Abstract
Sorry, not available.
Click the PDF button.
References
Information
Click the PDF button.
- 강영옥・조나혜・박소연・김지연, 2021, “합성곱신경망을 활용한 SNS 사진 분류 및 관광객과 거주자의 관광 활동 특성 분석,” 대한지리학회지, 56(3), 247-264.
- 강영옥・조나혜・이주윤・윤지영・이혜진, 2019, “경험적 모델과 머신러닝 기법을 활용한 SNS 사용자 분류방법 비교: 플리커 데이터의 관광객 분류방법,” 대한공간정보학회지, 27(4), 29-37.
- 김길수, 2019, “공공부문에서 인공지능 활용에 관한 연구,” 한국자치행정학보, 33(1), 27-47.
- 김경일, 2017, 「지혜의 심리학」, 서울: 진성북스.
- 김민성, 2013, “교사들이 인지하는 고등학생들의 한국지리 오개념,” 대한지리학회지, 48(3), 482-496.
- 김민성, 2016, “로우테크 원격탐사 활동의 교육적 효과: 비판적 공간사고력을 중심으로,” 한국지리환경교육학회지, 24(4), 115-130.
- 김영훈, 2019, “국토공간분석을 위한 인공지능 기법의 가능성 탐색,” 국토, 451, 94-95.
- 김현진・박정호・홍선주・박연정・김은영・최정윤・김유리, 2020, “학교교육에서 AI 활용에 대한 교사의 인식,” 교육공학연구, 36(3), 905-930.
- 모윤하, 2020, “디지털 스토리텔링을 위한 챗봇 개발,” 서울대학교 석사학위논문.
- 박지만・조두영・이상선・이민섭・남한식・양혜림, 2018, “인공지능과 국토정보를 활용한 노인복지 취약지구 추출방법에 관한 연구,” 지적과 국토정보, 48(1), 169-186.
- 박지수, 2021, “빅데이터 기반 사회과 핵심역량 도출과 평가도구 개발 및 검증: 지리 영역을 중심으로,” 부산대학교 박사학위논문.
- 박찬・김병석・전수연・전은경・홍수빈・진성임・문혜진・김성빈・정선재・강윤진・변문경・권해연・박서희・이정훈, 2020, 「4차산업혁명시대 인공지능 융합교육법: 우리아이 AI」, 서울: 다빈치books.
- 신동광, 2019, “영어 쓰기 능력 향상을 위한 AI 챗봇 활용 방안 탐색,” 교원교육, 35(1), 41-55.
- 이주호・정제영・정영식, 2021, 「AI 교육혁명」, 서울: 시원북스.
- 조원호・임용호・박기호, 2019, “합성곱 신경망을 이용한 딥러닝 기반의 토지피복 분류: 한국 토지피복을 대상으로,” 대한지리학회지, 54(1), 1-16.
- 최서원・남재현, 2019, “SW 교육 보조 도구로서의 AI 챗봇 활용,” 한국정보통신학회논문지, 23(12), 1693-1699.
- 홍정민, 2021, 「에듀테크의 미래」, 서울: 책밥.
- 황규호, 2020, “포스트 코로나 시대 국가교육과정의 과제,” 교육과정연구, 38(4), 83-106.
- 황홍섭, 2019, “빅데이터를 활용한 사회과 교수학습 모형의 탐색,” 사회과교육, 58(1), 63-98.
- Ahmad, M.F. and Ghapar, W.R.G.W.A., 2019, The era of artificial intelligence in Malaysian higher education: Impact and challenges in tangible mixed-reality learning system toward self exploration education (SEE), Procedia Computer Science, 163, 2-10.
- Alkhatlan, A. and Kalita, J., 2019, Intelligent tutoring systems: A comprehensive historical survey with recent developments, International Journal of Computer Applications, 181(43), 1-20.
- Asif, R., Merceron, A., Ali, S.A., and Haider, N.G., 2017, Analyzing undergraduate students’ performance using educational data mining, Computers & Education, 113, 177-194.
- Biswas, G., Leelawong, K., Schwartz, D., Vye, N., and The Teachable Agents Group at Vanderbilt, 2005, Learning by teaching: A new agent paradigm for educational software, Applied Artificial Intelligence, 19(3-4), 363-392.
- Biswas, G., Segedy, J.R., and Bunchongchit, K., 2016, From design to implementation to practice a learning by teaching system: Betty’s Brain, International Journal of Artificial Intelligence in Education, 26(1), 350-364.
- Berendt, B., Littlejohn, A., and Blakemore, M., 2020, AI in education: Learner choice and fundamental rights, Learning, Media and Technology, 45(3), 312-324.
