An integrated framework for course adapted student learning analytics dashboard. (March 2019)

• Record Type: Journal Article
• Authors: Aljohani, Naif Radi; Daud, Ali; Abbasi, Rabeeh Ayaz; Alowibdi, Jalal S.; Basheri, Mohammad; Aslam, Muhammad Ahtisham
• Is Part Of: Computers in human behavior. Volume 92(2019)
• Page Start: 679
• Page End: 690
• Publication Date: 2019-03
• Subjects: Learning analytics -- Learning management systems -- Learning framework -- Blackboard -- Cognitive computing; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2018.03.035
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: The advanced learning analytics research of the last years converges with the industry demand to enhance famous learning management systems with learning analytics capabilities promoting the efficiency of higher education. The exploitation of big volume learning data, is a critical challenge for the design of personalized curricula and learning experiences. The purpose of this research paper is to communicate a framework for Learning Analytics aiming to support the integrated management of end-to-end learning data. We present the research foundations of a research prototype for the integration of a Learning Analytics Dashboard: The AMBA Prototype with famous Learning Management Systems. Finally, we present the main findings of an empirical study that proves the capacity of learning analytics to enhance the learners' ecosystem with value adding learning services. The proposed framework exploits cognitive computing for the enhancement of decision making in education by proving the capacity of Learning Analytics to reveal hidden patterns of learners' behaviour and attitude. Highlights: New "Course-adapted student learning analytics framework" is proposed. Proposed framework is partially implemented in the form of AMBA tool. Impact of AMBA tool is extensively evaluated for student's engagement.