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Effects and acceptance of precision education in an AI-supported smart learning environment

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Abstract

The research presents precision education that aims to regulate students’ behaviors through the learning analytics dashboard (LAD) in the AI-supported smart learning environment (SLE). The LAD basically tracks and visualizes traces of learning actions to make students aware of their learning behaviors and reflect these against the agreed goals. This research aims to realize the digital transformation of the learning space, thereby improving students’ learning outcomes with the assistance of the learning dashboard. To examine whether there was a close relationship between the frequency of using the whole platform and academic results, the data was collected from 50 first-year university students who registered with the innovative thinking course. Based on the data, we constructed the Technology Acceptance Model (TAM) questionnaire and interview guide to realize the students’ acceptance and feedback towards the SLE. Students were clustered into high-mark and low-mark groups based on their final results. The Wilcoxon rank-sum test is used to identify a significant difference between the two groups using the precision education platform. Subsequently, the partial least squares structural equation modeling (PLS-SEM) is further utilized to analyze the relationship between system quality, perceived ease of use, and perceived usefulness on behavioral intention and learning transfer.

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Data availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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References

  • Abad-Segura, E., González-Zamar, M.-D., Infante Moro, J. C., & García, G. (2020). Sustainable management of digital transformation in higher education: Global research trends. Sustainability, 12(5), 2107. https://doi.org/10.3390/su12052107

    Article  Google Scholar 

  • Agarwal, R., & Prasad, J. (1999). Are Individual Differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x

    Article  Google Scholar 

  • Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., & Chamba-Eras, L. (2018). Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society, 17(4), 693–709. https://doi.org/10.1007/s10209-017-0525-0

    Article  Google Scholar 

  • Ajzen I.(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2),179–211. https://doi.org/10.1016/0749-5978(91)90020-T

  • Ajzen, I., & Fishbein, M. (2005). The Influence of Attitudes on Behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (p. 173–221). Lawrence Erlbaum Associates Publishers.

  • Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-020-10219-y

    Article  Google Scholar 

  • Alzougool, B. (2019). The use and continuance use of social media applications by small and medium enterprises in Kuwait. Global Knowledge, Memory and Communication, 68(6–7), 471–490. https://doi.org/10.1108/GKMC-11-2018-0094

    Article  Google Scholar 

  • Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014

    Article  Google Scholar 

  • Azevedo, R., Johnson, A., Chauncey, A., & Graesser, B. (2011). Use of hypermedia to convey and assess self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of Self-Regulation of Learning and Performance (pp. 102–121). New York, NY: Routledge. https://doi.org/10.4324/9780203839010

  • Barisa, M. F., & Tosun, N. (2013). Influence of E-portfolio supported education process to academic success of the students. Procedia - Social and Behavioral Sciences, 103(26), 492–499. https://doi.org/10.1016/j.sbspro.2013.10.365

    Article  Google Scholar 

  • Berrett, B., Murphy, J., & Sullivan, J. (2012). Administrator insights and reflections: Technology integration in schools. The Qualitative Report, 17(1), 200–221.

    Google Scholar 

  • Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11

  • Bölen, M. C. (2020). Exploring the determinants of users’ continuance intention in smartwatches. Technology in Society, 60, 101209. https://doi.org/10.1016/j.techsoc.2019.101209

    Article  Google Scholar 

  • Bull, S. (2004). Supporting learning with open learner models. Planning, 29(14), 1.

    MathSciNet  Google Scholar 

  • Chang, C.-C., Liang, C., Chao, P.-N., & Liao, Y.-M. (2018). Using e-portfolio for learning goal setting to facilitate self-regulated learning of high school students. Behaviour & Information Technology, 37(12), 1237–1257. https://doi.org/10.1080/0144929X.2018.1496275

    Article  Google Scholar 

  • Chau, P. Y. K., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191–229. https://doi.org/10.1080/07421222.2002.11045699

    Article  Google Scholar 

  • Cheung, M. L., Chau, K. Y., Lam, M. H. S., Tse, G., Ho, K. Y., Flint, S. W., Broom, D. R., Tso, E. K. H., & Lee, K. Y. (2019). Examining consumers’ adoption of wearable healthcare technology: The role of health attributes. International Journal of Environmental Research and Public Health, 16, 2257. https://doi.org/10.3390/ijerph16132257

    Article  Google Scholar 

  • Conner, M. and Sparks, P. (2015). Theory of planned behavior and health behavior. In M. Cooner and P. Sparks (eds), prediction health behavior: Research and practice with social cognition models. 3rd edition. Open University Press. 170–222.

