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題名 以深度學習藝術影像建立虛擬創客空間
Building a virtual makerspace with deep learning based art images作者 黃怜樺
Huang, Ling-Hua貢獻者 羅崇銘
Lo, Chung-Ming
黃怜樺
Huang, Ling-Hua關鍵詞 生成式人工智慧
大學圖書館
虛擬生成創客空間
Generative artificial intelligence
University libraries
Virtual generative makerspaces日期 2024 上傳時間 5-Aug-2024 13:32:56 (UTC+8) 摘要 大學圖書館是大學的心臟，支援學習和學術研究，受到學習與教學模式的改變，圖書館建立各種多元的學習空間來滿足師生需求，如資訊共享空間、學習共享空間、虛擬學習空間與創客空間等。創客空間是實現創客精神理念的場所，創客精神鼓勵跨領域學習，強調創造性的思維與解決問題的重要性。傳統的實體創客空間提供3D列印的設備，但受到空間與開館時間的限制而無法隨時提供創作服務，雖然延伸發展出虛擬創客空間，僅僅是提供線上會議或討論的形式，並非虛擬線上的創作。因此大學圖書館應建立一個提供虛擬創作平台的虛擬生成創客空間，結合實體創客空間與線上創作平台，提供生成式人工智慧的工具以創新方法滿足學生多元創作的需求，以Stable Diffusion為例，學生利用文字描述就能生成各種影像，還可搭配不同的外掛工具來調整影像的風格或變化，鼓勵學生發揮創意、實現創作的想法，利用其進行研究或完成各種學習活動。 為了評估「虛擬生成創客空間與創客精神之間的關聯」，以及對「虛擬生成創客空間使用性」的看法，本研究針對大學的學生和教職員為進行問卷調查，使用Google表單進行線上填答，並透過Facebook進行問卷發放，回收100份有效問卷。根據問卷分析，問卷的Cronbach's Alpha值為0.852，表示問卷具有一致性與信度。問卷的KMO值為0.855，以及Bartlett的球形檢定顯著性小於0.001，代表問卷適合進行因素分析，主成分分析顯示兩個主成分能夠解釋64.644%的變異性，使用李克特量表進行量測，虛擬生成創客空間與創客精神關聯的同意度平均值為4.46，虛擬生成創客空間使用性的同意度平均值為4.68，可以確定問卷能有效反映研究內容。研究結果也發現，無論是性別、單位與身份，都不會影響受訪者對於虛擬生成創客空間的看法。因此，研究結果可以作為未來大學圖書館建立虛擬生成創客空間之參考。
The university library is the heart of the university, supporting learning and academic research. With changes in learning and teaching modes, libraries have established various diverse learning spaces to meet the needs of teachers and students, such as information commons, virtual learning commons, and makerspaces, etc. The makerspace is a place to fulfill the maker spirit, which encourages interdisciplinary learning and emphasizes the importance of creative thinking and problem-solving. The traditional physical makerspaces provide 3D printing, but they cannot provide creative services at any time. Even though virtual makerspaces have been developed, they only offer online meetings or discussions, not virtually online creations. Therefore, the university library should establish a virtual generative makerspace that combines a physical makerspace with an online creation platform, and provides generative AI tools to innovatively meet the diverse creation needs of students. For instance, with Stable Diffusion, students can use prompt to generate different images or variations with different tools, encouraging students to demonstrate creativity, implement creative ideas, use it for research, or complete various learning activities. In order to evaluate the 'relationship between the virtual generative makerspace and the maker spirit,' and the 'usability of the virtual generative makerspace,' this study conducted a questionnaire survey on university students and faculty members. The online questionnaires were filled out through Google Forms and distributed through Facebook, and 100 valid questionnaires were collected. According to the questionnaire analysis, the Cronbach's Alpha of the questionnaire was 0.852, indicating that the questionnaire has consistency and reliability. The KMO of the questionnaire was 0.855, and the significance of Bartlett's test was less than 0.001, indicating that the questionnaire is suitable for factor analysis. Principal component analysis showed that the two main components could explain 64.644% of the variability. The Likert scale was used for measurement. The average degree of agreement of the connection between the virtual generative makerspace and maker spirit was 4.46, and the average degree of agreement of the usability of the virtual generative makerspace was 4.68. It can be confirmed that the questionnaire can effectively reflect the research content. The research results also found that gender, unit, and identity do not affect the respondents' views on the virtual generative makerspace. Therefore, the research results can be used as a reference for university libraries to establish a virtual generative makerspace in the future.參考文獻 Albert, G. (2021). The simple secret to better painting : how to immediately improve your art with the one rule of composition ([New edition] ed.). Echo Point Books & Media Brattleboro, Vermont. Allison, D., DeFrain, E., Hitt, B. D., & Tyler, D. C. (2019). Academic library as learning space and as collection: A learning commons' effects on collections and related resources and services. The Journal of Academic Librarianship, 45(3), 305-314. https://doi.org/10.1016/j.acalib.2019.04.004 Anderson, J. (2019). The use of the University of Tartu Art Museum collection in teaching between 1803 and 1918. History of Education, 48(5), 575-590. https://doi.org/10.1080/0046760X.2019.1615560 Andrews, M. E., Borrego, M., & Boklage, A. (2021). Self-efficacy and belonging: the impact of a university makerspace. International Journal of Stem Education, 8(1), Article 24. https://doi.org/10.1186/s40594-021-00285-0 Anonymous. (2011, 2011 Feb 12). Leaders: Print me a Stradivarius. The Economist, 398(8720), 11. Arango, L., Singaraju, S. P., & Niininen, O. (2023). Consumer Responses to AI-Generated Charitable Giving Ads. Journal of Advertising, 1-18. https://doi.org/10.1080/00913367.2023.2183285 Barnhardt, C., Cotter, S. A., Mitchell, G. L., Scheiman, M., & Kulp, M. T. (2012). Symptoms in children with convergence insufficiency: before and after treatment. Optometry and vision science: official publication of the American Academy of Optometry, 89(10), 1512. Beagle, D. (1999). Conceptualizing an Information Commons [Article]. Journal of Academic Librarianship, 25(2), 82-89. https://doi.org/10.1016/s0099-1333(99)80003-2 Beaumont, R. (2022). LAION-5B: A NEW ERA OF OPEN LARGE-SCALE MULTI-MODAL DATASETS. LAION. Retrieved 3 March from https://laion.ai/blog/laion-5b/ Beavers, K., Cady, J. E., Jiang, A., & McCoy, L. (2019). Establishing a maker culture beyond the makerspace. Library Hi Tech, 37(2), 219-232. https://doi.org/10.1108/lht-07-2018-0088 Beltzung, B., Pelé, M., Renoult, J. P., & Sueur, C. (2023). Deep learning for studying drawing behavior: A review. Frontiers in Psychology, 14, Article 992541. https://doi.org/10.3389/fpsyg.2023.992541 Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55(2), 155-162. https://doi.org/10.1016/j.bushor.2011.11.003 Bieraugel, M., & Neill, S. (2017). Ascending Bloom's Pyramid: Fostering Student Creativity and Innovation in Academic Library Spaces. College & Research Libraries, 78(1), 35-52. https://doi.org/10.5860/crl.78.1.35 Bryant, J., Matthews, G., & Walton, G. (2009). Academic libraries and social and learning space: A case study of Loughborough University Library, UK. Journal of Librarianship and Information Science, 41(1), 7-18. https://doi.org/10.1177/0961000608099895 Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8 Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3 Chen, C., Wu, Z., Lai, Y., Ou, W., Liao, T., & Zheng, Z. (2023). Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond. arXiv preprint arXiv:2306.00419. Chen, Y., & Wu, C. (2017). The hot spot transformation in the research evolution of maker. Scientometrics, 113(3), 1307-1324. https://doi.org/10.1007/s11192-017-2542-4 Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2023.2253861 Church, J. (2005). The evolving Information Commons. Library Hi Tech, 23(1), 75-81. https://doi.org/10.1108/07378830510586711 Cohn, G. (2018, Oct 25). AI Art at Christie’s Sells for $432,500. The New York Times. https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html Congdon, L. (2019). Find your artistic voice : the essential guide to working your creative magic. Chronicle Books. Cox, A. M. (2018). Space and embodiment in informal learning. HIGHER EDUCATION, 75(6), 1077-1090. https://doi.org/10.1007/s10734-017-0186-1 Croitoru, F. A., Hondru, V., Ionescu, R. T., & Shah, M. (2023). Diffusion Models in Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 10850-10869. https://doi.org/10.1109/TPAMI.2023.3261988 Curry, R. (2022). Insights from a cultural-historical HE library makerspace case study on the potential for academic libraries to lead on supporting ethical-making underpinned by 'Critical Material Literacy'. Journal of Librarianship and Information Science, Article 09610006221104796. https://doi.org/10.1177/09610006221104796 Davee, S., Regalla, L., & Chang, S. (2015). Makerspaces: Highlights of select literature. https://makered.org/wp-content/uploads/2015/08/Makerspace-Lit-Review-5B.pdf de Vicente-Yagüe-Jara, M. I., López-Martínez, O., Navarro-Navarro, V., & Cuéllar-Santiago, F. (2023). Writing, creativity, and artificial intelligence. ChatGPT in the university context. Comunicar, 31(77), 47-57. https://doi.org/10.3916/c77-2023-04 de Winter*, J. C. F., Dodou*, D., & Wieringa, P. A. (2009). Exploratory Factor Analysis With Small Sample Sizes. Multivariate Behavioral Research, 44(2), 147-181. https://doi.org/10.1080/00273170902794206 Dehouche, N., & Dehouche, K. (2023). What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education. Heliyon. Derevyanko, N., & Zalevska, O. (2023). Comparative analysis of neural networks Midjourney, Stable Diffusion, and DALL-E and ways of their implementation in the educational process of students of design specialities. Scientific Bulletin of Mukachevo State University. Series “Pedagogy and Psychology, 9(3), 36-44. Eriksson, E., Heath, C., Ljungstrand, P., & Parnes, P. (2018). Makerspace in school—Considerations from a large-scale national testbed. International Journal of Child-Computer Interaction, 16, 9-15. https://doi.org/10.1016/j.ijcci.2017.10.001 Farritor, S. (2017). UNIVERSITY-BASED MAKERSPACES: A SOURCE OF INNOVATION. Technology and Innovation, 19(1), 389-395. https://doi.org/10.21300/19.1.2017.389 Fido, D., Rao, J., & Harper, C. A. (2022). Celebrity status, sex, and variation in psychopathy predicts judgements of and proclivity to generate and distribute deepfake pornography. Computers in Human Behavior, 129, 107141. https://doi.org/https://doi.org/10.1016/j.chb.2021.107141 Figg, C., Khirwadkar, A., & Welbourn, S. (2020). Making ‘Math Making’ Virtual. Brock Education Journal, 29(2), 30. https://doi.org/10.26522/brocked.v29i2.836 Foley, C., Darcy, S., Hergesell, A., Almond, B., McDonald, M., Nguyen, L. T., & Morgan-Brett, E. (2023). Extracurricular activities, graduate attributes and serious leisure: competitive sport versus social-cultural clubs in campus life. Leisure Studies, 42(6), 971-988. https://doi.org/10.1080/02614367.2023.2168030 Fourie, I., & Meyer, A. (2015). What to make of makerspaces: Tools and DIY only or is there an interconnected information resources space? Library Hi Tech, 33(4), 519-525. https://doi.org/10.1108/LHT-09-2015-0092 Gierdowski, D., & Reis, D. (2015). The MobileMaker: an experiment with a Mobile Makerspace. Library Hi Tech, 33(4), 480-496. https://doi.org/10.1108/lht-06-2015-0067 Godhe, A.-L., Lilja, P., & Selwyn, N. (2019). Making sense of making: critical issues in the integration of maker education into schools. Technology, Pedagogy and Education, 28(3), 317-328. https://doi.org/10.1080/1475939X.2019.1610040 Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The qualitative report, 8(4), 597-607. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 2672-2680. Harley, B., Dreger, M., & Knobloch, P. (2001). The postmodern condition: students, the Web, and academic library services. Reference Services Review, 29(1), 23-32. https://doi.org/10.1108/00907320110366750 Harris, J., & Cooper, C. (2015). MAKE ROOM FOR A MAKERSPACE. Computers in Libraries, 35(2), 5-9. Hessman, T. (2013). Take 5: Q&A with Chuck Hull, Co-Founder, 3D Systems. Industry Week. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685. Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2 Jin, Z., & Song, Z. (2023). Generating coherent comic with rich story using ChatGPT and Stable Diffusion. arXiv preprint arXiv:2305.11067. Junjie, L., Jianjing, W., & Zhengfeng, J. (2020). Generative Adversarial Networks GAN Overview. Journal of Frontiers of Computer Science & Technology, 14(1), 1-17. https://doi.org/10.3778/j.issn.1673-9418.1910026 K, S., & Durgadevi, M. (2021, 8-10 July 2021). Generative Adversarial Network (GAN): a general review on different variants of GAN and applications. 2021 6th International Conference on Communication and Electronics Systems (ICCES), Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36. Kemp, J. (1994). Art in the library: Should academic libraries manage art? The Journal of Academic Librarianship, 20(3), 162-166. https://doi.org/https://doi.org/10.1016/0099-1333(94)90010-8 Kietzmann, J., Pitt, L., & Berthon, P. (2015). Disruptions, decisions, and destinations: Enter the age of 3-D printing and additive manufacturing. Business Horizons, 58(2), 209-215. https://doi.org/10.1016/j.bushor.2014.11.005 Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. kja, 70(1), 22-26. https://doi.org/10.4097/kjae.2017.70.1.22 Knibbe, J., Grossman, T., & Fitzmaurice, G. (2015, November15–18). Smart makerspace: An immersive instructional space for physical tasks. Proceedings of the 2015 International Conference on Interactive Tabletops & Surfaces, Madeira, Portugal. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Commun. ACM, 60(6), 84–90. https://doi.org/10.1145/3065386 Kwon, B.-R., & Lee, J. (2017). What makes a maker: the motivation for the maker movement in ICT [Article]. Information Technology for Development, 23(2), 318-335. https://doi.org/10.1080/02681102.2016.1238816 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 Lee, U. G., Han, A. R., Lee, J. J., Lee, E. S., Kim, J., Kim, H., & Lim, C. (2023). Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12150-4 Letnikova, G., & Xu, N. (2017). Academic library innovation through 3D printing services. Library Management, 38(4/5), 208-218. https://doi.org/10.1108/LM-12-2016-0094 Li, H. (2019). Special Section Introduction: Artificial Intelligence and Advertising. Journal of Advertising, 48(4), 333-337. https://doi.org/10.1080/00913367.2019.1654947 Liang, P. P., Zadeh, A., & Morency, L.-P. (2022). Foundations and recent trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430. Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 140, 5-53. Lindtner, S. (2014). Hackerspaces and the Internet of Things in China: How makers are reinventing industrial production, innovation, and the self. China Information, 28(2), 145-167. https://doi.org/10.1177/0920203x14529881 Lindtner, S. (2015). Hacking with Chinese Characteristics: The Promises of the Maker Movement against China's Manufacturing Culture. SCIENCE TECHNOLOGY & HUMAN VALUES, 40(5), 854-879. https://doi.org/10.1177/0162243915590861 Liu, M., & Hu, Y. (2023, 2023//). Application Potential of Stable Diffusion in Different Stages of Industrial Design. Artificial Intelligence in HCI, Cham. Liu, V., & Chilton, L. B. (2022). Design Guidelines for Prompt Engineering Text-to-Image Generative Models Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, <conf-loc>, <city>New Orleans</city>, <state>LA</state>, <country>USA</country>, </conf-loc>. https://doi.org/10.1145/3491102.3501825 Lo Conte, A. (2020). Antipodean Prints: Joseph Burke and the development of the University of Melbourne’s Print Collection. Journal of the Australian Library and Information Association, 69(2), 176-190. https://doi.org/10.1080/24750158.2020.1755924 Lock, J., Redmond, P., Orwin, L., Powell, A., Becker, S., Hollohan, P., & Johnson, C. (2020). Bridging distance: Practical and pedagogical implications of virtual Makerspaces. Journal of Computer Assisted Learning, 36(6), 957-968. https://doi.org/10.1111/jcal.12452 Loertscher, D. V. (2015, Oct 2015). The Virtual Makerspace: A New Possibility? Teacher Librarian, 43(1), 50-51,67. Loertscher, D. V., & Koechlin, C. (2012, Oct 2012). The Virtual Learning Commons and School Improvement. Teacher Librarian, 40(1), 20-24,24,63. Lu, Q., Yao, Y., Xiao, L., Yuan, M., Wang, J., & Zhu, X. (2024). Can ChatGPT effectively complement teacher assessment of undergraduate students’ academic writing? Assessment & Evaluation in Higher Education, 1-18. https://doi.org/10.1080/02602938.2024.2301722 Ma, H., & Zheng, H. (2024, 2024//). Text Semantics to Image Generation: A Method of Building Facades Design Base on Stable Diffusion Model. Phygital Intelligence, Singapore. MacWhinnie, L. A. (2003). The information commons: The academic library of the future [Article]. Portal-Libraries and the Academy, 3(2), 241-257. https://doi.org/10.1353/pla.2003.0040 Mann, L. (2018). Making a Place for Makerspaces in Information Literacy. Reference & User Services Quarterly, 58(2), 82-86. https://doi.org/10.5860/rusq.58.2.6927 Milton, C. L. (2023). ChatGPT and Forms of Deception. Nursing Science Quarterly, 36(3), 232-233. https://doi.org/10.1177/08943184231169753 Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student's t-test, analysis of variance, and covariance. Ann Card Anaesth, 22(4), 407-411. https://doi.org/10.4103/aca.ACA_94_19 Montgomery, S. E. (2014). Library Space Assessment: User Learning Behaviors in the Library. The Journal of Academic Librarianship, 40(1), 70-75. https://doi.org/10.1016/j.acalib.2013.11.003 Moorefield-Lang, H. (2015). Change in the Making: Makerspaces and the Ever-Changing Landscape of Libraries. TechTrends, 59(3), 107-112. https://doi.org/10.1007/s11528-015-0860-z Moorefield-Lang, H. M. (2015). When makerspaces go mobile: case studies of transportable maker locations. Library Hi Tech, 33(4), 462-471. https://doi.org/10.1108/lht-06-2015-0061 Morado, M. F., Melo, A. E., & Jarman, A. (2021). Learning by making: A framework to revisit practices in a constructionist learning environment. British Journal of Educational Technology, 52(3), 1093-1115. https://doi.org/10.1111/bjet.13083 Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum Sample Size Recommendations for Conducting Factor Analyses. International Journal of Testing, 5(2), 159-168. https://doi.org/10.1207/s15327574ijt0502_4 Nagle, S. B. (2021). Maker Services in Academic Libraries: A Review of Case Studies [Article]. New Review of Academic Librarianship, 27(2), 184-200. https://doi.org/10.1080/13614533.2020.1749093 Nascimento, S., & Polvora, A. (2018). Maker Cultures and the Prospects for Technological Action. Sci Eng Ethics, 24(3), 927-946. https://doi.org/10.1007/s11948-016-9796-8 Nitecki, D. A. (2011). Space Assessment as a Venue for Defining the Academic Library. The Library Quarterly, 81(1), 27-59. https://doi.org/10.1086/657446 Nunes, A. F., Monteiro, P. L., & Nunes, A. S. (2020). Factor structure of the convergence insufficiency symptom survey questionnaire. Plos One, 15(2), Article e0229511. https://doi.org/10.1371/journal.pone.0229511 Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2023). University Students' Perception and Use of ChatGPT: Generative Artificial Intelligence (AI) in Higher Education. International Journal of Human Computing Studies, 5. https://doi.org/10.5281/zenodo.10360697 Ojennus, P., & Watts, K. A. (2017). User preferences and library space at Whitworth University Library. Journal of Librarianship and Information Science, 49(3), 320-334. https://doi.org/10.1177/0961000615592947 Oppenlaender, J. (2023). A taxonomy of prompt modifiers for text-to-image generation. Behaviour & Information Technology, 1-14. https://doi.org/10.1080/0144929X.2023.2286532 Paananen, V., Oppenlaender, J., & Visuri, A. (2023). Using text-to-image generation for architectural design ideation. International Journal of Architectural Computing. https://doi.org/10.1177/14780771231222783 Pierard, C., & Bordeianu, S. (2016). Learning commons reference collections in ARL libraries. REFERENCE SERVICES REVIEW, 44(3), 411-430. https://doi.org/10.1108/RSR-02-2016-0014 Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision. International conference on machine learning, Randtke, W., Bareford, L., & Tooley, A. (2022). 3D Printing on a Shoestring: How to Avoid a Costly Ventilation Retrofit. Computers in Libraries, 42(7), 23-27. Reno, T. I. (2012). University of Nevada, Reno library first in nation to offer 3D printing campuswide. This Is Reno. https://thisisreno.com/2012/07/university-of-nevada-reno-library-first-in-nation-to-offer-3d-printing-campuswide/ Rice, S., Crouse, S. R., Winter, S. R., & Rice, C. (2024). The advantages and limitations of using ChatGPT to enhance technological research. Technology in Society, 76, Article 102426. https://doi.org/10.1016/j.techsoc.2023.102426 Rogers, A. S. (2016). The librarian’s role in academic makerspaces. Proceedings of the 1st International Symposium on Academic Makerspaces, Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10684-10695. Rouse, R., & Rouse, A. G. (2022). Taking the maker movement to school: A systematic review of preK-12 school-based makerspace research. Educational Research Review, 35, 100413. https://doi.org/10.1016/j.edurev.2021.100413 Saari, H., Åkerman, M., Kieslinger, B., Myllyoja, J., & Sipos, R. (2021). How Open Is the Maker Movement? Integrative Literature Review of the Openness Practices in the Global Maker Movement. Sustainability, 13(24). https://doi.org/10.3390/su132413559 Saltz, J. (2020). How to be an artist ([First hardcover] ed.). Riverhead Books. Santos, I., Castro, L., Rodriguez-Fernandez, N., Torrente-Patiño, Á., & Carballal, A. (2021). Artificial Neural Networks and Deep Learning in the Visual Arts: a review. Neural Computing and Applications, 33(1), 121-157. https://doi.org/10.1007/s00521-020-05565-4 Santos, I. M., Ali, N., & Hill, A. (2016). Students as Co-designers of a Virtual Learning Commons: Results of a Collaborative Action Research Study. The Journal of Academic Librarianship, 42(1), 8-14. https://doi.org/10.1016/j.acalib.2015.09.006 Santoso, S. M., & Wicker, S. B. (2014). The future of three-dimensional printing: