The 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, mainly in higher education.
We offer some PhD and PostDoc positions for doing research in Learning Analytics applied to Higher Education and MOOCs, among other topics. Our current research lines are:
- Using learning analytics to redefine the role of tutors and learners for effective feedback and assessment
- E-assessment and automatic feedback in online mathematics
- Dropout in higher education: analysis of causes and design of interventions
- Learning Analytics and Visualization to understand and improve engagement in MOOCs
This group is joined by researchers from three different departments at UOC (Computer Science, Multimedia and Telecommunications; Psychology and Educational Sciences; and Information and Communication Sciences). LAIKA also collaborates regularly with other research groups from leading national and international universities.
The group exploits the competitive advantage of being in a top online University, the UOC, as a living laboratory for research in teaching and learning, where tens of thousands of users daily interact with services, resources and among them, leaving a trail that need to be tracked to analyze and better understand the learning process. Moreover, it is important that the group also develop methodologies applicable to a set of broader contexts that can be transferred to other scenarios and situations supported by virtual learning environments as well as mixed (or blended) scenarios.
LAIKA team shares the vision and a common interest to analyse a complex ecosystem within a virtual framework in which it is essential to work from various disciplines in co-operation. LAIKA’s members have the know how in the design and evaluation of online educational settings, creating instruments for data collection and analysis using statistical and data mining techniques, as well as the triangulation with data obtained through qualitative techniques (see the list of publications). The fact that the group members have been involved in projects at different levels (projects, publications and thesis co-supervisions) enriches particularly the definition of common issues of interest that can be tackled from an interdisciplinary perspective, absolutely essential in the proposed framework of Learning Analytics.