Martin, P., Kubsch, M., Yik, B. J., Burlingham, B. T., Graulich, N., (2025). Adaptive, but Equitable? Exploring the Impact of Machine Learning-Based Adaptive Support on Educational Debts in Undergraduate Chemistry. Science Education.
https://doi.org/10.1002/sce.70042

Wulff, P., & Kubsch, M. (2025). Learning against the machine: the doubleedged sword of (Gen)AI in STEM education. International Journal of STEM Education, 12, 66. Springer Nature Link.
https://doi.org/10.1186/s40594-025-00588-6

Domenichini, D., Strauß, S., Gombert, S., Rummel, N., Drachsler, H., Neumann, K., Chiarello, F., Fantoni, G.,  & Kubsch, M.(2025). Leveraging AI and network analysis to uncover learning trajectories of energy to Foster knowledge-in-use in science education. Disciplinary and Interdisciplinary Science Education Research 7, 28. Springer Nature Link. 
https://doi.org/10.1186/s43031-025-00150-y 

Sorge, S., Wulff, P. & Kubsch, M. (2025). Using a large language model to provide individualized feedback for pre-service physics teachers’ written reflections. Disciplinary and Interdisciplinary Science Education Research 7, 25. Springer Open. https://doi.org/10.1186/s43031-025-00145-9 

Wyrwich, T., Domenichini, D., Gombert, S., Kubsch, M., & Neumann, K. (2025). Characterizing students’ energy learning trajectories. Disciplinary and Interdisciplinary Science Education Research 7, 23. Springer Open. https://doi.org/10.1186/s43031-025-00141-z 

Kranz, J., Dau, H., & Kubsch, M. (2025). Zukunft gestalten lernen in Zeiten der Klimakrise. Transformatives Lernen in der naturwissenschaftlichen Bildung. In D. Pijetlovic, J. von Au, & Virtuelle Akademie Nachhaltigkeit (VAN) (Hrsg.) Klimawandel und Bildung. Konzepte, Methoden und Perspektiven transformativer Klimabildung (S. 53-71). oekom.
https://doi.org/10.14512/9783987264818 

Hur, P., Yilmaz, C., & Kubsch, M. (2025). Mit KI einfacher differenzieren. Differenziertes Material für den Physikunterricht mithilfe von generativer KI erstellen. Naturwissenschaften im Unterricht Physik, 209, 11-18.

Kubsch, M. (2025). Certainly Productive — A Review of Productive Uncertainty in Science Education by Eve Manz: The Physics Educator, 07, 2580002. https://doi.org/10.1142/S2661339525800022 

Hur, P., & Kubsch, M. (2025). Navigating Trust and Transparency: The Role of XAI in Supporting Education and EU Regulatory Measures. In S. Papadakis (Ed.), AI in Early Education: Integrating Artificial Intelligence for Inclusive and Effective Learning (pp. 248-266). Wiley. https://doi.org/10.1002/9781394352821.ch15 

Tschisgale, P., Maus, H., Kieser, F., Kroehs, B., Petersen, S., & Wulff, P. (2025). Evaluating GPT- and reasoning-based large language models on Physics Olympiad problems: Surpassing human performance and implications for educational assessment. Physical Review Physics Education Research, 21 (2), 020115. https://doi.org/10.1103/6fmx-bsnl

Hur, P., Twidale, M., & Bosch, N. (2025). “Like driving in a storm at night”: How students use metaphors to describe confusion during learning. In A. Rajala, A. Cortez, R. Hofmann, A. Jornet, H. Lotz-Sisitka, & L. Markauskaite (Eds.), Proceedings of the 19th International Conference of the Learning Sciences – ICLS 2025 (pp. 2973–2975). International Society of the Learning Sciences. https://doi.org/10.22318/icls2025.443156

Kubsch, M., Neumann, K., Nordine, J., & Fortus, D. (2025). Investigating the role of perceived intra-unit coherence for science learning. In A. Rajala, A. Cortez, R. Hofmann, A. Jornet, H. Lotz-Sisitka, & L. Markauskaite (Eds.), Proceedings of the 19th International Conference of the Learning Sciences - ICLS 2025 (pp. 305–313). International Society of the Learning Sciences. https://doi.org/10.22318/icls2025.409417

Stinken-Rösner, L., Pannullo, L., & Kubsch, M. (2025). Ich gehöre dazu! Physikidentität: Wege zur Stärkung des Zugehörigkeitsgefühls in der Physik. Physik Journal, 6, 38.

