Learning analytic and educational data mining for learning science and technology

Abstract

Learning analytics is another trendy choice for technology-enhanced learning. It is not surprising, therefore, that learning analytics is the topic of many scientific papers as a new learning process and tool to improve learning performance. Some of the most common methods used in data mining for learning analytics, include: classification and prediction, clustering, outlier detection, relationship mining, social network analysis, process mining, text mining, distillation of data for human judgment, and discovery with models. The use of analytic learning and data mining in education and learning, generally to store information related to student backgrounds, about their interactions with the learning system and to understand how students interact with learning resources, learning styles, predictions of learning outcomes and their possibilities for completing studies they successfully. With this study teachers and school managers can develop new learning tools how to apply analytic learning to improve and predict student learning success.