Abstract Education Management Information System means to gather statistics by following people, models, methods, procedures, processes, rules and regulations with the emerging information technology to provide comprehensive, integrated, relevant, reliable, unambiguous and timely data to education leaders, decisions makers, planners and managers, to perform their responsibilities accurately and efficiently. Numbers of countries in the world have decentralized their EMIS systems so... Education Management Information System means to gather statistics by following people, models, methods, procedures, processes, rules and regulations with the emerging information technology to provide comprehensive, integrated, relevant, reliable, unambiguous and timely data to education leaders, decisions makers, planners and managers, to perform their responsibilities accurately and efficiently. Numbers of countries in the world have decentralized their EMIS systems so as to transfer and manage all EMIS activities from high level to low level in the best interest of the organization. But in various developing countries, it is still poor in terms of collection, analysis and production of data, generating its outputs and dissemination to develop better education policies. Work of one district do not complete without completion of the work of other district, which ultimately results delays in overall EMIS activities. This paper emphasizes on the capacity and performance of the different EMIS activities, necessary and mandatory for any efficient decentralized EMIS system by assessing, examining and identifying the deficiencies in them. Read More Read Less

Abstract There has been a lot of research work in relation to online laboratories, however the idea of students experiencing realism when performing experiments in such environments has not been the focus when setting up online or virtual Laboratories. The purpose of this paper is to find out the importance of realism experience (RE) in relation to laboratory experiments in a... There has been a lot of research work in relation to online laboratories, however the idea of students experiencing realism when performing experiments in such environments has not been the focus when setting up online or virtual Laboratories. The purpose of this paper is to find out the importance of realism experience (RE) in relation to laboratory experiments in a virtual environment, the process of identifying them, and how to ensure that they are present when developing virtual or online laboratories. A lot of research has been carried out on the understanding of subject content with virtual laboratories and there has been a lot of success with it, however, there are other experiences that must go with these experiments to enable students adapt to real life environment. In setting up a physical laboratory the idea of making sure students experience realism as part of the learning process is a matter of course and as such not much effort is put in when setting the objectives for the laboratory, however, the same cannot be said for setting up a virtual laboratory. The paper concludes with the processes that must be followed in ensuring that realism experience is embedded in the process of performing the laboratory experiments in a virtual environment and also to be able to determine whether a physical laboratory experiment could be performed in a virtual environment without losing any of the learning experiences required to give the student total learning experience as indicated by the Bloom’s taxonomy of learning domain. Read More Read Less

Abstract There had been many studies about the multi-representational learning of students. These studies helped us understand how multiple representations can induce understanding on certain concepts to students. Some of these studies are focused on Physics, particularly on the concepts of force. Over the years, students’ understanding on force concepts was measured through tests such as the Force Concept Inventory. From... There had been many studies about the multi-representational learning of students. These studies helped us understand how multiple representations can induce understanding on certain concepts to students. Some of these studies are focused on Physics, particularly on the concepts of force. Over the years, students’ understanding on force concepts was measured through tests such as the Force Concept Inventory. From this, Nieminen, Savinainen, and Viiri developed a new multiple-choice questionnaire which became known as the R-FCI or the Representational Variant of Force Concept Inventory. In this paper, the R-FCI is used to measure the consistency of the students on the force concepts. Lumping together in this study were ICT integration, scaffolding, student-centered mode of learning, and independent inquiry. Utilizing a modified quasiexperimental design, 3 comparable physics 1 classes were utilized. One class served as treatment (Xt ), another as positive control (Xco+ ), and the last one as negative control (Xco −- ). Both Xt and Xco+ received all the treatments from scaffolded lectures, hands-on investigation on DIY apparatus, group exploration on PhET simulations covering forces, and social interaction by online comment on submitted webquest activities. The Xco- however received only the usual classroom lecture mode and was not exposed to webquests and PhET simulations. These three classes took the R-FCI. Results showed that the study can only afford to conclude the significant effect of scaffoldings on raw scores. The scaffolded or treated classes, both Xt and Xco+, had no significant difference between them but had a significant difference with the unscaffolded or untreated class, Xco-. Read More Read Less

Abstract Cancer is the most familiar disease that affects human body. The time factor is very important to discover the abnormality issues in target patients, especially in various cancer tumors such as lung cancer, breast cancer etc., To increase the survival rate of cancer patients, as it is extremely poor, recently, image processing techniques have been used widely for earlier detection... Cancer is the most familiar disease that affects human body. The time factor is very important to discover the abnormality issues in target patients, especially in various cancer tumors such as lung cancer, breast cancer etc., To increase the survival rate of cancer patients, as it is extremely poor, recently, image processing techniques have been used widely for earlier detection and treatment with reduced risk. This paper presents a Fuzzy-Adaptive Resonance Theory (ART) neural network based approach for early detection of lung cancer from raw chest X-ray images. Image quality and accuracy in predicting the presence of nodules and clustering them into one of the four stages of cancer are the core factors of this paper. Removal of noise using various filters and segmentation of the lung to detect abnormal regions in the X-ray images has been carried out. Relying on extracted features like area, perimeter, shape of the detected nodules, a clustering method is used to cluster the nodules appropriately to verify whether a region is a malignant nodule or not in consent with the domain expert knowledge. Read More Read Less

Abstract Thunderstorm is a sudden electrical expulsion manifested by a blaze of lightening with a muffled sound. It is one of the most spectacular mesoscale weather phenomena in the atmosphere which occurs seasonally. On the other hand, prediction of thunderstorms is said to be the most complicated task in weather forecasting, due to its limited spatial and temporal extension either dynamically... Thunderstorm is a sudden electrical expulsion manifested by a blaze of lightening with a muffled sound. It is one of the most spectacular mesoscale weather phenomena in the atmosphere which occurs seasonally. On the other hand, prediction of thunderstorms is said to be the most complicated task in weather forecasting, due to its limited spatial and temporal extension either dynamically or physically. Every thunderstorm produce lightening, this kills more people every year than tornadoes. Heavy rain from thunderstorm leads to flash flooding, and causes extensive loss to property and other living organisms. Different scientific and technological researches are been carried on for the forecasting of this severe weather feature in advance to reduce damages. In this regard, many of the researchers proposed various methodologies like STP model, MOM model, CG model, LM model, QKP model, DBD model and so on for the detection, but neither of them could provide an accurate prediction. The present research adopted clustering and wavelet transform techniques in order to improve the prediction rate to a greater extent. This is the first research study carried on thunderstorm prediction using the clustering and wavelet techniques resulting with higher accuracy. The proposed model yields an average accuracy of 89.23% in the identification of thunderstorm. Read More Read Less