Browsing by Author "Kalegele, Khamisi"

Now showing 1 - 12 of 12

Analysis of factors influencing information access among rural communities in Tanzania
(Journal of Agricultural Extension and Rural Development, 2017-09) Ramadhani, Abswaidi; Sam, Anael; Kalegele, Khamisi
Information access in rural communities of Tanzania is very important as it keeps its members aware of what happened and what happening in the world. This importance can not be undermined since it helps farmers and other members of the rural community to make the right decision about their activities. Rural community areas are surrounded by many factors which in one way or another influence or hinder accessibility of information. Several works have been done on analysis of these factors; however most of them were conducted a number of years back. This study focused on providing the current survey on analysis of these factors. Specifically, the study analysed the following factors: source of power, penetration of mobile phones, subscription to networks, languages used and level of education, policy, government support, government administration and farming system. Mwanga District was used as a study area where descriptive research design was applied. Primary data were collected from farmers by using questionnaires while secondary data were collected from village reports and other research reports. The collected data were analysed by using Statistical Package for Social Science (SPSS). Findings show that in rural areas, there are unreliable source of power, higher penetrations of feature phones, high subscription to Vodacom network and high uses of Swahili language for communication, good government support and policy which favour farmer’s development. Generally, we recommend that the existing sources of information like information systems and other applications should be integrated to form one stop centre where all information can be accessed.
Classroom ICT integration in Tanzania: Opportunities and challenges from the perspectives of TPACK and SAMR models
(International Journal of Education and Development using Information and Communication Technology (IJEDICT), 2016) Kihoza, Patrick; Zlotnikova, Irina; Bada, Joseph; Kalegele, Khamisi
With the education systems demand of contemporary technologies, teacher trainees should be imparted with competencies and skills to integrate information and communication technology (ICT) into their future teaching and learning practices. This study assessed classroom ICTs integration opportunities and the challenges in relation to Technological Pedagogical and Content Knowledge (TPACK) and SAMR (Substitute, Augmentation, Modification, and Redefinition) models. The case study involved tutors and teacher trainees (N=206) from teacher training colleges. Results indicated that, majority of respondents have low pedagogical ICT competencies. However, tutors exhibited good knowledge level in all TPACK and SAMR constructs that we assessed, teacher trainees’ revealed poor skills and inefficient support on the use of basic ICTs (hardware, software, and associated peripherals). The impacts of TPACK and SAMR models characteristics related to the technology use planning and redesign of learning tasks was evident. Most of the challenges identified were associated to the lack of infrastructures, readiness to change and lack of competencies on pedagogical ICTs applications. Among others, we recommend the government to work on a harmonized ICT in education integration framework; that consider the existing opportunities and challenges facing Tanzania teacher training systems. Further work should focus on carrying out an experimental research design to unlock the existing ICT use realities.
Enhancing Management of Nutrition Information Using Mobile Application: Prenatal and Postnatal Requirements
(IST-Africa, 2017) Mduma, Neema; Kalegele, Khamisi
Malnutrition contributes to over one half of the deaths of children under age of five years in developing countries and is the single greatest cause of child mortality in Tanzania. Investigations reveal that the issue of malnutrition is aggravated by lack of nutritional information especially in rural communities. Absence of proper tools makes collection, management and access to nutrition information very difficult. The aim of this study is to improve accessibility of nutritional information by taking advantage of the advanced mobile technologies to integrate a mobile-based information management platform with existing Health Information Systems. The platform will give mothers instant access to nutritional tips, allow them to interact with nutrition practitioners and help in record keeping. In this paper, we present the requirements of a mobile application for managing prenatal and postnatal nutritional information. The requirements have been established from interviews with the various stakeholders and literature reviews. The established requirements become a necessary input towards development of a complete mobile-based nutrition information management platform, which is to be integrated with existing health information system.
An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools
(Journal of Information Systems Engineering & Management, 2019-08-22) Mduma, Neema; Kalegele, Khamisi; Machuve, Dina
When a student is absent from school for a continuous number of days as defined by the relevant authority, that student is considered to have dropped out of school. In Tanzania, for instance, drop-out is when a student is absent continuously for a period of 90 days. Despite the fact that several efforts have been made to improve the overall status of education at secondary level, the student drop-out problem still persists. Taking advantage of advancement in technology, several studies have used machine learning to address the student drop-out problem. However, most of the conducted studies have used datasets from developed countries, while developing countries are facing challenges on generating public datasets to be used to address this problem. Using a dataset from Tanzania which reflect a local scenario; this study presents an ensemble predictive model based prototype for student drop-out in secondary schools. The deployed model was developed by soft combining a tuned Logistic Regression and Multi-Layer Perceptron models. A feature engineering experiment was conducted to obtain the most important features for predicting student drop-out. Furthermore, a visualization module was integrated to assist interpretation of the machine learning results and we used flask framework in the development of the prototype.
