知识迁移能力在《材料力学》课程学习中的培养被引量：6

Cultivation of Knowledge Transfer Ability in Course of "Material Mechanics"

下载PDF

导出

摘要学生知识迁移能力的培养是提高学生学习效率的重要途径,知识迁移能力的提高对学习效果有显著的影响。本文以材料力学课程中拉压、扭转和弯曲应力理论学习过程为例,探讨如何培养学生的知识迁移能力,提高学生自身的学习能力。 The cultivation of students＇ knowledge transfer ability is an important way to improve students＇ learningefficiency.The improvement of knowledge transfer ability has a significant effect on the learning effect.Taking thetheoretical learning process of tension,torsion and bending stress in material mechanics course as an example,this paperdiscussed how to train students＇ knowledge transfer ability and improve their own learning ability.

作者刘灿昌刚宪约许英姿云海李磊 LIU Can-chang;GANG Xian-yue;XU Ying-zi;YUN Hai;LI Lei(School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo,Shandong255049,China)

机构地区山东理工大学交通与车辆工程学院

出处《教育教学论坛》 2018年第50期226-227,共2页 Education And Teaching Forum

基金山东省自然科学基金面上项目 ZR2017LA004

关键词学习迁移材料力学杆件变形 learning transfer Material Mechanics bar deformation

分类号 G642.0 [文化科学—高等教育学]

引文网络
相关文献

参考文献2

1孟佳娜,赵丹丹,于玉海,孙世昶.归纳式迁移学习在跨领域情感倾向性分析中的应用[J].南京大学学报（自然科学版）,2016,52(1):175-183. 被引量：2
2田有谷.论信息技术教学中学生知识迁移能力的培养[J].中国信息技术教育,2014(8):157-157. 被引量：1

二级参考文献24

1Pang B, Lee L, Shivakumar V. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Philadelphia, PA, USA, Proceeding of the ACL,2002,79--86.
2Rao D,Ravichandran 13. Semi supervised polarity lexicon induction. In: Proceedings of 12'h Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009, 675--682.
3Pan S, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engi- neering, 2009, (22) : 1345-- 1359.
4Cao X B, Wang Z, Yan P K, et al. Transfer learning for pedestrian detection. Neurocom- puting, 2013,100 : 51 -- 57.
5Zhuo H H,Yang Q. Action-model acquisition for planning via transfer learning. Artificial Intelligence, 2014,212 : 80 -- 103.
6Dai W Y, Yang Q, Xne G R, et al. Self-taught clustering. In: Proceedings of the 25th International Conference of Machine Learning. H elsinki, Finland .. ACM, 2008,200 -- 207.
7Meng J N,Lin H F, Li Y P. Knowledge transfer based on feature representation mapping for text classification. Expert Systems with Applications, 2011(38) :10562--10567.
8Meng J N,Lin H F, Yu Y H. Adaptive transfer learning for spam filtering. Journal of Computational Information Systems, 2010, 6) : 4581 --4589.
9Blitzer J, Dredze M, Pereira F. Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic-Association for Computational Linguistics, 2007 .. 440-- 447.
10Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. In.. Proceedings of Conference on Empirical Methods in Natural Language. Sydney, Australia .. Association for Computational Linguistics, 2006 .. 120 -- 128.