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Problem-Solving Strategies in Stoichiometry Across Two Intelligent Tutoring Systems: A Cross-National Study

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

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Organisationseinheiten

Externe Organisationen

  • Carnegie Mellon University
  • Universität Potsdam

Details

Titel in ÜbersetzungProblemlösungsstrategien in der Stöchiometrie mit zwei intelligenten Tutorensystemen: Eine länderübergreifende Studie
OriginalspracheEnglisch
Seiten (von - bis)384–400
Seitenumfang17
FachzeitschriftJournal of Science Education and Technology
Jahrgang34
Frühes Online-Datum11 Jan. 2025
PublikationsstatusElektronisch veröffentlicht (E-Pub) - 11 Jan. 2025

Abstract

Intelligent tutoring system (ITS) provides learners with step-by-step problem-solving support through scaffolding. Most ITSs have been developed in the USA and incorporate American instructional strategies. How do non-American students perceive and use ITS with different native problem-solving strategies? The present study compares Stoich Tutor, an ITS with a high level of scaffolding, with ORCCA, an ITS with dynamic scaffolds that can support a range of problem-solving strate- gies. We conducted a think-aloud study with university students in the USA (N = 10) and Germany (N = 11), where students worked with either Stoich Tutor and ORCCA before solving stoichiometry problems on paper. Two human coders derived a coding scheme to investigate the strategies American and German students employ during problem solving on paper without instructional support. We derive a taxonomy of three stoichiometry problem-solving strategies. Next to the American fac- tor labeling method, this taxonomy includes a strategy based on equation transformations and a previously undocumented strategy using abstract symbols to isolate a target variable and then pluck in given values and compute the solution. German students exclusively used the latter strategy, which was not explicitly supported by any of the two tutoring systems. Further, students who did not use the factor-label method for paper-based problem solving, most of whom were German, initially had difficulty setting appropriate goals and working with fractions in the Stoich Tutor. While German students preferred ORCCA based on short interviews, they more often successfully solved problems in Stoich Tutor. Therefore, Stoich Tutor, although misaligned with German instruction, could still support German students’ learning. Still, revisions to ITS based on local instructional cultures could make them potentially more effective and aligned with curricular goals.

ASJC Scopus Sachgebiete

Zitieren

Problem-Solving Strategies in Stoichiometry Across Two Intelligent Tutoring Systems: A Cross-National Study. / Borchers, Conrad; Fleischer, Hendrik; Yaron, David J. et al.
in: Journal of Science Education and Technology, Jahrgang 34, 04.2025, S. 384–400.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Borchers C, Fleischer H, Yaron DJ, McLaren BM, Scheiter K, Aleven V et al. Problem-Solving Strategies in Stoichiometry Across Two Intelligent Tutoring Systems: A Cross-National Study. Journal of Science Education and Technology. 2025 Apr;34:384–400. Epub 2025 Jan 11. doi: 10.1007/s10956-024-10197-7
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abstract = "Intelligent tutoring system (ITS) provides learners with step-by-step problem-solving support through scaffolding. Most ITSs have been developed in the USA and incorporate American instructional strategies. How do non-American students perceive and use ITS with different native problem-solving strategies? The present study compares Stoich Tutor, an ITS with a high level of scaffolding, with ORCCA, an ITS with dynamic scaffolds that can support a range of problem-solving strate- gies. We conducted a think-aloud study with university students in the USA (N = 10) and Germany (N = 11), where students worked with either Stoich Tutor and ORCCA before solving stoichiometry problems on paper. Two human coders derived a coding scheme to investigate the strategies American and German students employ during problem solving on paper without instructional support. We derive a taxonomy of three stoichiometry problem-solving strategies. Next to the American fac- tor labeling method, this taxonomy includes a strategy based on equation transformations and a previously undocumented strategy using abstract symbols to isolate a target variable and then pluck in given values and compute the solution. German students exclusively used the latter strategy, which was not explicitly supported by any of the two tutoring systems. Further, students who did not use the factor-label method for paper-based problem solving, most of whom were German, initially had difficulty setting appropriate goals and working with fractions in the Stoich Tutor. While German students preferred ORCCA based on short interviews, they more often successfully solved problems in Stoich Tutor. Therefore, Stoich Tutor, although misaligned with German instruction, could still support German students{\textquoteright} learning. Still, revisions to ITS based on local instructional cultures could make them potentially more effective and aligned with curricular goals.",
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AU - Borchers, Conrad

AU - Fleischer, Hendrik

AU - Yaron, David J.

AU - McLaren, Bruce M.

AU - Scheiter, Katharina

AU - Aleven, Vincent

AU - Schanze, Sascha

N1 - Publisher Copyright: © The Author(s) 2025.

PY - 2025/1/11

Y1 - 2025/1/11

N2 - Intelligent tutoring system (ITS) provides learners with step-by-step problem-solving support through scaffolding. Most ITSs have been developed in the USA and incorporate American instructional strategies. How do non-American students perceive and use ITS with different native problem-solving strategies? The present study compares Stoich Tutor, an ITS with a high level of scaffolding, with ORCCA, an ITS with dynamic scaffolds that can support a range of problem-solving strate- gies. We conducted a think-aloud study with university students in the USA (N = 10) and Germany (N = 11), where students worked with either Stoich Tutor and ORCCA before solving stoichiometry problems on paper. Two human coders derived a coding scheme to investigate the strategies American and German students employ during problem solving on paper without instructional support. We derive a taxonomy of three stoichiometry problem-solving strategies. Next to the American fac- tor labeling method, this taxonomy includes a strategy based on equation transformations and a previously undocumented strategy using abstract symbols to isolate a target variable and then pluck in given values and compute the solution. German students exclusively used the latter strategy, which was not explicitly supported by any of the two tutoring systems. Further, students who did not use the factor-label method for paper-based problem solving, most of whom were German, initially had difficulty setting appropriate goals and working with fractions in the Stoich Tutor. While German students preferred ORCCA based on short interviews, they more often successfully solved problems in Stoich Tutor. Therefore, Stoich Tutor, although misaligned with German instruction, could still support German students’ learning. Still, revisions to ITS based on local instructional cultures could make them potentially more effective and aligned with curricular goals.

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KW - Problem-Solving strategy

KW - Stoichometry

KW - Think-aloud protocols

KW - Stoichiometry

KW - Chemistry education

KW - Problem-solving strategy

KW - Intelligent tutoring systems

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DO - 10.1007/s10956-024-10197-7

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VL - 34

SP - 384

EP - 400

JO - Journal of Science Education and Technology

JF - Journal of Science Education and Technology

SN - 1059-0145

ER -

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