The effects of climate change such as drought, storms and extreme weather are affecting the earth. Greenhouse gases are the main cause of climate change and carbon dioxide (CO2) takes the larger proportion of the total greenhouse gases emitted. CO2 is projected to continuously increase based on simulation and data mining tools relying on historical data. Globally, 80% of CO2 emission is from combustion of fossil fuel mainly in manufacturing industry or transportation industry. Governments in both developed and developing countries have formulated policies to manage CO2 emission by orienting them towards consumers or manufacturers. In Zambia, CO2 emission control has mostly focused on transportation industry where carbon emission tax is charged on motor vehicles depending on combustion capacity. Transportation is an industry that has had a high increase of total CO2 emission currently standing at 35% but with average of 31.7 % from 1971 to 2014 in Zambia. The manufacturing sector, though being the highest emitter of CO2 has seen no policies formulated to regulate emissions. Predicated emission values for both transportation and manufacturing sector show a continuous domination of these two sectors regarding carbon emission. The task is then left with policy makers to introduce policies that will regulate emissions as the current carbon tax policy does not seem to be effective in reducing emissions. This paper brings out the trends in CO2 emission from fossil fuels in Zambia from 1964 to 2016. The paper also highlights the main industries contributing to CO2 emission, the policies implemented to control CO2 emission levels globally and in Zambia, provides a forecast for CO2 emission levels till 2021 and suggests future research directions based on the findings.

E-learning poses a challenge in a pedagogical perspective such as finding ways on how to motivate the students to learn in spite of the absence of a human instructor. Many researchers in the field have proposed and implemented various mechanisms to improve the learning process such as individualization and personalization. The main objectives is to maximize learning by dynamically selecting the closest teaching operation in order to achieve the learning goals. In this paper, a revolutionary technique has been proposed and implemented to perform individualization and personalization using reversed roulette wheel selection algorithm that runs at O(n). The technique is simpler to implement and is algorithmically less expensive compared to other revolutionary algorithms since it collects the dynamic real time performance such as examinations, reviews and study matrices. Results show that the implemented system is capable of recommending new learning sequences that lessens time of study based on their prior knowledge and real performance matrix.

<p>A in a university setting, class scheduling is vital for teaching and learning process. Academic institutions rely on time tables in their day to day activities. University Course Timeframe problem can be resolved by using multi-agent systems-based method which may increase the independence of each department's class scheduling, adaptability in a distributed environment and prevents conflicts between events or resources, and unforeseen allocation through intervention between agents in a dispersed environment. Class timing is performed manually in most of the higher educational institutions, which is a very challenging and time-consuming process. The main objective of the study is to build a multi-agent class timing system that automates the process of class scheduling of higher education institutions (HEIs) using the Prometheus methodology. The implementation of the Prometheus approach in the development of a multi-agent framework has resulted in a complete and comprehensive system covering all phases of software development as applied to the agent systems.</p>

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An e-learning website is not sufficient to fully attain the results of online education. There also is a need to align the educational objectives into the design of the assessment to improve and develop cognition, critical thinking and problem-solving skills. Previous studies have explored the potentials of the assessment models but few ventured into their implementation. Others only proposed and introduced conceptual frameworks. The implementation of these proposals, however, revealed that the question type in the assessment phase neglected to align their questionnaire formats into a cognitive schema. At present, the standard multiple-choice question is the most frequently used of the question type of e-learning assessments. However, if this type is the only format adopted by e-learning developers, then the potentially rich and embedded assessment of the computer platform will be given up. This paper focuses on the design of assessment questions, which is created and guided by the hierarchical Bloom cognitive taxonomy and by utilizing rich media formats. Results conducted for eighteen weeks show a dramatic increase in the academic performance of the students. Likewise, digital transcripts converted from the collected perceptions after training undergoes sentiment analysis have correlated with the student improved academic throughput.