How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning: A TAM-based Proposal
暂无分享,去创建一个
[1] Icek Ajzen,et al. From Intentions to Actions: A Theory of Planned Behavior , 1985 .
[2] Etienne Wenger,et al. Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .
[3] John Self. Artificial Intelligence and Human Learning: Intelligent Computer-Aided Instruction , 1988 .
[4] Simon Holland,et al. Artificial intelligence, education and music: the use of artificial intelligence to encourage and facilitate music composition by novices , 1989 .
[5] Fred D. Davis,et al. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .
[6] Fred D. Davis,et al. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .
[7] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[8] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[9] Ron Stevens,et al. The Use of Artificial Neural Nets (ANN) to Help Evaluate Student Problem Solving Strategies , 2000 .
[10] Christoph Peylo,et al. W2 - Adaptive and Intelligent Web-Based Education Systems , 2003, Intelligent Tutoring Systems.
[11] Siu-Ming Yiu,et al. SmartTutor: An intelligent tutoring system in web-based adult education , 2003, J. Syst. Softw..
[12] Chi-Jen Lin,et al. Redefining the learning companion: the past, present, and future of educational agents , 2003, Comput. Educ..
[13] George D. Magoulas,et al. Intelligent Learning Objects: An Agent Based Approach of Learning Objects , 2004, Intelligent Tutoring Systems.
[14] Hyacinth S. Nwana,et al. Intelligent tutoring systems: an overview , 1990, Artificial Intelligence Review.
[15] David J. McArthur,et al. The roles of artificial intelligence in education : current progress and future prospects: , 2005 .
[16] Huaiqing Wang,et al. Intelligent agent supported personalization for virtual learning environments , 2006, Decis. Support Syst..
[17] William R. King,et al. A meta-analysis of the technology acceptance model , 2006, Inf. Manag..
[18] C. Flavián,et al. How bricks‐and‐mortar attributes affect online banking adoption , 2006 .
[19] Timothy Teo,et al. Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model , 2007, J. Comput. Assist. Learn..
[20] Analía Amandi,et al. An enhanced Bayesian model to detect students' learning styles in Web-based courses , 2008, J. Comput. Assist. Learn..
[21] Viswanath Venkatesh,et al. Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..
[22] Shu Ching Yang,et al. A study of high school English teachers' behavior, concerns and beliefs in integrating information technology into English instruction , 2008, Comput. Hum. Behav..
[23] Tahir Cetin Akinci,et al. Evaluation of student performance in laboratory applications using fuzzy logic , 2010 .
[24] Daniel L. Schwartz,et al. Preparing students for future learning with Teachable Agents , 2010 .
[25] Richard E. Mayer,et al. Polite web-based intelligent tutors: Can they improve learning in classrooms? , 2011, Comput. Educ..
[26] Ángel Hernández García. Desarrollo de un modelo unificado de adopción del comercio electrónico entre empresas y consumidores finales. Aplicación al mercado español , 2012 .
[27] Peter Mikulecký,et al. Smart Environments for Smart Learning , 2012 .
[28] Manuel Contero,et al. Study on Parent's Acceptance of the Augmented Reality Use for Preschool Education , 2013, VARE.
[29] Maria Samarakou,et al. Implementation of artificial intelligence assessment in engineering laboratory education , 2014 .
[30] Sung-Joon Yoon,et al. Validation of Haptic Enabling Technology Acceptance Model (HE-TAM): Integration of IDT and TAM , 2014, Telematics Informatics.
[31] Vladimir Stantchev,et al. Cloud computing service for knowledge assessment and studies recommendation in crowdsourcing and collaborative learning environments based on social network analysis , 2015, Comput. Hum. Behav..
[32] Dilek Sultan Acarli,et al. Investigation of Pre-service Teachers’ Intentions to Use of Social Media in Teaching Activities within the Framework of Technology Acceptance Model☆ , 2015 .
[33] Yaping Zang,et al. Advances of flexible pressure sensors toward artificial intelligence and health care applications , 2015 .
[34] Timothy Teo,et al. Comparing pre-service and in-service teachers' acceptance of technology: Assessment of measurement invariance and latent mean differences , 2015, Comput. Educ..
[35] Siân Bayne. Teacherbot: interventions in automated teaching , 2015, Apertura.
[36] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[37] Pandian Vasant,et al. Handbook of Research on Artificial Intelligence Techniques and Algorithms , 2015 .
[38] Hüseyin Çakir,et al. Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review , 2015, ArXiv.
[39] Somnuk Phon-Amnuaisuk,et al. Mathematics Wall: Enriching Mathematics Education Through AI , 2015, ICSI.
