Lankeit E, Biehler R. The meaning landscape of the concept of the total derivative in multivariable real analysis textbooks: an analysis based on a new model of meaning. ZDM – Mathematics Education. Published online 2024. doi:10.1007/s11858-024-01584-w

Ostsieker L, Biehler R. Supporting Students in Developing Adequate Concept Images and Definitions at University: The Case of the Convergence of Sequences. In: Practice-Oriented Research in Tertiary Mathematics Education. Springer International Publishing; 2023:181-201. doi:10.1007/978-3-031-14175-1_9

Kortemeyer J, Biehler R. Analyzing the Interface Between Mathematics and Engineering in Basic Engineering Courses. In: Practice-Oriented Research in Tertiary Mathematics Education. Springer International Publishing; 2023:669-692. doi:10.1007/978-3-031-14175-1_32

Kortmeyer J, Biehler R. The use of integrals for accumulation and mean values in basic electrical engineering courses. In: Dreyfus T, Gonzalez-Martin AS, Nardi E, Monaghan J, Thompson PW, eds. The Learning and Teaching of Calculus Across Disciplines – Proceedings of the Second Calculus Conference. MatRIC; 2023:121-124.

Biehler R, Engel J, Frischemeier D. Stochastik: Leitidee Daten und Zufall. In: Bruder R, Büchter A, Gasteiger H, Schmidt-Thieme B, Weigand HG, eds. Handbuch Der Mathematikdidaktik. Springer; 2023. doi:10.1007/978-3-662-66604-3_8

Biehler R, Frischemeier D, Gould R, Pfannkuch M. Impacts of Digitalization on Content and Goals of Statistics Education. In: Pepin B, Gueudet G, Choppin J, eds. Handbook of Digital Resources in Mathematics Education. Springer International Publishing; 2023.

Biehler R, Krüger K. Von Glückstal (Ukraine) nach Frankfurt: Problemlösen, Algorithmen, Stochastik – Nachruf auf Arthur Engel. GDM-Mitteilungen. 2023;(115):57-65.

Biehler R, Liebendörfer M, Gueudet G, Rasmussen C, Winsløw C. Practice-Oriented Research in Tertiary Mathematics Education – An Introduction. In: Practice-Oriented Research in Tertiary Mathematics Education. Springer International Publishing; 2023. doi:10.1007/978-3-031-14175-1_1

Liebendörfer M, Büdenbender-Kuklinski C, Lankeit E, Schürmann M, Biehler R, Schaper N. Framing Goals of Mathematics Support Measures. In: Practice-Oriented Research in Tertiary Mathematics Education. Springer International Publishing; 2023:91-117. doi:10.1007/978-3-031-14175-1_5

Biehler R, Liebendörfer M, Gueudet G, Rasmussen C, Winsløw C, eds. Practice-Oriented Research in Tertiary Mathematics Education. Springer International Publishing; 2023. doi:10.1007/978-3-031-14175-1

Biehler R, Liebendörfer M, Schmitz A. Lernvideos und ihre Erstellung - Das Projekt studiVEMINTvideos. Mitteilungen der Gesellschaft für Didaktik der Mathematik. 2023;114:8-12.

Nieszporek R, Griese B, Biehler R. Facilitators’ Orientations Towards Learning Goals, Perceived Challenges, and Teaching Resources for a PD Course on Conditional Probability. In: Peters SA, Zapata-Cardona L, Bonafini F, Fan A, eds. Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics. International Association for Statistical Education; 2022. doi:10.52041/iase.icots11.t4b2

Biehler R. Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications. In: Peters SA, Zapata-Cardona L, Bonafini F, Fan A, eds. Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics. International Association for Statistical Education; 2022. doi:10.52041/iase.icots11.t1a2

Biehler R, De Veaux R, Engel J, Kazak S, Frischemeier D. Editorial: Research on Data Science Education. Statistics Education Research Journal. 2022;21(2). doi:10.52041/serj.v21i2.606

Biehler R, Langlotz H, Zappe W. Alles normal?! Daten auf Normalverteilung prüfen–mit schulischen Mitteln. mathematik lehren. 2022;2022(232):41–45.

Biehler R, Griese B. Modellieren im Stochastikunterricht: Annahmen hinterfragen, Ergebnisse validieren. mathematik lehren. 2022;2022(232):2–6.

Fleischer Y, Biehler R, Schulte C. Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. Statistics Education Research Journal. 2022;21(2). doi:10.52041/serj.v21i2.61

Griese B, Nieszporek R, Biehler R. Facilitators’ views on content goals, learning obstacles, and teaching resources in reference to conditional probability. In: ; 2022.

Lankeit E, Biehler R. Mathematik-Vorkurse zur Vorbereitung auf das Studium – Zielsetzungen und didaktische Konzepte. In: Hochmuth R, Biehler R, Liebendörfer M, Schaper N, eds. Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse. Springer; 2022:117-141. doi:10.1007/978-3-662-64833-9_4

Liebendörfer M, Göller R, Gildehaus L, et al. The role of learning strategies for performance in mathematics courses for engineers. International Journal of Mathematical Education in Science and Technology. 2022;53(5):1133-1152. doi:10.1080/0020739x.2021.2023772