Insights for academic analytics.

• Main Authors: Ya'Acob, Suraya, Abd. Talib, Muhammad Danial
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2022
• Subjects: LC Special aspects of education
• Online Access: http://eprints.utm.my/104608/1/MuhammadDanialAbdTalibSurayaYaacob2022_IsightsforAcademicAnalytics.pdf; http://eprints.utm.my/104608/; https://oiji.utm.my/index.php/oiji/article/view/180
• Institution: Universiti Teknologi Malaysia

Education plays a vital role in any civilisation; improving education thus comes priority. With tons of interest shown in analytics, it has become natural for the education world to dive into academic analytics (AA), which two primary methods are educational data mining (EDM) and learning analytics (LA). EDM and LA are used to predict students in academic difficulty, allow faculty and advisers to customise their learning path, or provide guidance tailored to unique learning needs. EDM is a method for extracting useful information that could potentially affect an organisation. LA is a method of collecting, understanding data to optimise the learning experience. This project aims to identify the business requirement specification (BRS) for the Razak Faculty of Technology and Informatics (RFTI). The BRS insight will create a foundation for academic analytics implementation at RFTI. The methodology used in the project is qualitative, with the data collected from the semi- structured interview. This project's end product is the BRS insight that can be used to apply AA at RFTI.