Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis
In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence...
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| Veröffentlicht in: | International journal of artificial intelligence in education 2021-06, Vol.31 (2), p.277-303 |
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| description | In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas. |
| doi_str_mv | 10.1007/s40593-021-00244-4 |
| format | Article |
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A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas.</description><identifier>ISSN: 1560-4292</identifier><identifier>EISSN: 1560-4306</identifier><identifier>DOI: 10.1007/s40593-021-00244-4</identifier><language>eng</language><publisher>New York: Springer New York</publisher><subject>Artificial Intelligence ; Bibliometrics ; Computer Science ; Computers and Education ; Cooperative Learning ; Data Analysis ; Data collection ; Data mining ; Deep learning ; Digital technology ; Distance learning ; Education ; Educational Environment ; Educational Research ; Educational Technology ; Educational Trends ; Electronic Learning ; Eye Movements ; Game Based Learning ; Information Retrieval ; Information technology ; Instructional Design ; Intelligent Tutoring Systems ; Interdisciplinary aspects ; Interdisciplinary studies ; International conferences ; Keywords ; Knowledge ; Learner Engagement ; Learning activities ; Learning Analytics ; Literature reviews ; Machine learning ; Natural Language Processing ; Neural networks ; Online Courses ; Online instruction ; Psychological Patterns ; Research methodology ; Student Evaluation ; Systematic review ; Teaching ; Teaching Methods ; Technology Uses in Education ; Trends ; Tutoring ; User generated content ; User Interfaces and Human Computer Interaction</subject><ispartof>International journal of artificial intelligence in education, 2021-06, Vol.31 (2), p.277-303</ispartof><rights>International Artificial Intelligence in Education Society 2021</rights><rights>International Artificial Intelligence in Education Society 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-ec70ce7000ddd64ca99b452e9601505d40789ee6808a61d21d728bdfd3501c313</citedby><cites>FETCH-LOGICAL-c385t-ec70ce7000ddd64ca99b452e9601505d40789ee6808a61d21d728bdfd3501c313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40593-021-00244-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2938256760?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1299850$$DView record in ERIC$$Hfree_for_read</backlink></links><search><creatorcontrib>Feng, Shihui</creatorcontrib><creatorcontrib>Law, Nancy</creatorcontrib><title>Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis</title><title>International journal of artificial intelligence in education</title><addtitle>Int J Artif Intell Educ</addtitle><description>In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas.</description><subject>Artificial Intelligence</subject><subject>Bibliometrics</subject><subject>Computer Science</subject><subject>Computers and Education</subject><subject>Cooperative Learning</subject><subject>Data Analysis</subject><subject>Data collection</subject><subject>Data mining</subject><subject>Deep learning</subject><subject>Digital technology</subject><subject>Distance learning</subject><subject>Education</subject><subject>Educational Environment</subject><subject>Educational Research</subject><subject>Educational Technology</subject><subject>Educational Trends</subject><subject>Electronic Learning</subject><subject>Eye Movements</subject><subject>Game Based Learning</subject><subject>Information Retrieval</subject><subject>Information technology</subject><subject>Instructional 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Shihui ; Law, Nancy</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-ec70ce7000ddd64ca99b452e9601505d40789ee6808a61d21d728bdfd3501c313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Intelligence</topic><topic>Bibliometrics</topic><topic>Computer Science</topic><topic>Computers and Education</topic><topic>Cooperative Learning</topic><topic>Data Analysis</topic><topic>Data collection</topic><topic>Data mining</topic><topic>Deep learning</topic><topic>Digital technology</topic><topic>Distance learning</topic><topic>Education</topic><topic>Educational Environment</topic><topic>Educational Research</topic><topic>Educational Technology</topic><topic>Educational Trends</topic><topic>Electronic Learning</topic><topic>Eye Movements</topic><topic>Game Based Learning</topic><topic>Information Retrieval</topic><topic>Information 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A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. 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| subjects | Artificial Intelligence Bibliometrics Computer Science Computers and Education Cooperative Learning Data Analysis Data collection Data mining Deep learning Digital technology Distance learning Education Educational Environment Educational Research Educational Technology Educational Trends Electronic Learning Eye Movements Game Based Learning Information Retrieval Information technology Instructional Design Intelligent Tutoring Systems Interdisciplinary aspects Interdisciplinary studies International conferences Keywords Knowledge Learner Engagement Learning activities Learning Analytics Literature reviews Machine learning Natural Language Processing Neural networks Online Courses Online instruction Psychological Patterns Research methodology Student Evaluation Systematic review Teaching Teaching Methods Technology Uses in Education Trends Tutoring User generated content User Interfaces and Human Computer Interaction |
| title | Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis |
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