Mümine Kaya Keleş
Mümine KAYA KELEŞ is a Computer Engineer at Adana Science and Technology University as an Assisstant Professor. Her research field is Data Mining, Plagiarism and Distance Education.
Chapters authored
A Review of Distance Learning and Learning Management Systems
In recent years, rapid developments in technology and the web have led to many changes in education. One of the most important changes in education is in the form of distance learning. Distance learning, which is used to define education where educators and learners are physically separated, is not a new concept; however, emerging technologies and the web allow web‐based distance learning and therefore increase its popularity. As a result of these developments, many universities have started to use web‐based distance learning systems to provide flexible education that is independent of time and place. In this chapter, we review all popular, widely used, and well‐known learning management systems and include detailed comparison of some of these systems to allow institutions to choose the right system for their distance education activities.
Part of the book: Virtual Learning
Determination and Classification of Crew Productivity with Data Mining Methods
Turkey is a developing country and the main axis of development is “construction.” The construction sector is in a position to create demand for goods and services produced by more than 200 subsectors, and this widespread impact is the most basic indicator of the sector’s “locomotive of the economy.” In the development of the construction industry, crew productivity plays a very important role. While businesses that do not measure their employees’ needs, their locations, and so on are suffering from various losses, rare businesses that take these parameters into account can profit. The identification of leadership types that will motivate employees has great importance in terms of construction businesses where the human element is the foreground. For this purpose, in the province of Adana, the relationship of productivity between the engineers working in construction companies and workers who work at lower departments of these engineers was examined. In this study, bidirectional multiple leadership questionnaire (MLQ) was applied to construction site managers and employees, and according to this survey data, leadership and motivations/productivities were classified using data mining methods. According to the classification analysis results, the most successful data mining algorithm was random forest algorithm with a rate of 81.3725%.
Part of the book: Data Mining