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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">EDTR</journal-id><journal-title-group><journal-title>Educational Theory Observation</journal-title></journal-title-group><issn>2995-5017</issn><eissn>2995-5025</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/EDTR.2025050027</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>统计学视角的学生课下学习模式及干预模式探究</title><url>https://artdesignp.com/journal/EDTR/3/5/10.61369/EDTR.2025050027</url><author>韩锋,易丹辉,张景胜,张琳琳,李青艳,高洪力</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>3</volume><issue>5</issue><history><date date-type="pub"><published-time>2025-05-20</published-time></date></history><abstract>高速发展的互联网给我们带来了便利，也带来一些挑战。混合式教学、项目式教学等模式下，如何充分利用好课下时间，在&amp;ldquo;双减&amp;rdquo;政策下，既能减去不必要的负担又能高效地获取知识对学生的发展尤为重要。当今家长们面临着工作压力及生活压力，同样需要减负。如何借助学生之间的互助互律恰当发挥小组学习的功效非常重要，而学习小组的恰当选择无疑需要借助科技的力量。本文借助社交网络方法与因果推断方法相融合，给出学生课下学习模式的一些建议，从而，给实际的精准教与学提供借鉴和参考。</abstract><keywords>因果推断,社交网络,学生课下学习模式,干预模式,度中心性算法</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]中共中央办公厅 国务院办公厅印发《关于进一步减轻义务教育阶段学生作业负担和校外培训负担的意见》．央视新闻 [引用日期2021-07-24].[2]Fisher, R.A. Statistical Methods for Research Workers[M].Oliver＆Boyd,1925.[3]Rubin,D.B.Estimating causal effects of treatments in randomized and nonrandomized studies[J].Journal of educational Psychology,1974,66:688 -701.[4]Hirano, K., Imbens, G. and Ridder, G.Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score[J].Econometrica, 2003,71(4):1161-1189.[5]Ding R X, Wang X, Shang K, et al. Social network analysis- based conflict relationship investigation and conflict degree-based consensus reaching process for large scale decision making using sparse representation[J]. Information Fusion, 2019,50:251-272.[6]郭奕, 徐亮, 熊雪军. 社交网络中意见领袖挖掘方法综述[J]. 计算机科学与探索, 2021, 15 (11): 2077-2092.[7]Rosenbaum,P. R.,and Rubin,D. B.The Central Role of the Propensity Score in Observational Studies for Causal Effects[J].Biometrika, 1983,70:41-55.[8]Efron, B., Tibshirani, R. (1993), An introduction to the Bootstrap. London: UK: Champman &amp;amp; Hall.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
