of this research are to assess the awareness level of cloud computing among administrators, teachers, and students; identify factors influencing this awareness; explore the perceived advantages and disadvantages of cloud adoption; and provide strategic recommendations to enhance awareness and integration. A mixed-methods approach was employed, combining quantitative data from 192 questionnaire respondents (120 students, 60 teachers, and 12 administrators) with qualitative insights from interviews, focus group discussions, and observation checklists. Thematic and descriptive statistical analyses revealed that only 35% of participants demonstrated a moderate to high level of awareness about cloud computing. Awareness was highest among administrators (58%) and lowest among students (25%). Key factors influencing awareness included access to ICT infrastructure, previous training on digital technologies, and internet availability. Participants identified advantages such as improved data management, collaborative learning, and reduced costs, while perceived disadvantages included concerns over data security, lack of technical support, and unreliable internet connectivity. The findings suggest an urgent need for targeted awareness programs, professional development, and infrastructure improvements. The study recommends that educational policymakers, ICT coordinators, and development partners prioritize the integration of cloud computing in school strategies to foster innovation and bridge the digital divide in low-resource environments.
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    An Assessment of the Role and Challenges of Information Technology in TVET Institutions: A Case Study of Mekelle, Tigray
    (Mekelle University, 2025-08-24) Berhe Gebreyohans
    This thesis examines the role of Information Technology (IT) in enhancing the effectiveness of Technical and Vocational Education and Training (TVET) institutions in Mekelle, Tigray, while identifying the challenges faced in integrating these technologies. As Ethiopia's economy undergoes rapid transformation, the need for a skilled workforce equipped with modern competencies becomes increasingly critical. This study employs a mixed-methods approach, combining qualitative interviews and quantitative surveys to gather comprehensive data from educators, students, and administrators across various TVET institutions. The findings reveal that the integration of IT significantly enhances pedagogical practices, promotes equitable access to educational resources, and prepares students for Industry 4.0 competencies. However, challenges such as inadequate infrastructure, limited digital literacy, insufficient funding, and resistance to change hinder effective implementation. The research highlights the urgent need for targeted policies and investments to address these barriers, including improved training for educators and enhanced access to technology. By situating the findings within the broader context of vocational education in Ethiopia, this study contributes to the academic literature on IT integration and offers actionable insights for policymakers and educational leaders. Ultimately, the research underscores the importance of leveraging IT to improve educational outcomes and align TVET programs with the dynamic needs of the labor market, thereby fostering economic growth and social equity in the region.
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    EFFECTS OF INFORMATION AND COMMUNICATION TECHNOLOGY ON STUDENTS’ LEARNING: A CASE OF SHIRE SECONDARY SCHOOL By:
    (2025-09-24) AbdelWASIE ADEM
    The study investigated the effect of ICT on students‟ learning by taking the case of shire Secondary school. It sought to establish the relationship between ICT and students‟ learning particularly looking at the availability, accessibility and user-ability of the ICT resources in shire secondary school. The rapid advancement of Information and Communication Technology (ICT) has transformed various sectors, particularly education. This thesis investigates the effects of ICT on students’ learning outcomes at Shire Secondary School in Northwestern Tigray. The study aims to evaluate how the integration of ICT in educational practices influences academic performance, student engagement, and overall learning experiences of students. The study was prompted due to the persistent report that students in shire secondary school are getting difficulties in their studies due to limited access and use of ICT resources. It was conducted through cross-sectional survey design; data was collected during the month of March 2017 using questionnaires, interview techniques from a sample of 265 respondents out of a parent population of 3114. In verifying the hypotheses, the researcher used Pearson correlation analysis method to find out whether students‟ learning was linearly correlated with ICT. The study established that the availability of ICT resources in the school is still very much wanting and very inadequate for the students to use. Because of the limited number of functional computers and the computer laboratory, accessibility is timetabled. It was found out that training was mainly limited to introduction to basic concepts of information technology, some application programs notably MS office suit and internet; contextual training of students on how to use ICT in learning was not in practice. The researcher concluded that availability, accessibility and user-ability of ICT resources significantly affect students learning in shire secondary school. Based on the above, the researcher recommends that there is need for the school to invest more in computers and related technology. Access to ICT tools should not be limited only in labs and library but expanded through establishment of ICT resource Centre. ICT training should not be limited to MS Office suites but rather aim at training students with the contextual skills to use ICT for their learning. The rapid advancement of Information and Communication Technology (ICT) has transformed various sectors, particularly education. This thesis investigates the effects of ICT on students’ learning outcomes at Shire Secondary School in Northwestern Tigray. The study aims to evaluate how the integration of ICT in educational practices influences academic performance, student engagement, and overall learning experiences of students.