Wyrwich, T., Kubsch, M., Drachsler, H., & Neumann, K. (2025). Tracking students’ progression in developing understanding of energy using AI technologies. Physical Review Physics Education Research, 21, 010152. https://doi.org/10.1103/PhysRevPhysEducRes.21.010152

Kubsch, M., Strauß, S., Grimm, A., Gombert, S., Drachsler, H., Neumann, K., & Neumann, N. (2025). Self-regulated learning in the digitally enhanced science classroom: Toward an early warning system. Educational Psychology Review, 37, 34. https://doi.org/10.1007/s10648-025-10011-9

Fiedler, K., Kubsch, M., Neumann, K., & Nordine, J. (2025). Supporting learning about energy with fields—evidence from a mixed-methods study. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.70006

Tschisgale, P., Wulff, P., & Kubsch, M. (2025). Erratum: Integrating artificial intelligence-based methods into qualitative research in physics education research: A case for computational grounded theory. [Physical Review: Physics Education Research, 19, 020123. (2023)]. Physical Review Physics Education Research, 21, 019901.https://doi.org/10.1103/PhysRevPhysEducRes.21.019901

Wulff, P., Kubsch, M., & Krist, K. (Eds.).(2025). Applying Machine Learning in Science Education Research. When, How, and Why? Springer. https://link.springer.com/book/10.1007/978-3-031-74227-9

Nehring, A., Buschhüter, D., Kubsch, M., Ludwig, T., Wulff, P., & Neumann, K. (2025). Künstliche Intelligenz in den Naturwissenschaftsdidaktiken – gekommen, um zu bleiben: Potenziale, Desiderata, Herausforderungen. Zeitschrift für Didaktik der Naturwissenschaften, 31, 2. Springer. https://doi.org/10.1007/s40573-025-00177-8

Tschisgale, P., Kubsch, M., Wulff, P., Petersen, St., & Neumann, K. (2025). Exploring the sequential structure of students’ physics problem-solving approaches using process mining and sequence analysis. Physical Review Physics Education Research, pp. 1-21. https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.21.010111

Kubsch, M., Grimm, A., Neumann, K., Drachsler, H., & Rummel, N. (2024). Using Evidence-Centered Design to Develop an Automated System for Tracking Students’ Physics Learning in a Digital Learning Environment. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. 230-240). Oxford University Press.
https://doi.org/10.1093/oso/9780198882077.001.0001

Borgards, L., Karademir, O., Strauß, S., Di Mitri, D., Kubsch, M., Brobeil, M., Grimm, A., Gombert, A., Neumann, K., Drachsler, H., Scheffel, M., & Rummel, N. (2024). Achieving tailored feedback by means of a teacher dashboard? Insights into teachers’ feedback practices. In R. F. Mello, N. Rummel, I. Jivet, G. Pishtari, & J. A. Ruipérez Valiente (Eds.), Technology-enhanced learning for inclusive and equitable quality education (pp. 75–80). Springer.
https://doi.org/10.1007/978-3-031-72312-4_8

Ludwig, T., Kubsch, M., Sorge, St., & Kardas, E. (2024, August). Quellen von Unsicherheit beim Experimentieren - Welche Rolle spielen verschiedene Arten von Unsicherheit beim Experimentieren? Beitrag präsentiert an der GDCP Jahrestagung 2023 in Hamburg, Deutschland (S. 846–849). https://gdcp-ev.de/wp-content/uploads/securepdfs/2024/07/Tagungsband_2024.pdf

Tautz, S., Sorge, St., & Kubsch, M. (2024). Mithilfe von Bayesian Updating Activities zur epistemischen Kognition? Beitrag präsentiert an der GDCP Jahrestagung 2023 in Hamburg, Deutschland (S. 746–749). https://gdcp-ev.de/wp-content/uploads/securepdfs/2024/07/Tagungsband_2024.pdf

