|
一、中文文獻 行政院(2018)。臺灣AI行動計畫(2018-2021年)。作者。 吳明隆、涂金堂(2007)。SPSS與統計應用分析。五南。 邱皓政(2002)。量化研究與統計分析:SPSS中文視窗版資料分析範例解析(二版)。五南。 施淑慎(2008)。學習情境中之自主支持與國中生成就相關歷程間關係之探討。教育與心理研究,31(2),1-26。 國家教育研究院(2018)。十二年國民基本教育「科技領域」科目課程綱要。作者。 張偉豪、鄭時宜(2012)。與結構方程模型共舞:曙光初現。前程文化。 教育部電子報(2020)。日本政府推動AI教育認定制度,培養先端領域人才。日本經濟新聞。https://epaper.edu.tw/windows.aspx?windows_sn=23069 陳正昌、程炳林、陳新豐、 劉子鍵(2003)。多變量分析方法-統計軟體應用。五南。 陳昇瑋、溫怡玲(2019)。人工智慧在台灣:產業轉型的契機與挑戰。天下雜誌。 曾衒銘(2021)。原來AI這麼簡單!熟練機器學習5大步驟,就算不會寫程式,也能成為AI高手。商周出版社。 謝慶華、段曉林、靳知勤、陳淑貞(2016)。國中自然與生活科技學習參與量表的發展與相關因素之探討。科學教育學刊,24(3),249-273。https://doi.org/ 10.6173/CJSE.2016.2403.02 二、英文文獻 Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036 Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.014 Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035 Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control (pp. 11-39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2 Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277. https://doi.org/10.1037/h0076477 Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389-412. https://doi.org/10.1016/j.compedu.2018.06.008 Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883-900. https://doi.org/10.1007/s10936-020-09752-1 Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., & Shuib, L. (2021). For sustainable application of mobile learning: An extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), 1856. https://doi.org/10.3390/su13041856 Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155. https://doi.org/10.14569/IJACSA.2014.050120 Ali, H., Ahmed, A. A., Tariq, T. G., & Safdar, H. (2013, May 7-9). Second Life (SL) in Education: The intensions to use at university of Bahrain. 2013 Fourth International Conference on e-Learning" Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity, Manama, Bahrain. Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019, August 11). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. 2019 EDUAI International Workshop on Education in Artificial Intelligence K-12, Macao, China. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411 Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386. https://doi.org/10.1002/pits.20303 Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44(5), 427-445. https://doi.org/10.1016/j.jsp.2006.04.002 Arenas Gaitán, J., Rondán Cataluña, F. J., & Ramírez Correa, P. (2010, November 3-5). Gender influence in perception and adoption of e-learning platforms. 9th WSEAS International Conference on Data Networks, Communications, Computers, Faro, Portugal. Arfi, W. B., Nasr, I. B., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688 Ashrafzadeh, A., & Sayadian, S. (2015). University instructors’ concerns and perceptions of technology integration. Computers in Human Behavior, 49, 62-73. https://doi.org/10.1016/j.chb.2015.01.071 Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327 Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776 Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180, 121692. https://doi.org/10.1016/j.techfore.2022.121692 Bervell, B., Umar, I. N., Kumar, J. A., Asante Somuah, B., & Arkorful, V. (2021). Blended Learning Acceptance Scale (BLAS) in distance higher education: Toward an initial development and validation. SAGE Open, 11(3), 1-19. https://doi.org/10.1177/21582440211040073 Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921 Bluemink, J., & Järvelä, S. (2004). Face-to-face encounters as contextual support for Web-based discussions in a teacher education course. The Internet and Higher Education, 7(3), 199-215. https://doi.org/10.1016/j.iheduc.2004.06.006 Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Sage. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons. Breivik, E., & Olsson, U. H. (2001). Adding variables to improve model fit: The effect of model size on fit assessment in LISREL. In R. Cudeck, S. Du Toit, & D. Sorbom (Eds.), Structural equation modeling: Present and future (pp. 169-194). Scientific Software International. Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. Springer. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage. Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016, February 12-17). IRobot: Teaching the basics of artificial intelligence in high schools. 