[40] Bart Rienties,et al. Why some teachers easily learn to use a new virtual learning environment: a technology acceptance perspective , 2016, Interact. Learn. Environ..
[41] Cathy O'Neil,et al. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2016, Vikalpa: The Journal for Decision Makers.
[42] Jon Mason,et al. Literate, Numerate, Discriminate – Realigning 21st Century Skills , 2016 .
[43] Michael J. Timms. Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms , 2016, International Journal of Artificial Intelligence in Education.
[44] Mingming Zhou,et al. Modelling Serbian pre-service teachers' attitudes towards computer use: A SEM and MIMIC approach , 2016, Comput. Educ..
[45] Thomas B. Sheridan,et al. Human–Robot Interaction , 2016, Hum. Factors.
[46] Mohamed A. Shahin,et al. State-of-the-art review of some artificial intelligence applications in pile foundations , 2016 .
[47] Gary K. W. Wong,et al. The behavioral intentions of Hong Kong primary teachers in adopting educational technology , 2016, Educational Technology Research and Development.
[48] Ido Roll,et al. Evolution and Revolution in Artificial Intelligence in Education , 2016, International Journal of Artificial Intelligence in Education.
[49] Sharon Kerr,et al. Exploring the impact of artificial intelligence on teaching and learning in higher education , 2017, Research and Practice in Technology Enhanced Learning.
[50] Francisco Jos Garca-Pealvo,et al. MLearning and pre-service teachers , 2017 .
[51] Ömer Faruk Ursavas,et al. The effects of cognitive style on Edmodo users’ behaviour: A structural equation modeling-based multi-group analysis , 2017 .
[52] Refik Caglar Kizilirmak,et al. Predicting Students’ GPA and Developing Intervention Strategies Based on Self-Regulatory Learning Behaviors , 2017, IEEE Access.
[53] Francisco José García-Peñalvo,et al. Technology Acceptance Among Teachers: An SLR on TAM and Teachers , 2017 .
[54] Thomas K. F. Chiu. Introducing electronic textbooks as daily-use technology in schools: A top-down adoption process , 2017, Br. J. Educ. Technol..
[55] Daniyal M. Alghazzawi,et al. A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms , 2017, J. Artif. Intell. Soft Comput. Res..
[56] Francisco José García-Peñalvo,et al. Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning , 2018, IEEE Access.
[57] Timothy Teo,et al. Explicating the influences that explain intention to use technology among English teachers in China , 2018, Interact. Learn. Environ..
[58] Carlos Delgado-Kloos,et al. Augmented reality for STEM learning: A systematic review , 2018, Computers & Education.
[59] Paul A. Pavlou,et al. Social identity and trust in internet-based voting adoption , 2018, Gov. Inf. Q..
[60] Pedro J. Muñoz Merino,et al. Learning analytics trends and challenges in engineering education: SNOLA special session , 2018, 2018 IEEE Global Engineering Education Conference (EDUCON).
[61] Francisco J. García-Peñalvo,et al. A Deep-Learning-Based Proposal to Aid Users in Quantum Computing Programming , 2018, HCI.
[62] Jie Hu,et al. The Exploration of a Machine Learning Approach for the Assessment of Learning Styles changes , 2018, Mechatron. Syst. Control..
[63] Ömer Faruk Ursavas,et al. How teachers’ personality affect on their behavioral intention to use tablet PC , 2018 .
[64] Binbin Zheng,et al. Chinese Language Teachers’ Perceptions of Technology and Instructional Use of Technology: A Path Analysis , 2018 .
[65] Aras Bozkurt,et al. Artificial Intelligence in Education: Current Insights and Future Perspectives , 2019 .
[66] Manisha Sharma,et al. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation , 2019, Int. J. Inf. Manag..
[67] Ronny Scherer,et al. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education , 2019, Comput. Educ..
[68] Francisco J. García-Peñalvo,et al. Cultural values and technology adoption: A model comparison with university teachers from China and Spain , 2019, Comput. Educ..
[69] Wei Zhang,et al. The roles of initial trust and perceived risk in public’s acceptance of automated vehicles , 2019, Transportation Research Part C: Emerging Technologies.
[70] Liston William Bailey. New Technology for the Classroom: Mobile Devices, Artificial Intelligence, Tutoring Systems, and Robotics , 2019 .
[71] Educational Technology and the New World of Persistent Learning , 2019, Advances in Educational Technologies and Instructional Design.
[72] Francisco J. García-Peñalvo,et al. Measuring Students' Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model , 2019, HCI.