基础数学技能和分数理解能力可预测百分比理解能力:来自智能辅导系统的证据

IF 6.7 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Markus Wolfgang Hermann Spitzer, Miguel Ruiz‐Garcia, Korbinian Moeller
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引用次数: 0

摘要

在智能辅导系统(ITSs)中促进有关百分比学习的研究十分有限。此外,也缺乏数据驱动的方法来改进智能辅导系统的设计,以促进有关百分比的学习。为了弥补这些不足,我们首先研究了学生在智能辅导系统中对基本数学技能(如算术、测量单位和几何)和分数的理解是否能预测他们对百分比的理解。然后,我们应用心理网络分析来评估基本数学概念、分数和百分比的 44 个子主题数据中的相互依存关系。我们利用了一个大型数据集,该数据集由使用 ITS bettermarks 的 2798 名学生组成,共处理了约 410 万个数学问题。我们发现,对高级算术、测量单位、几何和分数的理解能显著预测对百分比的理解。仔细观察后发现,分数文字题和分数/自然数乘除题等具有相似特征的问题最能预测学生的百分比理解能力。我们的研究结果表明,教学人员和软件开发人员可以考虑针对学习百分数有困难的学生,修订与百分数问题有相同特点的特定子课题。更广泛地说,我们的研究表明,以数据为驱动的方法来评估综合学习策略所涵盖的子课题之间的相互依存关系,可以为改进综合学习策略的设计提供实用的见解。至于百分比理解能力是否也能得到类似的预测结果--无论是在一般情况下还是在智能辅导系统中--目前只有有限的证据。来自此类智能辅导系统的过程数据可用于解决教育研究问题和优化数字学习软件。涉及百分比的问题通常是需要乘法和/或除法的文字问题。本文的补充内容 与分数的情况类似,学生在高级算术、测量单位和几何方面的成绩对百分数的成绩有显著的预测作用。学生的分数成绩对百分数成绩也有明显的预测作用。本文应用心理网络分析来评估一系列子课题(如分数乘除法、分数加减法和百分数计算)之间的具体相互依赖关系。结果表明,分数文字题和涉及乘除法的分数问题最能预测学生对百分数的理解程度。对实践和/或政策的启示 当学生在学习百分数时遇到困难时,可建议他们复习以往具有类似特征的数学概念 (如分数文字游戏、分数/自然数乘除法问题)。软件开发人员可以考虑在智能辅导系统中为遇到困难的学生提供这种数据驱动的复习建议。心理网络分析可作为一种学习分析方法,以易于访问的可视化方式说明大量不同子课题之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Basic mathematical skills and fraction understanding predict percentage understanding: Evidence from an intelligent tutoring system
Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data‐driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg, arithmetic, measurement units and geometry) and fractions within an ITS predicts their understanding of percentages. We then applied a psychological network analysis to evaluate interdependencies within the data on 44 subtopics of basic mathematical concepts, fractions and percentages. We leveraged a large‐scale dataset consisting of 2798 students using the ITS bettermarks and working on approximately 4.1 million mathematical problems. We found that advanced arithmetic, measurement units, geometry and fraction understanding significantly predicted percentage understanding. Closer inspection indicated that percentage understanding was best predicted by problems sharing similar features, such as fraction word problems and fraction/natural number multiplication/division problems. Our findings suggest that practitioners and software developers may consider revising specific subtopics which share features with percentage problems for students struggling with percentages. More broadly, our study demonstrates how evaluating interdependencies between subtopics covered within an ITS as a data‐driven approach can provide practical insights for improving the design of ITSs.Practitioner notesWhat is already known about this topic Longitudinal studies showed that basic mathematical skills predict fraction understanding. There is only limited evidence on whether similar predictions can be observed for percentage understanding—in general and within intelligent tutoring systems. Process data from such intelligent tutoring systems can be leveraged to pursue both educational research questions and optimizing digital learning software. Problems involving percentages typically are word problems requiring multiplications and/or divisions. What this paper adds Similar to the case of fractions, students' performance on advanced arithmetic, measurement units and geometry significantly predicted performance with percentages. Students' performance with fractions also predicted performance with percentages significantly. A psychological network analysis was applied to evaluate specific interdependencies between a range of subtopics (eg, Multiplying and dividing fractions, Adding and subtracting fractions and Calculating with percentages). Fraction word problems and fraction problems involving multiplication/division turned out to be the best predictors of understanding percentages. Implications for practice and/or policy When facing difficulties with percentages, revision of previous mathematical concepts sharing similar features (eg, fraction word problems, fraction/natural number multiplication/division problems) may be advised. Software developers may consider implementing such data‐driven revision recommendations for students facing difficulties within intelligent tutor systems. Psychological network analysis can be utilized as a learning analytics method for easy‐to‐access visualizations illustrating relationships between a large range of different subtopics.
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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