79 research outputs found
Continuous Professional development for primary school teachers: Needs and Factors Hindering Teachers Participation
No full textThe government of Ethiopia has recognized quality education as a key to transform the economic and social development of the country. Teachers" professional development has been one of the priorities of the education sector to the realization of the country"s ambitious goals. The aim of this study was to investigate the professional development needs of teachers and the hindering factors that affect teachers" participation in CPD. The study employed mixed research approach. The participants of the study were 624 primary school teachers who were randomly selected from government primary schools of North Gondar Zone, Ethiopia. Data were collected by questionnaire, interview and focus group discussion from teachers, school principals, cluster supervisors and Woreda education officers. Both quantitative and qualitative data were collected. The quantitative data were analysed by the help of percentage, mean, t-test and One-Way ANOVA. The qualitative data were analysed thematically. The study revealed that lack of incentives, work load, shortage of time, lack of support from stakeholders, and Lack of trained CPD facilitators were the most perceived barriers of CPD. Teachers were also asked to identify their professional development needs. Accordingly, subject matter knowledge, Knowledge of curriculum and classroom management skills were identified as their most preferred needs. Teaching students with special needs and action research were the least selected professional development needs. The t-test result shows that gender difference affects the perception of teachers toward the hindering factors and their professional development needs. Female teachers showed higher professional development needs than male teachers. ANOVA result shows that teachers teaching experience does not affect the perception of teachers professional development needs. But significance difference was observed between different experience groups about the hindering factors of CPD
The Global Cohort of Doctoral Students: Building Shared Global Health Research Capacity in High-Income and Low- and Middle-Income Countries
No full textDoctoral students in high- and low-income countries pursuing careers in global health face gaps in their training that could be readily filled through structured peer-learning activities with students based at partnering institutions in complimentary settings. We share lessons learned from the Global Cohort of Doctoral Students, a community of doctoral students based at the Harvard T. H. Chan School of Public Health, Haramaya University. University of Gondar, University of Botswana, and University of Rwanda College of Medicine and Health Sciences. Students in the Global Cohort program engage in collaborative research, forums for constructive feedback, and professional development activities. We describe the motivation for the program, core activities, and early successes.This work was funded by the Rose Traveling Fellowship and Deborah Rose Service Learning Fellowship at the Harvard T. H. Chan School of Public Health. The funding sources had no role in the writing of the manuscript or decision to submit it for publication.Iyer, HS (corresponding author), Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA.
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Improving Medium- and Long-Range Hydrological Forecasts with Ensemble Meteorological Forecasts and Climatic Information
No full textTitle: Improving Medium- and Long-Range Hydrological Forecasts with Ensemble Meteorological Forecasts and Climatic Information, Author: Getnet Y. Muluye, Location: MillsThe ability to provide reliable and accurate medium- and long-range hydrological
forecasts is fundamental for the effective operation and management of water resources
systems. The principal objectives of this thesis are (i) to develop a framework for
advancing the long-range forecasting skills of hydrological models by coupling pertinent
and leading climate information with regional hydro-meteorological variables; and (ii) to
develop effective mechanisms for integrating meteorological ensemble systems in a
hydrologic prediction system, which would be useful for risk analysis by policy makers
for operating both large-scale as well as small-scale water resources systems. This
research constitutes three principal components: long-range forecasts, downscaling, and
medium-range forecasts. For long-range hydrological forecasting, four data-driven models, including multilayer perceptron (MLP), time-lagged feedforward network (TLFN), Bayesian neural network (BNN) and recurrent multilayer perceptron (RMLP) were designed by
incorporating low-frequency climatic indices to forecast seasonal reservoir inflows. The
results indicated that the incorporation of modes of climatic indices in a hydrologic
forecasting model resulted in a considerable improvement in the seasonal forecast
accuracy. Furthermore, the extended Kalman filter approach was used to train the
recurrent multilayer perceptron for capturing the complexity associated with the long range
