An interface is very effective to encourage the users intention and engage them to achieve their destination in e-learning process. This research aims to explore the user needs through learner experience on using e-learning interface. The case study is needed to identify and to collect the learner problem in e-learning, especially in User Interface. It took three months to get the user interface problems, such as login page, choice menus, and table of contents, content presentation, interactive media, and presentation of exercises, example solutions, and self-assessment. The research took three months data collection of forty-seven students in Mulawarman University. Based on this research, it shows that the communicativeness of the interface is the most difficult part, followed by choices menu, and self-assessment. Meanwhile, the example solutions and login page are among the least difficult of the menu.
There is a great deal of knowledge in requirement elicitation process (REP), because there are different stakeholders with various knowledge backgrounds. Different backgrounds of knowledge lead to different ways of knowledge expression that negatively affect knowledge understandability and cause ambiguity. Knowledge ambiguity results in incorrect interpretation of knowledge and requirements. On the other hand, different stakeholders have different needs and expectations from the software to be developed. This problem causes conflicting information and also negatively affects the correctness of knowledge. Furthermore, stakeholders may ignore mentioning some knowledge because they think it is obvious or their requirements change over time, this negatively affects completeness of knowledge in REP. To mitigate these problems, it is necessary to identify and assess the knowledge in REP. Knowledge Audit (KA) is the process of knowledge analysis and assessment. Therefore, this research introduces a KA model to support knowledge communication among stakeholders through objectively assessing the knowledge in REP.
Risk mitigation has gained relevance during the last years and has helped to solve risk and improve decision making among decision makers in IT Governance. However, there is still a increasing need of developing innovative tools that can help IT Practitioners to solve risk in IT Governance. Existing risk mitigation approaches or tools lacks need for adequate data which is very important in mitigating risk and there is difficulty of mitigating risk generally in IT Governance. This paper present an autonomic computing model developed to mitigate risk; mainly operational and technical in IT Governance by measuring the risk and providing risk report to the management and staffs in organisations. Autonomic Computing Systems (ACSs) are systems that manage themselves. The core of Autonomic Computing Systems are type of agent with advanced capacities for reasoning to measure the risk probability and risk impact based on available data in the knowledge base or previous experiences. The Autonomic Computing Systems provide risk advice aimed at providing decision support to management hence mitigating risk in IT Governance. Data was collected via purposely sampling using interview by case study among 13 Malaysia universities. The data was analyzed by Nvivo to get an insight on the current risk mitigation practices and process, after which a risk mitigation model has been developed using autonomic agents.
This paper considers the importance of knowledge in software development organizations which are highly knowledge-intensive and focuses on knowledge audit in their requirement elicitation process. Requirement elicitation process involves a great deal of knowledge and there are several problems regarding eliciting and using the knowledge in this process. Misunderstanding, undefined scope, conflicting information and constant changes of requirements are some of the problems of requirement elicitation. A knowledge audit model is proposed in this paper to improve the requirement elicitation process by identifying knowledge components and knowledge sources existing in the requirement elicitation process as well as their relationships. A survey is then conducted to prove the validity of the model. The results support that the proposed knowledge components and knowledge audit model improves requirement elicitation.
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