TY - GEN
T1 - Constructing Intelligent Learning Dashboard for Online Learners
AU - Farahmand, Arta
AU - Akber Dewan, M. Ali
AU - Lin, Fuhua
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This research is motivated by the growing demand for online learning and the potential of using student-facing intelligent learning dashboards (SF-iLDs) to support online learners. SF-iLDs are designed to increase students' self-regulation, engagement, and course performance by creating visibility into their progress in the online courses. Data visualization and predictive modeling techniques are investigated and integrated into the SF-iLD designed in this study. A predictive model based on the learning management system (LMS) data (generated by both instructors and students) is used to extract and analyze valuable insights about learners' progress in the online courses. The data measures students' learning activities, such as grades on quizzes, assignments, exams, the number of logins, access to the course materials, and the overall course grade. These features are used to classify the learners into three groups: Persistent, Regular, and Irregular. Using this model, the course outcome and the learning gain can be predicted for the students based on their time management and performance in the course activities and assessments. Furthermore, data visualization in the SF-iLD enables students to track their performance in the course, which helps students to better understand their self-regulation ability in the online courses, which potentially influences their self-efficacy and performance in their courses.
AB - This research is motivated by the growing demand for online learning and the potential of using student-facing intelligent learning dashboards (SF-iLDs) to support online learners. SF-iLDs are designed to increase students' self-regulation, engagement, and course performance by creating visibility into their progress in the online courses. Data visualization and predictive modeling techniques are investigated and integrated into the SF-iLD designed in this study. A predictive model based on the learning management system (LMS) data (generated by both instructors and students) is used to extract and analyze valuable insights about learners' progress in the online courses. The data measures students' learning activities, such as grades on quizzes, assignments, exams, the number of logins, access to the course materials, and the overall course grade. These features are used to classify the learners into three groups: Persistent, Regular, and Irregular. Using this model, the course outcome and the learning gain can be predicted for the students based on their time management and performance in the course activities and assessments. Furthermore, data visualization in the SF-iLD enables students to track their performance in the course, which helps students to better understand their self-regulation ability in the online courses, which potentially influences their self-efficacy and performance in their courses.
KW - Intelligent Learning dashboard
KW - data mining
KW - information visualization
KW - learning management system
KW - machine learning
KW - online learning
KW - predictive modeling
KW - self-regulated learning
UR - http://www.scopus.com/inward/record.url?scp=85127603691&partnerID=8YFLogxK
U2 - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00141
DO - 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00141
M3 - Published Conference contribution
AN - SCOPUS:85127603691
T3 - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
SP - 849
EP - 857
BT - Proceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
T2 - 19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Y2 - 25 October 2021 through 28 October 2021
ER -