研究生: |
林羿婷 Lin, Yi-Ting |
---|---|
論文名稱: |
大學生使用經驗學習環於影像辨識平台上學習人工智慧應用程式之學習成效 Learning Effectiveness of Undergraduates Using the Experiential Learning Cycle to Learn Artificial Intelligence Application on Image Recognition Platform |
指導教授: |
許庭嘉
Hsu, Ting-Chia |
口試委員: |
區國良
Ou, Kuo-Liang 謝易錚 Hsieh, Yi-Zeng |
口試日期: | 2021/07/07 |
學位類別: |
碩士 Master |
系所名稱: |
科技應用與人力資源發展學系 Department of Technology Application and Human Resource Development |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 133 |
中文關鍵詞: | 運算思維教育 、人工智慧教育 、機器學習 、影像辨識 |
英文關鍵詞: | computational thinking, artificial intelligence course, machine learning course, image recognition |
研究方法: | 準實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202100856 |
論文種類: | 學術論文 |
相關次數: | 點閱:226 下載:0 |
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本研究發展一個人工智慧影像辨識課程,適合無運算思維及人工智慧基礎之大學生,培養學生運算思維能力與認識人工智慧,使用準實驗研究方法,對於大學生使用經驗學習環進行人工智慧影像辨識課程之學習成效進行研究。研究結果顯示採用不同的教學方式皆可以增加學生的運算思維與人工智慧概念,經驗學習環較適合初始具有較低運算思維能力與較低自我效能的學生,因為經驗學習環具有反思、抽象化及主動驗證歷程,能夠讓學生產生討論、合作與直接操作的行為,這些行為的出現能夠提升初始運算思維能力與自我效能較低的學生運算思維能力與自我效能程度。而主題式導向學習較適合初始具有一定運算思維能力及高自我效能的學生,因為其過去的學習經驗已經習慣講述式的示範教學方法,延續習慣的且表現不錯的學習方法較能夠維持良好的運算思維感知程度。
This research develops an artificial intelligence image recognition course, which is suitable for college students without computational thinking and artificial intelligence (AI) foundation. This course cultivates the computational thinking ability of students and their recognition of AI knowledge. This research uses quasi-experimental research methods to study learning effects. The research results show that different teaching methods can increase students' computational thinking and artificial intelligence concepts. The experiential learning cycle is more suitable for students who initially have lower computational thinking ability and lower self-efficacy. The stage of reflection, abstraction and operation can enable students to generate discussion, cooperation, and direct manipulation behaviors. These behavior patterns can enhance the computational thinking ability and self-efficacy. The subject-based learning is more suitable for students who have certain computational thinking ability and high self-efficacy initially. Their past learning experience has become accustomed to the lecture teaching method. Lecture continues the habit is better for them able to learn.
一、中文部分
吳正己、林育慈、陳怡芬、張凌倩、賴錦緣(2020)。素養導向系列叢書:中學資訊科技教材教法。五南出版社。
教育部(2010)。 教育制度(國情簡介)。取自 https://www.ey.gov.tw/state/7F30E01184C37F0E/c533c870-9854-4344-b325-0239147484bd
教育部(2014年11月28日)。十二年國民基本教育課程綱要總綱。取自 https://www.naer.edu.tw/ezfiles/0/1000/attach/87/pta_18543_581357_62438.pdf
教育部(2018年9月20日)。十二年國民基本教育課程綱要國民中學暨普通型高級中等學校-科技領域。取自 https://www.naer.edu.tw/ezfiles/0/1000/attach/52/pta_18529_8438379_60115.pdf
麥勒.理察(1997)。教育心理學: 認知取向(林清山譯)。臺北:遠流。
黃富順(2004)。成人學習。臺北:五南。
資訊及科技教育司(2019)。AI教育X教育AI-人工智慧教育及數位先進個人化、適性化學習時代來臨!。取自 https://www.edu.tw/News_Content.aspx?n=9E7AC85F1954DDA8&s=D4C4CD32CAE3FF5D
二、外文部分
Ai4k12. (2017). Five Big Ideas in AI. Retrieved from https://bit.ly/ai4k12-five-big-ideas
Alexander, B., Ashford-Rowe, K., Barajas-Murph, N., Dobbin, G., Knott, J., McCormack, M., ... & Weber, N. (2019). Horizon report 2019 higher education edition, 3-41. EDU19.
Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. In International Workshop on Education in Artificial Intelligence K-12 .EDUAI'19.
Andresen, S. L. (2002). John McCarthy: father of AI. IEEE Intelligent Systems, 17(5), 84-85. https://doi.org/10.1109/MIS.2002.1039837
Anfurrutia, F. I., Álvarez, A., Larrañaga, M., & López-Gil, J. (2018). Integrating Formative Feedback in Introductory Programming Modules. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(1), 3-10. https://doi.org/10.1109/RITA.2018.2801898
Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-57.
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). iRobot: Teaching an Evaluated, Competencies-Based Introductory Artificial Intelligence Class in Highschools. Lecture Notes in Computer Science, 9904(2016), 218-223.
Balanskat, A., & Engelhardt, K. (2014). Computing our future: Computer programming and coding-Priorities, school curricula and initiatives across Europe. European Schoolnet.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community?. Acm Inroads, 2(1), 48-54.
BBC, B. B. C. (2017). Introduction to computational thinking. Retrieved 5/24 from https://www.bbc.co.uk/bitesize/guides/zp92mp3/revision/1
Benaya, T., Zur, E., Dagiene, V., & Stupuriene, G. (2017). Computer Science High School Curriculum in Israel and Lithuania–Comparison and Teachers' Views. Baltic Journal of Modern Computing, 5(2), 164.
Bonwell, C. C., & Eison, J. A. (1991). Active Learning: Creating Excitement in the Classroom. 1991 ASHE-ERIC Higher Education Reports. ERIC Clearinghouse on Higher Education, The George Washington University, One Dupont Circle, Suite 630, Washington, DC 20036-1183.
Brackmann, C. P., Román-González, M., Robles, G., Moreno-León, J., Casali, A., & Barone, D. (2017). Development of computational thinking skills through unplugged activities in primary school. Proceedings of the 12th Workshop on Primary and Secondary Computing Education. ACM Press, New York, 65-72.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association, 1, 25.
Brown, N. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education (TOCE), 14(2), 1-22.
Budihal, S., Patil, U., & Iyer, N. (2020). An Integrated approach of course redesign towards enhancement of experiential learning. Procedia Computer Science, 172, 324-330.
Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860.
Bundy, A. (2007). Computational thinking is pervasive. 1(2), 67-69.
Chiu, S. K. (2019). Innovative experiential learning experience: Pedagogical adopting Kolb’s learning cycle at higher education in Hong Kong. Cogent Education, 6(1), 1644720. https://doi.org/10.1080/2331186X.2019.1644720
Chiu, T. K., & Chai, C.-s. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective. Sustainability, 12(14), 5568.
Committee, H. O. L. S. (2018). Ai in the uk: ready, willing and able. Authority of the House of Lords. Retrieved 6/28 from http://allcatsrgrey.org.uk/wp/download/informatics/100.pdf
Copeland, M. (2016). What’s the Difference Between Artificial Intelligence, Machine Learning and Deep Learning? Retrieved 6/27 from https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Cornell, R. M., Johnson, C. B., & Schwartz Jr, W. C. (2013). Enhancing student experiential learning with structured interviews. Journal of Education for Business, 88(3), 136-146.
CSTA, & ISTE. (2011). Operational Definition of Computational Thinking for K-12 Education. http://www.iste.org/docs/pdfs/Operational-Definition-of-Computational-Thinking.pdf
Czerkawski, B. C., & Lyman, E. W. (2015). Exploring issues about computational thinking in higher education. TechTrends, 59(2), 57-65.
del Olmo-Muñoz, J., Cózar-Gutiérrez, R., & González-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of Primary Education. Computers & Education, 150, 103832. https://doi.org/https://doi.org/10.1016/j.compedu.2020.103832
DeNero, J., & Klein, D. (2010). Teaching introductory artificial intelligence with pac-man. First AAAI Symposium on Educational Advances in Artificial Intelligence.
Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355-369. https://doi.org/10.1007/s40692-017-0090-9
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.
Employment, M. o. E. A. a. (2019). Finland to invest in the future skills of Europeans – training one per cent of EU citizens in the basics of AI. https://eu2019.fi/en/-/suomen-eu-puheenjohtajuuden-aloite-suomi-investoi-eurooppalaisten-tulevaisuustaitoihin-tavoitteena-kouluttaa-prosentti-eu-kansalaisista-tekoalyn-perus
Falloon, G. (2019). Using simulations to teach young students science concepts: An Experiential Learning theoretical analysis. Computers & Education, 135, 138-159. https://doi.org/https://doi.org/10.1016/j.compedu.2019.03.001
Fukui, M., Sasaki, Y., Hagikura, J., Hayashi, Y., & Hirashima, T. (2020). Development and experimental use of assembling venn diagram and yes/no chart exercise system cultivating computational thinking .Transactions of the Japanese Society for Artificial Intelligence, 35(6), 1-13. https://doi.org/10.1527/tjsai.35-6_D-JA7
Giannakos, M. N., Krogstie, J., & Chrisochoides, N. (2014). Reviewing the flipped classroom research: reflections for computer science education. Proceedings of the computer science education research conference, 23-29.
Glushkova, T. (2016). Application of block programming and game-based learning to enhance interest in computer science. Journal of Innovations and Sustainability, 2(1), 21-32.
Goel, A. K., & Joyner, D. A. (2017). Using AI to teach AI: lessons from an online AI class. AI Magazine, 38(2), 48-59.
Grissom, S. (2013). Introduction to special issue on alternatives to lecture in the computer science classroom. ACM Trans. Comput. Educ., 13(3), 1-5. https://doi.org/10.1145/2499947.2499948
Grover, S., & Basu, S. (2017). Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and boolean logic. Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education, 267-272.
Hou, H.-T., & Wang, S.-M. (2015). Analyzing Students' Cognitive Processing Patterns of a Socioscientific Issue Learning Activity with Online Discussion--A Preliminary Result of Lag Sequential Analysis. 2015 IIAI 4th International Congress on Advanced Applied Informatics, 335-338.
How, M.-L., & Hung, W. L. D. (2019). Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education. Education Sciences, 9(3), 184.
Hsu, T.-C., Abelson, H., Lao, N., Tseng, Y.-H., & Lin, Y.-T. (2021). Behavioral-pattern exploration and development of an instructional tool for young children to learn AI. Computers and Education: Artificial Intelligence, 2, 100012. https://doi.org/https://doi.org/10.1016/j.caeai.2021.100012
Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018, 2018/11/01/). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310. https://doi.org/https://doi.org/10.1016/j.compedu.2018.07.004
Huang, W., & Looi, C.-K. (2020). A critical review of literature on “unplugged” pedagogies in K-12 computer science and computational thinking education. Computer Science Education, 1-29.
Hutchinson, T. L., & Janiszewski Goodin, H. (2013). Nursing student anxiety as a context for teaching/learning. Journal of Holistic Nursing, 31(1), 19-24.
Johnson, D. G., & Verdicchio, M. (2017). AI Anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270. https://doi.org/https://doi.org/10.1002/asi.23867
Kazimoglu, C., Kiernan, M., Bacon, L., & Mackinnon, L. (2012, 2012/01/01/). A Serious Game for Developing Computational Thinking and Learning Introductory Computer Programming. Procedia - Social and Behavioral Sciences, 47, 1991-1999. https://doi.org/https://doi.org/10.1016/j.sbspro.2012.06.938
Keating, J., & Nourbakhsh, I. (2018). Teaching artificial intelligence and humanity. Communications of the ACM, 61(2), 29-32.
Kline, R. B. (2005). Principles and practice of structural equation modeling 2nd ed. New York: Guilford.
