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Academic resilience is evident in students who are living in vulnerable environments, yet achieve success in academic outcomes. As a result, substantial attention has been devoted to identifying the factors associated with academic resilience and supporting students to be resilient. This study used the Classification and Regression Tree and Multilevel Logistic Regression modeling to identify the potential factors related to students’ academic resilience. Using these tools, the study analyzed the B-S-J-G (China) sample in PISA 2015. The variables that significantly predicted whether a student is disadvantaged and resilient (DRS) or not resilient (DNRS) were shown to be: Proportion of teachers in school with master’s degrees, Proportion of teachers in school with bachelor’s degrees, Environmental awareness, Science learning time per week, Number of learning domains with additional instruction, and Students’ expected occupational status. These findings may enlighten governments, teachers, and parents on ways to assist students to be resilient.

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Digital problem-solving competence is widely recognized as one of the core skills of the 21st century. A number of important factors influence this competence; some are task-specific pertaining to the problem-solving processes while others are non-task-specific related to knowledge, skills, attitudes and beliefs of the problem solvers, as well as the student learning environment. This study sought to determine important factors that classify student problem-solver as “high-performing expert” versus “low-performing novice”, using computer-generated log files of an exemplary digital problem task assessed in Organization for Economic Co-operation and Development (OECD)’s Programme for International Student Assessment (PISA) 2012 Study. The participants comprise 11,599 fifteen-year-old students from 42 economies. Apart from multilevel logistic regression of problem-solving process and student questionnaire data, the secondary data analysis employed was a data-mining approach involving classification and regression trees. Five important factors were identified that are key to the discrimination of the “expert vs novice” dichotomy.

数字化环境下的问题解决能力被广泛认为是21世纪的核心技能之一。许多重要因素会影响该能力表现：有些因素针对于特定的问题并与问题解决的过程相关，而其它非问题特定的因素则与问题解决者的知识、技能、态度、信念以及学习环境相关。本研究旨在分析经合组织 (OECD) 国际学生评估项目 (PISA) 2012 年研究中基于计算机的示例问题解决测验的过程数据，挖掘能将学生区分为“问题解决专家”与“问题解决新手”的关键因素。PISA 2012 的受试学生包括来自42 个经济体的11,599 名15 岁学生。研究使用了多级逻辑回归来 分析问题解决过程和学生问卷数据；其次级数据分析采用了涉及分类和回归树的数据挖掘方法。研究结果表明有五个可区分专家与新手的重要因素。