The Real Story Behind Story Problems: Effects of Representations on Quantitative Reasoning

By K. Koedinger, Mitchell J. Nathan. 2004

473 citations recorded by the source corpus.

Subfields: Education, Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5b3ffcbd38b130e54e161b22e513302ad3f186ce

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In Education

In Experimental and Cognitive Psychology

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Citations in the corpus

The contributions of working memory and executive functioning to problem representation and solution generation in algebraic word problems (2009, background): “Alternatively, working memory might contribute to the ability to integrate or perform the two tasks in an iterative fashion (Kintsch, 1998; Koedinger & Nathan, 2004).”
A Is for Apple: Mnemonic Symbols Hinder the Interpretation of Algebraic Expressions (2010, background): “…of variables (as in the present study) but also for students’ understanding of the equal sign (Baroody & Ginsburg, 1983; McNeil & Alibali, 2005a; McNeil et al., 2006; Seo & Ginsburg, 2003) and for simple algebra problems involving two operations and one unknown (Koedinger & Nathan, 2004).”
"Playing the game" of story problems: Coordinating situation-based reasoning with algebraic representation (2012, background): “Students’ tendency to use arithmetic rather than algebraic approaches to solve algebra story problems has been welldocumented (Hall, Kibler, Wenger, & Truxaw, 1989; Koedinger & Nathan, 2004; Stacey & MacGregor, 1999).”
Supporting Algebraic Reasoning through Personalized Story Scenarios: How Situational Understanding Mediates Performance (2013, methodology): “Thus we build on previous work on informal strategies in algebra (Hall et al., 1989; Koedinger & Nathan, 2004; Nathan et al., 2002), and find that use of certain types of informal reasoning may be closely tied to the students’ level of connection to the story context.”
Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement (2006, methodology): “Difficulty factors have been used to empirically evaluate a small number of alternative models [6, 10, 11].”
Maps as Representations: Expert Novice Comparison of Projection Understanding (2002, background): “…concerning instruction and learning because external representational systems can be the focus of a significant component of reasoning within that domain (Downs, Liben, & Daggs, 1988; Dufour-Janvier, Bednarz, & Belanger, 1987; Koedinger & Nathan, 1999; Lehrer & Schauble, 2000; Leinhardt, 2001).”
Who Benefits from Diagrams and Illustrations in Math Problems? Ability and Attitudes Matter (2018, background): “The contextualization perspective holds that contextualizing or ‘grounding’ math problems in real-world scenarios supports successful problem solving (Belenky & Schalk, 2014; Goldstone & Son, 2005; Koedinger & Nathan, 2004).”
Concreteness Fading in Mathematics and Science Instruction: a Systematic Review (2014, background): “For example, solving problems in abstract form often leads to inefficient solution strategies (Koedinger and Nathan 2004), inflexible application of learned procedures (McNeil and Alibali 2005), and illogical errors (Carraher and Schliemann 1985; Stigler et al. 2010).”
Unpacking Pedagogical Content Knowledge: Conceptualizing and Measuring Teachers' Topic-Specific Knowledge of Students (2008, result): “This result contradicts research on actual student learning conducted by Nathan and others (e.g., Koedinger & Nathan, 2004).”
The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning (2012, background): “We address challenges of different domains and student characteristics in terms of differences in knowledge demands (section 3) and stage of learning (sections 4 and 6).”