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. FT Press.
Kong, S.-C., Lai, M., & Sun, D. (2020, 2020/07/01/). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers & Education, 151, 103872. https://doi.org/https://doi.org/10.1016/j.compedu.2020.103872
Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3(2), 154-171.
Kuo, W.-C., & Hsu, T.-C. (2020). Learning computational thinking without a computer: How computational participation happens in a computational thinking board game. The Asia-Pacific Education Researcher, 29(1), 67-83.
Kong, S.C., Hoppe, H.U., Hsu, T.C., Huang, R.H., Kuo, B.C., Li, K.Y., Looi, C.K., Milrad, M., Shih, J.L., Sin, K.F., Song, K.S., Specht, M., Sullivan, F., & Vahrenhold, J. (Eds.). (2020). Proceedings of International Conference on Computational Thinking Education 2020. Hong Kong: The Education University of Hong Kong.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. P. Education.
Maglogiannis, I. G. (2007). Emerging artificial intelligence applications in computer engineering: real word ai systems with applications in ehealth, hci, information retrieval and pervasive technologies (Vol. 160). Ios Press.
Marcelino, M. J., Pessoa, T., Vieira, C., Salvador, T., & Mendes, A. J. (2018). Learning computational thinking and scratch at distance. Computers in Human Behavior, 80, 470-477.
Margolis, H., & McCabe, P. P. (2006, Mar 2006). Improving Self-Efficacy and Motivation: What to Do, What to Say. Intervention in School and Clinic, 41(4), 218-227. https://doi.org/http://dx.doi.org/10.1177/10534512060410040401
Marques, L. S., Gresse von Wangenheim, C., & HAUCK, J. C. (2020). Teaching Machine Learning in School: A Systematic Mapping of the State of the Art. Informatics in Education, 19(2), 283-321.
Masapanta-Carrión, S., & Velázquez-Iturbide, J. Á. (2018). A systematic review of the use of Bloom's taxonomy in Computer Science education. Proceedings of the 49th acm technical symposium on computer science education. 441-446.
Mayer, R., & Mayer, R. E. (2005). The Cambridge handbook of multimedia learning. Cambridge university press.
Mitchell, T. M. (2006). The discipline of machine learning. Carnegie Mellon University
Olli Koski, & Husso, K. (2018). Work in the age of artificial intelligence:Four perspectives on the economy, employment, skills and ethics. Ministry of Economic Affairs and Employment Retrieved from http://urn.fi/URN:ISBN:978-952-327-311-5
Papadakis, S., Kalogiannakis, M., Orfanakis, V., & Zaranis, N. (2017). The appropriateness of scratch and app inventor as educational environments for teaching introductory programming in primary and secondary education. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 12(4), 58-77.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. Basic Books, Inc.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Paris: UNESCO.
Programme, S. g. a. s. o. t. A. I. (2019). Leading the way into the era of artificial intelligence Final report of Finland's Artificial Intelligence Programme 2019. Ministry of Economic Affairs and Employment Retrieved from http://urn.fi/URN:ISBN:978-952-327-437-2
Programme, S. G. o. t. A. I. (2017). Finland’s age of artificial intelligence Turning Finland into a leading country in the application of artificial intelligence. Objective and recommendations for measures. Ministry of Economic Affairs and Employment Retrieved from http://urn.fi/URN:ISBN:978-952-327-290-3
Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004). Self-efficacy and mental models in learning to program. Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education. 171-175.
Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2021). Evaluation of an Online Intervention to Teach Artificial Intelligence with LearningML to 10-16-Year-Old Students. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 177-183.
Rodríguez Corral, J. M., Civit Balcells, A., Morgado Estévez, A., Jiménez Moreno, G., & Ferreiro Ramos, M. J. (2014). A game-based approach to the teaching of object-oriented programming languages. Computers & Education, 73, 83-92. https://doi.org/https://doi.org/10.1016/j.compedu.2013.12.013
Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach. Malaysia: Pearson Education Limited.
