{"title":"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","authors":"Jingjing Ma, Qingtang Liu, Shufan Yu, Jindian Liu, Xiaojuan Li, Chunhua Wang","doi":"10.1111/bjet.13499","DOIUrl":null,"url":null,"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 (<jats:italic>N</jats:italic> = 52), AR and non‐CM (<jats:italic>N</jats:italic> = 51), non‐AR and CM (<jats:italic>N</jats:italic> = 40), non‐AR and non‐CM (<jats:italic>N</jats:italic> = 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.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Augmented reality proves to be a useful technological tool for improving science learning.</jats:list-item> <jats:list-item>The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures.</jats:list-item> <jats:list-item>Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\"bullet\"> <jats:list-item>This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level.</jats:list-item> <jats:list-item>This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning.</jats:list-item> </jats:list>Implications for practice and/or policy <jats:list list-type=\"bullet\"> <jats:list-item>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.</jats:list-item> <jats:list-item>There are multiple pathways to achieving good learning outcomes rather than a single optimal solution.</jats:list-item> <jats:list-item>The implementation of educational interventions should fully consider students' individual differences, such as prior knowledge, experience and attitudes.</jats:list-item> </jats:list></jats:boxed-text>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"359 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What factors influence scientific concept learning? A study based on the fuzzy‐set qualitative comparative analysis\",\"authors\":\"Jingjing Ma, Qingtang Liu, Shufan Yu, Jindian Liu, Xiaojuan Li, Chunhua Wang\",\"doi\":\"10.1111/bjet.13499\",\"DOIUrl\":null,\"url\":null,\"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 (<jats:italic>N</jats:italic> = 52), AR and non‐CM (<jats:italic>N</jats:italic> = 51), non‐AR and CM (<jats:italic>N</jats:italic> = 40), non‐AR and non‐CM (<jats:italic>N</jats:italic> = 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.<jats:label/><jats:boxed-text content-type=\\\"box\\\" position=\\\"anchor\\\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\\\"bullet\\\"> <jats:list-item>Augmented reality proves to be a useful technological tool for improving science learning.</jats:list-item> <jats:list-item>The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures.</jats:list-item> <jats:list-item>Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\\\"bullet\\\"> <jats:list-item>This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level.</jats:list-item> <jats:list-item>This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning.</jats:list-item> </jats:list>Implications for practice and/or policy <jats:list list-type=\\\"bullet\\\"> <jats:list-item>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.</jats:list-item> <jats:list-item>There are multiple pathways to achieving good learning outcomes rather than a single optimal solution.</jats:list-item> <jats:list-item>The implementation of educational interventions should fully consider students' individual differences, such as prior knowledge, experience and attitudes.</jats:list-item> </jats:list></jats:boxed-text>\",\"PeriodicalId\":48315,\"journal\":{\"name\":\"British Journal of Educational Technology\",\"volume\":\"359 1\",\"pages\":\"\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Educational Technology\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1111/bjet.13499\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Educational Technology","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1111/bjet.13499","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
What factors influence scientific concept learning? A study based on the fuzzy‐set qualitative comparative analysis
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.
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.