Personal Electronic Learning Environment

Learning Analytics

The “Personal Electronic Learning Environment” together with our self-learning modules enables individual project learning with regular formative assessments in large-scale lectures at BSc-level. With this project were able to show how important regular feedback is for student motivation, and that it can significantly increase student activity during the semester.


We teach three computer science service-lectures with over 1000 BSc-students from five different departments and 50 teaching assistants. Students learn basics in programming, data visualization, data management, and databases to professionalize their digital literacy competences for their future activities in research and profession.
Only about 10% of our students have prior knowledge in computer science, 90% are novices. To enable all students to prepare themselves individually and independently to solve demanding project tasks with a scientific context, students get access to a series of self-learning modules including online tutorials, videos, quizzes, mock-exam questions, etc. with a total workload of 4-8 hours each. A corresponding blended learning concept organizes individual learning of basic computer science concepts and its transfer into practice independent from student prior knowledge.
At the end of each sequence, students have to present and discuss their individual project solutions in an individualized, 15-minute face-to-face discussion with a teaching assistant. With the Personal Electronic Learning Environment, developed by the project team, individual face-to-face discussions can be organized with large cohorts of several hundred students. As soon as a student has completed their project, they register online for a personal presentation meeting and they are randomly assigned to a teaching assistant by the system. Per semester, our teaching assistants complete over 4000 formative face-to-face assessments with individual peer feedback. For quality control the presentations were rated by both teaching assistants and students.

Slideshow is being loaded..

The following outcomes could be achieved with this project:
• Higher student motivation: Interdisciplinary projects with real data from students’ fields of studies motivates them to learn computer science concepts.
• Student centered learning: The focus of a course has shifted from big cohorts in a lecture hall to self-directed individual learning and coaching.
• More student activity, self-control and responsibility: Depending on their prior knowledge, students prepare themselves with the help of the provided self-learning modules to solve their project tasks and communicate their individual solution to a teaching assistant.
• Deeper learning based on understanding: Since the exercise correction was replaced by formative assessments, students’ individual concept understanding became more important. This enables a stable knowledge structure.
• Equal opportunity and fairness: All students get the same information and they are able to make progress independent of their prior knowledge. The one to one situation has proved to be less stressful for students than learning in exercise groups.
• New role for teaching assistants: In our courses, teaching assistants no longer teach their student groups, they coach students one-by-one and give them individual feedback. They learn important skills for their future careers.
Our Self-learning units are widely used in basic computer science courses at ETH. The personal electronic environment is currently used in a course at D-MATH.


{{ projectHeader.title }}