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, United States. https://doi.org/10.1609/aaai.v30i1.9864 Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809-826. https://doi.org/10.14742/ajet.81 Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741-752. https://doi.org/10.1016/j.chb.2010.01.011 Cheong, M. C. S. (2018). Artificial intelligence in healthcare. Journal of Biomedical & Laboratory Sciences, 30(2), 33-37. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003 Chiu, T. K. (2021). A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends, 65(5), 796-807. https://doi.org/10.1007/s11528-021-00637-1 Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52. https://doi.org/10.1016/j.compedu.2015.09.013 Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73. https://doi.org/10.2307/3150876 Cohen, S., & Williamson, G. (1988). Perceived stress in a probability sample of the United States. In S. Spacapan, & S. Oskamp (Eds.), The social psychology of health (pp. 31-68). Sage. Connell, J. (1991). Competence, autonomy, and relatedness: A motivational analysis of self-system processes. Minnesota Symposia on Child Psychology, 23, 43-78. Dabbagh, N. (2007). The online learner: Characteristics and pedagogical implications. Contemporary Issues in Technology and Teacher Education, 7(3), 217-226. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. de Blanes Sebastián, M. G., Guede, J. R. S., & Antonovica, A. (2022). Tam versus utaut models: A contrasting study of scholarly production and its bibliometric analysis. TECHNO REVIEW. International Technology, Science and Society Review/Revista Internacional de Tecnología, Ciencia y Sociedad, 12(3), 1-27. https://doi.org/10.37467/revtechno.v11.4445 De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688-696. https://doi.org/10.1016/j.compedu.2011.09.013 del Barrio-García, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students' perceptions. Journal of Educational Technology & Society, 18(3), 129-141. Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 12(2), 259-274. https://doi.org/10.2307/249524 Druga, S., Williams, R., Breazeal, C., & Resnick, M. (2017, June 27-30). " Hey Google is it ok if I eat you?" Initial explorations in child-agent interaction. 2017 IDC Conference on Interaction Design and Children, Stanford, CA, United States. https://doi.org/10.1145/3078072.3084330 Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares: Concepts, methods and applications. Springer. Evangelista, I., Blesio, G., & Benatti, E. (2018, November 12-16). Why are we not teaching machine learning at high school? 2018 WEEF-GEDC World Engineering Education Forum-Global Engineering Deans Council, Albuquerque, NM, United States. https://doi.org/10.1109/WEEF-GEDC.2018.8629750 Figg, C., & Jamani, K.J. (2011). Exploring teacher knowledge and actions supporting technology-enhanced teaching in elementary schools: Two approaches by pre-service teachers. Australasian Journal of Educational Technology, 27, 1227-1246. https://doi.org/10.14742/ajet.914 Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117-142. https://doi.org/10.3102/00346543059002117 Fokides, E. (2017). Greek pre-service teachers’ intentions to use computers as in-service teachers. Contemporary Educational Technology, 8(1), 56-75. https://doi.org/10.30935/cedtech/6187 Fornell, C., & Larcker, D. (1987). A second generation of multivariate analysis: Classification of methods and implications for marketing research. Review of Marketing, 51, 407-450. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.2307/3150980 Fredericks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763-782). Springer Science+Business. https://doi.org/10.1007/978-1-4614-2018-7_37 Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. H. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development. (pp. 305-321). Springer Science + Business Media. Fredricks, J. A., McColskey, W., Meli, J., Mordica, J., Montrosse, B., & Mooney, K. (2011). Measuring student engagement in upper elementary through high school: A description of 21 instruments. Issues and Answers Report, 98, 26-114. Fredricks, J. F., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059 Fried, L. J., & Konza, D. M. (2013). Using self-determination theory to investigate student engagement in the classroom. International Journal of Pedagogy and Curriculum, 19(2), 27-40. https://doi.org/10.18848/2327-7963/CGP/v19i02/48898 Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133-139. https://doi.org/10.1108/IJILT-09-2016-0048 Gallini, J. K., & Barron, D. (2001). Participants’ perceptions of web-infused environments: A survey of teaching beliefs, learning approaches, and communication. Journal of Research on Technology in Education, 34(2), 139-156. https://doi.org/10.1080/15391523.2001.10782341 Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-78. https://doi.org/10.17705/1CAIS.00407 Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107. https://doi.org/10.2307/2334290 George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. Gibbs, G. (2012). Implications of ‘Dimensions of Quality’ in a market environment. Higher Education Academy. Greene, T. G., Marti, C. N., & McClenney, K. (2008). The effort-outcome gap: Differences for African American and Hispanic community college students in student engagement and academic achievement. The Journal of Higher Education, 79(5), 513-539. https://doi.org/10.1353/jhe.0.0018 Greenwood, C. R., Horton, B. T., & Utley, C. A. (2002). Academic engagement: Current perspectives on research and practices. School Psychology Review, 31(3), 328-349. https://doi.org/10.1080/02796015.2002.12086159 Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611-642. https://doi.org/10.2307/25148742 Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238-1249. https://doi.org/10.1016/j.jbusres.2008.01.012 Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128 He, Y., Chen, Q., & Kitkuakul, S. (2018). Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy. Cogent Business & Management, 5(1), 1459006. https://doi.org/10.1080/23311975.2018.1459006 Heafner, T. L., & Friedman, A. M. (2008). Wikis and constructivism in secondary social studies: Fostering a deeper understanding. Computers in the Schools, 25(3-4), 288-302. https://doi.org/10.1080/07380560802371003 Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances international marketing (pp. 277-320). Bingley. Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (2019, May 4-9). Can children understand machine learning concepts? The effect of uncovering black boxes. 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, United Kingdom. https://doi.org/10.1145/3290605.3300645 Ho, J. W., Scadding, M., Kong, S. C., Andone, D., Biswas, G., Hoppe, H. U., & Hsu, T. C. (2019, June 13-15). Classroom activities for teaching artificial intelligence to primary school students. 2019 CTE International Conference on Computational Thinking Education, Hong Kong, China. Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi, K., Schunn, C., & Sirkiä, T. (2020). Improving engagement in program construction examples for learning Python programming. International Journal of Artificial Intelligence in Education, 30(2), 299-336. https://doi.org/10.1007/s40593-020-00197-0 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 Hulland, J. (1999). Use of Partial Least Squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7 Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159-164. https://doi.org/10.1016/j.procs.2017.01.196 Imtiaz, M. A., & Maarop, N. (2014). A review of technology acceptance studies in the field of education. Jurnal Teknologi, 69(2), 27-32. https://doi.org/10.11113/jt.v69.3101 Iqbal, J., & Sidhu, M. S. (2022). Acceptance of dance training system based on augmented reality and Technology Acceptance Model (TAM). Virtual Reality, 26(1), 33-54. https://doi.org/10.1007/s10055-021-00529-y Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199-218. https://doi.org/10.1086/376806 Jo¨reskog, K., & So¨rbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International Inc. Kaloti-Hallak, F., Armoni, M., & Ben-Ari, M. (2015). Students' attitudes and motivation during robotics activities. In Proceedings of the workshop in primary and secondary computing education (pp. 102-110). Association for Computing Machinery. https://doi.org/10.1145/2818314.2818317 Kandemir, M. A., Franklin, T., Perkmen, S., & Yıldız, Y. (2022). Developing a mobile learning acceptance scale for mathematics. Canadian Journal of Science, Mathematics and Technology Education, 22(2), 392-404. https://doi.org/10.1007/s42330-022-00216-3 Kay, R. (2011). Evaluating learning, design, and engagement in Web-Based Learning Tools (WBLTs): The WBLT Evaluation Scale. Computers