Mapping Artificial Intelligence in Education Research: A Network-Based Keyword Analysis

Autor/inn/en	Feng, Shihui; Law, Nancy
Titel	Mapping Artificial Intelligence in Education Research: A Network-Based Keyword Analysis
Quelle	Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ In: International Journal of Artificial Intelligence in Education, 31 (2021) 2, S. 277-303Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-021-00244-4
Schlagwörter	Forschungsbericht; Artificial Intelligence; Technology Uses in Education; Educational Research; Intelligent Tutoring Systems; Online Courses; Electronic Learning; Game Based Learning; Cooperative Learning; Student Evaluation; Psychological Patterns; Learner Engagement; Instructional Design; Natural Language Processing; Information Retrieval; Learning Analytics; Eye Movements; Educational Trends + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Bildungsforschung; Pädagogische Forschung; Intelligentes Tutorsystem; Online course; Online-Kurs; Kooperatives Lernen; Schulnote; Studentische Bewertung; Lesson concept; Lessonplan; Unterrichtsentwurf; Natürliche Sprache; Augenbewegung; Bildungsentwicklung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01