Journal Articles

Permanent URI for this collection

http://repository.must.ac.tz/handle/123456789/63

Browse

Browsing Journal Articles by Issue Date

Filter results by year or month
Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Using Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology.
    (EJISDC, 2017) Mwalumbwe, Imani; Mtebe, Joel S.
    The past decade has seen the rapid adoption and use of various Learning Management Systems (LMS) in Africa, and Tanzania in particular. Institutions have been spending thousands of dollars to implement these systems in a bid to improve the quality of education as well as increasing students’ enrolments through distance and blended learning. However, the impact of these system on improving students’ performance has been a popular subject of research in recent years. Studies have been relying on data from users’ opinions and subjective interpretation through surveys to determine the effectiveness of LMS usage on students’ learning performance. The use of such data is normally subject to the possibility of distortion or low reliability. Therefore, this study designed and developed Learning Analytics tool and used the tool to determine the causation between LMS usage and students’ performance. Data from LMS log of two courses delivered at Mbeya University of Science and Technology (MUST) were extracted using developed Learning Analytics tool and subjected into linear regression analysis with students’ final results. The study found that discussion posts, peer interaction, and exercises were determined to be significant factors for students’ academic achievement in blended learning at MUST. Nonetheless, time spend in the LMS, number of downloads, and login frequency were found to have no significant impact on students’ learning performance. The implications of these results on improving students’ learning are discussed.
  • Thumbnail Image
    Item
    ICT Adoption and Access Among Small-Scale Tea Growers in Rungwe, Tanzania
    (Mbeya University of Science and Technology (MUST), 2023) Madembwe, Peter; Kusyama, Sadiki; Minga, Lusajo
    Tanzania is known for its high-quality tea production, with Rungwe District being a major producer. Though the sector faces various challenges, including low productivity, limited market access, and poor quality, the adoption of Information and Communication Technology (ICT) is likely to solve these challenges and enhance the overall performance of the tea industry in Rungwe. However, there is a shortage of data on ICT usage in Africa, specifically in Tanzania. This paper intends to explore the extent to which ICT has been adopted among Tanzanian Small-Scale Tea Growers (SSTGs) in Rungwe district and its impact on the tea industry. The study found that SSTGs in Rungwe have been adopting various ICT tools and applications to enhance their tea production processes, access information on market trends and opportunities, and access knowledge and training. These tools include radios, television, mobile apps, the internet (online platforms and YouTube), and sensor-based technologies, which are used to monitor crop growth, soil moisture, and weather conditions. The tools have enabled farmers to make more informed decisions about their crop management and improve their yields. The adoption of ICT in small-scale tea production in Rungwe has had a positive impact on the sector, enhancing productivity and efficiency, improving market access, and increasing the income and livelihoods of SSTGs.
  • Thumbnail Image
    Item
    Public Perception of Climate Risk and Adaptation in Tanzania: a Systematic Review
    (Sokoine University of Agriculture, 2023) Nyinondi, P.S; Sospeter,M
    Climate change is a pressing global challenge of the 21st century, with impacts including global warming, drought, famine, floods, tropical storms, and cyclones. One of the biggest challenges to mitigating climate change is people's perception of its risks. This study provides valuable insights on the public perception of climate risk and adaptation in Tanzania through a systematic review of peer-reviewed papers. The search was conducted using keywords related to climate change awareness, knowledge, perception, attitude, and risk adaptation from the Sokoine University of Agriculture Institutional repository (SUAIR) for publications between 2010 and 2022, 48 peer reviewed articles were reviewed. The review found that there is a high level of awareness (87.5%) of climate change, with many (77%) recognizing its impacts on their daily lives in terms of economic activities and gender roles. However, the perception of climate risk varies depending on factors such as gender, location, and socioeconomic status. For example, people living in rural areas perceived climate risks such as floods and drought more than those in urban areas did. Attitudes towards climate change adaptation also vary among different groups, with some people such as farmers more resilient and willing to adapt than pastoralists, people living in urban areas than people living in rural areas. The review identifies knowledge gaps in understanding the causes and impacts of climate change. Overall, this systematic review provides a comprehensive picture of current knowledge and understanding of the public perception of risk adaptation in Tanzania, highlighting areas for further research and policy action. Keywords: Climate change, Perception, Adaptation, Risk, Tanzania
  • Thumbnail Image
    Item
    Improving Network Security: An Intrusion Detection System (IDS) Dataset from Higher Learning Institutions, Mbeya University of Science and Technology (MUST), Tanzania.
    (EANSO, 2024-01-07) Sindika, Daud M.; Dr. Nicholaus, Mrindoko R.; Dr. Hamadi, Nabahani B
    Nowadays, Internet-driven culture securing computer networks in Higher Learning Institutions (HLIs) has become a major responsibility. Intrusion Detection Systems (IDS) are crucial for protecting networks from unauthorized activity and cyber threats. This paper examines the process of improving network security by creating a comprehensive IDS dataset using real traffic from HLIs, highlighting the importance of accurate and representative data in improving the system's ability to identify and mitigate future cyber-attacks. The IDS model was created using a variety of machine learning (ML) techniques. Metrics like accuracy, precision, recall, and score were used to assess the performance of each model. The dataset used for training and testing was real-world network traffic data obtained from the institution's computer network. The results showed that the developed IDS obtained exceptional accuracy rates, with Random Forest, Gradient Boosting, and XGBoost models all achieving an accuracy of around 93%. Precision and recall values were likewise quite high across all algorithms. Furthermore, the study discovered that data quality has a substantial impact on IDS performance. Proper data preparation, feature engineering, and noise removal were found to be helpful in improving model accuracy and reducing false positives. While the IDS models performed well throughout validation and testing, implementing such systems in a production setting necessitates careful thought. As a result, the essay also examined the procedures for testing and deploying the IDS models in a real-world scenario. It underlined the significance of ongoing monitoring and maintenance in order to keep the model effective in identifying intrusions. The research aids in the progress of network security in HLI. Educational institutions can better protect their precious assets and sensitive information from cyberattacks by understanding the impact of data quality on IDS performance and implementing effective deployment techniques.