Discovering the structure of mathematical problem solving

被引:25
|
作者
Anderson, John R. [1 ]
Lee, Hee Seung [2 ]
Fincham, Jon M. [1 ]
机构
[1] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA
[2] Yonsei Univ, Dept Educ, Seoul 120749, South Korea
关键词
fMRI; Hidden Markov model; Mathematical problem solving; Multivariate pattern analysis; FORWARD-BACKWARD ALGORITHM; PREFRONTAL CORTEX; DEFAULT NETWORK; INTRAPARIETAL SULCUS; PARIETAL CORTEX; BRAIN; ACTIVATION; VISUALIZATION; DYSCALCULIA; SOFTWARE;
D O I
10.1016/j.neuroimage.2014.04.031
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The goal of this research is to discover the stages of mathematical problem solving, the factors that influence the duration of these stages, and how these stages are related to the learning of a new mathematical competence. Using a combination of multivariate pattern analysis (MVPA) and hidden Markov models (HMM), we found that participants went through 5 major phases in solving a class of problems: A Define Phase where they identified the problem to be solved, an Encode Phase where they encoded the needed information, a Compute Phase where they performed the necessary arithmetic calculations, a Transform Phase where they performed any mathematical transformations, and a Respond Phase where they entered an answer. The Define Phase is characterized by activity in visual attention and default network regions, the Encode Phase by activity in visual regions, the Compute Phase by activity in regions active in mathematical tasks, the Transform Phase by activity in mathematical and response regions, and the Respond phase by activity in motor regions. The duration of the Compute and Transform Phases were the only ones that varied with condition. Two features distinguished the mastery trials on which participants came to understand a new problem type. First the duration of late phases of the problem solution increased. Second, there was increased activation in the rostrolateral prefrontal cortex (RLPFC) and angular gyrus (AG), regions associated with metacognition. This indicates the importance of reflection to successful learning. (C) 2014 Elsevier Inc All rights reserved.
引用
收藏
页码:163 / 177
页数:15
相关论文
共 50 条
  • [21] Metacognitive macroevaluations in mathematical problem solving
    Desoete, A
    Roeyers, H
    LEARNING AND INSTRUCTION, 2006, 16 (01) : 12 - 25
  • [22] Discovering optimal brain states for problem solving with EEG
    Wu, Ying Choon
    Jung, Melody
    Lock, Derrick
    Chao, Eric
    Jung, Tzyy-Ping
    2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2013, : 774 - 777
  • [23] Enactive metaphors in mathematical problem solving
    Diaz-Rojas, Daniela
    Soto-Andrade, Jorge
    PROCEEDINGS OF THE TENTH CONGRESS OF THE EUROPEAN SOCIETY FOR RESEARCH IN MATHEMATICS EDUCATION (CERME10), 2017, : 3904 - 3911
  • [24] MATHEMATICAL TOPOLOGY - SOLVING A KNOTTY PROBLEM
    STEWART, I
    NATURE, 1985, 317 (6035) : 290 - 290
  • [25] Mathematical Document Retrieval for Problem Solving
    Samarasinghe, Sidath Harshanath
    Hui, Siu Cheung
    2009 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND TECHNOLOGY, VOL I, PROCEEDINGS, 2009, : 583 - 587
  • [26] Productive failure in mathematical problem solving
    Manu Kapur
    Instructional Science, 2010, 38 : 523 - 550
  • [27] Intuition and Visualization in Mathematical Problem Solving
    Valeria Giardino
    Topoi, 2010, 29 : 29 - 39
  • [28] Developing mathematical problem solving skills
    Abidin, B
    Hartley, JR
    JOURNAL OF COMPUTER ASSISTED LEARNING, 1998, 14 (04) : 278 - 291
  • [29] MATHEMATICAL PROBLEM-SOLVING BY ANALOGY
    NOVICK, LR
    HOLYOAK, KJ
    BULLETIN OF THE PSYCHONOMIC SOCIETY, 1990, 28 (06) : 494 - 494
  • [30] THE ROLE OF THE AESTHETIC IN MATHEMATICAL PROBLEM SOLVING
    Sinclair, Nathalie
    Berneche, Christian
    INTERDISCIPLINARITY FOR THE TWENTY-FIRST CENTURY, 2011, 11 : 49 - +