Karademir, O., Borgards, L., Di Mitri, D., Strauß, S., Kubsch, M., Brobeil, M., Grimm, A., Gombert, S., Rummel, N., Neumann, K., & Drachsler, H. (2024). Following the Impact Chain of the LA Cockpit. An Intervention Study Investigating a Teacher Dashboard’s Effect on Student Learning. Journal of Learning Analytics, (pp. 1–14).
https://doi.org/10.18608/jla.2024.8399

Nordine, J., Kubsch, M., Fortus, D., Krajcik, J., & Neumann, K. (2024). Middle school students' use of the energy concept to engage in new learning: What ideas matter? Journal of Research in Science Teaching, 1–32.
https://doi.org/10.1002/tea.21950

Karademir, O., Di Mitri, D., Schneider, J., Jivet, I., Allmang, J., Gombert,S.,Kubsch, M., Neumann, K., & Drachsler, H. (2024). I don't have time! But keep me in the loop: Co-designing requirements for a learning analytics cockpit with teachers. Journal of Computer Assisted Learning,1–19. https://doi.org/10.1111/jcal.12997

Fiedler, K., Kubsch, M., Neumann, K., & Nordine, J. (2024). Der potentiellen Energie ein Zuhause geben. In M. Hopf & M. Becker (Hrsg.), Energie (S. 10–15). Verein zur Förderung des physikalischen und chemischen Unterrichts.

Kubsch, M., & Nordine, J. (2024). Auf das System kommt es an. In M. Hopf & M. Becker (Hrsg.), Energie (S. 16–20). Verein zur Förderung des physikalischen und chemischen Unterrichts.

Tschisgale, P., Steegh, A., Petersen, S., Kubsch, M., Wulff, P. & Neuman, K. (2024). Are science competitions meeting their intentions? a case study on affective and cognitive predictors of success in the Physics Olympiad.
Discip Interdscip Sci Educ Res. https://doi.org/10.1186/s43031-024-00102-y

Kubsch, M. (2024). What affects the continued learning about energy? Evidence from a 4-year longitudinal study.
Journal of Research in Science Teaching, (pp. 1–23). https://doi.org/10.1002/tea.21931

Tschisgale, P., Steegh, A., Kubsch, M., Petersen, S., & Neumann, K. (2024). Towards a more individualised support of science competition participants – identification and examination of participant profiles based on cognitive and affective characteristics. International Journal of Science Education. https://doi.org/10.1080/09500693.2023.2300147

Graulich, N., Arnold, J., Sorge, S., & Kubsch, M. (2024). Lehrkräftebildung von morgen - Beiträge der Naturwissenschaftsdidaktiken zur Förderung überfachlicher Kompetenzen. Waxmann. https://doi.org/10.31244/9783830997962

Nauermann, L., Sorge, S., Garrecht, C., Bernholt, S., Kubsch, M., & Steegh, A. (2024). Gemeinsam globale Herausforderungen angehen - Ein Seminarkonzept zur Erstellung digitaler Unterrichtsmaterialien in den Naturwissenschaften. In Graulich, N., Arnold, J., Sorge, S., & Kubsch, M. (Hrsg.). Lehrkräftebildung von morgen - Beiträge der Naturwissenschaftsdidaktiken zur Förderung überfachlicher Kompetenzen (S. 159–169). Waxmann. https://doi.org/10.31244/9783830997962.17

Kubsch, M., & Neumann, I. (2024). Science Denial im naturwissenschaftlichen Unterricht begegnen.
In Graulich, N., Arnold, J., Sorge, S., & Kubsch, M. (Hrsg.). Lehrkräftebildung von morgen - Beiträge der Naturwissenschaftsdidaktiken zur Förderung überfachlicher Kompetenzen (S. 255-260). Waxmann. https://doi.org/10.31244/9783830997962.27

Kubsch, M., Strauß, S., & Bernholt, S. (2023). Integrating perspectives to promote knowledge integration: How knowledge integration, learning progressions and instructional science can complement each other.
In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Hrsg.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1897-1898). International Society of the Learning Sciences. https://doi.org/10.22318/icls2023.643889

