K.-U. Bux, J. Hilgert, and T. Weich, “Poisson transforms for trees of bounded degree,”10.4171/jst/414__Journal of Spectral Theory, vol. 12, no. 2, pp. 659–681, 2022, doi:10.4171/jst/414 .

J. Hilgert, T. Weich, and K.-U. Bux, “Poisson transforms for trees of bounded degree,” J. of Spectral Theory, vol. 12, pp. 659–681, 2022.

J. Hilgert, “Ethan D. Bolker und Maura B. Mast: Common Sense Mathematics, Second Edition. AMS/MAA Press 2021,”10.1007/s00591-021-00314-7__Mathematische Semesterberichte, vol. 69. pp. 151–153, 2022, doi:10.1007/s00591-021-00314-7 .

H. Garcke, B. Kovács, and D. Trautwein, “Viscoelastic Cahn–Hilliard models for tumor growth,”10.1142/s0218202522500634__Mathematical Models and Methods in Applied Sciences, vol. 32, no. 13, pp. 2673–2758, 2022, doi:10.1142/s0218202522500634 .

C. M. Elliott, H. Garcke, and B. Kovács, “Numerical analysis for the interaction of mean curvature flow and diffusion on closed surfaces,”10.1007/s00211-022-01301-3__Numerische Mathematik, vol. 151, no. 4, pp. 873–925, 2022, doi:10.1007/s00211-022-01301-3 .

J. Nick, B. Kovács, and C. Lubich, “Time-dependent electromagnetic scattering from thin layers,”10.1007/s00211-022-01277-0__Numerische Mathematik, vol. 150, no. 4, pp. 1123–1164, 2022, doi:10.1007/s00211-022-01277-0 .

B. Kovács and B. Li, “Maximal regularity of backward difference time discretization for evolving surface PDEs and its application to nonlinear problems,”10.1093/imanum/drac033__IMA Journal of Numerical Analysis, 2022, doi:10.1093/imanum/drac033 .

R. Altmann, B. Kovács, and C. Zimmer, “Bulk–surface Lie splitting for parabolic problems with dynamic boundary conditions,”10.1093/imanum/drac002__IMA Journal of Numerical Analysis, vol. 43, no. 2, pp. 950–975, 2022, doi:10.1093/imanum/drac002 .

P. Csomós, B. Farkas, and B. Kovács, “Error estimates for a splitting integrator for abstract semilinear boundary coupled systems,”10.1093/imanum/drac079__IMA Journal of Numerical Analysis, 2022, doi:10.1093/imanum/drac079 .

C. A. Beschle and B. Kovács, “Stability and error estimates for non-linear Cahn–Hilliard-type equations on evolving surfaces,”10.1007/s00211-022-01280-5__Numerische Mathematik, vol. 151, no. 1, pp. 1–48, 2022, doi:10.1007/s00211-022-01280-5 .

J. Bohn, M. Feischl, and B. Kovács, “FEM-BEM Coupling for the Maxwell–Landau–Lifshitz–Gilbert Equations via Convolution Quadrature: Weak Form and Numerical Approximation,”10.1515/cmam-2022-0145__Computational Methods in Applied Mathematics, vol. 23, no. 1, pp. 19–48, 2022, doi:10.1515/cmam-2022-0145 .

G. Werth, “Neue Wege im mathematischen Unterricht - Auf den Spuren Mathilde Vaertings,”https://doi.org/10.37626/GA9783959872089.0__presented at the 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik, Frankfurt am. Main, 2022, doi:https://doi.org/10.37626/GA9783959872089.0 .

S. N. Malik and S. Rezat, “Linguistic features of word problems that cause difficulties for learners across the curriculum: A literature review,” 2022.

S. Rezat, S. N. Malik, and M. Leifeld, “Scaffolding Close Reading of Mathematical Text in Pre-service Primary Teacher Education at the Tertiary Level: Design and Evaluation,”10.1007/s10763-022-10309-y__International Journal of Science and Mathematics Education, vol. 20, no. , pp. 215–236, 2022, doi:10.1007/s10763-022-10309-y .

J. Dröse, B. Griese, and L. Wessel, “Prospective teachers’ diagnostic judgments on students’ understanding of conditional probabilities,” presented at the TwelfthCongressoftheEuropeanSocietyforResearchin Mathematics Education(CERM), Bozen-Bolzano,Italy, 2022.

