混合式STEM学习对乡村小学生科学学习表现的影响机制---基于NCA和fsQCA的组态路径分析

Mechanisms of blended STEM learning on rural primary school students' scientific learning performance: a configurational path analysis based on NCA and fsQCA

摘要

混合式STEM学习是破解乡村科学教育资源困境的有效策略.为探究其多要素协同机制及在乡村情境下的作用路径,以一所小规模乡村学校14名五年级学生为研究对象,采用必要条件分析(necessary condition analysis ,NCA )与模糊集定性比较分析 (fuzzy-set qualitative comparative analysis ,fsQCA )相结合的方法 ,探讨科学兴趣、自我效能感、先验知识、混合式学习与STEM学习5个要素对学生科学学习表现的影响机制.结果表明:科学兴趣是高科学学习表现的必要条件;兴趣驱动型、知识导向型与优势叠加型3条差异化路径可取得高学习表现,而资源失配型组态易导致低学习表现;在资源约束条件下,混合式STEM学习通过探究实践与技术支架的协同作用,形成动机补偿机制,实现教育资源与学生特征的精准匹配.上述发现揭示了资源受限的乡村教育情境下,混合式STEM学习对乡村小学生科学学习表现的多路径作用机制,为乡村STEM教育从“资源均等”转向“精准适配”提供了实证依据.

Abstract

Blended STEM learning is an effective strategy to address the resource difficulties in rural science education. To explore its multi-factor interaction mechanisms and context-specific pathways in rural settings ,this study took 14 fifth-grade students from a small-scale rural school as participants. The method combined necessary condition analysis (NCA )and fuzzy-set qualitative comparative analysis (fsQCA )to investigate how scientific interest ,self-efficacy ,prior knowledge ,blended learning ,and STEM learning jointly influence students' scientific learning performance. The results showed that scientific interest was a necessary condition for high scientific learning performance. Three differentiated pathways ,namely the interest-driven type ,the knowledge-oriented type ,and the advantage-compounding type ,could achieve high learning performance ,whereas a resource-mismatch configuration tended to lead to low performance. Under resource constraints ,blended STEM learning ,through the synergy of exploratory practice and technical support ,formed a motivation compensation mechanism ,achieving a precise match between educational resources and student characteristics. These findings reveal the multi-pathway mechanisms of blended STEM learning on rural primary school students' scientific learning performance in resource-limited rural educational contexts ,providing empirical evidence for shifting rural STEM education from resource equalization to precision adaptation.

关键词

混合式STEM学习 / 乡村小规模学校 / 科学学习表现 / 教育数字化转型 / 教育公平

Key words

blended STEM learning / rural small-scale schools / scientific learning performance / digital transformation in education / educational equity

参考文献

[1] 傅骞, 刘鹏飞. 从验证到创造:中小学STEM教育应用模式研究[J]. 中国电化教育, 2016(4): 71-78.

[2] 孙宇杰, 杨卫安. 科学资本理论视域下乡村中小学科学教育的问题透视与路径纾解[J]. 中国电化教育, 2024(8): 17-24.

[3] 付卫东, 汪琪. 数字化赋能乡村科学教育:重点、难点及推进策略[J]. 当代教育论坛, 2025(3): 59-68.

[4] 郭绍青, 王家阳. 教育智能化:技术赋能乡村教育公平的新路径[J]. 中国电化教育, 2025(2): 67-74.

[5] GENG X Y, SU Y S. Enhancing K-12 students' STEM learning through the integration of the metaverse into online and blended environments: a meta-analysis[J]. International Journal of Science and Mathematics Education, 2024, 22: 111-143.

[6] LEON C, LIPUMA J, OVIEDO-TORRES X. Artificial intelligence in STEM education: a transdisciplinary framework for engagement and innovation[J]. Frontiers in Education, 2025, 10: 1619888.

[7] 缪巧玲, 王继新, 田俊, 等. 乡村学校科学教育高质量发展:价值意蕴、困境存因与可能路径[J]. 现代远距离教育, 2024(4): 29-38.

[8] 郭丛斌, 吴宇川, 沙桀民, 等. 我国中小学科学教育的师资基础:挑战与应对:基于对16841名中小学教师的问卷调查[J]. 中国教育学刊, 2024(6): 77-83.

[9] 郑永和, 杨宣洋, 王晶莹, 等. 我国小学科学教师队伍现状、影响与建议:基于31个省份的大规模调研[J]. 华东师范大学学报(教育科学版), 2023, 41(4): 1-21.

[10] 李伟, 邬志辉. 补齐乡村中小学STEM教育短板:美澳两国的实践与启示[J]. 比较教育学报, 2025(2): 51-65.

