Debarshi Nath
About Me
I am a doctoral research scholar in the Joint PhD programme at IIT Bombay-Monash Research Academy. I am affiliated to the Interdisciplinary Programme in Educational Technology at Indian Institute of Technology Bombay, India and Centre of Learning Analytics at Monash (CoLAM) under Department of Human-Centred Computing, Faculty of IT at Monash University, Australia. My supervisors are Prof. Ramkumar Rajendran and Prof. Dragan Gasevic.Â
My research interests are in the field of Learning Analytics, Multi-modal Analytics in Self-Regulated Learning, and Machine Learning.
Education
Publications
From PhD
Debarshi Nath, Dragan Gasevic, Yizhou Fan, Ramkumar Rajendran, CTAM4SRL: A Consolidated Temporal Analytic Method for Analysis of Self-Regulated Learning, in 14th International Conference on Learning Analytics and Knowledge (LAK 2024), March 18-22, 2024
Debarshi Nath, Dragan Gasevic, Ramkumar Rajendran, A Trace-Based Generalized Multimodal SRL Framework for Reading-Writing Tasks, in Doctoral Consortium of 16th International Conference on Educational Data Mining (EDM 2023), July 11-14, 2023
Spruha Satavlekar, Debarshi Nath, Rajashri Priyadarshini, Prajish Prasad, Daevesh Kumar Singh, Ramkumar Rajendran, Unravelling Learner Interaction Strategies in VeriSIM for Software Design Diagrams, in proceedings of 21st IEEE International Conference on Advanced Learning Technologies (ICALT 2021), July 12-15, 2021
From MTech
Debarshi Nath, Anubhav, Mrigank Singh, Divyashikha Sethia, Diksha Kalra, S. Indu, A Comparative Study of Subject-Dependent and Subject-Independent Strategies for EEG-Based Emotion Recognition using LSTM Network, in proceedings of 4th International Conference on Compute and Data Analysis (ICCDA 2020), March 9-12, 2020, pp. 142-147
Anubhav, Debarshi Nath, Mrigank Singh, Divyashikha Sethia, Diksha Kalra, S. Indu, An Efficient Approach to EEG-Based Emotion Recognition using LSTM Network, in proceedings of 16th IEEE Colloquium on Signal Processing and its Applications (CSPA 2020), February 28-29, 2020, pp. 88-92