Abstract-There has been growing concerns about the rising costs of doing business and environmental degradation world over. Green ICT has been proposed to provide solutions to the two issues yet it is not being implemented fully in developing economies like Kenya. For its implementation, it is critical to establish the level of green ICT readiness of organisations to inform where to start and where to put more emphasis. Over the past few years this has been done using Molla's G-readiness model. However this model assumes the basic level of Greadiness to be same for both developed and developing economies to be the same with regard to ICT personnel preparedness. Based on green ICT readiness in Kenya, the relationship between ICT personnel's gender, age and training with the G-readiness variables as proposed in Molla's Greadiness model was investigated. The study surveyed ICT personnel in four cases using a questionnaire on a seven scale likert scale. It established that there exists a significant relationship between the ICT personnel related variables and the G-readiness variables. Based on the findings on the relationship, the study extended Molla's G-readiness model to include a sixth dimension of personnel readiness.
Executive SummaryEvery year, the Joint Admission Board (JAB) is tasked to determine those students who are expected to join various Kenyan public universities under the government sponsorship scheme. This exercise is usually extensive because of the large number of qualified students compared to the very limited number of slots at various institutions and the shortage of funding from the government. Further, this is made complex by the fact that the selections are done against a predefined cluster subjects vis a vis the student's preferred and applied for academic courses. Minimum requirements exist for each course and only students having the prescribed grades in specific subjects are eligible to join that course. Due to this, students are often admitted to courses they consider irrelevant to their career prospects and not their preferred choices.This process is tiresome, costly, and prone to bias, errors, or favour, leading to disadvantaging innocent students. This paper examines the potential use of artificial neural networks at the JAB for the process of selecting students for university courses. Based on the fact that Artificial Neural Networks (ANNs) have been tested and used in classification, the paper explains how a trained neural network can be used to perform the students' placement effectively and efficiently. JAB will be able, therefore, to undertake the students' placement thoroughly and be able to accomplish it with minimal wastage of time and resources respectively without having to utilise unnecessary effort. The paper outlines how the various metrics can be coded and used as input to the ANNs. Ultimately, the paper underscores the various merits that would accompany the adoption of this technique. By making use of neural networks in the university career choices, student placement at JAB will enhance the chances of students being placed into courses they prefer as part of their career choice. This is likely to motivate the students, making them work harder and leading to improved performance and improved completion rate. The ANN application may also reduce the cost spend on the application processing and the time the applicants have to wait for the outcome. The ANN application could further increase the chances of high quality applicants getting admission to career courses for which they qualify.
ICT is driving all areas of the economy and is likely to dictate the future for all genders. The narrow definition of ICT has greatly impacted on the female gender choosing ICT as a career of choice. There are few women in the ICT careers. The study sought to determine the nature of ICT career gender exclusion, status and trend of ICT job opportunities, source of ICT gender career exclusion and the contribution of the narrow definition to the exclusion. A mixed method of survey and desktop method was employed in this study. A structured questionnaire was used in this study in order to identify the factors that influence ICT career choice amongst Kenyan lady students. A purposive sample of Information Technology and Computer Science undergraduate university students (77 females, 56 males; age range 17 to 35 years) and 10 postgraduate students in Information Technology from two public universities participated in the study. The paper discusses the emerging unfilled ICT jobs. The study established that the narrow definition negatively influences ICT as a career of Choice among girls. Broadening ICT definition to include ICT related careers that have more social rather than technical aspect accordingly is likely to influence more women to join the field.
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