in Human Behavior, 27(5), 1849-1856. https://doi.org/10.1016/j.chb.2011.04.007 Kong, Q. P., Wong, N. Y., & Lam, C. C. (2003). Student engagement in mathematics: Development of instrument and validation of construct. Mathematics Education Research Journal, 15(1), 4-21. https://doi.org/10.1007/BF03217366 Kong, S. C. (2011). An evaluation study of the use of a cognitive tool in a one-to-one classroom for promoting classroom-based dialogic interaction. Computers & Education, 57(3), 1851-1864. https://doi.org/10.1016/j.compedu.2011.04.008 Kuh, G. (2001). Assessing what really matters to student learning: Inside the national survey of student engagement. Change, 33(3), 10-17. https://doi.org/10.1080/00091380109601795 Kuh, G. D. (2003). What we're learning about student engagement from NSSE: Benchmarks for effective educational practices. Change, 35(2), 24-32. https://doi.org/10.1080/00091380309604090 Kuh, G. D., Kinzie, J., Cruce, T., Shoup, R., & Gonyea, R. M. (2007). Connecting the dots: Multi-faceted analyses of the relationships between student engagement results from the NSSE, and the institutional practices and conditions that foster student success. Indiana University Center for Postsecondary Research. Leach, L., & Zepke, N. (2012). Student engagement in learning: Facets of a complex interaction. In I. Solomonides, A. Reid, & P. Petocz (Eds.), Engaging with learning in higher education (pp. 231-255). Libri Publishers. Lee, W., & Reeve, J. (2012). Teachers’ estimates of their students’ motivation and engagement: Being in synch with students. Educational Psychology, 32(6), 727-747. https://doi.org/10.1080/01443410.2012.732385 Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827. https://doi.org/ 10.1016/j.ijhm.2011.09.018 Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4 Li, Y., Bebiroglu, N., Phelps, E., Lerner, R. M., & Lerner, J. V. (2008). Out-of-school time activity participation, school engagement and positive youth development: Findings from the 4-H study of positive youth development. Journal of Youth Development, 3(3), 1-16. https://doi.org/10.5195/jyd.2008.284 Liem, G. A. D., & Martin, A. J. (2012). The motivation and engagement scale: Theoretical framework, psychometric properties, and applied yields. Australian Psychologist, 47(1), 3-13. https://doi.org/10.1111/j.1742-9544.2011.00049.x Lin, K.-M., Chen, N.-S., & Fang, K. (2010). Understanding e-learning continuance intention: A negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77-89. https://doi.org/10.1080/01449291003752948 Lin, P., Van Brummelen, J., Lukin, G., Williams, R., & Breazeal, C. (2020, February 7-12). Zhorai: Designing a conversational agent for children to explore machine learning concepts. 2020 AAAI Conference on Artificial Intelligence, New York, NY, United States. https://doi.org/10.1609/aaai.v34i09.7061 MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193-210. https://doi.org/10.1207/s15327906mbr3202_5 MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710-730. https://doi.org/10.1037/0021-9010.90.4.710 Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5 Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32(5), 669-685. https://doi.org/10.1080/09500690902792385 Mariescu-Istodor, R., & Jormanainen, I. (2019, November 21-24). Machine learning for high school students. 19th Koli Calling International Conference on Computing Education Research, Koli, Finland. Marks, H. M. (2000). Student engagement in instructional activity: Patterns in the elementary, middle, and high school years. American Eudcational Research Journal, 37(1), 153-184. https://doi.org/10.2307/1163475 Martin, A. J., Way, J., Bobis, J., & Anderson, J. (2015). Exploring the ups and downs of mathematics engagement in the middle years of school. The Journal of Early Adolescence, 35(2), 199-244. https://doi.org/10.1177/027243161452936 Martin, J., Birks, J., & Hunt, F. (2010). Designing for users: Online information literacy in the Middle East. Portal: Libraries and the Academy, 10(1), 57-73. https://doi.org/10.1353/pla.0.0086 Martinho, D. S., Santos, E. M., Miguel, M. I., & Cordeiro, D. S. (2018). Factors that influence the adoption of postgraduate online courses. International Journal of Emerging Technologies in Learning (iJET), 13(12), 123-141. https://doi.org/10.3991/ijet.v13i12.8864 McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analysis. Psychological Methods, 7, 64-82. https://doi.org/10.1037/1082-989X.7.1.64 McKinnon, M., & Vos, J. (2015). Engagement as a threshold concept for science education and science communication. International Journal of Science Education, Part B, 5(4), 297-318. https://doi.org/10.1080/21548455.2014.986770 Meyer, D. K., & Turner, J. C. (2002). Discovering emotion in classroom motivation research. Educational Psychologist, 37(2), 107-114. https://doi.org/10.1207/S15326985EP3702_5 Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88(2), 203-214. https://doi.org/10.1037/0022-0663.88.2.203 Missett, T. C., Reed, C. B., Scot, T. P., Callahan, C. M., & Slade, M. (2010). Describing learning in an advanced online case-based course in environmental science. Journal of Advanced Academics, 22(1), 10-50. https://doi.org/10.1177/1932202X1002200102 Mittal, N., Chaudhary, M., & Alavi, S. (2017). Development and validation of teachers mobile learning acceptance scale for higher education teachers. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 7(1), 76-98. https://doi.org/10.4018/IJCBPL.2017010106 Nakamaru, S. (2011). Investment and return: Wiki engagement in a “remedial” ESL writing course. Journal of Research on Technology in Education, 44(4), 273-291. https://doi.org/10.1080/15391523.2012.10782591 Naps, T. L., Rößling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C., Korhonen, A., Malmi, L., McNally, M., Rodger, S., & Velázquez-Iturbide, J. (2002, June 24-28). Exploring the role of visualization and engagement in computer science education. 7th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, Aarhus, Denmark. Narahara, T., & Kobayashi, Y. (2018, December 4-7). Personalizing homemade bots with plug & play AI for STEAM education. SIGGRAPH Asia 2018 Technical Briefs, Tokyo, Japan. Nasri, W., & Charfeddine, L. (2012). Factors affecting the adoption of internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The Journal of High Technology Management Research, 23(1), 1-14. https://doi.org/10.1016/j.hitech.2012.03.001 Newbery, G. (2012). The psychology of being engaged and its implications for promoting engagement. In I. Solomonides, A. Reid, & P. Petocz (Eds.), Engaging with learning in higher education (pp. 47-69). Libri. Nunnally, J. C., & Bernstein, I. H. (1994). Validity. Psychometric Theory, 3(1), 99-132. Ozdemir, S., & Susarla, D. (2018). Feature engineering made easy: Identify unique features from your dataset in order to build powerful machine learning systems. Packt Publishing Ltd. Park, N., Lee, K. M., & Cheong, P. H. (2007). University instructors’ acceptance of electronic courseware: An application of the technology acceptance model. Journal of Computer-Mediated Communication, 13(1), 163-186. Pascarella, E. T., Seifert, T. A., & Blaich, C. (2010). How effective are the NSSE benchmarks in predicting important educational outcomes? Change: The Magazine of Higher Learning, 42(1), 16-22. https://doi.org/10.1080/00091380903449060 Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275 Peters, A. K., & Pears, A. (2013, March 21-24). Engagement in computer science and IT--What! A matter of identity? 2013 Learning and Teaching in Computing and Engineering, Washington, DC, United States. https://doi.org/10.1109/LaTiCE.2013.42 Pike, G. R. (2013). NSSE benchmarks and institutional outcomes: A note on the importance of considering the intended uses of a measure in validity studies. Research in Higher Education, 54(2), 149-170. https://doi.org/10.1111/j.1083-6101.2007.00391.x Podsakoff, N. P., Shen, W., & Podsakoff, P. M. (2006). The role of formative measurement models in strategic management research: Review, critique, and implications for future research. In A. Ketchen & D. D. Bergh (Eds.), Research methodology in strategy and management (pp. 197-252). Emerald Group Publishing Limited. https://doi.org/10.1016/S1479-8387(06)03008-6 Price, L., Richardson, J. T., & Jelfs, A. (2007). Face‐to‐face versus online tutoring support in distance education. Studies in Higher Education, 32(1), 1-20. https://doi.org/10.1080/03075070601004366 Puriwat, W., & Tripopsakul, S. (2021). Understanding food delivery mobile application technology adoption: A UTAUT model integrating perceived fear of COVID-19. Emerging Science Journal, 5, 