Lankeit, E., &; Biehler, R. (2024). 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.https://doi.org/10.1007/s11858-024-01584-w 

Ostsieker, L., &; Biehler, R. (2023). 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 (pp. 181–201). Springer International Publishing.https://doi.org/10.1007/978-3-031-14175-1_9 

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

Kortmeyer, J., &; Biehler, R. (2023). The use of integrals for accumulation and mean values in basic electrical engineering courses. In T. Dreyfus, A. S. Gonzalez-Martin, E. Nardi, J. Monaghan, &; P. W. Thompson (Eds.), The Learning and Teaching of Calculus Across Disciplines – Proceedings of the Second Calculus Conference (pp. 121–124). MatRIC.

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

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

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

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

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

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

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

Nieszporek, R., Griese, B., &; Biehler, R. (2022). Facilitators’ Orientations Towards Learning Goals, Perceived Challenges, and Teaching Resources for a PD Course on Conditional Probability. In S. A. Peters, L. Zapata-Cardona, F. Bonafini, &; A. Fan (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.https://doi.org/10.52041/iase.icots11.t4b2 

Biehler, R. (2022). Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications. In S. A. Peters, L. Zapata-Cardona, F. Bonafini, &; A. Fan (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.https://doi.org/10.52041/iase.icots11.t1a2 

Biehler, R., De Veaux, R., Engel, J., Kazak, S., &; Frischemeier, D. (2022). Editorial: Research on Data Science Education. Statistics Education Research Journal, 21(2), Article 1.https://doi.org/10.52041/serj.v21i2.606 

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

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

Fleischer, Y., Biehler, R., &; Schulte, C. (2022). Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. Statistics Education Research Journal, 21(2), Article 7.https://doi.org/10.52041/serj.v21i2.61 

Griese, B., Nieszporek, R., &; Biehler, R. (2022). Facilitators’ views on content goals, learning obstacles, and teaching resources in reference to conditional probability. Twelfth Congress of the European Society for Research in Mathematics Education (CERM), Bozen-Bolzano, Italy.

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

Liebendörfer, M., Göller, R., Gildehaus, L., Kortemeyer, J., Biehler, R., Hochmuth, R., Ostsieker, L., Rode, J., &; Schaper, N. (2022). The role of learning strategies for performance in mathematics courses for engineers. International Journal of Mathematical Education in Science and Technology, 53(5), 1133–1152.https://doi.org/10.1080/0020739x.2021.2023772