Framework for integrating fragmented information systems: Case of livestock information systems
(International Journal of Computer Engineering Research, 2017-07) Ramadhani, Abswaidi; Sam, Anael E.; Kalegele, Khamisi
Information System (IS) is used for information communication. But the problem with the existing Agro- ISs is that they are not integrated. To integrate it, we need to have an integration framework. Generally, much has been done on the designing framework for IS integration. However, there are credible things which can be added to improve these frameworks. A framework can be designed in such a way that it guides users on technologies to be used, skills needed for the task, goals, outcome and output of integration. This is what this study focused to contribute. To design the framework, guiding factors were established. These factors were the one which surround farmers and in one way or another hinder or support accessibility of information from integrated ISs. The factors were discussed and presented in the mapping matrix. Under each factor, viability of technology on the ground was established and compared against the available technology. From the mapping matrix, scenarios on possible applications were established. Guides on Technologies, skills, goals, output and outcome of integration were established to form a framework for integrating applications and ISs. Then the framework was demonstrated empirically and compared with the existing frameworks. Results show that the proposed framework is more comprehensive in terms of guides it provides, than the existing frameworks.
An Integrated Mobile Application for Enhancing Management of Nutrition Information in Arusha Tanzania.
(International Journal of Computer Science and Information Security, 2015-07) Mduma, Neema; Kalegele, Khamisi
Based on the fact that management of nutrition information is still a problem in many developing countries including Tanzania and nutrition information is only verbally provided without emphasis, this study proposes mobile application for enhancing management of nutrition information. The paper discusses the implementation of an integrated mobile application for enhancing management of nutrition information based on literature review and interviews, which were conducted in Arusha region for the collection of key information and details required for designing the mobile application. In this application, PHP technique has been used to build the application logic and MySQL technology for developing the back-end database. Using XML and Java, we have built an application interface that provides easy interactive view.
Machine learning approach for reducing students dropout rates
(International Journal of Advanced Computer Research, 2019-05-06) Mduma, Neema; Kalegele, Khamisi; Machuve, Dina
serious issue in developing countries. On the other hand, machine learning techniques have gained much attention on addressing this problem. This paper, presents a thorough analysis of four supervised learning classifiers that represent linear, ensemble, instance and neural networks on Uwezo Annual Learning Assessment datasets for Tanzania as a case study. The goal of the study is to provide data-driven algorithm recommendations to current researchers on the topic. Using three metrics: geometric mean, F-measure and adjusted geometric mean, we assessed and quantified the effect of different sampling techniques on the imbalanced dataset for model selection. We further indicate the significance of hyper parameter tuning in improving predictive performance. The results indicate that two classifiers: logistic regression and multilayer perceptron achieve the highest performance when over-sampling technique was employed. Furthermore, hyper parameter tuning improves each algorithm's performance compared to its baseline settings and stacking these classifiers improves the overall predictive performance. Keywords Machine learning (ML), Imbalanced
An overview of existing frameworks for integrating fragmented information systems
(International Journal of Information Sciences and Techniques (IJIST, 2016-11) Ramadhani, Abswaidi; Sam, Anael; Kalegele, Khamisi
Literatures show that there are several structured integration frameworks which emerged with the aim of facilitating application integration. But weakness and strength of these frameworks are not known. This paper aimed at reviewing these frameworks with the focus on identifying their weakness and strength. To accomplish this, recommended comparison factors were identified and used to compare these frameworks. Findings shows that most of these structure frameworks are custom based on their motives. They focus on integrating applications from different sectors within an organization for the purpose of eliminating communication inefficiencies. There is no framework which guides application’s integrators on goals of integrations, outcomes of integration, outputs of integration and skills which will be required for types of applications expected to be integrated. The study recommended further study on integration-framework especial on designing unstructured framework which will support and guide application’s integrators with consideration on consumer’s surrounding environment
A Review on Development of GIS and m-Health Based Patient Registration System to Enhance Support for Epidemiological Analysis: A Case Study of Tanzania Hospitals
((IJCSIS) International Journal of Computer Science and Information Security, 2015-08) Leo, Judith; Michael, Kisangiri; Kalegele, Khamisi
Over the past decades, there has been great advances in ICTs, which has led to the evolution and deployment of mobile phone application technology and GIS in the health sector. Despite of the expanded use of advanced ICT in the health sector, there is still ineffective data collection and presentation of patient and general health data in Tanzanian HIS. This paper shows different proposed and used GIS and mobile applications in perfecting HIS systems. It further proposes the best way on how these technologies can be used to provide effective data collection and presentation. Based on the discussions, a module is proposed to be integrated into the HIS. The ultimate goal of this paper is to improve collection and presentation of health/patient data, in order to enable enhancement of epidemiological analysis.
