Andrea Horbach

Andrea Horbach serves as Full Professor for Teaching and Learning in the Digital World with a Focus on Machine Learning and Learning Analytics at the Leibniz Institute for Science and Mathematics Education (IPN) and the University of Kiel since 2024. Her interdisciplinary work bridges educational research and data science within Germany's premier science education institute.

Her academic foundation includes:

• PhD in Computational Linguistics from Saarland University (2018)
• Diploma in Computational Linguistics from Saarland University (2008)
• Advanced studies supported by the German National Merit Foundation scholarship (2003-2007)

Her research pioneers Educational Natural Language Processing applications, developing machine learning systems for automated assessment and language learning support. Specializing in argument mining, error detection, and hierarchical scoring models, she transforms educational measurement through data-driven methodologies that enhance both formative and summative evaluations in digital learning environments.

Her 2025 publications reveal a concentrated focus on solving critical challenges in educational NLP: optimizing argument mining for incomplete student writing, implementing cascading scoring architectures for large-scale assessments, and creating specialized error detection tools for Germanic language learners. These works collectively advance the integration of robust machine learning techniques into authentic educational contexts where data quality and learner diversity present significant challenges.

Her recognition includes:

• German National Merit Foundation scholarship (2003-2007)

Her leadership trajectory—from Junior Research Group Leader at FernUniversität in Hagen to her current professorship—demonstrates sustained research impact in educational technology. While specific grant details aren't provided, her position directing methodological research at IPN indicates substantial involvement in nationally funded educational data science initiatives and graduate student mentorship in computational education.

She leads the Educational Measurement and Data Science department at IPN, driving innovation in the institute's 'Methodological Research and Machine Learning' research line. Her team develops cutting-edge assessment tools that interface with IPN's broader mission in domain-specific learning and teacher professional development, creating synergies between theoretical educational research and practical AI applications.