The meaning landscape of the concept of the total derivative in multivariable real analysis textbooks: an analysis based on a new model of meaning
E. Lankeit, R. Biehler, ZDM – Mathematics Education (2024).

Supporting Students in Developing Adequate Concept Images and Definitions at University: The Case of the Convergence of Sequences
L. Ostsieker, R. Biehler, in: Practice-Oriented Research in Tertiary Mathematics Education, Springer International Publishing, Cham, 2023, pp. 181–201.

Analyzing the Interface Between Mathematics and Engineering in Basic Engineering Courses
J. Kortemeyer, R. Biehler, in: Practice-Oriented Research in Tertiary Mathematics Education, Springer International Publishing, Cham, 2023, pp. 669–692.

The use of integrals for accumulation and mean values in basic electrical engineering courses
J. Kortmeyer, R. Biehler, 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, MatRIC, 2023, pp. 121–124.

Stochastik: Leitidee Daten und Zufall
R. Biehler, J. Engel, D. Frischemeier, in: R. Bruder, A. Büchter, H. Gasteiger, B. Schmidt-Thieme, HG. Weigand (Eds.), Handbuch Der Mathematikdidaktik, Springer, Berlin, Heidelberg, 2023.

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

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

Practice-Oriented Research in Tertiary Mathematics Education – An Introduction
R. Biehler, M. Liebendörfer, G. Gueudet, C. Rasmussen, C. Winsløw, in: Practice-Oriented Research in Tertiary Mathematics Education, Springer International Publishing, Cham, 2023.

Framing Goals of Mathematics Support Measures
M. Liebendörfer, C. Büdenbender-Kuklinski, E. Lankeit, M. Schürmann, R. Biehler, N. Schaper, in: Practice-Oriented Research in Tertiary Mathematics Education, Springer International Publishing, Cham, 2023, pp. 91–117.

Practice-Oriented Research in Tertiary Mathematics Education
R. Biehler, M. Liebendörfer, G. Gueudet, C. Rasmussen, C. Winsløw, eds., Practice-Oriented Research in Tertiary Mathematics Education, Springer International Publishing, Cham, 2023.

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

Facilitators’ Orientations Towards Learning Goals, Perceived Challenges, and Teaching Resources for a PD Course on Conditional Probability
R. Nieszporek, B. Griese, R. Biehler, 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, Rosario, Argentina, 2022.

Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications
R. Biehler, 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, 2022.

Editorial: Research on Data Science Education
R. Biehler, R. De Veaux, J. Engel, S. Kazak, D. Frischemeier, Statistics Education Research Journal 21 (2022).

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

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

Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks
Y. Fleischer, R. Biehler, C. Schulte, Statistics Education Research Journal 21 (2022).

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

Mathematik-Vorkurse zur Vorbereitung auf das Studium – Zielsetzungen und didaktische Konzepte
E. Lankeit, R. Biehler, in: R. Hochmuth, R. Biehler, M. Liebendörfer, N. Schaper (Eds.), Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, Springer, Berlin, Heidelberg, 2022, pp. 117–141.

The role of learning strategies for performance in mathematics courses for engineers
M. Liebendörfer, R. Göller, L. Gildehaus, J. Kortemeyer, R. Biehler, R. Hochmuth, L. Ostsieker, J. Rode, N. Schaper, International Journal of Mathematical Education in Science and Technology 53 (2022) 1133–1152.