A Solution to Manage the Full Life Cycle of Learning Analytics in a Learning Management System: AnalyTIC

• Published in: IEEE-RITA Vol. 14; no. 4; pp. 127 - 134
• Main Authors: Simanca Herrera, Fredys Alberto, Crespo, Ruben Gonzalez, Baena, Luis Rodriguez, Burgos, Daniel
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-11-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classrooms; Educational technology; Electronic learning; Information retrieval; Learning; Learning analytics; Learning management systems; Learning systems; Life cycle engineering; Mathematical analysis; Mentoring; online education; personalized learning; personalized mentoring; Students; Teachers
• ISSN: 1932-8540; 1932-8540; 2374-0132
• DOI: 10.1109/RITA.2019.2950148

Learning Analytics (LA) has a significant impact in learning and teaching processes. These processes can be improved using the available data retrieved from students' activity inside the virtual classrooms of a learning management system (LMS). This process requires the development of a tool that allows one to handle the retrieved information properly. This paper presents a solution to this need, in the form of a development model and actual implementation of an LA tool. Four phases (Explanation, Diagnosis, Prediction and Prescription) are implemented in the tool, allowing a teacher to track students' activity in a virtual classroom via the Sakai LMS. It also allows for the identification of users who face challenges in their academic process and the initiation of personalised mentoring by the teacher or tutor. The use of the tool was tested on groups of students in an algorithms course in the periods 2017-1, 2017-2, 2018-1 and 2018-2, with a total of 90 students - in parallel with the control groups in the same periods that totalled 95 students - obtaining superior averages in the test groups versus the control groups, which evidenced the functionality and utility of the software.