In the last few years, most educational institutions have adopted different solutions based on e-learning in order to both supplement classroom teaching or offer entirely online training. Other institutions like the Open University of Catalonia (UOC) have been online educational communities from their inception. In both cases there is a virtual space in which students interact with other students and teachers, as well as the resources and services offered by the institution. In this setting, which goes beyond areas outside the traditional classroom, it is a challenge to analyze the complexity of the learning process, from the student and teacher’s perspective, taking into account the different spaces of interaction with quantitative data collected automatically, including surveys and qualitative interviews.
Learning Analytics is an emerging discipline that includes a set of techniques and methodologies to capture, measure, analyze and visualize information relating to the learning process of students in virtual environments, with the aim of extracting useful knowledge. That knowledge is applied to understand and optimize the learning process, reintroducing it in the form of action. Learning Analytics is based on an interdisciplinary evidence-based research which combines research from two different angles: the underlying conception of learning or the educational context (Learning); and the application of new computational, statistical and visualization methods (Analytics) to their understanding. And finally, our challenge is to measure the real impact of each proposed solution in a continuous cycle of observation, analysis and improvement action.
To sum up, there are four key elements which shape LAIKA as a singular research group:
- Interdisciplinarity: research problems require an interdisciplinary approach and a team with complementary profiles. LAIKA aggregates four different disciplines.
- Innovation in new scenarios for teaching and learning: LAIKA is involved with several innovation projects concerning from e-assessment to MOOCs.
- Network of top Open Universities in Europe: LAIKA is collaborating actively, and leading initiatives, with Open University UK, Open University Nl and UNED.
- Evidence-based research: an excellent position to develop data-driven research based on analytics using data from UOC students.
Specifically, LAIKA’s activity is organized into four main research lines:
(1) Using learning analytics to redefine the role of tutors and learners for effective feedback and assessment
This research line uses data analysis to find and analyse indicators in order to measure the relation between the students academic achievement and, for instance, the tutors use of educational interactive (online) tools. We also analyse the influence of the educational personal feedback in the learning process and the relevance, for instance, of the emotional factors in this process. This research line has a special interest in analysing the relation between the students profile with the dropout in online courses related with the educational tutors feedback process.
(2) E-assessment and automatic feedback in online mathematics
The main objetive of this research line is to analize the effectiveness of quizzes with automatic feedback in online mathematics. Different educational scenarios could be considered:
a) online subject with a methodology based on regular activity through quizzes.
b) active learning through flipped classroom model with autonomous work based on quizzes and automatic feedback
(3) Dropout in higher education: analysis of causes and design of interventions
Higher education is a very expensive process which pursues the empowerment of highly qualified citizens, a key asset in the information society. Nevertheless, in some cases, the educational system fails to provide the appropriate support to all learners. Dropout rates are very high, especially among freshmen, resulting in frustration for both the learner and institutional managers. Dropout is, indeed, a failure of society and it needs to be analyzed using a multidimensional approach. In this research line we propose to adopt a wide perspective to better understand dropout in higher education, covering from data gathering up to intervention design, by means of surveys, learning analytics, and decision making. Typical research questions in this topic include characterization and early detection of students at risk of dropping out, internal and external factors related to dropout and the evaluation of effective measures for improving retention and re-enrolment, among others.
(4) Learning Analytics to understand and improve engagement in MOOCs
The phenomenon of massive open online courses (MOOCs) has created a new educational paradigm that, due to its particular characteristics, it is interesting to be studied from the perspective of Learning Analytics. Thousands of students enroll in many courses every week, but only a very small percentage completes a given course in a satisfactory manner. The diversity of user profiles, backgrounds and interests makes very difficult to provide a single learning experience following a one-size-fits-all mode. In this context we analyse participants’ behaviour in different MOOCs.