Cheating In Academia- Different Attitudes and Remedies

Universities have taken vastly different approaches to curving cheating and maintaining academic integrity throughout their academic programs in both traditional (in-class_ and Distance Learning (online) offerings. This ongoing research is focused upon assessing the wide spectrum of (technology-based and non-technology-based) approaches addressing these problems. In this paper the authors identify the research methods being used to develop an understanding of the extent of the problem and to identify reasonable solutions. The research tools include surveys of university students, instructors, and administrators, as well as select business organizations. Preliminary results for the student survey are presented.</jats:p

Method to Generate Building Damage Maps by Combining Aerial Image Processing and Crowdsourcing

Building damage maps that show the damage status of buildings are an essential information source for various disaster countermeasures, such as evacuation, rescue, and reconstruction. Therefore, they must be generated as quickly as possible. However, to generate a building damage map, it is necessary to collect disaster information and estimate the damage situation over a wide area, which is time consuming. (In this paper, we consider disaster information collection as capturing aerial images.) In recent years, crowdsourcing has been widely used to understand the damage situation. Crowdsourcing achieves large-scale work by dividing it into microtasks that can be solved by anyone and by distributing the microtasks among an unspecified number of workers. We believe that crowdsourcing is suitable for gathering information and assessing damage situations as it can adjust the type and number of workers in a scalable manner and allocate resources according to the size of the disaster. Therefore, crowdsourcing has been used for gathering information and assessing the situation during disaster management. However, usually, the two types of crowdsourcing tasks (i.e., gathering information and assessing the damage) are performed independently; consequently, the collected information is often not utilized effectively. More efficient work can be expected by linking the two crowdsourcing tasks. This paper proposes a framework for efficiently generating a building damage map by combining the two methods of information collection on disaster areas and assessment of disaster situations using aerial image processing. The results of an experiment using a prototype of our proposed framework clarify the range of applications in the collection and assessment crowdsourcing tasks. The experimental results indicate the feasibility of understanding disaster situations using our method. In addition, it is possible to install artificial intelligence workers that can support human workers to estimate the damage situation more quickly.</jats:p