What factors influence scientific concept learning? A study based on the fuzzy‐set qualitative comparative analysis

IF 6.7 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH British Journal of Educational Technology Pub Date : 2024-06-25 DOI:10.1111/bjet.13499
Jingjing Ma, Qingtang Liu, Shufan Yu, Jindian Liu, Xiaojuan Li, Chunhua Wang
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Abstract

This research employs the fuzzy‐set qualitative comparative analysis (fsQCA) method to investigate the configurations of multiple factors influencing scientific concept learning, including augmented reality (AR) technology, the concept map (CM) strategy and individual differences (eg, prior knowledge, experience and attitudes). A quasi‐experiment was conducted with 194 seventh‐grade students divided into four groups: AR and CM (N = 52), AR and non‐CM (N = 51), non‐AR and CM (N = 40), non‐AR and non‐CM (N = 51). These students participated in a science lesson on ‘The structure of peach blossom’. This study represents students' science learning outcomes by measuring their academic performance and cognitive load. The fsQCA results reveal that: (1) factors influencing students' academic performance and cognitive load are interdependent, and a single factor cannot constitute a necessary condition for learning outcomes; (2) multiple pathways can lead to the same learning outcome, challenging the notion of a singular best path derived from traditional analysis methods; (3) the configurations of good and poor learning outcomes exhibit asymmetry. For example, high prior knowledge exists in both configurations leading to good and poor learning outcomes, depending on how other conditions are combined.Practitioner notesWhat is already known about this topic Augmented reality proves to be a useful technological tool for improving science learning. The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures. Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning. What this paper adds This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level. This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning. Implications for practice and/or policy No single factor present or absent is necessary for learning outcomes, but the combinations of AR and concept map strategy always obtain satisfactory learning outcomes. There are multiple pathways to achieving good learning outcomes rather than a single optimal solution. The implementation of educational interventions should fully consider students' individual differences, such as prior knowledge, experience and attitudes.
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影响科学概念学习的因素有哪些?基于模糊集定性比较分析的研究
本研究采用模糊集定性比较分析(fsQCA)方法,研究影响科学概念学习的多种因素的配置,包括增强现实(AR)技术、概念图(CM)策略和个体差异(如已有知识、经验和态度)。我们对 194 名七年级学生进行了一项准实验,分为四组:AR和CM组(52人)、AR和非CM组(51人)、非AR和CM组(40人)、非AR和非CM组(51人)。这些学生参加了 "桃花的结构 "科学课。本研究通过测量学生的学习成绩和认知负荷来反映学生的科学学习成果。研究结果表明(1) 影响学生学业成绩和认知负荷的因素是相互依存的,单一因素不能构成学习结果的必要条件;(2) 多种路径可以导致相同的学习结果,这对传统分析方法得出的单一最佳路径的概念提出了挑战;(3) 好的和差的学习结果的配置表现出不对称性。例如,高先验知识存在于导致好的和差的学习结果的两种配置中,这取决于其他条件是如何组合的。概念图可以引导学生描述概念之间的关系,并在新知识和现有知识结构之间建立联系。个体差异被强调为控制学习效果的重要外部因素。本文的补充 本研究创新性地采用了fsQCA分析方法,从细微处揭示了科学概念学习过程的复杂现象。本研究探讨了个体差异如何与 AR 和概念图策略相互作用,从而影响科学概念学习。对实践和/或政策的启示 任何单一因素的存在或不存在都不是学习成果的必要条件,但AR和概念图策略的组合总能获得令人满意的学习成果。取得良好学习效果有多种途径,而不是单一的最佳解决方案。教育干预措施的实施应充分考虑学生的个体差异,如先前的知识、经验和态度。
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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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