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Article History

Accepted: 26 December 2024

First Online: 11 January 2025

Declarations

: The authors have no known conflict of interest to disclose. The authors have no relevant financial or non-financial interests to disclose. Research activities conducted in the USA were approved by Carnegie Mellon University’s Institutional Review Board and included informed consent. Research activities conducted in Germany were regulated under a data protection declaration, serving as informed consent. Carnegie Mellon University’s Graduate Student Assembly/Provost GuSH Grant funding was used to support this research under the project name “Scaffolding effects on problem-solving in intelligent tutoring systems.” Research materials are available upon request by accessing and . All data analysis code is publicly available at: . The log data stemming from student-tutor interactions during research activities in the USA are available at . Three conference papers using the US sample have been published (see citations below) with no ideas, analyses, or other manuscript content re-used in the present manuscript:

: Borchers, C., Zhang, J., Baker, R. S., & Aleven, V. (2024). Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 529–539). 

: Zhang, J., Borchers, C., Aleven, V., & Baker, R. S. (2024). Using Large Language Models to Detect Self-Regulated Learning in Think-Aloud Protocols. Proceedings of the 17th International Conference on Educational Data Mining (EDM). 

: Zhang, J., Borchers, C., & Barany, A. (2024). Studying the Interplay of Self-regulated Learning Cycles and Scaffolding Through Ordered Network Analysis Across Three Tutoring Systems. In: Kim, Y.J., Swiecki, Z. (eds) Advances in Quantitative Ethnography. ICQE 2024. Communications in Computer and Information Science, vol 2278. Springer, Cham.