Kubsch, M., Sorge, S., & Wulff P. (2023). Emotionen beim Reflektieren in der Lehrkräftebildung.
In Mientus, L., Klempin, C., & Nowak, A. (Hrsg.), Reflexion in der Lehrkräftebildung. Empirisch – Phasenübergreifend – Interdisziplinär (4th ed., pp. 261-269). Universitätsverlag Potsdam. https://doi.org/10.25932/publishup-59171  

Taylor, J. A., Bowen, G. M., Kubsch, M., Summers, R., Sezen-Barrie, A., Patrick, P., Lachapelle, C., Warfa, A., & Guzey, S. S. (2023). Crossing boundaries between research and practitioner communities: The role of research use and cross-community journal authorship. Journal of Research in Science Teaching, pp. 1–28.
https://doi.org/10.1002/tea.21914

Krist, C., & Kubsch, M. (2023). Bias, bias everywhere: A response to Li et al. and Zhai and Nehm. Journal of Research in Science Teaching, 60(10), pp. 2395–2399. https://doi.org/10.1002/tea.21913

Tschisgale, P., Wulff, P. & Kubsch, M. (2023). Integrating artificial intelligence-based methods into qualitative research in PER – A case for computational grounded theory. Physical Review Physics Education Research, 19, 020123. https://link.aps.org/doi/10.1103/PhysRevPhysEducRes.19.020123 

Fiedler, K., Kubsch, M., Neumann, K., & Nordine, J. (2023). Fields in middle school energy instruction to support continued learning of energy. Physical Review Physics Education Research, 19, 010122.
https://doi.org/10.1103/PhysRevPhysEducRes.19.010122 

Kubsch, M., Neumann, K., Rochnia, M., & Gräsel, C. (2023). 50 Jahre Unterrichtswissenschaften – Themen der empirischen Lehr-Lern-Forschung im Wandel. Unterrichtswissenschaft, 51 (S. 15–37).
https://doi.org/10.1007/s42010-023-00164-3

Grimm, A., Steegh, A., Colakogul, J., Kubsch, M., & Neumann, K. (2023). Positioning Responsible Learning Analytics in the Context of STEM Identities of Under-Served Students. Frontiers in Education, 7, 1082748.
https://doi.org/10.3389/feduc.2022.1082748

Grimm, A., Kubsch, M., Steegh, A., & Neumann, K. (2023). Learning Analytics in Physics Education: Ethical, Equity-Focused Decision-Making Lacks Guidance! Journal of Learning Analytics, 10(1), pp. 71-84.
https://doi.org/10.18608/jla.2023.7793

Gombert, S., Di Mitri, D., Karademir, O., Kubsch, M., Kolbe, H., Tautz, S., Grimm, A., Bohm, I., Neumann, K., & Drachsler, H. (2023). Coding energy knowledge in constructed responses with explainable NLP models.
Journal of Computer Assisted Learning, 39(3), pp. 767–786. https://doi.org/10.1111/jcal.12767

Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., Fiedler, D., Strauß, S., Cress, U., Drachsler, H., Neumann, K., & Rummel, R. (2022). Towards Learning Progression Analytics – Developing Learning Environments for the Automated Analysis of Learning using Evidence Centered Design.
Frontiers in Education, 7:981910. https://doi.org/10.3389/feduc.2022.981910

Kubsch, M.,Rosenberg, J. M., & Krist, C. (2022). Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research.
Journal of Research in Science Teaching, 60(2), pp. 423–447. https://doi.org/10.1002/tea.21803

Kubsch, M., Fortus, D., Neumann, K., Nordine, J., & Krajcik, J. (2022). The interplay of students’ motivational profiles and science learning. Journal of Research in Science Teaching, 60(1), pp. 3–25. https://doi.org/10.1002/tea.21789

Rosenberg, J., Kubsch, M., Dogucu, M., & Wagenmakers, E. (2022). A Bayesian perspective on uncertainty and probability in science education. Science & Education, 31. https://doi.org/10.1007/s11191-022-00341-3

Kubsch, M., Wulff, P., & Buschhüter, D. (2022). Schwerpunkttagung Maschinelles Lernen und computerbasierte Textanalysen: Potentiale und Herausforderungen für die Naturwissenschaftsdidaktik.
In Habig, S. & van Vorst, H. (Hrsg.). Unsicherheit als Element von naturwissenschaftsbezogenen Bildungsprozessen (48.: 2021 : Online). GDCP Tagungsband 2022. https://doi.org/10.25656/01:25241

Kubsch, M., & Weßnigk, S. (2022). Die Wärmebildkamera im Kontext Mechanik gewinnbringend einsetzen. MNU.