Intellectual property or intellectual confinement? New Media & Society, 18(1), 138-155. https://doi.org/10.1177/1461444814538647 Sarpong, D., Ofosu, G., Botchie, D., & Clear, F. (2020). Do-it-yourself (DiY) science: The proliferation, relevance and concerns. Technological Forecasting and Social Change, 158, 120127. https://doi.org/10.1016/j.techfore.2020.120127 Scalfani, V. F., & Sahib, J. (2013). A model for managing 3D printing services in academic libraries. Issues in Science and Technology Librarianship, 72(Spring), 1-13. Schad, M., & Jones, W. M. (2020). The Maker Movement and Education: A Systematic Review of the Literature. Journal of Research on Technology in Education, 52(1), 65-78. https://doi.org/10.1080/15391523.2019.1688739 Seal, R. A. (2007). The information commons handbook. PORTAL-LIBRARIES AND THE ACADEMY, 7(3), 389-390. https://doi.org/10.1353/pla.2007.0037 Sheikh, A. (2015). Development of Information Commons in University Libraries of Pakistan: The Current Scenario. The Journal of Academic Librarianship, 41(2), 130-139. https://doi.org/10.1016/j.acalib.2015.01.002 Smith, J. S., Hsu, Y.-C., Zhang, L., Hua, T., Kira, Z., Shen, Y., & Jin, H. (2023). Continual diffusion: Continual customization of text-to-image diffusion with c-lora. arXiv preprint arXiv:2304.06027. StabilityAI. (2022). Stable Diffusion Public Release. Stability AI. Retrieved 3 March from https://stability.ai/news/stable-diffusion-public-release StabilityAI. (2023a). Announcing SDXL 1.0. Stability AI. Retrieved 26 July from https://stability.ai/news/stable-diffusion-sdxl-1-announcement StabilityAI. (2023b). Celebrating one year(ish) of Stable Diffusion … and what a year it’s been! . Stability AI. Retrieved 3 Oct from https://stability.ai/news/celebrating-one-year-of-stable-diffusion Stöckl, A. (2023). Evaluating a Synthetic Image Dataset Generated with Stable Diffusion. Proceedings of Eighth International Congress on Information and Communication Technology, Singapore. Su, Y., Lin, Y., & Lai, C. (2023). Collaborating with ChatGPT in argumentative writing classrooms. Assessing Writing, 57, 100752. https://doi.org/https://doi.org/10.1016/j.asw.2023.100752 Sweeny, R. W. (2017). Making and breaking in an art education makerspace. Journal of Innovation and Entrepreneurship, 6(1), 1-10. https://doi.org/https://doi.org/10.1186/s13731-017-0071-2 Tabares, R., & Boni, A. (2022). Maker culture and its potential for STEM education. INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION. https://doi.org/10.1007/s10798-021-09725-y Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. (2019). Deep learning in spiking neural networks. Neural Networks, 111, 47-63. https://doi.org/10.1016/j.neunet.2018.12.002 Thomas, B., Van Horne, S., Jacobson, W., & Anson, M. (2015). The design and assessment of the Learning Commons at the University of Iowa. The Journal of Academic Librarianship, 41(6), 804-813. https://doi.org/10.1016/j.acalib.2015.09.005 Turner, A., Welch, B., & Reynolds, S. (2013). Learning Spaces in Academic Libraries – A Review of the Evolving Trends. Australian Academic & Research Libraries, 44(4), 226-234. https://doi.org/10.1080/00048623.2013.857383 Vahdat, A., & Kreis, K. (2022, April 26). Improving Diffusion Models as an Alternative To GANs, Part 1. Technical Blog. https://developer.nvidia.com/blog/improving-diffusion-models-as-an-alternative-to-gans-part-1/ Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Verdonck, M., Greenaway, R., Kennedy-Behr, A., & Askew, E. (2019). Student experiences of learning in a technology-enabled learning space. INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL, 56(3), 270-281. https://doi.org/10.1080/14703297.2018.1515645 Vuopala, E., Guzmán Medrano, D., Aljabaly, M., Hietavirta, D., Malacara, L., & Pan, C. (2020). Implementing a maker culture in elementary school – students’ perspectives. Technology, Pedagogy and Education, 29(5), 649-664. https://doi.org/10.1080/1475939X.2020.1796776 Wang, C., & Chung, J. (2023). Research on AI Painting Generation Technology Based on the [Stable Diffusion]. International journal of advanced smart convergence, 12(2), 90-95. Wang, S., & Kim, S. (2022). Users’ emotional and behavioral responses to deepfake videos of K-pop idols. Computers in Human Behavior, 134, 107305. https://doi.org/https://doi.org/10.1016/j.chb.2022.107305 Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian journal of paramedicine, 8, 1-13. Wong, A., & Partridge, H. (2016). Making as Learning: Makerspaces in Universities. Australian Academic & Research Libraries, 47(3), 143-159. https://doi.org/10.1080/00048623.2016.1228163 Wu, J., Gan, W., Chen, Z., Wan, S., & Lin, H. (2023). Ai-generated content (aigc): A survey. arXiv preprint arXiv:2304.06632. Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136. https://doi.org/10.1109/JAS.2023.123618 Wu, X., Sun, K., Zhu, F., Zhao, R., & Li, H. (2023). Human Preference Score: Better Aligning Text-to-Image Models with Human Preference. arXiv preprint arXiv:2303.14420. Wu, Y., Mou, Y., Li, Z., & Xu, K. (2020). Investigating American and Chinese Subjects’ explicit and implicit perceptions of AI-Generated artistic work. Computers in Human Behavior, 104, 106186. https://doi.org/https://doi.org/10.1016/j.chb.2019.106186 Xu, J., Zhang, X., Li, H., Yoo, C., & Pan, Y. (2023). Is Everyone an Artist? A Study on User Experience of AI-Based Painting System. Applied Sciences, 13(11), 6496. https://www.mdpi.com/2076-3417/13/11/6496 Yaddanapudi, S., & Yaddanapudi, L. (2019). How to design a questionnaire. Indian Journal of Anesthesia, 63(5), 335-337. https://doi.org/https://doi.org/10.4103/ija.IJA_334_19 Yang, S., Li, S., & Tong, C. (2023). The Effectiveness of Artificial Intelligence Teaching Methods in Art Subject Classrooms. Journal of Artificial Intelligence Practice, 6(7), 52-59. Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418. Zhan, Q., Chen, X., & Retnawati, E. (2022). Exploring a construct model for university makerspaces beyond curriculum. Education and Information Technologies, 27(3), 3467-3493. https://doi.org/10.1007/s10639-021-10761-3 Zhang, C., Zhang, C., Zhang, M., & Kweon, I. S. (2023). Text-to-image Diffusion Model in Generative AI: A Survey. arXiv preprint arXiv:2303.07909. Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., Dam, S. K., Thwal, C. M., Tun, Y. L., & Huy, L. L. (2023). A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? arXiv preprint arXiv:2303.11717. Zhang, L., Rao, A., & Agrawala, M. (2023). Adding conditional control to text-to-image diffusion models. Proceedings of the IEEE/CVF International Conference on Computer Vision. 描述 碩士
國立政治大學
圖書資訊學數位碩士在職專班
