英文摘要 |
Purpose The purpose of this study is to make use of the computergenerated log files to derive task-specific indicator variables of problem-solving processes of an exemplary problem task (TRAFFIC) to examine factors of relative importance and thereby classify and differentiate high-performing problem-solving experts from lowperforming problem-solving novices. Added to the task-specific indicators are non-task-specific variables collated from questionnaires administered in the Programme for International Student Assessment (PISA) 2012 study. Design/methodology/approach The participants are 2,651 fifteen-year-old high-performing problem-solving experts and low-performing problem-solving novices who have responded to the TRAFFIC problem task coming from the top ten high-performing economies in the PISA 2012 digital problem-solving study. The educational data mining tool Classification and Regression Tree (CART) is the main analytic technique used. Factors found for the students of the top ten highperforming Eastern economies are compared with those of highperforming Western economies. Findings The factors affecting student performance in Eastern and Western high-performing economies share commonalities and differences. In the Eastern economies, the factors identified in descending order of relative importance are: Discovery of the optimal solution path of the problem task, mathematics self-efficacy, and experience with pure mathematics tasks at school. In the Western economies, the factors identified in descending order of relative importance are: Mathematics self- efficacy, discovery of the optimal solution path of the problem task, familiarity with mathematical concepts, and mathematics work ethics. Originality/value Based on the findings, educational practitioners may be informed how to design problem-based learning (PBL) in their respective economies. Furthermore, it is hoped that the methodologies developed are useful in furnishing new ideas to the future studies of digital problem solving. |