Special Session 3:

Learning Analytics in Education and Enterprise


Special Session Chair:

Professor Jamila Mustafina

iTEC Research Lab, Kazan Federal University, Russia


This special session is organised and supported by Kazan Federal University, Russia

Session Committee:

Dr. Ayrat Khasyanov
Higher School of Information Technologies and Information Systems, Kazan Federal University, Russia
Dr. Lenar Galiullin
Higher School of Engineering, Kazan Federal University
Egor Petrov
PhD student, Kazan federal University
Sing Ying Tan,
PhD student, Liverpool John Moores University

New educational technologies such as LMS, student information systems mobile computing, social media, lecture capturing makes it possible to collect unprecedented amounts of data – big data on teaching learning and institutional processes. Digital traces created as a result of interaction with the technology can automatically be processed and identified into patterns. These patterns then are used to better understand and improve educational models, teaching practices as well as institutional processes. This emphasizes Learning Analytics as an area that is evidence-based and aimed at providing the decision-making process of the whole learning ecosystem – students, parents, instructors, deans, university presidents, governments and other private and public organizations. This special session aims to provide the researchers with the opportunity to present their latest research developments in Learning Analytics, Educational Data Mining, Academic Analytics and other related areas covered by data analysis.


  • Identification and explanation of useful data features for analyzing, understanding and improving learning and teaching
  • Evaluation of the learning progress through analysis of learner actions
  • New ways to store, share and preserve learning and teaching traces
  • New learning/teaching theories or reinterpretations of existing theories based on large-scale data analysis
  • Particular aspects of a learning/teaching processes in the context of data science
  • Creating mathematical, statistical or computational models of a learning/teaching process
  • Assessment of the impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.)
  • Effectiveness of learning analytics implementations or educational initiatives guided by learning analytics
  • Ethics of Learning Analytics
  • Learning Analytics strategies and policies