110913012資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110913012 資料類型 thesis dc.contributor.advisor 羅崇銘 zh_TW dc.contributor.advisor Lo, Chung-Ming en_US dc.contributor.author (Authors) 黃怜樺 zh_TW dc.contributor.author (Authors) Huang, Ling-Hua en_US dc.creator (作者) 黃怜樺 zh_TW dc.creator (作者) Huang, Ling-Hua en_US dc.date (日期) 2024 en_US dc.date.accessioned 5-Aug-2024 13:32:56 (UTC+8) - dc.date.available 5-Aug-2024 13:32:56 (UTC+8) - dc.date.issued (上傳時間) 5-Aug-2024 13:32:56 (UTC+8) - dc.identifier (Other Identifiers) G0110913012 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152684 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊學數位碩士在職專班 zh_TW dc.description (描述) 110913012 zh_TW dc.description.abstract (摘要) 大學圖書館是大學的心臟，支援學習和學術研究，受到學習與教學模式的改變，圖書館建立各種多元的學習空間來滿足師生需求，如資訊共享空間、學習共享空間、虛擬學習空間與創客空間等。創客空間是實現創客精神理念的場所，創客精神鼓勵跨領域學習，強調創造性的思維與解決問題的重要性。傳統的實體創客空間提供3D列印的設備，但受到空間與開館時間的限制而無法隨時提供創作服務，雖然延伸發展出虛擬創客空間，僅僅是提供線上會議或討論的形式，並非虛擬線上的創作。因此大學圖書館應建立一個提供虛擬創作平台的虛擬生成創客空間，結合實體創客空間與線上創作平台，提供生成式人工智慧的工具以創新方法滿足學生多元創作的需求，以Stable Diffusion為例，學生利用文字描述就能生成各種影像，還可搭配不同的外掛工具來調整影像的風格或變化，鼓勵學生發揮創意、實現創作的想法，利用其進行研究或完成各種學習活動。 為了評估「虛擬生成創客空間與創客精神之間的關聯」，以及對「虛擬生成創客空間使用性」的看法，本研究針對大學的學生和教職員為進行問卷調查，使用Google表單進行線上填答，並透過Facebook進行問卷發放，回收100份有效問卷。根據問卷分析，問卷的Cronbach's Alpha值為0.852，表示問卷具有一致性與信度。問卷的KMO值為0.855，以及Bartlett的球形檢定顯著性小於0.001，代表問卷適合進行因素分析，主成分分析顯示兩個主成分能夠解釋64.644%的變異性，使用李克特量表進行量測，虛擬生成創客空間與創客精神關聯的同意度平均值為4.46，虛擬生成創客空間使用性的同意度平均值為4.68，可以確定問卷能有效反映研究內容。研究結果也發現，無論是性別、單位與身份，都不會影響受訪者對於虛擬生成創客空間的看法。因此，研究結果可以作為未來大學圖書館建立虛擬生成創客空間之參考。 zh_TW dc.description.abstract (摘要) The university library is the heart of the university, supporting learning and academic research. With changes in learning and teaching modes, libraries have established various diverse learning spaces to meet the needs of teachers and students, such as information commons, virtual learning commons, and makerspaces, etc. The makerspace is a place to fulfill the maker spirit, which encourages interdisciplinary learning and emphasizes the importance of creative thinking and problem-solving. The traditional physical makerspaces provide 3D printing, but they cannot provide creative services at any time. Even though virtual makerspaces have been developed, they only offer online meetings or discussions, not virtually online creations. Therefore, the university library should establish a virtual generative makerspace that combines a physical makerspace with an online creation platform, and provides generative AI tools to innovatively meet the diverse creation needs of students. For instance, with Stable Diffusion, students can use prompt to generate different images or variations with different tools, encouraging students to demonstrate creativity, implement creative ideas, use it for research, or complete various learning activities. In order to evaluate the 'relationship between the virtual generative makerspace and the maker spirit,' and the 'usability of the virtual generative makerspace,' this study conducted a questionnaire survey on university students and faculty members. The online questionnaires were filled out through Google Forms and distributed through Facebook, and 100 valid questionnaires were collected. According to the questionnaire analysis, the Cronbach's Alpha of the questionnaire was 0.852, indicating that the questionnaire has consistency and reliability. The KMO of the questionnaire was 0.855, and the significance of Bartlett's test was less than 0.001, indicating that the questionnaire is suitable for factor analysis. Principal component analysis showed that the two main components could explain 64.644% of the variability. The Likert scale was used for measurement. The average degree of agreement of the connection between the virtual generative makerspace and maker spirit was 4.46, and the average degree of agreement of the usability of the virtual generative makerspace was 4.68. It can be confirmed that the questionnaire can effectively reflect the research content. The research results also found that gender, unit, and identity do not affect the respondents' views on the virtual generative makerspace. Therefore, the research results can be used as a reference for university libraries to establish a virtual generative makerspace in the future. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 圖書館空間 1 第二節 創客空間 7 第三節 研究目的 16 第二章 文獻探討 17 第三章 影像生成 25 第一節 生成式人工智慧 25 第二節 大學創作實例 35 第三節 研究問卷 43 壹、問卷設計 43 貳、統計分析 48 第四章 研究結果 50 第五章 討論與結論 61 第六章 未來展望 65 參考文獻 68 zh_TW dc.format.extent 1224798 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110913012 en_US dc.subject (關鍵詞) 生成式人工智慧 zh_TW dc.subject (關鍵詞) 大學圖書館 zh_TW dc.subject (關鍵詞) 虛擬生成創客空間 zh_TW dc.subject (關鍵詞) Generative artificial intelligence en_US dc.subject (關鍵詞) University libraries en_US dc.subject (關鍵詞) Virtual generative makerspaces en_US dc.title (題名) 以深度學習藝術影像建立虛擬創客空間 zh_TW dc.title (題名) Building a virtual makerspace with deep learning based art images en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Albert, G. (2021). The simple secret to better painting : how to immediately improve your art with the one rule of composition ([New edition] ed.). Echo Point Books & Media Brattleboro, Vermont. Allison, D., DeFrain, E., Hitt, B. D., & Tyler, D. C. (2019). Academic library as learning space and as collection: A learning commons' effects on collections and related resources and services. The Journal of Academic Librarianship, 45(3), 305-314. https://doi.org/10.1016/j.acalib.2019.04.004 Anderson, J. (2019). The use of the University of Tartu Art Museum collection in teaching between 1803 and 1918. History of Education, 48(5), 575-590. https://doi.org/10.1080/0046760X.2019.1615560 Andrews, M. E., Borrego, M., & Boklage, A. (2021). Self-efficacy and belonging: the impact of a university makerspace. International Journal of Stem Education, 8(1), Article 24. https://doi.org/10.1186/s40594-021-00285-0 Anonymous. (2011, 2011 Feb 12). Leaders: Print me a Stradivarius. The Economist, 398(8720), 11. Arango, L., Singaraju, S. P., & Niininen, O. (2023). Consumer Responses to AI-Generated Charitable Giving Ads. Journal of Advertising, 1-18. https://doi.org/10.1080/00913367.2023.2183285 Barnhardt, C., Cotter, S. A., Mitchell, G. L., Scheiman, M., & Kulp, M. T. (2012). Symptoms in children with convergence insufficiency: before and after treatment. Optometry and vision science: official publication of the American Academy of Optometry, 89(10), 1512. Beagle, D. (1999). Conceptualizing an Information Commons [Article]. Journal of Academic Librarianship, 25(2), 82-89. https://doi.org/10.1016/s0099-1333(99)80003-2 Beaumont, R. (2022). LAION-5B: A NEW ERA OF OPEN LARGE-SCALE MULTI-MODAL DATASETS. LAION. Retrieved 3 March from https://laion.ai/blog/laion-5b/ Beavers, K., Cady, J. E., Jiang, A., & McCoy, L. (2019). Establishing a maker culture beyond the makerspace. Library Hi Tech, 37(2), 219-232. https://doi.org/10.1108/lht-07-2018-0088 Beltzung, B., Pelé, M., Renoult, J. P., & Sueur, C. (2023). Deep learning for studying drawing behavior: A review. Frontiers in Psychology, 14, Article 992541. https://doi.org/10.3389/fpsyg.2023.992541 Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55(2), 155-162. https://doi.org/10.1016/j.bushor.2011.11.003 Bieraugel, M., & Neill, S. (2017). Ascending Bloom's Pyramid: Fostering Student Creativity and Innovation in Academic Library Spaces. College & Research Libraries, 78(1), 35-52. https://doi.org/10.5860/crl.78.1.35 Bryant, J., Matthews, G., & Walton, G. (2009). Academic libraries and social and learning space: A case study of Loughborough University Library, UK. Journal of Librarianship and Information Science, 41(1), 7-18. https://doi.org/10.1177/0961000608099895 Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8 Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3 Chen, C., Wu, Z., Lai, Y., Ou, W., Liao, T., & Zheng, Z. (2023). Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond. arXiv preprint arXiv:2306.00419. Chen, Y., & Wu, C. (2017). The hot spot transformation in the research evolution of maker. Scientometrics, 113(3), 1307-1324. https://doi.org/10.1007/s11192-017-2542-4 Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2023.2253861 Church, J. (2005). The evolving Information Commons. Library Hi Tech, 23(1), 75-81. https://doi.org/10.1108/07378830510586711 Cohn, G. (2018, Oct 25). AI Art at Christie’s Sells for $432,500. The New York Times. https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html Congdon, L. (2019). Find your artistic voice : the essential guide to working your creative magic. Chronicle Books. Cox, A. M. (2018). Space and embodiment in informal learning. HIGHER EDUCATION, 75(6), 1077-1090. https://doi.org/10.1007/s10734-017-0186-1 Croitoru, F. A., Hondru, V., Ionescu, R. T., & Shah, M. (2023). Diffusion Models in Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9), 10850-10869. https://doi.org/10.1109/TPAMI.2023.3261988 Curry, R. (2022). Insights from a cultural-historical HE library makerspace case study on the potential for academic libraries to lead on supporting ethical-making underpinned by 'Critical Material Literacy'. Journal of Librarianship and Information Science, Article 09610006221104796. https://doi.org/10.1177/09610006221104796 Davee, S., Regalla, L., & Chang, S. (2015). Makerspaces: Highlights of select literature. https://makered.org/wp-content/uploads/2015/08/Makerspace-Lit-Review-5B.pdf de Vicente-Yagüe-Jara, M. I., López-Martínez, O., Navarro-Navarro, V., & Cuéllar-Santiago, F. (2023). Writing, creativity, and artificial intelligence. ChatGPT in the university context. Comunicar, 31(77), 47-57. https://doi.org/10.3916/c77-2023-04 de Winter*, J. C. F., Dodou*, D., & Wieringa, P. A. (2009). Exploratory Factor Analysis With Small Sample Sizes. Multivariate Behavioral Research, 44(2), 147-181. https://doi.org/10.1080/00273170902794206 Dehouche, N., & Dehouche, K. (2023). What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education. Heliyon. Derevyanko, N., & Zalevska, O. (2023). Comparative analysis of neural networks Midjourney, Stable Diffusion, and DALL-E and ways of their implementation in the educational process of students of design specialities. Scientific Bulletin of Mukachevo State University. Series “Pedagogy and Psychology, 9(3), 36-44. Eriksson, E., Heath, C., Ljungstrand, P., & Parnes, P. (2018). Makerspace in school—Considerations from a large-scale national testbed. International Journal of Child-Computer Interaction, 16, 9-15. https://doi.org/10.1016/j.ijcci.2017.10.001 Farritor, S. (2017). UNIVERSITY-BASED MAKERSPACES: A SOURCE OF INNOVATION. Technology and Innovation, 19(1), 389-395. https://doi.org/10.21300/19.1.2017.389 Fido, D., Rao, J., & Harper, C. A. (2022). Celebrity status, sex, and variation in psychopathy predicts judgements of and proclivity to generate and distribute deepfake pornography. Computers in Human Behavior, 129, 107141. https://doi.org/https://doi.org/10.1016/j.chb.2021.107141 Figg, C., Khirwadkar, A., & Welbourn, S. (2020). Making ‘Math Making’ Virtual. Brock Education Journal, 29(2), 30. https://doi.org/10.26522/brocked.v29i2.836 Foley, C., Darcy, S., Hergesell, A., Almond, B., McDonald, M., Nguyen, L. T., & Morgan-Brett, E. (2023). Extracurricular activities, graduate attributes and serious leisure: competitive sport versus social-cultural clubs in campus life. Leisure Studies, 42(6), 971-988. https://doi.org/10.1080/02614367.2023.2168030 Fourie, I., & Meyer, A. (2015). What to make of makerspaces: Tools and DIY only or is there an interconnected information resources space? Library Hi Tech, 33(4), 519-525. https://doi.org/10.1108/LHT-09-2015-0092 Gierdowski, D., & Reis, D. (2015). The MobileMaker: an experiment with a Mobile Makerspace. Library Hi Tech, 33(4), 480-496. https://doi.org/10.1108/lht-06-2015-0067 Godhe, A.-L., Lilja, P., & Selwyn, N. (2019). Making sense of making: critical issues in the integration of maker education into schools. Technology, Pedagogy and Education, 28(3), 317-328. https://doi.org/10.1080/1475939X.2019.1610040 Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The qualitative report, 8(4), 597-607. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 2672-2680. Harley, B., Dreger, M., & Knobloch, P. (2001). The postmodern condition: students, the Web, and academic library services. Reference Services Review, 29(1), 23-32. https://doi.org/10.1108/00907320110366750 Harris, J., & Cooper, C. (2015). MAKE ROOM FOR A MAKERSPACE. Computers in Libraries, 35(2), 5-9. Hessman, T. (2013). Take 5: Q&A with Chuck Hull, Co-Founder, 3D Systems. Industry Week. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685. Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2 Jin, Z., & Song, Z. (2023). Generating coherent comic with rich story using ChatGPT and Stable Diffusion. arXiv preprint arXiv:2305.11067. Junjie, L., Jianjing, W., & Zhengfeng, J. (2020). Generative Adversarial Networks GAN Overview. Journal of Frontiers of Computer Science & Technology, 14(1), 1-17. https://doi.org/10.3778/j.issn.1673-9418.1910026 K, S., & Durgadevi, M. (2021, 8-10 July 2021). Generative Adversarial Network (GAN): a general review on different variants of GAN and applications. 2021 6th International Conference on Communication and Electronics Systems (ICCES), Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36. Kemp, J. (1994). Art in the library: Should academic libraries manage art? The Journal of Academic Librarianship, 20(3), 162-166. https://doi.org/https://doi.org/10.1016/0099-1333(94)90010-8 Kietzmann, J., Pitt, L., & Berthon, P. (2015). Disruptions, decisions, and destinations: Enter the age of 3-D printing and additive manufacturing. Business Horizons, 58(2), 209-215. https://doi.org/10.1016/j.bushor.2014.11.005 Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. kja, 70(1), 22-26. https://doi.org/10.4097/kjae.2017.70.1.22 Knibbe, J., Grossman, T., & Fitzmaurice, G. (2015, November15–18). Smart makerspace: An immersive instructional space for physical tasks. Proceedings of the 2015 International Conference on Interactive Tabletops & Surfaces, Madeira, Portugal. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Commun. ACM, 60(6), 84–90. https://doi.org/10.1145/3065386 Kwon, B.-R., & Lee, J. (2017). What makes a maker: the motivation for the maker movement in ICT [Article]. Information Technology for Development, 23(2), 318-335. https://doi.org/10.1080/02681102.2016.1238816 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 Lee, U. G., Han, A. R., Lee, J. J., Lee, E. S., Kim, J., Kim, H., & Lim, C. (2023). Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12150-4 Letnikova, G., & Xu, N. (2017). Academic library innovation through 3D printing services. Library Management, 38(4/5), 208-218. https://doi.org/10.1108/LM-12-2016-0094 Li, H. (2019). Special Section Introduction: Artificial Intelligence and Advertising. Journal of Advertising, 48(4), 333-337. https://doi.org/10.1080/00913367.2019.1654947 Liang, P. P., Zadeh, A., & Morency, L.