streamflow forecasting. Results showed that the proposed methodology was able to
provide a robust modeling framework capable of capturing the complex dynamics of the
hydrologic system. Different statistical methods were developed and evaluated for downscaling local scale information of precipitation and temperature from the numerical weather prediction model output. Three different methods were considered: (i) hybrids; (ii) neural networks; and (iii) nearest neighbor-based approaches. The findings revealed that the skills in the downscaled temperature forecasts were superior to those in the downscaled precipitation forecasts. In particular, for downscaling daily precipitation, the artificial neural network-logistic regression (ANN-Logst), partial least squares (PLS) regression and recurrent
multilayer perceptron trained with the extended Kalman filter (EKF) models yielded
greater skill values, and the conditional resampling method (SDSM) and K-nearest
neighbor (KNN) based models showed potential for characterizing the variability in daily
precipitation. For the case of medium-range hydrological forecasting, the downscaled and the raw numerical model outputs were forced into an HBV hydrologic model in order to
generate an ensemble of reservoir inflows. The simulation results indicated that the
downscaled-based flows had greater skill values, and yielded more accurate forecasts
than the raw-based flows. The potential economic values of flow forecasts were further
assessed based on a simple optimal decision-making, cost-loss analysis technique. The
principal outcomes emerging from the analyses included: (i) the economic benefits
associated with probabilistic flow forecasts were more useful than their deterministic
counterparts; and (ii) the downscaled-based flow forecasts offered greater benefits, which
are applicable to a much wider range of users, than the raw-based flow forecasts.ThesisDoctor of Philosophy (PhD
Ethnobotanical study of medicinal plants used against human ailments in Gubalafto District, Northern Ethiopia
No full textAbstract Background Traditional medicinal plant species documentation is very crucial in Ethiopia for biodiversity conservation, bioactive chemical extractions and indigenous knowledge retention. Having first observed the inhabitants of Gubalafto District (Northern Ethiopia), the author gathered, recorded, and documented the human traditional medicinal plant species and the associated indigenous knowledge. Methods The study was conducted from February 2013 to January 2015 and used descriptive field survey design. Eighty-four informants were selected from seven study kebeles (sub-districts) in the District through purposive, snowball, and random sampling techniques. Both quantitative and qualitative data were collected through semi-structured interviews, guided field walks, demonstrations, and focus group discussions with the help of guided questions. Data were organized and analyzed by descriptive statistics with SPSS version 20 and Microsoft Office Excel 2007. Results A total of 135 medicinal plant species within 120 genera and 64 families were documented. Among the species, Ocimum lamiifolium and Rhamnus prinoides scored the highest informant citations and fidelity level value, respectively. In the study area, Asteraceae with 8.1% and herbs with 50.4% plant species were the most used sources for their medicinal uses. A total of 65 ailments were identified as being treated by traditional medicinal plants, among which stomachache (abdominal health problems) was frequently reported. Solanum incanum was reported for the treatment of many of the reported diseases. The leaf, fresh parts, and crushed forms of the medicinal plants were the most preferred in remedy preparations. Oral application was the highest reported administration for 110 preparations. A majority of medicinal plant species existed in the wild without any particular conservation effort. Few informants (about 5%) had only brief notes about the traditional medicinal plants. Ninety percent of the respondents have learned indigenous medicinal plants knowledge from their family members and friends secretly. Orthodox Church schools were found the main place for 65% of healer’s indigenous knowledge origin and experiences. Elders, aged between 40 and 84 years, gave detailed descriptions about traditional medicinal plants. Conclusions Traditional medicinal plants and associated indigenous knowledge are the main systems to maintain human health in Gubalafto District. But minimal conservation measures were recorded in the community. Thus, in-situ and ex-situ conservation practices and sustainable utilization are required in the District
Predictors of the Achievement of Primary School Students in Sciences and Mathematics in North Western Ethiopia
