<p>This paper details the design and development of <i>MathFlowLens</i>, a visualization tool that illustrates students’ procedural pathways in algebraic problem solving and provides valuable insights into various mathematical strategies they use. MathFlowLens was built using the middle-school student (<i>N</i> = 1,649) log data from a gamified learning platform, From Here To There! (FH2T), and was developed in two phases. First, by using pathfinding algorithms, we identified four distinct types of students problem solving pathways in the platform: optimal, suboptimal, dead-end, and incomplete pathways. Second, we created sequential network visualizations based on the identified classifications to present these distinct procedural pathways. Furthermore, we tested the applicability of this tool by examining the relations between the identified classifications and students’ performance on a posttest assessing three facets of algebraic knowledge: conceptual knowledge, procedural knowledge, and procedural flexibility. To examine the relations with algebraic knowledge, we focused on the subset of students who completed both the pre- and post-test (N = 778). The results indicated that students who took dead-end pathways more frequently, which we posited as exploratory behavior, had higher conceptual and procedural knowledge scores than those who did not. This finding highlights the importance of fostering the exploration of multiple procedural pathways, regardless of failure, to bolster the acquisition of algebraic knowledge. This study demonstrates that MathFlowLens, a novel method for visualizing students’ solution pathways, can provide valuable insights into their solution strategies and mathematical problem solving processes.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

MathFlowLens: a classification and visualization tool for analyzing students’ procedural pathways

  • Siddhartha Pradhan,
  • Erin Ottmar,
  • Ashish Gurung,
  • Ji-Eun Lee

摘要

This paper details the design and development of MathFlowLens, a visualization tool that illustrates students’ procedural pathways in algebraic problem solving and provides valuable insights into various mathematical strategies they use. MathFlowLens was built using the middle-school student (N = 1,649) log data from a gamified learning platform, From Here To There! (FH2T), and was developed in two phases. First, by using pathfinding algorithms, we identified four distinct types of students problem solving pathways in the platform: optimal, suboptimal, dead-end, and incomplete pathways. Second, we created sequential network visualizations based on the identified classifications to present these distinct procedural pathways. Furthermore, we tested the applicability of this tool by examining the relations between the identified classifications and students’ performance on a posttest assessing three facets of algebraic knowledge: conceptual knowledge, procedural knowledge, and procedural flexibility. To examine the relations with algebraic knowledge, we focused on the subset of students who completed both the pre- and post-test (N = 778). The results indicated that students who took dead-end pathways more frequently, which we posited as exploratory behavior, had higher conceptual and procedural knowledge scores than those who did not. This finding highlights the importance of fostering the exploration of multiple procedural pathways, regardless of failure, to bolster the acquisition of algebraic knowledge. This study demonstrates that MathFlowLens, a novel method for visualizing students’ solution pathways, can provide valuable insights into their solution strategies and mathematical problem solving processes.