-P. (2022). Foundations and recent trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430. Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 140, 5-53. Lindtner, S. (2014). Hackerspaces and the Internet of Things in China: How makers are reinventing industrial production, innovation, and the self. China Information, 28(2), 145-167. https://doi.org/10.1177/0920203x14529881 Lindtner, S. (2015). Hacking with Chinese Characteristics: The Promises of the Maker Movement against China's Manufacturing Culture. SCIENCE TECHNOLOGY & HUMAN VALUES, 40(5), 854-879. https://doi.org/10.1177/0162243915590861 Liu, M., & Hu, Y. (2023, 2023//). Application Potential of Stable Diffusion in Different Stages of Industrial Design. Artificial Intelligence in HCI, Cham. Liu, V., & Chilton, L. B. (2022). Design Guidelines for Prompt Engineering Text-to-Image Generative Models Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, <conf-loc>, <city>New Orleans</city>, <state>LA</state>, <country>USA</country>, </conf-loc>. https://doi.org/10.1145/3491102.3501825 Lo Conte, A. (2020). Antipodean Prints: Joseph Burke and the development of the University of Melbourne’s Print Collection. Journal of the Australian Library and Information Association, 69(2), 176-190. https://doi.org/10.1080/24750158.2020.1755924 Lock, J., Redmond, P., Orwin, L., Powell, A., Becker, S., Hollohan, P., & Johnson, C. (2020). Bridging distance: Practical and pedagogical implications of virtual Makerspaces. Journal of Computer Assisted Learning, 36(6), 957-968. https://doi.org/10.1111/jcal.12452 Loertscher, D. V. (2015, Oct 2015). The Virtual Makerspace: A New Possibility? Teacher Librarian, 43(1), 50-51,67. Loertscher, D. V., & Koechlin, C. (2012, Oct 2012). The Virtual Learning Commons and School Improvement. Teacher Librarian, 40(1), 20-24,24,63. Lu, Q., Yao, Y., Xiao, L., Yuan, M., Wang, J., & Zhu, X. (2024). Can ChatGPT effectively complement teacher assessment of undergraduate students’ academic writing? Assessment & Evaluation in Higher Education, 1-18. https://doi.org/10.1080/02602938.2024.2301722 Ma, H., & Zheng, H. (2024, 2024//). Text Semantics to Image Generation: A Method of Building Facades Design Base on Stable Diffusion Model. Phygital Intelligence, Singapore. MacWhinnie, L. A. (2003). The information commons: The academic library of the future [Article]. Portal-Libraries and the Academy, 3(2), 241-257. https://doi.org/10.1353/pla.2003.0040 Mann, L. (2018). Making a Place for Makerspaces in Information Literacy. Reference & User Services Quarterly, 58(2), 82-86. https://doi.org/10.5860/rusq.58.2.6927 Milton, C. L. (2023). ChatGPT and Forms of Deception. Nursing Science Quarterly, 36(3), 232-233. https://doi.org/10.1177/08943184231169753 Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student's t-test, analysis of variance, and covariance. Ann Card Anaesth, 22(4), 407-411. https://doi.org/10.4103/aca.ACA_94_19 Montgomery, S. E. (2014). Library Space Assessment: User Learning Behaviors in the Library. The Journal of Academic Librarianship, 40(1), 70-75. https://doi.org/10.1016/j.acalib.2013.11.003 Moorefield-Lang, H. (2015). Change in the Making: Makerspaces and the Ever-Changing Landscape of Libraries. TechTrends, 59(3), 107-112. https://doi.org/10.1007/s11528-015-0860-z Moorefield-Lang, H. M. (2015). When makerspaces go mobile: case studies of transportable maker locations. Library Hi Tech, 33(4), 462-471. https://doi.org/10.1108/lht-06-2015-0061 Morado, M. F., Melo, A. E., & Jarman, A. (2021). Learning by making: A framework to revisit practices in a constructionist learning environment. British Journal of Educational Technology, 52(3), 1093-1115. https://doi.org/10.1111/bjet.13083 Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum Sample Size Recommendations for Conducting Factor Analyses. International Journal of Testing, 5(2), 159-168. https://doi.org/10.1207/s15327574ijt0502_4 Nagle, S. B. (2021). Maker Services in Academic Libraries: A Review of Case Studies [Article]. New Review of Academic Librarianship, 27(2), 184-200. https://doi.org/10.1080/13614533.2020.1749093 Nascimento, S., & Polvora, A. (2018). Maker Cultures and the Prospects for Technological Action. Sci Eng Ethics, 24(3), 927-946. https://doi.org/10.1007/s11948-016-9796-8 Nitecki, D. A. (2011). Space Assessment as a Venue for Defining the Academic Library. The Library Quarterly, 81(1), 27-59. https://doi.org/10.1086/657446 Nunes, A. F., Monteiro, P. L., & Nunes, A. S. (2020). Factor structure of the convergence insufficiency symptom survey questionnaire. Plos One, 15(2), Article e0229511. https://doi.org/10.1371/journal.pone.0229511 Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2023). University Students' Perception and Use of ChatGPT: Generative Artificial Intelligence (AI) in Higher Education. International Journal of Human Computing Studies, 5. https://doi.org/10.5281/zenodo.10360697 Ojennus, P., & Watts, K. A. (2017). User preferences and library space at Whitworth University Library. Journal of Librarianship and Information Science, 49(3), 320-334. https://doi.org/10.1177/0961000615592947 Oppenlaender, J. (2023). A taxonomy of prompt modifiers for text-to-image generation. Behaviour & Information Technology, 1-14. https://doi.org/10.1080/0144929X.2023.2286532 Paananen, V., Oppenlaender, J., & Visuri, A. (2023). Using text-to-image generation for architectural design ideation. International Journal of Architectural Computing. https://doi.org/10.1177/14780771231222783 Pierard, C., & Bordeianu, S. (2016). Learning commons reference collections in ARL libraries. REFERENCE SERVICES REVIEW, 44(3), 411-430. https://doi.org/10.1108/RSR-02-2016-0014 Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision. International conference on machine learning, Randtke, W., Bareford, L., & Tooley, A. (2022). 3D Printing on a Shoestring: How to Avoid a Costly Ventilation Retrofit. Computers in Libraries, 42(7), 23-27. Reno, T. I. (2012). University of Nevada, Reno library first in nation to offer 3D printing campuswide. This Is Reno. https://thisisreno.com/2012/07/university-of-nevada-reno-library-first-in-nation-to-offer-3d-printing-campuswide/ Rice, S., Crouse, S. R., Winter, S. R., & Rice, C. (2024). The advantages and limitations of using ChatGPT to enhance technological research. Technology in Society, 76, Article 102426. https://doi.org/10.1016/j.techsoc.2023.102426 Rogers, A. S. (2016). The librarian’s role in academic makerspaces. Proceedings of the 1st International Symposium on Academic Makerspaces, Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10684-10695. Rouse, R., & Rouse, A. G. (2022). Taking the maker movement to school: A systematic review of preK-12 school-based makerspace research. Educational Research Review, 35, 100413. https://doi.org/10.1016/j.edurev.2021.100413 Saari, H., Åkerman, M., Kieslinger, B., Myllyoja, J., & Sipos, R. (2021). How Open Is the Maker Movement? Integrative Literature Review of the Openness Practices in the Global Maker Movement. Sustainability, 13(24). https://doi.org/10.3390/su132413559 Saltz, J. (2020). How to be an artist ([First hardcover] ed.). Riverhead