Integration of conversational agents and learning analytics in MOOCs

This research will leverage conversational agents (CA) to guide and support student dialogue using natural language both in individual and collaborative settings. Integrating CA into MOOCs to trigger peer interaction in discussion groups is expected to considerably increase the engagement and the commitment of online students (and, consequently, reduce the dropout rate of MOOCs). Moreover, this research line will use learning analytics techniques, as a method to support teachers’ orchestration and students’ learning during MOOCs by evaluating students' interaction and participation.

ICT education through formative assessment, learning analytics and gamification

The main goal of this research line is to design and build a set of e-learning tools and services to provide support to the learning process in university degrees in the field of ICT. The expected benefits will have a repercussion on the students (improvement of the educational experience, greater participation and performance, lower drop-out rate) and on the lecturers, managers and academic coordinators (resources for monitoring a course, making decisions and predictions).

Multimodal emotion-awareness e-learning tools

This research line aims to enhance existing e-learning platforms by developing tools and services which support the detection and representation of learners’ emotions, as well as emotion-based learning adaptation and affective feedback. To this end, the research will apply novel emotion detection models to rich multimodal data collected using state of the art channels, advanced sensors and novel adaptive interfaces. The research results will demonstrate a positive impact of emotion-aware e-learning on decreasing learner drop-out rates, increasing satisfaction and improving learning performance, thus making learning as a whole a better experience.

Cloud, cluster and distributed computing for e-learning

This research line will leverage intensive computational capabilities of cloud, cluster and distributed computing for e-learning in order to integrate adaptive and personalized approaches capable of identifying learners’ requirements (using artificial intelligence and data mining techniques), building user models based on navigation patterns in virtual campuses, intelligently monitoring progress to provide purposeful and meaningful advice to both learners and teachers, among others.

Information models for enhancing security in e-learning

This research line focuses on incorporating information security properties and services into online e-learning. The main goal is to design innovative security solutions, based on methodical approaches, to provide e-learning designers and managers with guidelines for incorporating security into online learning. These guidelines include all the processes involved in e-learning design and management such as security analysis, learning activities design, detection of anomalous actions, and trustworthiness data processing.

Integrating business intelligence and learning analytics systems to create global analytical information systems for universities

This research line considers and combines learning analytics and business intelligence systems to create institutional and transversal analytical information systems for universities. The purpose is to demonstrate that developing analytical information systems in universities is a grand challenge for information systems research, showing the benefits of doing so by integrating both approaches and developing analytical information systems for universities that use that global approach.