[11] CHANG Y, LEE E. Addressing the challenges of online and blended STEM learning with grounded design[J]. Australasian Journal of Educational Technology, 2022, 38(5): 163-179.

[12] MÄKELÄ T, FENYVESI K, KANKAANRANTA M, et al. Co-designing a pedagogical framework and principles for a hybrid STEM learning environment design[J]. Educational Technology Research and Development, 2022, 70(4): 1329-1357.

[13] SUPRIYADI A, DESY D, SUHARYAT Y, et al. The effectiveness of STEM-integrated blended learning on Indonesia student scientific literacy: a meta-analysis[J]. International Journal of Education and Literature, 2023, 2(1): 41-48.

[14] AKÇAYIR M, AKÇAYIR G, PEKTAŞ H M, et al. Augmented reality in science laboratories: the effects of augmented reality on university students' laboratory skills and attitudes towards science laboratories[J]. Computers in Human Behavior, 2016, 57: 334-342.

[15] DICKES A C, KAMARAINEN A, METCALF S J, et al. Scaffolding ecosystems science practice by blending immersive environments and computational modeling[J]. British Journal of Educational Technology, 2019, 50(5): 2181-2202.

[16] CHIRIKOV I, SEMENOVA T, MALOSHONOK N, et al. Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost[J]. Science Advances, 2020, 6(15): eaay5324.

[17] BAZELAIS P, DOLECK T. Blended learning and traditional learning: a comparative study of college mechanics courses[J]. Education and Information Technologies, 2018, 23(6): 2889-2900.

[18] HSIAO J C, CHEN S K, CHEN W, et al. Developing a plugged-in class observation protocol in high-school blended STEM classes: student engagement, teacher behaviors and student-teacher interaction patterns[J]. Computers & Education, 2022, 178: 104403.

[19] MONKE F, GUIDRY K R, PUSECKER K L, et al. Blended learning in computing education: It's here but does it work?[J]. Education and Information Technologies, 2020, 25(1): 83-104.

[20] EVENHOUSE D, LEE Y, BERGER E, et al. Engineering student experience and self-direction in implementations of blended learning: a cross-institutional analysis[J]. International Journal of STEM Education, 2023, 10(1): 19.

[21] JOVANOVIC J, SAQR M, JOKSIMOVIC S, et al. Students matter the most in learning analytics: the effects of internal and instructional conditions in predicting academic success[J]. Computers & Education, 2021, 172: 104251.

[22] HUI Y K, LI C, QIAN S, et al. Learning engagement via promoting situational interest in a blended learning environment[J]. Journal of Computing in Higher Education, 2019, 31(2): 408-425.

[23] HANFF, ELLIS R A. Self-reported and digital-trace measures of computer science students' self-regulated learning in blended course designs[J]. Education and Information Technologies, 2023, 28(10): 13253-13268.

[24] LE B, LAWRIE G A, WANG J T H. Student self-perception on digital literacy in STEM blended learning environments[J]. Journal of Science Education and Technology, 2022, 31(3): 303-321.

[25] 孔晶, 杨媛, 廖倩, 等. 国内外基础教育领域跨学科学习实践研究:基于系统性文献综述法[J]. 现代教育技术, 2024, 34(6): 63-70.

[26] BANDURA A, HALL P. Albert bandura and social learning theory[J]. Learning Theories for Early Years, 2018, 78: 35-36.

[27] MASON L, BOSCOLO P, TORNATORA M C, et al. Besides knowledge: a cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science[J]. Instructional Science, 2013, 41(1): 49-79.

[28] HARTELT T, MARTENS H. Self-regulatory and metacognitive instruction regarding student conceptions: influence on students' self-efficacy and cognitive load[J]. Frontiers in Psychology, 2024, 15: 1450947.

[29] BERGEY B W, KETELHUT D J, LIANG S F, et al. Scientific inquiry self-efficacy and computer games self-efficacy as predictors and outcomes of middle school boys' and girls' performance in a science assessment in a virtual environment[J]. Journal of Science Education and Technology, 2015, 24(5): 696-708.

[30] HECHT M, KNUTSON K, CROWLEY K. Becoming a naturalist: interest development across the learning ecology[J]. Science Education, 2019, 103(3): 691-713.

[31] TANG X, ZHANG D H. How informal science learning experience influences students' science performance: a cross-cultural study based on PISA 2015[J]. International Journal of Science Education, 2020, 42(4): 598-616.

[32] AGUILERA D, PERALES-PALACIOS F J. Learning biology and geology through a participative teaching approach: the effect on student attitudes towards science and academic performance[J]. Journal of Biological Education, 2020, 54(3): 245-261.