94-104. https://doi.org/10.28991/esj-2021-SPER-08 Rajak, M., & Shaw, K. (2021). An extension of technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800. https://doi.org/10.1016/j.techsoc.2021.101800 Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579-595. https://doi.org/10.1037/a0032690 Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332-344. https://doi.org/10.1016/j.ijresmar.2009.08.001 Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827-1843. https://doi.org/10.1016/j.respol.2015.06.006 Ryan, A. M., & Patrick, H. (2001). The classroom social environment and change in adolescents’ motivation and engagement during middle school. American Education Research Journal, 38(2), 437-460. https://doi.org/10.3102/00028312038002437 Sabuncuoglu, A. (2020, June 15-19). Designing one year curriculum to teach artificial intelligence for middle school. 2020 ACM Conference on Innovation and Technology in Computer Science Education, New York, NY, United States. https://doi.org/10.1145/3341525.3387364 Sadi, Ö. (2017). Relational analysis of high school students’ cognitive self-regulated learning strategies and conceptions of learning biology. Eurasia Journal of Mathematics, Science and Technology Education, 13(6), 1701-1722. https://doi.org/10.12973/eurasia.2017.00693a Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640. https://doi.org/10.1016/j.chb.2010.06.011 Scherer, R., Siddiq, F., & Tondeur, J. (2019). The Technology Acceptance Model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009 Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Psychology Press. https://doi.org/10.4324/9781410610904 Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, 20(4-5), 461-472. https://doi.org/10.1016/j.intcom.2008.04.004 Sezer, B., & Yilmaz, R. (2019). Learning Management System Acceptance Scale (LMSAS): A validity and reliability study. Australasian Journal of Educational Technology, 35(3), 15-30. https://doi.org/10.14742/ajet.3959 Sinclair, J., Butler, M., Morgan, M., & Kalvala, S. (2015, August 17-21). Measures of student engagement in computer science. 2015 ACM Conference on Innovation and Technology in Computer Science Education, London, United Kingdom. https://doi.org/10.1145/2729094.2742586 Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100(4), 765-781. https://doi.org/10.1037/a0012840 Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of The Royal Statistical Society: Series B (Methodological), 36(2), 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077. https://doi.org/10.1016/j.chb.2011.08.005 Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204. https://doi.org/10.1111/j.1467-8535.2010.01157.x Suo, W. J., Goi, C. L., Goi, M. T., & Sim, A. K. (2022). Factors influencing behavioural intention to adopt the QR-code payment: Extending UTAUT2 model. International Journal of Asian Business and Information Management (IJABIM), 13(2), 1-22. https://doi.org/10.4018/IJABIM.20220701.oa8 Swesi, A. T., Masud, J., & Nath, M. (2016). Nickel selenide as a high-efficiency catalyst for oxygen evolution reaction. Energy & Environmental Science, 9(5), 1771-1782. https://doi.org/10.1039/C5EE02463C Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, demographic characteristics and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72-89. https://doi.org/10.5539/hes.v6n3p72 Teo, T. (2010). Development and validation of the E-learning Acceptance Measure (ElAM). The Internet and Higher Education, 13(3), 148-152. https://doi.org/10.1016/j.iheduc.2010.02.001 Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008 Teo, T. (Ed.). (2011). Technology acceptance in education. Springer Science & Business Media. Teo, T., Milutinović, V., Zhou, M., & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), 811-827. https://doi.org/10.1080/10494820.2016.1189943 Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., & Vartiainen, H. (2020, July 6-9). Co-designing machine learning apps in K–12 with primary school children. 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia. https://doi.org/10.1109/ICALT49669.2020.00099 Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, January 27-February 1). Envisioning AI for K-12: What should every child know about AI? 2019 AAAI Conference on Artificial Intelligence, Honolulu, HI, United States. https://doi.org/10.1609/aaai.v33i01.33019795 Trowler, V. (2010). Student engagement literature review. The Higher Education Academy, 11(1), 1-15. Ullman, J. B., & Bentler, P. M. (2013). Structural equation modeling. