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001484018 001__ 1484018
001484018 003__ OCoLC
001484018 005__ 20240117003310.0
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001484018 020__ $$a9789819979479$$q(electronic bk.)
001484018 020__ $$a9819979471$$q(electronic bk.)
001484018 020__ $$z9819979463
001484018 020__ $$z9789819979462
001484018 0247_ $$a10.1007/978-981-99-7947-9$$2doi
001484018 035__ $$aSP(OCoLC)1409202757
001484018 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE
001484018 049__ $$aISEA
001484018 050_4 $$aLB1028.3
001484018 08204 $$a371.33$$223/eng/20231121
001484018 1112_ $$aInternational Conference on Artificial Intelligence in Education Technology$$n(4th :$$d2023 :$$cBerlin, Germany).
001484018 24510 $$aArtificial intelligence in education technologies :$$bnew development and innovative practices : proceedings of 2023 4th International Conference on Artificial Intelligence in Education Technology /$$cTim Schlippe, Eric C.K. Cheng, Tianchong Wang, editors.
001484018 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001484018 264_4 $$c©2023
001484018 300__ $$a1 online resource (xi, 340 pages) :$$billustrations (chiefly color).
001484018 336__ $$atext$$btxt$$2rdacontent
001484018 337__ $$acomputer$$bc$$2rdamedia
001484018 338__ $$aonline resource$$bcr$$2rdacarrier
001484018 4901_ $$aLecture notes on data engineering and communications technologies ;$$v190
001484018 500__ $$aInternational conference proceedings.
001484018 500__ $$aIncludes author index.
001484018 5058_ $$aIntro -- Preface -- Conference Committee -- Contents -- Educational Data Mining and Learning Analysis -- A Qualitative Evaluation of an AI-Based Study Progress Forecast -- 1 Introduction -- 2 Theoretical Background -- 3 Application Design and Architecture -- 4 Evaluation -- 4.1 Participants -- 4.2 Data Collection -- 4.3 Data Analysis -- 4.4 Results -- 5 Discussion -- 6 Conclusion -- References -- AI for Coding Education Meta-analyses: An Open-Science Approach that Combines Human and Machine Intelligence -- 1 Introduction -- 2 Related Work -- 3 Background and Goal -- 3.1 Meta-Analysis
001484018 5058_ $$a3.2 Scaffolding -- 3.3 MACROs -- 4 Two Example Autocoders -- 4.1 Education Level Autocoder -- 4.2 Education Population Autocoder -- 4.3 Human-in-the-loop -- 5 Conclusion -- References -- Results Analysis of the Opinion Survey for Mechanical Engineering Students of a Course Taught in Face-to-Face vs. Online Format -- 1 Introduction -- 2 Methodology -- 2.1 Research Context and Participants -- 2.2 Research Purpose -- 2.3 Data Analysis -- 3 Prediction Model Using Machine Learning Tools -- 3.1 Correlations of Variables -- 3.2 Training and Testing Phase -- 3.3 Results Phase -- 4 Conclusions
001484018 5058_ $$a5 Future Work -- References -- KNIGHT Learning Analytics Architecture for Betterment of Student Education -- 1 Introduction -- 2 Background -- 3 KNIGHT LA Architecture -- 3.1 Users of the System -- 3.2 Learning Materials and Applications -- 3.3 Learning Record Store -- 4 Use Case-Scenario -- 5 Conclusion and Future Work -- References -- An Innovative Model for Teacher Presence or Not in Video for Online Instruction Based on Neural Theory -- 1 Introduction -- 2 Review of the Literature -- 2.1 Online Teaching and Learning -- 2.2 Multimedia Learning Theory -- 2.3 Attention Span -- 2.4 Neuroscience
001484018 5058_ $$a3 The Model for Multimedia Effect -- 4 Case Study -- 5 Surveys for Students and Teachers -- 6 Results and Discussion -- 7 Conclusion -- References -- Using Relational Dialectics to Better Understand the Impact of Computer-Mediated Communication from the Perspective of Online Teaching -- 1 Introduction -- 2 Literature Review -- 2.1 Relational Dialectics -- 2.2 Binary Thinking -- 2.3 Computer-Mediated Communication -- 3 Methodology -- 3.1 Research Design -- 3.2 Sample and Data Collection, Analysis -- 4 Findings/Results -- 5 Discussion -- 6 Conclusion -- 7 Future Research Directions and Limitations
001484018 506__ $$aAccess limited to authorized users.
001484018 520__ $$aThis book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. It is a timely and up-to-date publication responsive to the rapid development of AI technologies, practices and their increasingly complex interplay with the education domain. It promotes the cross-fertilisation of knowledge and ideas from researchers in various fields to construct the interdisciplinary research area of AI in Education. These subject areas include computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology and linguistics. The feature of this book will contribute from diverse perspectives to form a dynamic picture of AI in Education. It also includes various domain-specific areas for which AI and other education technology systems have been designed or used in an attempt to address challenges and transform educational practice. This timely publication is in line with UNESCOs Beijing Consensus on Artificial Intelligence and Education. It is committed to exploring how AI may play a role in bringing more innovative practices, transforming education, and triggering an exponential leap towards the achievement of the Education 2030 Agenda. Providing broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education. .
001484018 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 21, 2023).
001484018 650_0 $$aArtificial intelligence$$xEducational applications$$vCongresses.$$xMedical applications$$0(DLC)sh 88003000
001484018 650_0 $$aEducational technology$$vCongresses.$$zGreat Britain$$0(DLC)sh2008119094
001484018 655_7 $$aConference papers and proceedings.$$2lcgft
001484018 655_0 $$aElectronic books.
001484018 7001_ $$aSchlippe, Tim,$$eeditor.
001484018 7001_ $$aCheng, Eric C. K.,$$eeditor.
001484018 7001_ $$aWang, Tianchong,$$eeditor.
001484018 77608 $$iPrint version: $$z9819979463$$z9789819979462$$w(OCoLC)1402201620
001484018 830_0 $$aLecture notes on data engineering and communications technologies ;$$vv. 190.
001484018 852__ $$bebk
001484018 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-7947-9$$zOnline Access$$91397441.1
001484018 909CO $$ooai:library.usi.edu:1484018$$pGLOBAL_SET
001484018 980__ $$aBIB
001484018 980__ $$aEBOOK
001484018 982__ $$aEbook
001484018 983__ $$aOnline
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