Digital Mentoring

Open learning materials and analytics for mathematics in the study entrance phase. Choose a version and start training. More specific information for students & study beginners, lecturers, developers and researchers can be found below.

KLAUS

MathMagician

Moodle Default

For Students & Study Beginners

Select your favorite version above and start working on the tasks. Each version contains the same tasks and you will be provided with feedback on your answers. By working your way through the topics and responding to the feedback, you can refresh your mathematical knowledge and gradually close any gaps.

For Lecturers

The learning materials and analysis methods offered are open source and can be copied and modified as required. For example, they can be transferred to your learning management system. The installation of a plugin is not necessary. For a demonstration of the learning data analysis, please visit the demo course filled with generated user data. You will automatically be given teacher authorization, which allows you to look behind the scenes as well as to download and copy elements.

For Developers

The error-adaptive feedback in the learning materials is provided using the STACK plugin. The resulting task set (Moodle standard version) is then enriched with graphical elements, instant feedback and gamification elements using Javascript. The learning data analysis is also based on Javascript, which is why it can be executed without having to install a plugin beforehand. The Javascript loads pages from the learning management system in the background and extracts information such as access times and answers. This information is then processed anonymously to create, e.g., a dashboard. Further information, such as step-by-step instructions, is available in the project's repository.

For Researchers

The similarities and differences in the versions, their influence on the students' behavior and the resulting implications for digital mathematical learning in the introductory phase of studies were addressed in the following scientific publications.

• Neugebauer, M.; Erlebach, R.; Kaufmann, C.; Mohr, J.; Frochte, J. (2024): Efficient Learning Processes By Design: Analysis of Usage Patterns in Differently Designed Digital Self-Learning Environments. Proceedings of the 16th International Conference on Computer Supported Education. https://doi.org/10.5220/0012558200003693
• Neugebauer, M., Tousside, B., Frochte, J. (2023). Success Factors for Mathematical E-Learning Exercises Focusing First-Year Students. In: Proceedings of the 15th International Conference on Computer Supported Education (CSEDU). https://doi.org/10.5220/0011858400003470
• Neugebauer, M, Frochte, J. (2023). Steigerung von Lernerfolg und Motivation durch gamifizierte Mathematik-Aufgaben in Lernmanagementsystemen. In: 21. Fachtagung Bildungstechnologien (DELFI). https://doi.org/10.18420/delfi2023-39

Learning data analysis using Javascript was presented for the first time in the following article.

• Neugebauer, M. (2024): Lightweight Learning Analytics Dashboard for Analyzing the Impact of Feedback & Design on Learning in Mathematical E-Learning. Proceedings 18. Workshop Mathematik in ingenieurwissenschaftlichen Studiengängen. Preprint