DFI Speaker Series: Multimodal Learning Analytics Approaches in e-Learning

DFI Speaker Series: Multimodal Learning Analytics Approaches in e-Learning

Enhancing the Learning Experience of MOOC Learners with a Learning Analytics System and a Multimodal Learning Analytics

By Digital Futures Institute

Date and time

Starts on Monday, October 16, 2023 · 12pm EDT

Location

Russell Hall 4th Fl | Smith Learning Theater | Teachers College, Columbia University

525 West 120th Street New York, NY 10027

About this event

In this seminar, Dr. Ruth Cobos will share her research experience involving various Learning Analytics approaches developed by her and her team colleagues within the context of MOOCs at the Autonomous University of Madrid or Universidad Autónoma de Madrid (UAM, Spain). Initially, she will introduce edX-LIMS (System for Learning Intervention and Monitoring in edX MOOCs) and its application over more than 3 years within an edX MOOC. edX-LIMS stands as a Learning Analytics system that incorporates an intervention mechanism, analyzing learners' performance within the course and delivering feedback through a web-based Learner Dashboard. Additionally, the system provides MOOC instructors with a web-based Instructor Dashboard, facilitating the monitoring of learners' progress and offering assistance accordingly.

Next, she will introduce M2LADS (Multimodal Learning Analytics Dashboard System) and its application involving data collected from MOOC learners. Within UAM, she and her team have monitored the engagement of more than one hundred learners participating in a MOOC learning session, with M2LADS being the platform for data management. During these sessions, they have gathered multimodal data from biometric and behavioral signals, encompassing metrics like electroencephalogram data for cognitive attention assessment, heart rate for emotional evaluation, and visual attention derived from video recordings. M2LADS offers a means to capture a comprehensive understanding of learners' experiences throughout their interactions with the MOOC. This amassed data holds the potential to enhance learning outcomes through feedback visualizations and interventions, in addition to refining learning analytics models and improving the content of the MOOC.

Bio:

Ruth Cobos is Associate Professor in the Department of Computer Science at Universidad Autónoma de Madrid (UAM, Spain, https://www.uam.es/). She received her M.Sc. and Ph.D. degrees in Computer Engineering from UAM. She has been member of the research group GHIA (Grupo de Herramientas Interactivas Avanzadas, https://vghia.ii.uam.es/) at UAM since 1999 and member of the research group CONTIC (Grupo de Excelencia Cognición y Contexto: el aprendizaje colaborativo mediado por ordenador) at Universitat de Lleida (UdL) since 2005. She is the Principal Investigator from UAM of the eMadrid Research Network (http://www.emadridnet.org/) and of the Spanish Network of Learning Analytics – SNOLA (https://snola.es/). She has participated in more than fifteen R&D projects. During 2008 and 2009 she was de Principal Investigator of a project funded by the Agencia Española de Cooperación Internacional para el Desarrollo (AECID), which involves researchers from UAM, from UdL and from two universities at Colombia. As Rector’s Delegate for Educational Technologies (2014-2016) she directed the technical office at UAM for the generation and creation of UAM MOOCs at edX. UAM has been member of edX consortium since 2014 (https://www.edx.org/es/school/uamx). Nowadays, she is the coordinator of the MOOC instructor team of the course entitled “Introducción al desarrollo de aplicaciones web” (https://www.edx.org/es/course/introduccion-al-desarrollo-de-aplicaciones-web-2). She received the Meritorious Service Award 2022 (e-Madrid Excellence Network) for outstanding contribution to the development conference of EDUCON - IEEE Global Engineering Education. Currently, her main research areas are e-Learning, Blended Learning, MOOCs, SPOCs, Learning Analytics, Multimodal Learning Analytics, Educational Technologies, Artificial Intelligence in Education, Machine Learning, Sentiment Analysis and Natural Language Processing. More information at: https://vghia.ii.uam.es/member?filepath=cobos_ruth.txt"

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