A. Dellori and L. Wessel, “Entwicklung und Erprobung von professionsorientierten Lernumgebungen zur Wissensvernetzung in der Algebra,”10.17877/DE290R-23598__Beiträge zum Mathematikunterricht 2022, pp. 665–668, 2022, doi:10.17877/DE290R-23598 .

A. Dellori and L. Wessel, “Design principles for intertwining local and nonlocal mathematics - The case of relating registers and representations in abstract algebra,” in Proceedings of INDRUM2022, M. Trigueros, B. Barquero, R. Hochmuth, and J. Peters, Eds. 2022, pp. 572–573.

A. Dellori and L. Wessel, “Pre-service Teachers’ Professional Development: Relating Abstract Algebra and School Algebra,” in Proceedings of the 24th Annual Conference on Research in Undergraduate Mathematics Education, S. S. Karunakaran and A. Higgins, Eds. Boston: MA, 2022, p. 1177.

L. Wessel and J. Dröse, “Schreiben will gelernt sein: Schreiblerngelegenheiten adaptiv gestalten,” mathematik lehren, vol. 233, pp. 33–36, 2022.

R. Nieszporek, B. Griese, and R. Biehler, “Facilitators’ Orientations Towards Learning Goals, Perceived Challenges, and Teaching Resources for a PD Course on Conditional Probability,” in Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, S. A. Peters, L. Zapata-Cardona, F. Bonafini, and A. Fan, Eds. Rosario, Argentina: International Association for Statistical Education, 2022.

R. Biehler, “Revisiting Fundamental Ideas for Statistics Education From the Perspective of Machine Learning and Its Applications,”10.52041/iase.icots11.t1a2__in Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, 2022, doi:10.52041/iase.icots11.t1a2 .

R. Biehler, R. De Veaux, J. Engel, S. Kazak, and D. Frischemeier, “Editorial: Research on Data Science Education,”10.52041/serj.v21i2.606__Statistics Education Research Journal, vol. 21, no. 2, Art. no. 1, 2022, doi:10.52041/serj.v21i2.606 .

R. Biehler, H. Langlotz, and W. Zappe, “Alles normal?! Daten auf Normalverteilung prüfen–mit schulischen Mitteln,” mathematik lehren, vol. 2022, no. 232, pp. 41–45, 2022.

R. Biehler and B. Griese, “Modellieren im Stochastikunterricht: Annahmen hinterfragen, Ergebnisse validieren,” mathematik lehren, vol. 2022, no. 232, pp. 2–6, 2022.

Y. Fleischer, R. Biehler, and C. Schulte, “Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks,”10.52041/serj.v21i2.61__Statistics Education Research Journal, vol. 21, no. 2, Art. no. 7, 2022, doi:10.52041/serj.v21i2.61 .

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

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

M. Liebendörfer et al., “The role of learning strategies for performance in mathematics courses for engineers,”10.1080/0020739x.2021.2023772__International Journal of Mathematical Education in Science and Technology, vol. 53, no. 5, pp. 1133–1152, 2022, doi:10.1080/0020739x.2021.2023772 .

R. Hochmuth, R. Biehler, M. Liebendörfer, and N. Schaper, Eds., Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse. Berlin, Heidelberg: Springer, 2022.

J. Ridgway, P. Campos, and R. Biehler, “Data Science, Statistics, and Civic Statistics: Education for a Fast Changing World,” in Statistics for Empowerment and Social Engagement, Cham: Springer International Publishing, 2022.

T. Bauer, R. Biehler, and E. Lankeit, “Mini-Aufgaben in mathematischen Übungsgruppen zur Analysis: Charakteristika von Aufgaben und Abstimmungsverhalten von Studierenden,” in Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, R. Hochmuth, R. Biehler, M. Liebendörfer, and N. Schaper, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022, pp. 515–543.

E. Lankeit and R. Biehler, “Vorkurse und ihre Wirkungen im Übergang Schule – Hochschule,” in Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, R. Hochmuth, R. Biehler, M. Liebendörfer, and N. Schaper, Eds. Berlin, Heidelberg: Springer, 2022, pp. 293–363.

M. Liebendörfer et al., “Enriching videos with interactive questions to enhance students’ cognitive activity: concept and implementation,” presented at the Twelfth Congress of the European Society for Research in Mathematics Education (CERM), Bozen-Bolzano, Italy, 2022.

