Teaching

Technology-enhanced, student-centered learning

‚ÄčSoco-teaching-portfolio

We offer lectures, seminars, and practical courses in learning technologies, web technologies, data science and visual analytics for students of Applied Computer Science, Komedia, and ISE at bachelor and master level. We teach how data analytics and visualization can open up new ways of interactions in social media, in learning environments and at the workplace. We follow a technology-enhanced, student-centered learning approach, by giving students the opportunity to learn self-paced, project-oriented and active in groups, mediated by technology-enhanced learning environments. Our students can thus get familiar with a self-regulated, inquiry- and problem-based approach to learning that can drive their practice when they graduate from university.

Current Courses (Winter Semester 2022/23)

Check out our list of courses in the previous semesters.

Open Theses

Type

Topic

Contact

Master's or Bachelor's Thesis (Komedia)

[This thesis can be written in English or German]

Understanding the Impact of Explanation Scope on the Perception of an Explainable Scientific Literature Recommender System M.Sc. Mouadh Guesmi

Master's or Bachelor's Thesis (Komedia)

[This thesis can be written in English or German]

Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Scientific Literature Recommender System M.Sc. Mouadh Guesmi

Master's or Bachelor's Thesis (Komedia)

[This thesis can be written in English or German]

Effects of Transparent User Models on the Perception of an Explainable Scientific Literature Recommender System M.Sc. Mouadh Guesmi
Master's Thesis Spark-based Indicator Execution in OpenLAP M.Sc. Shoeb Joarder
Master's Thesis Supporting Indicator Reuse and Recommendation in OpenLAP M.Sc. Shoeb Joarder
Master's Thesis A Toolkit for xAPI-based Data Collection in OpenLAP M.Sc. Shoeb Joarder
Master's Thesis Privacy-Preserving Learner Data Management in OpenLAP M.Sc. Shoeb Joarder


Additional topics in the areas of data science, learning analytics, visual analytics, explainable recommendation, and human-AI interaction are available on request.

Check out our theses overview page for more details on how to apply.