Sands, P., Yadav, A., & Good, J. (2018). Computational thinking in K-12: In-service teacher perceptions of computational thinking. In Computational thinking in the STEM disciplines, 151-164. Springer.
Scherer, R., Siddiq, F., & Viveros, B. S. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior, 109, 106349.
Schwarzer, R., Bäßler, J., Kwiatek, P., Schröder, K., & Zhang, J. X. (1997). The assessment of optimistic self‐beliefs: comparison of the German, Spanish, and Chinese versions of the general self‐efficacy scale. Applied Psychology, 46(1), 69-88.
Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. U. o. Southampton. https://eprints.soton.ac.uk/356481/
Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469-495.
Simon, B., Parris, J., & Spacco, J. (2013). How we teach impacts student learning: peer instruction vs. lecture in CS0 Proceeding of the 44th ACM technical symposium on Computer science education, Denver, Colorado, USA. https://doi.org/10.1145/2445196.2445215
Singh, N. (2017). How to Get Started as a Developer in AI. Retrieved 6/27 from https://software.intel.com/content/www/us/en/develop/articles/how-to-get-started-as-a-developer-in-ai.html
T. Baker, L. Smith. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798.
Toikkanen, T., & Leinonen, T. (2017). The code ABC MOOC: Experiences from a coding and computational thinking MOOC for Finnish primary school teachers. In Emerging research, practice, and policy on computational thinking, 239-248. Springer.
Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers & Education, 120, 64-74. https://doi.org/https://doi.org/10.1016/j.compedu.2018.01.011
Tran, Y. (2019). Computational thinking equity in elementary classrooms: What third-grade students know and can do. Journal of Educational Computing Research, 57(1), 3-31.
Tsai, C.-Y. (2019). Improving students' understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95, 224-232. https://doi.org/https://doi.org/10.1016/j.chb.2018.11.038
Tsai, M.-J., Wang, C.-Y., & Hsu, P.-F. (2019). Developing the Computer Programming Self-Efficacy Scale for Computer Literacy Education. Journal of Educational Computing Research, 56(8), 1345-1360. https://doi.org/10.1177/0735633117746747
Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Luxembourg: Publications Office of the European Union.
Wang, Y.-Y., & Wang, Y.-S. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments. 1-16.
Weintrop, D. (2019). Block-based programming in computer science education. Communications of the ACM, 62(8), 22-25.
Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: students' perceptions of blocks-based programming. Proceedings of the 14th international conference on interaction design and children. 199-208.
Williams, R., Park, H. W., & Breazeal, C. (2019). A is for artificial intelligence: the impact of artificial intelligence activities on young children's perceptions of robots. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.1-11.
Wilson, A., Hainey, T., & Connolly, T. M. (2013). Using Scratch with primary school children: an evaluation of games constructed to gauge understanding of programming concepts. International Journal of Game-Based Learning (IJGBL), 3(1), 93-109.
Wing, J. (2011). Research notebook: Computational thinking—What and why. The link magazine, 6, 20-23.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
Wing, J. M. (2008). Computational thinking and thinking about computing. 366(1881), 3717-3725. https://doi.org/doi:10.1098/rsta.2008.0118
Wong, G. K., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K-12: where to start? ACM Inroads, 11(1), 20-29.
Wynn, Andrew Hanson. (2018). The Effect of Experiential Learning on the Mathematics Achievement and Mathematics Anxiety of African-American Students. Doctoral Dissertations and Projects. Retrieved from https://digitalcommons.liberty.edu/doctoral/1937
Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016). Expanding computer science education in schools: understanding teacher experiences and challenges. Computer Science Education, 26(4), 235-254.
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 1-16.
Yan, L. (2009). Teaching Object-Oriented Programming with Games. 2009 Sixth International Conference on Information Technology: New Generations, Las Vegas, NV, USA, pp. 969–974.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019, 2019/10/28). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI. Future of Humanity Institute. University of Oxford
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1-38.
Zhao, W., & Shute, V. J. (2019). Can playing a video game foster computational thinking skills? Computers & Education, 141, 103633. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103633
Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 3(1), 1-130.