A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data
(International Journal of Computing and Digital Systems, 2024-08) Lukwaro, Elia; Kalegele, Khamisi; Nyambo, Devotha
There has been a significant increase in academic processes to ensure the quality of educational resources such as curricula, examinations, and educational content. This has drawn attention to studies exploring the use of text mining, learning machines, and auto-analytic tools like natural language processing (NLP) to interpret and evaluate the quality of these educational resources. Auto-analytical techniques are required to evaluate the quality of educational content; otherwise, manual evaluation can be burdensome and improperly influenced by human instincts. This study employs a methodical approach to comprehensively survey NLP techniques for extracting syntactic and semantic features to analyze and comprehend educational content. NLP, in combination with machine learning, is an ideal tool for automatically evaluating the aspects of higher education quality. This is because they include features that aid in textual content comprehension as well as implementing natural language techniques that provide an interpretive interface between humans and machines. The review highlights the limitations of NLP in evaluating educational data, including the need for sentence-level understanding and the need for research to address challenges like noise in text data, domain-specific language variations, and improving model robustness for effective feature extraction in educational contexts. The findings of this review hold substantial benefits for various stakeholders, including education regulatory bodies, researchers, higher education institutions, and NLP researchers. Notably, the study equips NLP researchers with valuable insights into document analysis’s current strengths and weaknesses. The accumulated evidence can provide the skills to develop NLP-based applications for evaluating the relevant and quality aspects of education in higher educational settings. Furthermore, NLP researchers can be updated on the strengths and limitations of document analysis, allowing them to apply effective text representation approaches and implement the appropriate algorithm and techniques for NLP tasks, particularly in educational data. Keywords: NLP, syntactic features, semantic feat
A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction
(Data Science Journal, 2019-04-17) Mduma, Neema; Kalegele, Khamisi; Machuve, Dina
School dropout is absenteeism from school for no good reason for a continuous number of days. Addressing this challenge requires a thorough understanding of the underlying issues and effective planning for interventions. Over the years machine learning has gained much attention on addressing the problem of students dropout. This is because machine learning techniques can effectively facilitate determination of at-risk students and timely planning for interventions. In order to collect, organize, and synthesize existing knowledge in the field of machine learning on addressing student dropout; literature in academic journals, books and case studies have been surveyed. The survey reveal that, several machine learning algorithms have been proposed in literature. However, most of those algorithms have been developed and tested in developed countries. Hence, developing countries are facing lack of research on the use of machine learning on addressing this problem. Furthermore, many studies focus on addressing student dropout using student level datasets. However, developing countries need to include school level datasets due to the issue of limited resources. Therefore, this paper presents an overview of machine learning in education with the focus on techniques for student dropout prediction. Furthermore, the paper highlights open challenges for future research directions.
Web Services for Transforming e-Cultural Heritage Management in Tanzania
(Modern Education and Computer Science Press, 2017-12-08) Monko, Gloriana Joseph; Kalegele, Khamisi; Machuve, Dina
Globally, applications of Information and Communication Technologies (ICTs) in services management and delivery have positively transformed various sectors of economies. Similar transformations have been witnessed in Tanzania, where mobile money and the Internet have transformed the way businesses are done. However, the transformations have not taken place across all sectors of the Tanzanian economy with some sectors lagging further behind than others. Cultural heritage sector, in particular, has been slow in leveraging ICT to transform the delivery of services. The sector is characterized by poor consumption of heritage sites and artworks, neglect, low publicity, weak branding, to mention a few. Although the achieved level of skills in Internet and Web have enabled the proliferation of many useful Websites which offer services to the public, information gathering remains a huge challenge considering the size of the country, its cultural heritage from 126 tribes and the vast nature reserves. We have investigated the usefulness of Web services in promoting mechanisms to gather and disseminate information about e-Cultural Heritage in Tanzania. In our investigations, we analyzed stakeholders of the sector and realized that Web services can be used to foster mechanisms within specific groups using crowdsourcing techniques. We then proposed specific Web services that cater for information flow chain of Tanzanian's artwork industry and developed an e-cultural heritage portal for demonstration. We tested the system with a sample of selected stakeholders and the initial results indicated that the locals’ readiness for such a system is strong enough and that apart from developers benefiting from the Web services, accessibility of information and stakeholders’ visibility will improve.