No full textThis study explores the key factors that shape students’ performance in science and mathematics among primary school students in Northwestern Ethiopia. Using a cross-sectional design, data were gathered from 2,928 students enrolled in 24 primary schools between January and April 2016. The schools and participants were selected through a two-stage stratified random sampling method. Information was collected using structured questionnaires and interviews with students, teachers, and school administrators. The results indicate that poor teacher performance, especially in science and mathematics, and a shortage of qualified teachers are major barriers to student achievement. Low teacher motivation and limited access to learning resources, such as textbooks and library facilities, further compound the problem. Gender differences were also considered that male students generally scored higher in science and mathematics, whereas female students showed strong interest and attitudes toward language subjects. Factor analysis revealed weak connections between teacher–student interactions and overall school engagement, suggesting gaps in the learning environment. Multivariate analysis identified school type, gender, availability of textbooks, and access to teaching materials as significant predictors of academic success. Multilevel modeling showed considerable variation in student achievement between schools, with an intra-class correlation coefficient (ICC) of 0.664, highlighting the strong role of school-level conditions in shaping learning outcomes. Overall, the findings call for comprehensive measures to strengthen school infrastructure, improve teacher training and motivation, and provide better academic support for students both at school and at home. Strengthening these areas is essential to raise achievement levels in science and mathematics and to ensure more equitable educational outcomes across schools in the region
Landscape Targeted Crop-Fertilizer Response in the Highlands of Ethiopia Version 1.0
No full textThe dataset is meant for developing fertilizer management decision support tool for an effective crop-nutrient management. The dataset is developed on the basis of landscape targeting on-farm trials on crop-nutrient response and crop yield gap assessment across the Africa Rising target districts and other scaling up locations in the Ethiopian highlands.Africa RISIN
Dynamic Bayesian network modeling for longitudinal data on child undernutrition in Ethiopia (2002-2016)
No full textAbstract
Introduction:
Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002–2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with the highest values of mutual information and target entropy reduction, along with the lowest value of target entropy, was considered to have the strongest predictive power in the dataset.
Results:
This study revealed that long-term participation in programs increased the likelihood of children being in a normal nutritional state. Key factors influencing the nutritional status of children under two years of age include the mother’s education level, her subjective well-being, and the household’s wealth quintile. Children with educated parents were more likely to have a healthy nutritional status. Additionally, the causal pathway of intervention programs → wealth quintile → child nutritional status consistently exceeded 90% in Waves 3, 4, and 5, indicating a strong relationship. Similarly, the relationship between intervention programs → food security → child nutritional status was nearly perfect at 99.99% in Waves 4 and 5, indicating a strong association. Finally, the study revealed that household participation in intervention programs significantly reduces undernutrition in best-case scenarios, while the absence of support poses a higher risk in worst-case conditions.
Conclusion:
The comprehensive intervention program strongly improved household wealth, food security, and maternal well-being, which in turn affected children’s nutritional status.Abstract
Introduction:
Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002–2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with the highest values of mutual information and target entropy reduction, along with the lowest value of target entropy, was considered to have the strongest predictive power in the dataset.
Results:
This study revealed that long-term participation in programs increased the likelihood of children being in a normal nutritional state. Key factors influencing the nutritional status of children under two years of age include the mother’s education level, her subjective well-being, and the household’s wealth quintile. Children with educated parents were more likely to have a healthy nutritional status. Additionally, the causal pathway of intervention programs → wealth quintile → child nutritional status consistently exceeded 90% in Waves 3, 4, and 5, indicating a strong relationship. Similarly, the relationship between intervention programs → food security → child nutritional status was nearly perfect at 99.99% in Waves 4 and 5, indicating a strong association. Finally, the study revealed that household participation in intervention programs significantly reduces undernutrition in best-case scenarios, while the absence of support poses a higher risk in worst-case conditions.
Conclusion:
The comprehensive intervention program strongly improved household wealth, food security, and maternal well-being, which in turn affected children’s nutritional status
Assessment on the Livestock and Feed Production and Utilization Practice in Basona Worena Woreda of North Shewa, Ethiopia
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