Dr. Xu Du
National Engineering Research Center for E-Learning,
Central China Normal University
Dr. Jui-Long Hung
Department of Educational Technology
Boise State University
Dr. Chih-Hsiung Tu,
College of Education
Northern Arizona University
Learning Analytics (LA) and Educational Data Mining (EDM) are highly related subjects that overlap in definition and scope. Although both communities of researchers within LA and EDM have similarities where learning science and analytic techniques intersect, there are some significant differences between them in terms of origins, techniques, fields of emphasis and types of discovery. EDM refers to computerized methods and tools for automatically detecting and extracting meaningful patterns and information from large collections of data from educational settings. LA is focused on understanding and optimizing learning and learning environments by measuring, gathering, analyzing and reporting of data about learners and learning contexts.
The aim for this special issue on Applications in LA and EDM cover all aspects of data analytics in supporting teaching, learning, and administration for researchers in P-16 education. The development of technology enriched formats of instructional delivery, such as various categories of blended and online learning. Traditionally, the main data source of LA and EDM research replied on the database in the Learning Management System (LMS). The development of IoT or sensors, at some levels, make up the gap of activity tracking outside the LMS. The special issue endeavors to publish research and practice which explores the applications of Learning Analysis and Educational Data Mining by including data sources outside the Learning Management Systems, such as open data, in classroom devices, IoT, mobile devices, academic data warehouse, and other devices which can track, diagnose, and store learning activities.
We are also interested in innovative approaches of feature extraction, pattern identification/recognition, data anonymization, modeling, and intervention to support innovative applications of Machine Learning and Deep Learning in Education.
- Innovative applications of learning analytics and educational data mining
• Deep learning applications in education
• Initiatives or analytics in Open Education Data
• Analytics in smart learning environment
• Emerging trends of data and text mining in education
• Learning analytics in formal, non-formal, and informal learning environments
• Data standards and feature extractions in learning analytics and data visualization
• Emerging trends of academic analytics, game-based analytics, mobile learning analytics
• Emerging trends of social media interactions and social analytics in teaching and learning
• Systematic review of learning analytics and educational data mining in practice
• Case study of learning analytics adoption for supporting educational decisions.
• Problems or concerns associated with educational data analytics and adaptive learning
• Case study in big data analytics
Submissions should comply with the journal author guidelines which are here. They should be made through ScholarOne Manuscripts, the online submission and peer review system. Registration and access is available here.
Initial submissions due date: August 31, 2018
Preliminary Feedback notification: October 15, 2018
Revised submissions due: November 30, 2018
Peer review / editorial decisions due: December 31, 2018
Final submissions due: January 30th, 2019
Expected publication: Spring / Summer 2019.