Books. Santos, I., Castro, L., Rodriguez-Fernandez, N., Torrente-Patiño, Á., & Carballal, A. (2021). Artificial Neural Networks and Deep Learning in the Visual Arts: a review. Neural Computing and Applications, 33(1), 121-157. https://doi.org/10.1007/s00521-020-05565-4 Santos, I. M., Ali, N., & Hill, A. (2016). Students as Co-designers of a Virtual Learning Commons: Results of a Collaborative Action Research Study. The Journal of Academic Librarianship, 42(1), 8-14. https://doi.org/10.1016/j.acalib.2015.09.006 Santoso, S. M., & Wicker, S. B. (2014). The future of three-dimensional printing: Intellectual property or intellectual confinement? New Media & Society, 18(1), 138-155. https://doi.org/10.1177/1461444814538647 Sarpong, D., Ofosu, G., Botchie, D., & Clear, F. (2020). Do-it-yourself (DiY) science: The proliferation, relevance and concerns. Technological Forecasting and Social Change, 158, 120127. https://doi.org/10.1016/j.techfore.2020.120127 Scalfani, V. F., & Sahib, J. (2013). A model for managing 3D printing services in academic libraries. Issues in Science and Technology Librarianship, 72(Spring), 1-13. Schad, M., & Jones, W. M. (2020). The Maker Movement and Education: A Systematic Review of the Literature. Journal of Research on Technology in Education, 52(1), 65-78. https://doi.org/10.1080/15391523.2019.1688739 Seal, R. A. (2007). The information commons handbook. PORTAL-LIBRARIES AND THE ACADEMY, 7(3), 389-390. https://doi.org/10.1353/pla.2007.0037 Sheikh, A. (2015). Development of Information Commons in University Libraries of Pakistan: The Current Scenario. The Journal of Academic Librarianship, 41(2), 130-139. https://doi.org/10.1016/j.acalib.2015.01.002 Smith, J. S., Hsu, Y.-C., Zhang, L., Hua, T., Kira, Z., Shen, Y., & Jin, H. (2023). Continual diffusion: Continual customization of text-to-image diffusion with c-lora. arXiv preprint arXiv:2304.06027. StabilityAI. (2022). Stable Diffusion Public Release. Stability AI. Retrieved 3 March from https://stability.ai/news/stable-diffusion-public-release StabilityAI. (2023a). Announcing SDXL 1.0. Stability AI. Retrieved 26 July from https://stability.ai/news/stable-diffusion-sdxl-1-announcement StabilityAI. (2023b). Celebrating one year(ish) of Stable Diffusion … and what a year it’s been! . Stability AI. Retrieved 3 Oct from https://stability.ai/news/celebrating-one-year-of-stable-diffusion Stöckl, A. (2023). Evaluating a Synthetic Image Dataset Generated with Stable Diffusion. Proceedings of Eighth International Congress on Information and Communication Technology, Singapore. Su, Y., Lin, Y., & Lai, C. (2023). Collaborating with ChatGPT in argumentative writing classrooms. Assessing Writing, 57, 100752. https://doi.org/https://doi.org/10.1016/j.asw.2023.100752 Sweeny, R. W. (2017). Making and breaking in an art education makerspace. Journal of Innovation and Entrepreneurship, 6(1), 1-10. https://doi.org/https://doi.org/10.1186/s13731-017-0071-2 Tabares, R., & Boni, A. (2022). Maker culture and its potential for STEM education. INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION. https://doi.org/10.1007/s10798-021-09725-y Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. (2019). Deep learning in spiking neural networks. Neural Networks, 111, 47-63. https://doi.org/10.1016/j.neunet.2018.12.002 Thomas, B., Van Horne, S., Jacobson, W., & Anson, M. (2015). The design and assessment of the Learning Commons at the University of Iowa. The Journal of Academic Librarianship, 41(6), 804-813. https://doi.org/10.1016/j.acalib.2015.09.005 Turner, A., Welch, B., & Reynolds, S. (2013). Learning Spaces in Academic Libraries – A Review of the Evolving Trends. Australian Academic & Research Libraries, 44(4), 226-234. https://doi.org/10.1080/00048623.2013.857383 Vahdat, A., & Kreis, K. (2022, April 26). Improving Diffusion Models as an Alternative To GANs, Part 1. Technical Blog. https://developer.nvidia.com/blog/improving-diffusion-models-as-an-alternative-to-gans-part-1/ Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Verdonck, M., Greenaway, R., Kennedy-Behr, A., & Askew, E. (2019). Student experiences of learning in a technology-enabled learning space. INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL, 56(3), 270-281. https://doi.org/10.1080/14703297.2018.1515645 Vuopala, E., Guzmán Medrano, D., Aljabaly, M., Hietavirta, D., Malacara, L., & Pan, C. (2020). Implementing a maker culture in elementary school – students’ perspectives. Technology, Pedagogy and Education, 29(5), 649-664. https://doi.org/10.1080/1475939X.2020.1796776 Wang, C., & Chung, J. (2023). Research on AI Painting Generation Technology Based on the [Stable Diffusion]. International journal of advanced smart convergence, 12(2), 90-95. Wang, S., & Kim, S. (2022). Users’ emotional and behavioral responses to deepfake videos of K-pop idols. Computers in Human Behavior, 134, 107305. https://doi.org/https://doi.org/10.1016/j.chb.2022.107305 Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian journal of paramedicine, 8, 1-13. Wong, A., & Partridge, H. (2016). Making as Learning: Makerspaces in Universities. Australian Academic & Research Libraries, 47(3), 143-159. https://doi.org/10.1080/00048623.2016.1228163 Wu, J., Gan, W., Chen, Z., Wan, S., & Lin, H. (2023). Ai-generated content (aigc): A survey. arXiv preprint arXiv:2304.06632. Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136. https://doi.org/10.1109/JAS.2023.123618 Wu, X., Sun, K., Zhu, F., Zhao, R., & Li, H. (2023). Human Preference Score: Better Aligning Text-to-Image Models with Human Preference. arXiv preprint arXiv:2303.14420. Wu, Y., Mou, Y., Li, Z., & Xu, K. (2020). Investigating American and Chinese Subjects’ explicit and implicit perceptions of AI-Generated artistic work. Computers in Human Behavior, 104, 106186. https://doi.org/https://doi.org/10.1016/j.chb.2019.106186 Xu, J., Zhang, X., Li, H., Yoo, C., & Pan, Y. (2023). Is Everyone an Artist? A Study on User Experience of AI-Based Painting System. Applied Sciences, 13(11), 6496. https://www.mdpi.com/2076-3417/13/11/6496 Yaddanapudi, S., & Yaddanapudi, L. (2019). How to design a questionnaire. Indian Journal of Anesthesia, 63(5), 335-337. https://doi.org/https://doi.org/10.4103/ija.IJA_334_19 Yang, S., Li, S., & Tong, C. (2023). The Effectiveness of Artificial Intelligence Teaching Methods in Art Subject Classrooms. Journal of Artificial Intelligence Practice, 6(7), 52-59. Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418. Zhan, Q., Chen, X., & Retnawati, E. (2022). Exploring a construct model for university makerspaces beyond curriculum. Education and Information Technologies, 27(3), 3467-3493. https://doi.org/10.1007/s10639-021-10761-3 Zhang, C., Zhang, C., Zhang, M., & Kweon, I. S. (2023). Text-to-image Diffusion Model in Generative AI: A Survey. arXiv preprint arXiv:2303.07909. Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., Dam, S. K., Thwal, C. M., Tun, Y. L., & Huy, L. L. (2023). A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? arXiv preprint arXiv:2303.11717. Zhang, L., Rao, A., & Agrawala, M. (2023). Adding conditional control to text-to-image diffusion models. Proceedings of the IEEE/CVF International Conference on Computer Vision. zh_TW
