版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡介
1、 高等教育人工智能應(yīng)用研究綜述教育工作者的角色何在? 參考文獻(xiàn):acikkar,m.,& akay,m. f.(2009). support vector machines for predicting the admission decision of a candidate to the school of physical education and sports at cukurova university. expert systems with applications,36(3 part 2),7228-7233. ht
2、tps://adamson,david,dyke,g.,jang,h.,& penstein rosé,c.(2014). towards an agile approach to adapting dynamic collaboration support to student needs. international journal of artificial intelligence in education,24(1),92-124. https:/agaoglu,m.(2016). predicting instructor performance u
3、sing data mining techniques in higher education. ieee access,4,2379-2387. https://ahmad,h.,& rashid,t.(2016). lecturer performance analysis using multiple classifiers. journal of computer science,12(5),255-264. https://alfarsi,g. m. s.,omar,k. a. m.,& alsinani,m. j.(2017). a ru
4、le-based system for advising undergraduate students. journal of theoretical and applied information technology,95(11). retrieved from http:/alkhasawneh,r.,& hargraves,r. h.(2014). developing a hybrid model to predict student first year retention in stem disciplines using machine learning techniq
5、ues. journal of stem education:innovations & research,15(3),35-42. https:/aluko,r. o.,adenuga,o. a.,kukoyi,p. o.,soyingbe,a. a.,& oyedeji,j. o.(2016). predicting the academic success of architecture students by pre-enrolment requirement:using machine-learning techniques. construction economi
6、cs and building,16(4),86-98. https:// 16i4.5184aluthman,e. s.(2016). the effect of using automated essay evaluation on esl undergraduate studentswriting skill. international journal of english linguistics,6(5),54-67. https://amigud,a.,arnedo-moreno,j.,daradoumis,t.,& guerrero-rolda
7、n,a.-e.(2017). using learning analytics for preserving academic integrity. international review of research in open and distance learning,18(5),192-210. doi:10.19173/irrodl.v18i5.3103andris,c.,cowen,d.,& wittenbach,j.(2013). support vector machine for spatial variation. transactions in gis,17(1)
8、,41-61. https://aparicio,f.,morales-botello,m. l.,rubio,m.,hernando,a.,muñoz,r.,lópez-fernández,h.,buenaga,m. de.(2018). perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects. intern
9、ational journal of medical informatics,112(december 2017),21-33. https://babic,i. d.(2017). machine learning methods in predicting the student academic motivation. croatian operational research review,8(2),443-461. https://bahadr,e.(2016). using neural network and logistic regression a
10、nalysis to predict prospective mathematics teachersacademic success upon entering graduate education. kuram ve uygulamada egitim bilimleri,16(3),943-964. https://bakeman,r.,& gottman,j. m.(1997). observing interaction-an introduction to sequential analysis. cambridge:cambridge university
11、press.baker,r. s.(2016). stupid tutoring systems,intelligent humans. international journal of artificial intelligence in education,26(2),600-614. https:/baker,t.,& smith,l.(2019). educ-ai-tion rebooted?exploring the future of artificial intelligence in schools and colleges. retrieved from nesta
12、foundation website:https:/ of ai and education v5 web.pdfbarker,t.(2010). an automated feedback system based on adaptive testing:extending the model. international journal of emerging technologies in learning,5(2),11-14. https://barker,t.(2011). an automated individual feedback and marking sy
13、stem:an empirical study. electronic journal of e-learning,9(1),1-14. https:/bartolomé,a.,castañeda,l.,& adell,j.(2018). personalisation in educational technology:the absence of underlying pedagogies. international journal of educational technology in higher education,15(14). https:/ben
14、-zvi,t.(2012). measuring the perceived effectiveness of decision support systems and their impact on performance. decision support systems,54(1),248-256. https://biletska,o.,biletskiy,y.,li,h.,& vovk,r.(2010). a semantic approach to expert system for e-assessment of credentials and compet
15、encies. expert systems with applications,37(10),7003-7014. https://blikstein,p.,worsley,m.,piech,c.,sahami,m.,cooper,s.,& koller,d.(2014). programming pluralism:using learning analytics to detect patterns in the learning of computer programming. journal of the learning sciences,23(4),561-
16、599. https://brunton,j.,& thomas,j.(2012). information management in systematic reviews. in d. gough,s. oliver,& j. thomas(eds.),an introduction to systematic reviews(pp. 83-106). london:sage.calvo,r. a.,orourke,s. t.,jones,j.,yacef,k.,& reimann,p.(2011). collaborative writing sup
17、port tools on the cloud. ieee transactions on learning technologies,4(1),88-97. https:/camacho,d.,& moreno,m. d. r.(2007). towards an automatic monitoring for higher education learning design. international journal of metadata,semantics and ontologies,2(1),1-1. https://casamayor,a.,amandi