- Bringula, R.P., Fosgate Jr, I.C.O., Garcia, N.P.R., and Yorobe, J.L.M., 2018, Effects of pedagogical agents on students’ mathematics performance: A comparison between two versions, Journal of Educational Computing Research, 56(5), 701-722.
- Carbonell, J.R., 1970, AI in CAI: An artificial-intelligence approach to computer-assisted instruction, IEEE Transactions on Man-Machine Systems, 11(4), 190-202.
- Chen, L., Chen, P., and Lin, Z., 2020, Artificial intelligence in education: A review, IEEE Access, 8, 75264-75278.
- Chou, C.Y. and Chan, T.W., 2016, Reciprocal tutoring: Design with cognitive load sharing, International Journal of Artificial Intelligence in Education, 26(1), 512-535.
- Crevier, D., 1993, AI: The Tumultuous History of the Search for Artificial Intelligence, NY: Basic Books.
- D’Mello, S.K. and Graesser, A., 2010, Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features, User Modeling and User-Adapted Interaction, 20(2), 147-187.
- D’Mello, S.K. and Graesser, A., 2013, AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back, ACM Transactions on Interactive Intelligent Systems, 2(4), 1-39.
- D’Mello, S.K., Picard, R.W., and Graesser, A., 2007, Toward an affect-sensitive AutoTutor, IEEE Intelligent Systems, 22(4), 53-61.
- du Boulay, B., 2016, Artificial intelligence as an effective classroom assistant, IEEE Intelligent Systems, 31(6), 76-81.
- du Boulay, B., 2019, Escape from the Skinner Box: The case for contemporary intelligent learning environments, British Journal of Educational Technology, 50(6), 2902-2919.
- Fischer, C., Pardos, Z.A., Baker, R.S., Williams, J.J., Smyth, P., Yu, R., Slater, S., Baker, R., and Warschauer, M., 2020, Mining big data in education: Affordances and challenges, Review of Research in Education, 44(1), 130-160.
- Goldsmith, A.S. and Emrick, B.C., 2019, ASSISTments Cross-Platform Mobile App, retrieved from https://core.ac.uk/download/pdf/213002639.pdf
- Graesser, A.C., Chipman, P., Haynes, B.C., and Olney, A., 2005a, AutoTutor: An intelligent tutoring system with mixed-initiative dialogue, IEEE Transactions on Education, 48(4), 612-618.
- Graesser, A.C., McNamara, D.S., and VanLehn, K., 2005b, Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART, Educational Psychologist, 40(4), 225-234.
- Heffernan, N.T. and Heffernan, C.L., 2014, The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching, International Journal of Artificial Intelligence in Education, 24(4), 470-497.
- Heffernan, N.T., Turner, T.E., Lourenco, A.L., Macasek, M.A., Nuzzo-Jones, G., and Koedinger, K.R., 2006, The ASSISTment builder: Towards an analysis of cost effectiveness of ITS creation, Flairs Conference, 515-520.
- Holmes, W., Bialik, M., and Fadel, C., 2019, Artificial Intelligence in Education: Promises and Implications for Teaching & Learning, Boston, MA: Center for Curriculum Redesign(정제영・이선복 역, 2020, 「인공지능 시대의 미래교육: 가르침과 배움의 함의」, 서울: 박영story).
- Holstein, K., McLaren, B.M., and Aleven, V., 2018, Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms, in Rosé, C.P., Martínez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., and du Boulay, B., eds., Artificial Intelligence in Education, Cham: Springer, 154-168.
- Holstein, K., McLaren, B.M., and Aleven, V., 2019, Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity, Journal of Learning Analytics, 6(2), 27-52.
- Ideland, M., 2021, Google and the end of the teacher? How a figuration of the teacher is produced through an ed-tech discourse, Learning, Media and Technology, 46(1), 33-46.
- Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B., 2020, GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond, International Journal of Geographical Information Science, 34(4), 625-636.
- Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, J., and Goldstein, D.S., 2013, Estimating the effect of web-based homework, in Lane, H.C., Yacef, K., Mostow, J., and Pavlik, P., eds., Artificial Intelligence in Education, Berlin: Springer, 824-827.
- Kinnebrew, J.S., Segedy, J.R., and Biswas, G., 2015, Integrating model-driven and data-driven techniques for analyzing learning behaviors in open-ended learning environments, IEEE Transactions on Learning Technologies, 10(2), 140-153.
- Knox, J., 2020, Artificial intelligence and education in China, Learning, Media and Technology, 45(3), 298-311.
