A Comprehensive Data Mining Framework Used To Extract Academic Advising Knowledge From Social Media Data

In higher education institutions, students' academic advising is an essential activity for improving the students' academic performance. However, academic advising tasks demand a considerable amount of effort and time by the advisors, especially in case of universities with a large number of enrolled students. This practice also entails a variety of means to deliver academic advising services. Passionate use of social media by students urges the universities to make use of social media network for delivering the massive educational and academic advising services. Hence, there is a vast amount of unstructured data available that can generate themes of students' academic needs. This paper proposes a comprehensive framework of social media academic advising service that uses this amount of unstructured data and converts it into a structured data. Data mining techniques could be used to extract knowledge, features, and patterns that help to enhance the students' learning. This approach is expected to facilitate in developing an e-academic advising system, which will be beneficial especially for universities with a large number of students. Discovered knowledge will support the academic advising systems to intervene if needed and take actions to enhance the students' academic performance.