农科教育智能化转型实证探索

——融合因果推断与多元数据的路径分析

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中图分类号：G640 文献标志码：A 文章编号：2096-9902（2025）23-0066-05

DOI：10.20028/j.zhnydk.2025.23.015

Abstract：Toscientificallyassesstheeffectivenessandunderlyingmechanismsofintellgenttechnologyinagriculturalscienceeducationreform，thisstudyconstructsacausalanalysisframeworkbasedoncounterfactualcausalinferencetheoryemployingaquasi-experimentaldesignandmulti-sourcedataintegrationtechniques.Alongitudinalquasi-experimentaldesignwasimplementedwithexperimentalandcontrolgroups，utilizingdiference-in-diferences（DID）modelsandpropensityscorematching （PSM）.Thestudyntegatsclassroobevatios，leaingeaviorlgsndcogitiveessmentstoevaluatetheesofinteligenttchnologonducationTersultsindicateateintegatinofintellgenttchnologysignificantlyehancestfciency of agricultural science education（ β =7.6， P <0.01），comprehensively optimizes learning experiences （ES=O.85），and strengthens practical skill development（ η2 =0.21）.Multi-source evidence validation further demonstrates that students in the experimental group exhibit significantly higher knowledge transfer eficiency（t=5.32， p <0.001）and complex problem-solving ability（ F =18.64， （2号 p<0.01 ）.Thesefindingssuggesttatintellgenttechnologyefectivelyaddresssthetradionalbotleneckof"knowledgetoskil transformation"inagriculturaleducationthroughcognitivescafoldingconstructionandadaptivelearningpathoptimization.This studynotonlyestablishesacausalinferenceparadigmforevaluatingeducationaltechnologyefectivenessbutalsoprovidesevidence-based insights for the digital transformation of agricultural higher education.

Keywords：inteligent technology;agricultural education;causal inference;multiple evidencefusion;teachingreform

在高等农科教育人才培养中，教与学的主导和主动程度会直接影响农科院校本科生的培养质量。（剩余7610字）