18、,a.,& campo,m.(2009). intelligent assistance for teachers in collaborative e-learning environments. computers & education,53(4),1147-1154. https://castañeda,l. & selwyn,n.(2018). more than tools?making sense of the ongoing digitizations of higher education. international jour
19、nal of educational technology in higher education,15(22). https:/chaudhri,v. k.,cheng,b.,overtholtzer,a.,roschelle,j.,spaulding,a.,clark,p.,gunning,d.(2013). inquire biology:a textbook that answers questions. ai magazine,34(3),55-55. https://chen,j.-f.,& do,q. h.(2014). training neural ne
20、tworks to predict student academic performance:a comparison of cuckoo search and gravitational search algorithms. international journal of computational intelligence and applications,13(1). https:/chi,m.,vanlehn,k.,litman,d.,& jordan,p.(2011). empirically evaluating the application of reinforcem
21、ent learning to the induction of effective and adaptive pedagogical strategies. user modeling and user-adapted interaction,21(1),137-180. doi 10.1007/s11257-010-9093-1chodorow,m.,gamon,m.,& tetreault,j.(2010). the utility of article and preposition error correction systems for english language l
22、earners:feedback and assessment. language testing,27(3),419-436. https:/chou,c.-y.,huang,b.-h.,& lin,c.-j.(2011). complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. computers & education,57(4),2303-2312. https:/cobos,c.,rodri
23、guez,o.,rivera,j.,betancourt,j.,mendoza,m.,león,e.,& herrera-viedma,e.(2013). a hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes. information processing and management,49(3),607-625. https://cohen,j.(1960). a coef
24、ficient of agreement for nominal scales. educational and psychological measurement,20,37-46. https:/contact north.(2018). ten facts about artificial intelligence in teaching and learning. retrieved from https:/crown,s.,fuentes,a.,jones,r.,nambiar,r.,& crown,d.(2011). anne g. neering:interactive
25、chatbot to engage and motivate engineering students. computers in education journal,21(2),24-34.decarlo,p.,& rizk,n.(2010). the design and development of an expert system prototype for enhancing exam quality. international journal of advanced corporate learning,3(3),10-13. https://delen,d
26、.(2011). predicting student attrition with data mining methods. journal of college student retention:research,theory and practice,13(1),17-35. https://delen,d.(2010). a comparative analysis of machine learning techniques for student retention management. decision support systems,49(4),498-506
27、. https://dikli,s.(2010). the nature of automated essay scoring feedback. calico journal,28(1),99-134. doi:10.11139/cj.28.1.99-134dobre,i.(2014). assessing the students knowledge in informatics discipline using the meteor metric. mediterranean journal of social sciences,5(19),84-92. doi:10.59
28、01/mjss.2014.v5n19p84dodigovic,m.(2007). artificial intelligence and second language learning:an efficient approach to error remediation. language awareness,16(2),99-113. https://duarte,m.,butz,b.,miller,s.,& mahalingam,a.(2008). an intelligent universal virtual laboratory(uvl). ieee tran
29、sactions on education,51(1),2-9. doi:10.1109/ssst.2002.1027009duffy,m. c.,& azevedo,r.(2015). motivation matters:interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. computers in human behavior,52,338-348. https://
30、duzhin,f.,& gustafsson,a.(2018). machine learning-based app for self-evaluation of teacher-specific instructional style and tools. education sciences,8(1). https:/easterday,m. w.,rees lewis,d. g.,& gerber,e. m.(2018). the logic of design research. learning:research and practice,4(2),131-160.
31、 https://educause.(2018). horizon report:2018 higher education edition. retrieved from educause learning initiative and the new media consortium website:https:/educause.(2019). horizon report:2019 higher education edition. retrieved from educause learning initiative and the new media consorti
32、um website:https:/feghali,t.,zbib,i.,& hallal,s.(2011). a web-based decision support tool for academic advising. educational technology and society,14(1),82-94. https:/feng,s.,zhou,s.,& liu,y.(2011). research on data mining in university admissions decision-making. international journal of a
33、dvancements in computing technology,3(6),176-186. https://fleiss,j. l.(1981). statistical methods for rates and proportions. new york:wiley.ge,c.,& xie,j.(2015). application of grey forecasting model based on improved residual correction in the cost estimation of university education. int
34、ernational journal of emerging technologies in learning,10(8),30-33. doi:10.3991/ijet.v10i8.5215gierl,m.,latifi,s.,lai,h.,boulais,a.,& champlain,a.(2014). automated essay scoring and the future of educational assessment in medical education. medical education,48(10),950-962. https://gough
