Dr. Sarah Bichler

• Bichler, S., Stadler, M., Bühner, M., Greiff, S., & Fischer, F. (2022). Learning to solve ill‐defined statistics problems: does self‐explanation quality mediate the worked example effect? Instructional Science, 50, 335–359. Link
• Bichler, S., Schwaighofer, M., Stadler, M., Bühner, M., Greiff, S., & Fischer, F. (2019). How working memory capacity and shifting matter for learning with worked examples – A replication study. Journal of Educational Psychology, 112(7), 1320–1337. Link
• Bichler, S., Bradford, A., Riordan, B., & Linn, M. C. (2022). How do middle school students think about climate change? In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022 (pp. 2198–2199). International Society of the Learning Sciences. Link
• Gerard, L., Bichler, S., Bradford, A., Linn, M. C., Steimel, K., & Riordan, B. (2022). Designing an Adaptive Dialogue to Promote Science Understanding. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022 (pp. 1653–1656). International Society of the Learning Sciences. Link

• 06/2022 Postdoc Teaching and Learning Research, LMU Munich
• 2019-2022 Postdoc, Graduate School of Education, University of California Berkeley, Berkeley, USA (Technology Enhanced Learning in Science TELS); Projects STRIDES & NLP-TIPS
• 11/2019 PhD Learning Sciences, Reason (Elitenetzwerk Bayern), Munich Center of the Learning Sciences, LMU Munich
• Master Psychology/Learning Sciences, LMU München
• Bachelor Pedagogy/Educational Sciences, LMU Munich

• Interaction of Individual Learning Preferences and Learning Environment / Aptitude Theory
• Responsive Instruction
• Adaptive and individualized learning environments
• Development, design and implementation of teaching and learning technologies
• Development, design and implementation of digital learning units (elementary and middle school in physics, biology and climate change)
• Natural language processing (NLP), learning analytics & dashboards
• Knowledge capture and self-explanation
• Instructional support & scaffolding
• Role of prior knowledge, executive functions and intelligence for learning
• Quantification of qualitative data
• Moderated and mediated regression analyses

• Bachelor Pedagogy/Education Sciences
• Teacher Training
• Master Learning Sciences

The research group Teaching-Learning Research (Dr. Sarah Bichler & Prof. Sarah Hofer) offers methodological consulting for students writing their theses at Faculty 11. If you have any questions or problems concerning your quantitative or qualitative research work, please contact us at any time via eMail:

methodenberatung-paedagogik@psy.lmu.de