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology: Research methods in psychology (pp. 661-690). John Wiley & Sons Inc. Ulnicane, I. (2022). Artificial intelligence in the European Union. In T. Hoerber, G. Weber, & I. Cabras (Eds.), The Routledge handbook of European integrations (pp. 254-269). Routledge. Ünal, R., & Yel, S. (2019). Development of a social acceptance scale for inclusive education. Universal Journal of Educational Research, 7(10), 2187-2198. https://doi.org/10.13189/ujer.2019.071017 Ustun, A. B., Karaoglan-Yilmaz, F. G., & Yilmaz, R. (2022). Educational UTAUT-based virtual reality acceptance scale: A validity and reliability study. Virtual Reality, 6, 27-48 https://doi.org/10.1007/s10055-022-00717-4 Vazhayil, A., Shetty, R., Bhavani, R. R., & Akshay, N. (2019, December 9-11). Focusing on teacher education to introduce AI in schools: Perspectives and illustrative findings. 2019 IEEE Tenth International Conference on Technology for Education, Goa, India. https://doi.org/10.1109/T4E.2019.00021 Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16-26. https://doi.org/10.1016/j.learninstruc.2016.01.008 Weerasinghe, S., & Hindagolla, M. (2017). Technology acceptance model in the domains of LIS and education: A review of selected literature. Library Philosophy and Practice, 1-27. Weng, F., Yang, R. J., Ho, H. J., & Su, H. M. (2018). A TAM-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1(3), 36. https://doi.org/10.3390/asi1030036 Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195. https://doi.org/10.2307/20650284 Williams, L. J., & Hazer, J. T. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of Applied Psychology, 71(2), 219-231. https://doi.org/10.1037/0021-9010.71.2.219 Williams, R., Park, H. W., & Breazeal, C. (2019, May 4-9). A is for artificial intelligence: The impact of artificial intelligence activities on young children's perceptions of robots. 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, United Kingdom. https://doi.org/10.1145/3290605.3300677 Wise, A. F., Speer, J., Marbouti, F., & Hsiao, Y. T. (2013). Broadening the notion of participation in online discussions: Examining patterns in learners’ online listening behaviors. Instructional Science, 41(2), 323-343. https://doi.org/10.1007/s11251-012-9230-9 Witten, I. H., & Frank, E. (2002). Data mining: Practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76-77. https://doi.org/10.1145/507338.507355 Wong, G. K. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6), 713-735. https://doi.org/10.14742/ajet.1890 Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage Learning. Xu, Y. (2010). Examining the effects of digital feedback on student engagement and achievement. Journal of Educational Computing Research, 43(3), 275-291. https://doi.org/10.2190/EC.43.3.a Yang, Y. F. (2011). Engaging students in an online situated language learning environment. Computer Assisted Language Learning, 24(2), 181-198. https://doi.org/10.1080/09588221.2010.538700 Zare, H., & Yazdanparast, S. (2013). The causal Model of effective factors on intention to use of information technology among payamnoor and traditional universities students. Life Science Journal, 10(2), 46-50. Zhang, X., De Pablos, P. O., & Zhou, Z. (2013). Effect of knowledge sharing visibility on incentive-based relationship in electronic knowledge management systems: An empirical investigation. Computers in Human Behavior, 29(2), 307-313. https://doi.org/10.1016/j.chb.2012.01.029 Zhang, Y., Wang, J., Bolduc, F., Murray, W. G., & Staffen, W. (2019, January 27-February 1). A preliminary report of integrating science and computing teaching using logic programming. 2019 AAAI Conference on Artificial Intelligence, Honolulu, HI, United States. https://doi.org/10.1609/aaai.v33i01.33019737 Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64, 101515. https://doi.org/10.1016/j.techsoc.2020.101515 Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI learning experiences for K-12: Emerging works, future opportunities and a design framework. arXiv preprint arXiv:2009.10228. https://doi.org/10.48550/arXiv.2009.10228
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