The STELA project aims to enhance the successful transition from secondary to higher education using learning analytics. It is a 3-year ERASMUS+ funded project involving universities from Belgium, the Netherlands, Austria, and the UK. The project will develop dashboards using learning analytics data to provide formative feedback to students, teachers, and counselors during the transition period. It will test this approach through case studies in different educational contexts and make policy recommendations for implementing learning analytics in higher education. Existing learning analytics applications at partner universities focus on monitoring student engagement and progress, and providing additional academic skills feedback. Measurable results so far include increased student activity and course success rates.

1. STELA
PROJECT
SUCCESSFUL TRANSITION FROM
SECONDARY TO HIGHER EDUCATION
USING LEARNING ANALYTICS

2. Contributor and Timing
– ERASMUS +
– 3-year project (1/11/2015-31/10/2018)

3. Partners
– Project Coordinator:KATHOLIEKE UNIVERSITEIT LEUVEN
– TU Delft (The Netherlands), TU Graz (Austria), Nottingham Trent
University (United Kingdom), and SEFI (European Society of
Engineering Education).

4. The Aim
– The main goal of the project is to enhance a successful transition from
secondary to higher education by means of learning analytics. To this end the
project will develop, test, and assess a learning analytics approach that focuses
on providing formative and summative feedback to students in the transition.
On top of the development of a student dashboard, the project will develop
dashboards for the student counselors and teachers, hereby disclosing a vast
amount of information that can be used to improve counseling and teaching
practices.

5. Context of the Project
– The focus is the summer before entering university + 1st year experience

6. A successful transition from secondary to
higher education can be characterized by
different features:
– Academic achievement (e.g. credits obtained, GPA, timely graduation)
– Realistic academic self-concept and expectations (e.g. correct positioning
with respect to peers)
– Well-being, good perceived-fit, good quality of motivation, and
– In-time re-orientation of field of study in case of wrong study choice

7. Questions of the Project
– What are relevant student actions in the transition from SE to HE?
– How can data on relevant actions be captured?
– Which algorithms are suited for analyzing the collected data and to summarize it
into indicators for a successful transition?
– What kind of information in the form of formative feedback has to be presented
to the different stakeholders?
– How are awareness and self-reflection enabled for different kinds of users
through appropriate devices?
– How can the impact of the developed learning analytics dashboard applications
be evaluated and measured?

8. The project uses a four-step
approach:
– Theoretical and scientific underpinning
– Development and implementation pathways
– Execution of case studies in a variety of contexts (admission policy, different
disciplines, traditions in student counseling, etc.)
– Policy recommendations towards the implementation of learning analytics in
higher education

9. Approach
– The project ensures that the different stakeholders for learning analytics during
the transition are consulted throughout the entire project. On the other hand
students will be involved throughout the project (e.g. through questionnaires
and contact with students through student counselors) and vendors of learning
analytics will be consulted.

10. Approach
– The project ensures that the developed learning analytics innovations can be
easily mainstreamed. This is guaranteed by case studies in different educational
contexts (admission policy, educational disciplines, approach to student
counseling, etc.).
– The project uses an holistic approach of learning analytics that includes a broad
spectrum of educational activities and aims at including the full student
experience: pre-enrolment in university, learning design, teaching/learning,
assessment, and evaluation.

11. OVERVIEW OF
EXISTING
GUIDANCE IN THE
TRANSITION FROM
SECONDARY TO
HIGHER
EDUCATION /
INTAKE GUIDANCE

12. KU The engineering positioning test –
ijkingstoets
– First for the students that succeed (60%) in the test, it gives confidence in their
abilities for the engineering program. Second for the students that nearly
failed the test, it is a stimulus to make extra efforts, take a summer course, or
an individual study program. Third for the students that badly failed the test,
it encourages them to analyze their performance, and if they do not expect to
be able to do better, to stimulate to choose another study program.

13. EXISTING
LEARNING
ANALYTICS
APPLICATIONS
IN
UNIVERSITIES

14. KU LEUVEN
– LARAe (Learning Analytics Reflection & Awareness environment) consists of multiple dashboards
exploring different ways of presenting learning traces to both teacher and student. These learner
traces consist of Twitter and blog posts/comments, as a reporting tool. Using badges as the main
progress visualization technique, both personal and group dashboards have been deployed for
students to stay aware of their progress regarding course goals. A teacher-oriented version visualizes
details of progress over time, provides an overview of the entire class progression, and lets teachers
compare (groups of) students. Another approach is an RSS client augmented with learning analytics
data: as a student and/or teacher, RSS readers provide an easy way to keep track of activities across
multiple blog posts. By adding visual information regarding age of posts, activity on posts and social
interactions between student groups, students can easily identify interesting topics and plan their
peer review work, while teachers can keep track of both teacher and student activities, and
intervene when necessary.

15. DELFT UNIVERSITY OF
TECHNOLOGY (TU DELFT)
– By using the MAIS project, the TU Delfts want to make use of Measuring, Analysing, Informing, and Steering (MAIS),
to investigate whether study results can be improved by tackling students’ procrastination by using the Blackboard
Retention Center. The Blackboard Retention Center (RC) aids the instructor in monitoring the progress of the
students. By setting a number of criteria, the instructor can generate an overview of students who are lagging
behind. The instructor can then opt to warn those students in a simple way. With the help of a number of scenarios,
TU Delft can investigate with which types of courses and with which boundary conditions the RC can be used to
improve communication between instructors and students about progress, and reduce procrastination. TU Delft’s
Learning Analytics Pilot helps instructors find out how active their students are in the instructor’s Blackboard
course. This way, instructors are more involved in the progress students make in their course. This does require
instructors to a Blackboard course that is suitable for measuring progress. This requires weekly content updates,
regular assignments, and/or tests. In this pilot instructors are asked to perform a progresscheck a number of times,
and send warnings to students who appear to be lagging behind (done by using the RC). A final meeting is then
organised to evaluate the project.

16. GRAZ UNIVERSITY OF
TECHNOLOGY (TU GRAZ)
– Currently there is no existing learning analytics application at the TU Graz.

17. Measurable Results of the Project, Tinne De
Laet (the head of the Tutorial Services of
Engineering Science) stated:
– TU Delft found that students are more active when confronted with activity profiles of
past successful students in the learning tracker.
– Courses offering the learning tracker have a higher success rate than courses not
offering the learning tracker.
– KU Leuven has offered additional feedback to 1500 students in 13 programmes.
– Concerning the academic skills feedback dashboard, KU Leuven found that while
students with weaker academic skills go to the dashboard less, once on the dashboard
they interact more (viewing the relevant information, clicking on additional tips, etc.)
– TU Graz has set up an open-source software solution that is now ready to support our
interventions.

18. Learning Tracker

19. Outputs
– WP1-A1: Current situation
– WP1-A2: The transition from secondary to higher education and learning analytics: literature
survey
– WP2-A3: Questionnaires for the transition from SE to HE
– WP2-A4: Baseline measurement of challenges in the transition from secondary to higher
education
– WP4: Data collection for learning analytics
– WP5: Data visualization and data analysis for learning analytics
– WP6: Case studies
– WP7:Workshops
– WP8: Policy Reform

20. THANK YOU
FOR
LISTENING!