Learning Analytics Dashboards for Advisors -- A Systematic Literature Review

Learning Analytics Dashboard for Advisors is designed to provide data-driven insights and visualizations to support advisors in their decision-making regarding student academic progress, engagement, targeted support, and overall success. This study explores the current state of the art in learning analytics dashboards, focusing on specific requirements for advisors. By examining existing literature and case studies, this research investigates the key features and functionalities essential for an effective learning analytics dashboard tailored to advisor needs. This study also aims to provide a comprehensive understanding of the landscape of learning analytics dashboards for advisors, offering insights into the advancements, opportunities, and challenges in their development by synthesizing the current trends from a total of 21 research papers used for analysis. The findings will contribute to the design and implementation of new features in learning analytics dashboards that empower advisors to provide proactive and individualized support, ultimately fostering student retention and academic success

Learning analytics and dashboards, examining course design and students’ behavior:A case study in Saudi Arabian Higher Education

The use of Technology in Saudi Arabian Higher education is constantly evolving. With the thousands of students’ transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics (LA) in the Middle East and Gulf Cooperation Council region have increased in the recent years. This research is an exploratory case study at the University of Business and Technology (UBT), a private university in Jeddah, Saudi Arabia. The research aims to examine UBT’s rich learning analytics and discover the knowledge behind it. 900,000 records of Moodle analytical data were collected from two time periods: Fall 2018, and a consecutive 4-year historic data. Romero et al., (2008) educational data mining process was applied on three analytical reports: Students statistics, Activity and Log reports. Statistical and trend analysis were applied to examine and interpret the collected data. A significant positive correlation was found (0.265) between students’ final grades and their LMS movements in the course. The study also highlighted a trace of certain LMS engagement patterns associated with high GPA students such as viewing discussions, viewing profiles, and reviewing quizzes attempts. Additional data mining has also revealed high percentage of Turnitin and Moodle assignments’ usage. These trigger an insight recommendation for what lecturers should incorporate in their course design and what motivates students to engage and perform better. Self-regulated learning (SRL) questionnaires have been used to examine students’ and lecturers’ behavior towards Moodle Learning analytics and the completion progress dashboard. A positive association of self-control and monitoring, SRL behavior elements, to high GPA students was a main questionnaire finding. Recommendations include highlighting the need to build automated data mining tools that facilitate the capture of complex Learning Analytics data and refining it to enable interpreting and predicting the actions needed in higher education learning environments