- Knox, J., Williamson, B., and Bayne, S., 2020, Machine behaviourism: Future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), 31-45.
- Koedinger, K.R., McLaughlin, E.A., and Heffernan, N.T., 2010, A quasi-experimental evaluation of an on-line formative assessment and tutoring system, Journal of Educational Computing Research, 43(4), 489-510.
- Kural, M. and Kocakülah, M.S., 2016, Teaching for hot conceptual change: Towards a new model, beyond the cold and warm ones, European Journal of Education Studies, 2(8), 1-40.
- Leelawong, K. and Biswas, G., 2008, Designing learning by teaching agents: The Betty's Brain system, International Journal of Artificial Intelligence in Education, 18(3), 181-208.
- Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S.J.H., Ogata, H., Baltes. J., Guerra, R., Li, P., and Tsai, C.C., 2020, Challenges and future directions of big data and artificial intelligence in education, Frontiers in Psychology, 11, Article 580820.
- Luckin, R. and Cukurova, M., 2019, Designing educational technologies in the age of AI: A learning sciences- driven approach, British Journal of Educational Technology, 50(6), 2824-2838.
- Mason, L., Gava, M., and Boldrin, A., 2008, On warm conceptual change: The interplay of text, epistemological beliefs, and topic interest, Journal of Educational Psychology, 100(2), 291-309.
- McStay, A., 2020, Emotional AI and EdTech: Serving the public good?, Learning, Media and Technology, 45(3), 270-283.
- Mendicino, M., Razzaq, L., and Heffernan, N.T., 2009, A comparison of traditional homework to computer- supported homework, Journal of Research on Technology in Education, 41(3), 331-359.
- Morgan, J. and Lambert, D., 2005, Geography: Teaching School Subjects 11-19, NY: Routledge.
- Nye, B.D., Graesser, A.C., and Hu, X., 2014, AutoTutor and family: A review of 17 years of natural language tutoring, International Journal of Artificial Intelligence in Education, 24(4), 427-469.
- Pareto, L., 2014, A teachable agent game engaging primary school children to learn arithmetic concepts and reasoning, International Journal of Artificial Intelligence in Education, 24(3), 251-283.
- Pintrich, P.R., Marx, R.W., and Boyle, R.A., 1993, Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change, Review of Educational Research, 63(2), 167-199.
- Rajendran, R., Iyer, S., and Murthy, S., 2018, Personalized affective feedback to address students’ frustration in ITS, IEEE Transactions on Learning Technologies, 12(1), 87-97.
- Roschelle, J., Feng, M., Murphy, R.F., and Mason, C.A., 2016, Online mathematics homework increases student achievement, AERA Open, 2(4), 1-12.
- Romero, C. and Ventura, S., 2010, Educational data mining: A review of the state of the art, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 40(6), 601-618.
- Scheuer, O. and McLaren, B.M., 2012, Educational data mining, Encyclopedia of the Sciences of Learning, 1075-1079.
- Selwyn, N., Hillman, T., Eynon, R., Ferreira, G., Knox, J., Macgilchrist, F., and Sancho-Gil, J.M., 2020, What’s next for Ed-Tech? Critical hopes and concerns for the 2020s, Learning, Media and Technology, 45(1), 1-6.
- Tan, J., Beers, C.D., Gupta, R., and Biswas, G., 2005, Computer games as intelligent learning environments: A river ecosystem adventure, Artificial Intelligence in Education, 646-653.
- Timms, M.J., 2016, Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms, International Journal of Artificial Intelligence in Education, 26(2), 701-712.
- Williamson, B., 2020, New digital laboratories of experimental knowledge production: Artificial intelligence and education research, London Review of Education, 18(2), 209-220.
- Williamson, B. and Eynon, R., 2020, Historical threads, missing links, and future directions in AI in education, Learning, Media and Technology, 45(3), 223-235.
- Wilson, C. and Scott, B., 2017, Adaptive systems in education: A review and conceptual unification, The International Journal of Information and Learning Technology, 34(1), 2-19.
- Yannier, N., Hudson, S.E., and Koedinger, K.R., 2020, Active learning is about more than hands-on: A mixed-reality AI system to support STEM education, International Journal of Artificial Intelligence in Education, 30(1), 74-96.
- Publisher :The Association of Korean Geographers
- Publisher(Ko) :한국지리학회
- Journal Title :Journal of the Association of Korean Geographers
- Journal Title(Ko) :한국지리학회지
- Volume : 10
- No :3
- Pages :329~345
- DOI :https://doi.org/10.25202/JAKG.10.3.1


Journal of the Association of Korean Geographers





