STEAM University Learning Research Group

Main investigator:
Sancho Vinuesa, Teresa
Social sciences
Area of specialization:
Education and ICT, Internet technologies and artificial intelligence
Affiliation center:
UNESCO codes:
530602, 120318, 120310, 120317, 630707
  • Teaching innovation
  • Open access
  • Elearning
  • Computational analysis methods
  • Data visualization
  • Drop-out in education
  • Higher education

Learning Analytics for Innovation and Knowledge Application in Higher Education (LAIKA) is an interdisciplinary research group that addresses complex problems in teaching and learning contexts, primarily in higher education.

LAIKA carries out interdisciplinary research based on indicators that combine research from two different areas: the conception of underlying learning or the educational context (learning) and the use of new computational, statistical and visualization analysis methods (analytics) to understand it.

Specifically, the group's activity is organized around three main research lines:

  • Line 1: Student monitoring, assessment and feedback.
  • Line 2: Development of a predictive model for analysing student drop-out in higher education.
  • Line 3: Analysis of open education practices (MOOC, repositories, social media).

Student monitoring: assessment and feedback

The processes used by students are analysed at different levels: session (what do they do when they log on?), semester (how are they progressing in the course?) and programme (how do they acquire and develop skills at different times of life?). On all three levels, it must be possible to keep a record of what students do, why they do it (motivations, expectations, etc.) and how they do it (academic performance, satisfaction), and be able to intervene by means of adequate feedback (positioning, interaction, etc.).

Development of a predictive model for analysing student drop-out in education

The research focuses on the aspects that describe students' medium- and long-term relationship with the educational institution from two viewpoints, with the goal of providing more effective support, in the form of recommendation, monitoring and positioning systems regarding their skill development, in order to detect the students who are at risk of dropping out.

Analysis of open education practices (MOOC, repositories, social media)

This line analyses the use made by students of digital resources, their integration in formal education practices and the development of indicators related with student monitoring, satisfaction and assessment.