مقاله انگلیسی رایگان در مورد داده کاوی در تحقیقات کلاس درس فناوری آموزشی – الزویر 2017

Data mining has long been used in marketing, advertising, health, engineering, and information systems. At its core, data mining is an inductive, analytic, and exploratory approach, which is concerned with knowledge discovery through identification of patterns within large sets of data. In the last 10 years, the field of Educational Data Mining (EDM) has emerged as a distinct area of research concerned with using data mining techniques to answer educational questions, such as, “What are the difficulties students encounter during a learning activity?”, “What sequences of computer interactions lead to successful problem-solving performance?”, and “What sequences of actions characterize high performers and low performers in problem-solving activity?” EDM can also provide new insights into “wicked” educational problems, such as, “What are the differences in the ways students experience learning,” and “How can learning designs account for variations in students’ learning experiences?” In particular, EDM is concerned with developing methods for analyzing data from an educational system in order to detect patterns in large datasets that would otherwise be very difficult or even impossible to analyze due to the vast volume of data within which they exist (Romero & Ventura, 2013). Consequently, results from data mining can be used for deciding about how to improve the teaching and learning process as well as how to design or redesign a learning environment (Romero & Ventura, 2007; Ingram, 1999). Data mining techniques have been mostly used within the context of web-based or e-learning education in order to: (a) suggest activities, resources, learning paths, and tasks for improving learners’ performance and adapting learning experience (Tang & McCalla, 2005); (b) provide feedback to teachers and instructional designers in regards to learners’ difficulties with the content and structure of a course, so that revisions can be made to facilitate students’ learning (Merceron & Yacef, 2010; Zaiane & Luo, 2001); (c) predict learners’ performance (Ahmed & Elaraby, 2014); and (d) inform administrators about the effectiveness of instructional programs, so that better planning and allocation of human and material resources can be achieved (Romero & Ventura, 2007).