Learning Analytics for Innovation and Knowledge Application

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).

Researchers

Classifications

  • Ministry Area: LLENGUATGES I SISTEMES INFORMÀTICS, MÈTODES D'INVESTIGACIÓ I DIAGNÒSTIC EN EDUCACIÓ, DIDÀCTICA DE LA MATEMÀTICA
  • Group Recognition: Grup de recerca consolidat per la Generalitat de Catalunya
  • Application Area: Social sciences

beta Prevailing specialties (top 10) Obtained from publications help
Obtained from publications

The displayed thematic specialties have been obtained through the application of artificial intelligence models, derived as a result of the Hercules Project from those publications with an abstract, provided that the record does not come from commercial databases, which impose restrictions on data usage.

The list may contain errors. Under evaluation and improvement. Shared to gather community suggestions.

  1. Education (Social Sciences) Filter
  2. Library and Information Sciences (Social Sciences) Filter
  3. Communication (Social Sciences) Filter
  4. Information Systems (Computer Science) Filter
  5. Physics Education (Physics and Astronomy) Filter
  6. Computers and Society (Computer Science) Filter
  7. Computer Graphics and Computer-Aided Design (Computer Science) Filter
  8. Cultural Studies (Social Sciences) Filter
  9. General Computer Science (Computer Science) Filter
  10. Human-Computer Interaction (Computer Science) Filter

Former members (9)