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    DEVELOPING PREDICTIVE DATA MINING MODEL FOR GRADE 11TH STUDENT PERFORMANCE: IN-CASE OF ELU WOREDA SECONDARY SCHOOL
    (Mekelle University, 2025-08-24) BEDADA DEMTEW
    Education is a key factor for achieving continuing economic progress. Many students are getting less in their result for many reasons. More academic institutes now store massive student educational and related data. A large number of students will enter every year. So, the demanding growth of data in education sectors continues. Handling and analyzing such a high amount of raw data manually for performance evaluation creates dissatisfaction, boring and unsuccessful. Manually handling and evaluating such a big amount of data for performance evaluation leads to discontent, boredom, and failure. To put it another way, traditional methodologies are overly complex and difficult to analyze and evaluate. To solve such kind’s issues, we use a data mining technique, which is a set of machine learning algorithms, to examine the data .In other words automated discovery of previously unknown, valid, novel, useful, and understandable patterns in school databases. The study's major goal is to use data mining to create a predictive model for student academic performance in Oromia National Region State's South West Shoa zone Elu woreda Teji Senior Secondary School and Asgori secondary schools. This can greatly was support policymakers, planners, and education providers working on the control of student performance. The methodology used for this research will a hybrid six-step CRISP Knowledge Discovery Process. The essential data would be gathered from a school data ware house created specifically for student result purposes, would be collected from 2011 to 2016 E.C. The researcher used three popular data mining algorithms (J48 Decision Trees, Random forest and Naive Bayes Classifier) to develop the predictive model using a larger dataset (4000 cases). The researcher used 10-fold cross-validation and percentage split test mode for data mining algorithms of the three predictive models for performance comparison purposes. The results indicated that the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split testis the better predictor due to the nature of data which is categorical for which this two algorithm is better. Both algorithm (the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split test) has 100% accuracy on the given school dataset and Naïve Bayes came out to be the second with an accuracy of 97.5%
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    Design and Implementation of Big Data Analytics Framework at Abyssinia Bank: A Case Study in the Oromia Special Zone, Amhara Region
    (Mekelle University, 2025-08-24) Bayew Girma
    The Ethiopian banking industry is one of the fastest growing in the economy, Abyssinia Bank is one of the first banks in Ethiopia and it is among the top list of banks in the country. Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. The rapid emergence of big data presents significant opportunities for organizations to enhance decision-making processes through data driven insights. This thesis explores the design and implementation of a big data analytics framework at Abyssinia Bank, focusing on its application in the Oromia Special Zone of the Amhara Region. This study aims to identify specific use cases where big data analytics can improve operational efficiency, customer service, and strategic decision-making. Through a mixed-methods approach that incorporates quantitative analysis of existing data and qualitative interviews with key stakeholders, this research will contribute to a deeper understanding of how big data can transform banking practices in emerging markets.