Fiedler, K., Kubsch, M., Neumann, K., & Nordine, J. (2022). Der potentiellen Energie ein Zuhause geben – Felder als didaktisches Hilfsmittel im Anfangsunterricht zum Energiekonzept. Zeitschrift für Didaktik der Naturwissenschaften, 28 (1), 6. https://doi.org/10.1007/s40573-022-00143-8

Kubsch, M., & Hamerski, P. C. (2022). Dynamic Energy Transfer Models. The Physics Teacher, 60(7), pp. 583–585. https://doi.org/10.1119/5.0037727

Kubsch, M., Caballero, D., & Uribe, P. (2022). Once More with Feeling: Emotions in Multimodal Learning Analytics.
In M. Giannakos, D. Spikol, D. Di Mitri, K. Sharma, X. Ochoa, & R. Hammad (Hrsg.). The Multimodal Learning Analytics Handbook. Springer International Publishing. https://doi.org/10.1007/978-3-031-08076-0_11

Kubsch, M., Sorge, S., Arnold, J., & Graulich, N. (2021). Lehrkräftebildung neu gedacht – Ein Praxishandbuch für die Lehre in den Naturwissenschaften und deren Didaktiken. Waxmann.
https://doi.org/10.31244/9783830993490

Kubsch, M., Rosenberg, J. M., & Krist, C. (2021). Beyond Supervision: Human / Machine Distributed Learning in Learning Sciences Research. In de Vries, E., Hod, Y., & Ahn, J. (Hrsg.). Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 897-898). International Society of the Learning Sciences. https://repository.isls.org//handle/1/7609 

Rosenberg, J. M., & Kubsch, M. (2021). Considering K-12 Learners’ Use of Bayesian Methods.
In E. de Vries, Y. Hod, & J. Ahn (Eds.). Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 947-948). International Society of the Learning Sciences. https://repository.isls.org//handle/1/7635

Sorge, S., Kubsch, M., Breuer, J., Syskowski, S., & Wöhlke, C. (2021). Lehrkräftebildung neu gedacht – Ergebnisse des GDCP Hackathon 2020. In S. Habig (Hrsg.). Naturwissenschaftlicher Unterricht und Lehrerbildung im Umbruch?. Gesellschaft für Didaktik der Chemie und Physik, Online Jahrestagung 2020. (41st ed., S. 45-47).

Kubsch, M., Opitz, S., Nordine, J., Neumann, K., Fortus, D., & Krajcik, J. (2021). Exploring a pathway towards energy conservation through emphasizing the connections between energy, systems, and fields.
Disciplinary and Interdisciplinary Science Education Research, 3 (1). https://doi.org/10.1186/s43031-020-00030-7

Kubsch, M., Stamer, I., Steiner, M., Neumann, K., & Parchmann, I. (2021). Beyond p-values: Using Bayesian Data Analysis in Science Education Research. Practical Assessment, Research and Evaluation.
https://doi.org/10.7275/vzpw-ng13 

Kubsch, M., & Sorge, S. (2021). Unterstützungsmöglichkeiten beim Erklären und Argumentieren im Physikunterricht.
MNU.

Fischer, J., Steinmann, T., Kubsch, M., Laumann, D., Weßnigk, S., Neumann, K., & Kerres, M. (2021). Die Rettung der Phänomene! - Phänomenbasiertes Lernen initiieren und strukturieren durch Leitfragen. MNU.

