Detecting strengths and weaknesses in learning mathematics through a model classifying mathematical skills

By reviewing the literature on mathematical learning disabilities (MLD) and low academic mathematics (LA), we divided the mathematical skills involved in mathematical learning into four areas (core number, memory, inference, and visual space) We propose a model to do. In this paper, we presented a new computer-based mathematical task experiment aimed at acquiring ability from each field, and modeled 165 typical fifth and sixth graders (MLD = 9 and LA = 17 ). )sample. Interpretive and confirmative factor analysis of the data obtained, and K mean clustering analysis. The results demonstrate strong evidence supporting the reliability of the model, and the population is grouped into six distinguishable performance groups, MLD and LA students are distributed in five clusters. These findings support a hypothesis that the difficulty of learning mathematics may come from various sources and provide a way to outline the student's mathematical learning profile.

The essence of mathematics is found in many problems, but it depends on the solid foundation of core skills and concepts. In standard course mode, students can complete themes at a certain rate. Since it is rare that learning is linear, this inevitably leaves core gaps in knowledge for students. This problem is especially serious in mathematics and other areas, as curriculum knowledge is highly interdependent. For example, until you learn the basics of integers and math, students hardly want to learn scores. In more detail, they need to master the score before using the concept of probability.

By reviewing the literature on mathematical learning disabilities (MLD) and low academic mathematics (LA), we divided the mathematical skills involved in mathematical learning into four areas (core number, memory, inference, and visual space) We propose a model to do. In this paper, we presented a new computer-based mathematical task experiment aimed at acquiring ability from each field, and modeled 165 typical fifth and sixth graders (MLD = 9 and LA = 17 ). )sample. Interpretive and confirmative factor analysis of the data obtained, and K mean clustering analysis. This result shows strong evidence to support model reliability, aggregates the population into six distinguishable performance groups, MLD and LA students are distributed in five clusters.

Detect the advantages and disadvantages of classifying mathematical skills and learning mathematics