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    NEURAL NETWORK APPROACHES FOR ACCURATE AFAN OROMO SPELL CHECKING AND CORRECTION
    (Mekelle University, 2025-09-24) Ayenalem Dejene
    Afan Oromo, a widely spoken Cushitic language, lacks advanced natural language processing (NLP) tools like spell checkers due to limited resources and linguistic expertise. Both native and non-native speakers face challenges in writing Afan Oromo correctly, partly because its Latin-based Qubee script was adopted in 1991. Traditional spell-checking methods, such as dictionary lookup and rule-based approaches, are inadequate for Afan Oromo’s highly inflectional morphology. This thesis proposes a neural network-based spell checker using a sequence-to-sequence (Seq2Seq) model with Long Short-Term Memory (LSTM) layers. A corpus of 596,948 words was collected from BBC Afan Oromoo using Sketch Engine, ensuring compliance with BBC’s terms of service. The model was trained to detect and correct spelling errors, achieving 100% error recall and 52.47% precision. This work is the first to apply neural networks to Afan Oromo spell checking, offering a scalable solution for under-resourced languages.
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    Practices, Challenges and Implementations of ICT in Selected Secondary Schools of Zone Two Afar.
    (Mekelle University, 2025-09-24) Anwar Hussien
    Information and communication technologies (ICT) have become common place entity in all aspects of life. The purpose of this study is to assess the existing practices, challenges and prospects of ICT in in selected secondary schools Of Zone Two Afar. The main research questions raised is assessment of extent of ICT implementation in the schools, based on the perceptions of students’, teachers’ and principals. The research utilized a quantitative research method and a descriptive survey research design was used to analyze. From available sampling methods, Proportionate sampling method was employed to determine size of each subgroup from the selected sample population. The study used both primary and secondary data sources to gather input for the study. The researcher collected data from teachers, students, principals from the schools. The major data collection instruments include Questionnaires and document analysis. The major findings of the study are lack of infrastructure and teaching learning aid shortage in the school. This study showed additional ICT training has to be arranged to equip teachers and students with required level of operational skill. And on the other hand, there was good perception on use of ICT in educational process. Lastly, the study revealed that the implementation of ICT in Abala and Erebty secondary schools is being tested with various challenges. These challenges include absence of operational design or model as well as arrangement of ICT infrastructure, lack of training for teachers and limited ICT knowledge and skills with both students and teachers; limited technical support during teaching and learning process and lack of proper ICT policies the schools. There is a gap in teachers owning facilitation and improvement of continuous support, monitoring and evaluation of ICT Policies. Hence, the research concluded that the inadequate practice, poor perception and the above-mentioned challenges hindered a well-versed implementation of ICT in the schools.
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    Diagnosis of Diabetes Using Data Mining Techniques
    (Mekelle University, 2025-09-24) AMANA TESHI GEMMEDA
    Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to the body's inability to produce or effectively use insulin. It is a significant and growing health concern worldwide, affecting millions of individuals and posing a considerable burden on healthcare systems. Diabetes can lead to various complications, including cardiovascular disease, kidney damage, nerve damage, and vision impairment. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Traditional diagnostic methods rely on clinical symptoms, medical history, and laboratory tests. However, with the advancements in technology and the availability of large healthcare datasets, data mining techniques have emerged as a promising approach for diabetes diagnosis. Diabetes diagnosis plays a crucial role in effective disease management and prevention of complications. This study explores the application of data mining techniques for diabetes diagnosis, aiming to improve diagnostic accuracy and support healthcare decision-making. Four classification models (Decision Tree, SVM, Naïve Bayes, and Neural Network) are constructed and evaluated using a comprehensive diabetes dataset. Performance evaluation metrics, including TP rate, FP rate, accuracy, precision, recall, and F1-score, are employed to assess the models. The results indicate that the Decision Tree model consistently outperformed the other models, achieving high accuracy and F1-scores in both evaluation scenarios. The Naïve Bayes, SVM, and Neural Network models also showed reasonable performance, although slightly lower than the Decision Tree model. Based on the findings, it is recommended to utilize the Decision Tree model for diabetes diagnosis due to its high accuracy and F1-scores. However, further research and validation on larger and more diverse datasets are needed to ensure the generalizability of these results. Additionally, exploring the combination of different data mining techniques or the incorporation of additional factors, such as genetic data or lifestyle factors, could enhance the predictive capabilities of the models.