Bowen, M., Taylor, J., Patrick, P., Summers, R., Kubsch, M., Warfa, A., Sezen-Barrie, A., Guzey, S. & Lachapelle, C. (2020). Understanding the Use of Academic Research in Science Education Practitioner Journals. Journal of the Canadian Association for Curriculum Studies, 18 (1), pp. 151–153. https://doi.org/10.25071/1916-4467.40452

Kubsch, M., Nordine, J., Fortus, D., Krajcik, J. & Neumann, K. (2020). Supporting Students in UsingEnergy Ideas to Interpret Phenomena: The Role of an Energy Representation. International Journal of Science and Mathematics Education, 18 (8), pp. 1635–1654. https://doi.org/10.1007/s10763-019-10035-y

Kubsch, M., Touitou, I., Nordine, J., Fortus, D., Neumann, K. & Krajcik, J. (2020). Transferring Knowledge in a Knowledge-in-Use Task—Investigating the Role of Knowledge Organization. Education Sciences, 10 (1), 20. Article. http://dx.doi.org/10.3390/educsci10010020

Stamer, I., Kubsch, M., Thiele, M., Höffler, T., Schwarzer, S. & Parchmann, I. (2019). Scientists, Their Work, and how Others Perceive Them: Self-Perceptions of Scientists and Students’ Stereotypes. Research in Subject-matter Teaching and Learning (2nd, pp. 85-101). https://doi.org/10.23770/rt1826

Kubsch, M. & Cukurova, M. (2019). Diagnosing Student Competencies about the Concept of Energy in a Digital Learning Environment. In Lautenbach, C., Fischer, J., Zlatkin-Troitschanskaia, O., Toepper, M. & Pant, H. A (Hrsg.), Student Learning Outcomes Assessment in Higher Education - Ideas, Approaches and Concepts for Research, Transfer and Implementation: KoKoHs Working Papers.

Fortus, D., Kubsch, M., Bielik, T., Krajcik, J., Lehavi, Y., Neumann, K., Nordine, J., Opitz, S. & Touitou, I. (2019). Systems, transfer, and fields: Evaluating a new approach to energy instruction.
Journal of Research in Science Teaching, 56 (10), pp. 1341–1361. https://doi.org/10.1002/tea.21556

Kubsch, M., Nordine, J., Neumann, K., Fortus, D. & Krajcik, J. (2019). Probing the Relation between Students’ Integrated Knowledge and Knowledge-in-Use about Energy using Network Analysis.
Eurasia Journal of Mathematics, Science and Technology Education, 15(8). https://doi.org/10.29333/ejmste/104404

Kubsch, M., Nordine, J., Neumann, K., Fortus, D. & Krajcik, J. (2019). Measuring Integrated Knowledge – A Network Analytical Approach. In Rethinking Learning in the Digital Age. Making the Learning Sciences Count.

Kubsch, M., Nordine, J. & Neumann, K. (2018). Der System-Transfer-Ansatz. Den Energietransfer zwischen Systemen ins Zentrum stellen. Naturwissenschaften im Unterricht. Physik, 29 (164), S. 24–27.

Kubsch, M., Nordine, J., Neumann, K., Fortus, D. & Krajcik, J. (2018). Lerntrajektorien im Energiekonzept.
In C. Maurer (Hrsg.), Qualitätsvoller Chemie- und Physikunterricht - normative und empirische Dimensionen, S. 38.

Kubsch, M. & Nordine, J. (2018). Energietransferdiagramme als kognitive Unterstützung in der Mittelstufe.
In C. Maurer (Hrsg.), Implementation fachdidaktischer Innovation im Spiegel von Forschung und Praxis, S. 37.

Kubsch, M., Nordine, J. & Hadinek, D. (2017). Using smartphone thermal cameras to engage students’ misconceptions about energy. The Physics Teacher, 55 (8), pp. 504–505. https://doi.org/10.1119/1.5008354

Kubsch, M., Illenseer, T. F. & Duschl, W. J. (2016). Accretion disk dynamics: α-viscosity in self-similar self-gravitating models. Astronomy & Astrophysics, 588, A22. https://doi.org/10.1051/0004-6361/201527092

Meißner, M., & Haertig, H. (2014). Smartphone astronomy. The Physics Teacher, 52 (7), pp. 440–441. https://doi.org/10.1119/1.4895369