STELA is an acronym for “Successful Transition from secondary to higher Education using Learning Analytics”.

The project is funded by Erasmus+ (562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD).


Main Goal

The main goal of the project was to enhance a successful transition from secondary to higher education by means of learning analytics. To this end the project developed, tested, and deployed a learning analytics approach that focuses on providing formative and summative feedback to students in the transition. On top of the development of  student dashboards, the project also focused on how to support teachers and student advisers, hereby aiming at improving advising and teaching practices.

To realize this ambitious goal the project gathered a multidisciplinary team of learning analytics researchers, educational technology experts, experts in the transition from secondary to higher education, and practitioners. Thanks to this multidisciplinary team, the project tackled all the different steps required for the application of learning analytics: data collection, data analysis, data visualization, dashboard design, dashboard development, and last but not least the actual implementation and thorough evaluation of the learning analytics approach.


Seven keywords define the particular focus and context of the STELA project,

Learning Analytics

Learning Analytics is the key tool of the project. To this end the project uses the definition of Learning Analytics provided by Erik Duval "Learning analytics is about collecting traces that learners leave behind and using those traces to improve learning".

First-year Experience

The project focuses on the challenging transition from secondary to higher education ranging from initiatives for prospective students up to the end of the first year. Both initiatives focusing on online learners and regular campus students are part of the project.

Actual Implementations

The project is based on actual Learning Analytics implementations in the transition from secondary to higher education. The case studies are the core of the project and leave the "lab"-setting but realize actual embedding in educational practice.

Student-centered Approach

The Learning Analytics interventions in the project are student-centered, i.e. they mainly aim at having a direct connection to the student. The main approach of the project is to design, develop, implement, and evaluate student-facing learning dashboards.

Inclusive Approach

The target audience of the project's initiatives are all students. The aim is that all students are supported by the use of learning analytics. Rather than a particular focus on at-risk students or the most promising students, the projects uses an inclusive approach where the different subgroups within the student population are jointly addressed.

Available Data

Rather than creating new data traces the project's goal is first the use the available data as good as possible and hereby primary targets that is or could be readily available at any higher education institute. This decision directly supports the scalability and transferability of the developed Learning Analytics initiatives.

Scalability and Transferability

The STELA project aims at solutions that are scalable and transferable Learning Analytics initiatives. This means that the initiatives could be easily extended to new modules, courses, programs, and even other institutes.