35、,d.,oliver,s.,& thomas,j.(2017). an introduction to systematic reviews(2nd edition). los angeles:sage.gutierrez,g.,canul-reich,j.,ochoa zezzatti,a.,margain,l.,& ponce,j.(2018). mining:students comments about teacher performance assessment using machine learning algorithms. international jour
36、nal of combinatorial optimization problems and informatics,9(3),26-40. https:/hall,o. p.,& ko,k.(2008). customized content delivery for graduate management education:application to business statistics. journal of statistics education,16(3). https://haugeland,j.(1985). artificial intellige
37、nce:the very idea. cambridge,mass.:mit presshew,k. f.,lan,m.,tang,y.,jia,c.,& lo,c. k.(2019). where is the“theory”within the field of educational technology research?british journal of educational technology,50(3),956-971. https://hinojo-lucena,f.-j.,aznar-díaz,i.,cáceres-reche,
38、m.-p.,& romero-rodríguez,j.-m.(2019). artificial intelligence in higher education:a bibliometric study on its impact in the scientific literature. education sciences,9(1),51. https:/hoffait,a.-s.,& schyns,m.(2017). early detection of university students with potential difficulties. deci
39、sion support systems,101,1-11. https://hooshyar,d.,ahmad,r.,yousefi,m.,yusop,f.,& horng,s.(2015). a flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers. journal of computer assisted learning,31(4),345-361. https://howard,c.,jordan,
40、p.,di eugenio,b.,& katz,s.(2017). shifting the load:a peer dialogue agent that encourages its human collaborator to contribute more to problem solving. international journal of artificial intelligence in education,27(1),101-129. https:/howard,e.,meehan,m.,& parnell,a.(2018). contrasting pred
41、iction methods for early warning systems at undergraduate level. internet and higher education,37,66-75. https://huang,c.-j.,chen,c.-h.,luo,y.-c.,chen,h.-x.,& chuang,y.-t.(2008). developing an intelligent diagnosis and assessment e-learning tool for introductory programming. educational t
42、echnology & society,11(4),139-157. https:/huang,j.,& chen,z.(2016). the research and design of web-based intelligent tutoring system. international journal of multimedia and ubiquitous engineering,11(6),337-348. https://huang,s. p.(2018). effects of using artificial intelligence teach
43、ing system for environmental education on environmental knowledge and attitude. eurasia journal of mathematics,science and technology education,14(7),3277-3284. https:/hussain,m.,zhu,w.,zhang,w.,& abidi,s. m. r.(2018). student engagement predictions in an e-learning system and their impact on st
44、udent course assessment scores. computational intelligence and neuroscience. https:/iglesias,a.,martinez,p.,aler,r.,& fernandez,f.(2009). reinforcement learning of pedagogical policies in adaptive and intelligent educational systems. knowledge-based. systems,22(4),266-270. https:/e-jackson,m.,&a
45、mp; cossitt,b.(2015). is intelligent online tutoring software useful in refreshing financial accounting knowledge?advances in accounting education:teaching and curriculum innovations,16,1-19. https:/jain,g. p.,gurupur,v. p.,schroeder,j. l.,& faulkenberry,e. d.(2014). artificial intelligence-base
46、d student learning evaluation:a concept map-based approach for analyzing a students understanding of a topic. ieee transactions on learning technologies,7(3),267-279. doi:10.1109/tlt.2014.2330297jeschike,m.,jeschke,s.,pfeiffer,o.,reinhard,r.,& richter,t.(2007). equipping virtual laboratories wit
47、h intelligent training scenarios. aace journal,15(4),413-436. https:/ji,y.,& liu,y.(2014). development of an intelligent teaching system based on 3d technology in the course of digital animation production. international journal of emerging technologies in learning,9(9),81-86. https://jia,j.(2009). an ai framework to teach english as a foreign language:csiec. ai magazin
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 人人文庫網(wǎng)僅提供信息存儲空間,僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時也不承擔(dān)用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。
最新文檔
- 小學(xué)考試管理制度
- 買賣合同(供進(jìn)口成套設(shè)備用)5篇
- 二零二五年度駕校應(yīng)急處理與安全保障合同3篇
- 第17章-第1節(jié)-總需求曲線教材課程
- 《科幻小說賞析與寫作》 課件 第3、4章 “太空歌劇”的探索與開拓-《2001太空漫游》;“生命奇跡”的重述與復(fù)魅-《弗蘭肯斯坦》
- 二零二五年度網(wǎng)絡(luò)安全風(fēng)險(xiǎn)評估與維保服務(wù)合同3篇
- 2024年隴南市精神病康復(fù)醫(yī)院高層次衛(wèi)技人才招聘筆試歷年參考題庫頻考點(diǎn)附帶答案
- 二零二五年度高端制造項(xiàng)目反擔(dān)保協(xié)議3篇
- 2024年陽江市人民醫(yī)院高層次衛(wèi)技人才招聘筆試歷年參考題庫頻考點(diǎn)附帶答案
- 2024年河南機(jī)電職業(yè)學(xué)院高職單招語文歷年參考題庫含答案解析
- 視覺傳達(dá)設(shè)計(jì)史平面設(shè)計(jì)的起源與發(fā)展課件
- 醫(yī)技溝通與合作課件
- 醫(yī)學(xué)專業(yè)醫(yī)學(xué)統(tǒng)計(jì)學(xué)試題(答案見標(biāo)注) (三)
- cnas實(shí)驗(yàn)室規(guī)劃方案
- 脊髓損傷的病理生理和病因
- 肝內(nèi)膽管癌術(shù)后護(hù)理查房課件
- 職工心理健康知識手冊
- 工程量自動計(jì)算表格新
- 新時期學(xué)校德育工作的思路與方法
- 切爾諾貝利核電站事故工程倫理分析
- 合同備忘錄范本
評論
0/150
提交評論