[33] CHEN M P, WONG Y T, WANG L C. Effects of type of exploratory strategy and prior knowledge on middle school students' learning of chemical formulas from a 3D role-playing game[J]. Educational Technology Research and Development, 2014, 62(2): 163-185.

[34] YANG X T, RAHIMI S, SHUTE V, et al. The relationship among prior knowledge, accessing learning supports, learning outcomes, and game performance in educational games[J]. Educational Technology Research and Development, 2021, 69(2): 1055-1075.

[35] DEWI A K, LESTARI S M P, SANDAYANTI V. Can self-efficacy have a role in learning interest[J]. Psikostudia: Jurnal Psikologi, 2023, 12(2): 302.

[36] GUO L J, HE Y Y, WANG S L. An evaluation of English-medium instruction in higher education: influencing factors and effects[J]. Journal of Multilingual and Multicultural Development, 2024, 45(9): 3567-3584.

[37] ÖCAL T, WELSH C, DURGUNOGLU A, et al. Predictors of adolescents' science learning in blended learning: prior knowledge, reading proficiency, self-efficacy and regulation[J]. Yıldız Journal of Educational Research, 2021, 2(6): 61-71.

[38] DUL J. Conducting necessary condition analysis for business and management students[M]. London: SAGE Publications, 2019.

[39] 杜运周, 贾良定. 组态视角与定性比较分析(QCA):管理学研究的一条新道路[J]. 管理世界, 2017, 33(6): 155-167.

[40] MURPHY C, BEGGS J. Primary science in the UK: a scoping study[R]. London: Wellcome Trust, 2005.

[41] WISE. WISE features[EB/OL]. [2025-07-12]. https://wise.berkeley.edu/features.

[42] WHITE P J, ARDOIN N M, EAMES C, et al. Agency in the anthropocene: supporting document to the PISA 2025 Science Framework[R]. Paris: OECD Publishing, 2023.

[43] 黄璐, 裴新宁. 科学身份认同:青少年科学素质的本体性指征[J]. 科普研究, 2024, 19(5): 55-63.

[44] RIHOUX B, RAGIN C C. Configurational comparative methods: qualitative comparative analysis (QCA) and related techniques[M]. Thousand Oaks: SAGE Publications, 2009: 87-93.

[45] 黄璐. 基于网络的科学探究促进小学生科学身份认同发展的研究[D].上海:华东师范大学, 2020.

[46] COLES E. The development of science identity: an evaluation of youth development programs at the Museum of Science and Industry, Chicago[D]. Chicago: Loyola University Chicago, 2012.

[47] CHANG B L. Effects of racialized tracking on racial gaps in science self-efficacy, identity, engagement, and aspirations: connection to science and school segregation[D]. University Park, PA: The Pennsylvania State University, 2016.

[48] SCHNEIDER C Q, WAGEMANN C. Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis[M]. Cambridge: Cambridge University Press, 2012: 23-41.

[49] FISS P C. Building better causal theories: a fuzzy set approach to typologies in organization research[J]. The Academy of Management Journal, 2011, 54(2): 393-420.

[50] DUL J, VAN DER LAAN E, KUIK R. A statistical significance test for necessary condition analysis[J]. Organizational Research Methods, 2020, 23(2): 385-395.

[51] 杜运周, 刘秋辰, 陈凯薇, 等. 营商环境生态、全要素生产率与城市高质量发展的多元模式:基于复杂系统观的组态分析[J]. 管理世界, 2022, 38(9): 127-144.

[52] 张明, 杜运周. 组织与管理研究中QCA方法的应用:定位、策略和方向[J]. 管理学报, 2019, 16(9): 1312-1323.

[53] 杨嘉欣. 四年级农村留守儿童科学动机信念与科学学业成绩的关系[D].北京:北京师范大学, 2021.

[54] FAMERO A. Perceptions and predictors of science motivation under the realm of self-determination theory among grade 10 public school students[J]. Romblon State University Research Journal, 2022, 3(2): 14-21.

[55] KEHR H M. Integrating implicit motives, explicit motives, and perceived abilities: the compensatory model of work motivation and volition[J]. The Academy of Management Review, 2004, 29(3): 479-499.

[56] 黄璐, 裴新宁. 科学理性主义视野下的STEM教育思考:知识融通[J]. 比较教育研究, 2018, 40(6): 27-34.

[57] LIU J X, LIU Q T, YU S F, et al. Which types of learners are suitable for the virtual reality environment: a fsQCA approach[C]// 2022 8th International Conference of the Immersive Learning Research Network (iLRN). Vienna: IEEE, 2022: 1-5.

基金资助

全国教育科学规划课题项目(BCA240049)