M. Schürmann et al., Dokumentation der Erhebungsinstrumente des Projekts WiGeMath. LibreCat University, 2022.

R. Hochmuth et al., “Einführung in das WiGeMath-Projekt: Wirkung und Gelingensbedingungen von Unterstützungsmaßnahmen für mathematikbezogenes Lernen in der Studieneingangsphase,” in Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, H. Reinhard, R. Biehler, M. Liebendörfer, and N. Schaper, Eds. Berlin, Heidelberg: Springer, 2022, pp. 3–31.

M. Liebendörfer et al., “Ein Rahmenmodell für hochschuldidaktische Maßnahmen in der Mathematik,” in Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, R. Hochmuth, R. Biehler, M. Liebendörfer, and N. Schaper, Eds. Berlin, Heidelberg: Springer, 2022, pp. 33–65.

Y. Fleischer, S. Hüsing, R. Biehler, S. Podworny, and C. Schulte, “Jupyter Notebooks for Teaching, Learning, and Doing Data Science,”10.52041/iase.icots11.t10e3__in Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics, 2022, doi:10.52041/iase.icots11.t10e3 .

L. Wessel and T. Leuders, “Profession-Specific Curriculum Design in Mathematics Teacher Education: Connecting Disciplinary Practice to the Learning of Group Theory,” in Practice-Oriented Research in Tertiary Mathematics Education, R. Biehler, M. Liebendörfer, G. Gueudet, C. Rasmussen, and C. Winslow, Eds. Cham: Springer International Publishing, 2022, pp. 349–368.

S. Podworny, D. Frischemeier, and R. Biehler, “Civic Statistics for Prospective Teachers: Developing Content and Pedagogical Content Knowledge Through Project Work,” in Statistics for Empowerment and Social Engagement, Cham: Springer International Publishing, 2022.

D. Frischemeier, S. Podworny, and R. Biehler, “Data Visualization Packages for Non-inferential Civic Statistics in High School Classrooms,” in Statistics for Empowerment and Social Engagement, Cham: Springer International Publishing, 2022, pp. 199–236.

S. Podworny, Y. Fleischer, D. Stroop, and R. Biehler, “An example of rich, real and multivariate survey data for use in school,” presented at the Twelfth Congress of the European Society for Research in Mathematics Education (CERM), Bozen-Bolzano, Italy, 2022.

T. Kolbe and L. Wessel, “Self-regulated learning in a mathematics course for engineers in the first semester: insights into students reported resource management and cognitive strategies,” in Fourth conference of the International Network for Didactic Research in University Mathematics, Hannover, 2022, pp. 632–641.

M. Schürmann et al., “Evaluation von Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen,” in Unterstützungsmaßnahmen in mathematikbezogenen Studiengängen - Konzepte, Praxisbeispiele und Untersuchungsergebnisse, R. Hochmuth, R. Biehler, M. Liebendörfer, and N. Schaper, Eds. Berlin, Heidelberg: Springer, 2022, pp. 67–113.

J. Schönherr, S. Schukajlow, J. Blomberg, and C. Leopold, “Effects of drawing instructions and strategic knowledge on mathematical modeling performance: Mediated by the use of the drawing strategy,”10.1002/acp.3930__Applied Cognitive Psychology, , vol. 36, no. 2, pp. 402–417, 2022, doi:10.1002/acp.3930 .

J. Schönherr, S. Schukajlow, and C. Leopold, “Drawing instructions, strategic knowledge, strategy-based motivation, and students’ use of drawings,” in Proceedings of the 45th PME Conference, 2022, vol. 3, pp. 347–354.

F. Nüske, P. Gelß, S. Klus, and C. Clementi, “Tensor-based computation of metastable and coherent sets,”10.1016/j.physd.2021.133018__Physica D: Nonlinear Phenomena, Art. no. 133018, 2021, doi:10.1016/j.physd.2021.133018 .

S. Klus, P. Gelß, F. Nüske, and F. Noé, “Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry,”10.1088/2632-2153/ac14ad__Machine Learning: Science and Technology, Art. no. 045016, 2021, doi:10.1088/2632-2153/ac14ad .

C. Goelz et al., “Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults,” Cognitive Neurodynamics, 2021.

M. B. Berkemeier and S. Peitz, “Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models,” Mathematical and Computational Applications, vol. 26, no. 2, 2021.

F. Nüske, P. Koltai, L. Boninsegna, and C. Clementi, “Spectral Properties of Effective Dynamics from Conditional Expectations,” Entropy, 2021.