Dr. Sarah Bichler

• ORCID: https://orcid.org/0000-0002-8229-4414

• Twitter: https://twitter.com/SarahBichler4

• Open Science Framework (OSF): https://osf.io/hqfge/
• ResearchGate: https://www.researchgate.net/profile/Sarah-Bichler

Supervision of theses

Bachelor Pedagogy/Education Sciences, Teacher Training, Master Learning Sciences
Current topics and information by e-mail

Method consulting

Curriculum vitae

• 06/2022 Postdoc Lehr-Lernforschung, LMU München
• 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 München
• Master Psychologie/Learning Sciences, LMU München
• Bachelor Pädagogik/Bildungswissenschaften, LMU München

Research focus

• 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

Selected publications

• 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. https://doi.org/10.1007/s11251-022-09579-4
• 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. https://doi.org/10.1037/edu0000433
• 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. https://2022.isls.org/proceedings/
• 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. https://2022.isls.org/proceedings/
• Riordan, B., Bichler, S., Bradford, A., & Linn, M. (2020). Analyzing saliency in neural models for scoring content in science explanations. In Y. Belinkov, A. Alishahi, G. Chrupała, D. Hupkes, Y. Pinter, & S. Hassan (Chairs), BlackboxNLP 2020: Analyzing and interpreting neural networks for NLP [Symposium]. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), online conference. https://2020.emnlp.org/