  • Cook, C. R., Kilgusb, S. P., & Burns, M. K. (2018). Advancing the science and practice of precision education to enhance student outcomes. Journal of School Psychology, 66, 4–10. https://doi.org/10.1016/j.jsp.2017.11.004

    Article  Google Scholar 

  • Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M. (2018). The NISPI framework: Analysing collaborative problem-solving from students’ physical interactions. Computers and Education, 116, 93–109. https://doi.org/10.1016/j.compedu.2017.08.007

    Article  Google Scholar 

  • Davis G.B. (2000) Information systems conceptual foundations: Looking backward and forward. In: Baskerville R., Stage J., DeGross J.I. (eds) Organizational and social perspectives on information technology. IFIP — The international federation for information processing, vol 41. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35505-4_5

  • 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.

    Article  Google Scholar 

  • Du Boulay, B. (2019). Escape from the Skinner Box: The case for contemporary intelligent learning environments. British Journal of Educational Technology, 50(6), 2902–2919. https://doi.org/10.1111/bjet.12860

    Article  Google Scholar 

  • Duval, E. (2011). Attention please!: learning analytics for visualization and recommendation. Paper presented at the 1st International Conference on Learning Analytics and Knowledge, Alberta, Canada. https://doi.org/10.1145/2090116.2090118

  • Fleaca (2017). Embedding digital teaching and learning practices in the modernization of higher education institutions. In Proceedings of the SGEM2017 International Multidisciplinary Scientific GeoConference: SGEM (pp. 20–25).Albena, Bulgaria.

  • Fonseca, D., Conde, M. Á., & García-Peñalvo, F. J. (2017). Improving the information society skills: Is knowledge accessible for all? Universal Access in the Information Society, 17, 229–245. https://doi.org/10.1007/s10209-017-0548-6

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

    Article  Google Scholar 

  • Garson, G. D. (2014). Partial least squares: Regression and structural equation models. Asheboro, NC: Statistical Associates

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

    MATH  Google Scholar 

  • Hart, S. A. (2016). Precision education initiative: Moving toward personalized education. Mind, Brain, and Education, 10(4), 209–211. https://doi.org/10.1111/mbe.12109

    Article  Google Scholar 

  • Hattie, J. (2013). Calibration and confidence: Where to next? Learning and Instruction, 24, 62–66. https://doi.org/10.1016/j.learninstruc.2012.05.009

    Article  Google Scholar 

  • Henderson, D. J., & Russell, R. R. (2005). Human capital and convergence: A production-frontier approach. International Economic Review, 46(4), 1167–1205. https://doi.org/10.1111/j.1468-2354.2005.00364.x

    Article  Google Scholar 

  • Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-Learning system and their impact on student course assessment scores. Computational Intelligence and Neuroscience, 2018. https://doi.org/10.1155/2018/634718610.1155/2018/6347186.

  • Hwang, G.-J., Tsai, C.-C., & Yang, S.J.-H. (2008). Criteria, strategies and research issues of context-aware ubiquitous learning. Journal of Educational Technology & Society, 11(2), 81–91.

    Google Scholar 

  • Johnson, D. W., Johnson, R. T., & Stanne, M. B. (2000). Cooperative learning methods: a meta-analysis. Retrieved June 20, 2020, https://www.researchgate.net/publication/220040324_Cooperative_learning_methods_A_meta-analysis

  • Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K., (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium

  • Kaminskyi O.Y., Yereshko J., & Kyrychenko S.O. (2018). Digital transformation of University Education in Ukraine: Trajectories of Development in the conditions of new technological and economic order. Information Technologies and Learning Tools, 64(2), 128–137. https://doi.org/10.33407/itlt.v64i2.2083

  • Kay, J. (1997). Learner know Thyself: Student Models to Give Learner Control and Responsibility. In Z. Halim, T. Ottomann & Z. Razak (Eds.), Proceedings of International Conference on Computers in Education(pp. 17–24), Association for the Advancement of Computing in Education(AACE).

  • Khorashadi Zadeh, M., Karkon, A., & Golnari, H. (2017). The effect of information technology on the quality of accounting information. Journal of System Management, 3(3), 61–76.

    Google Scholar 

  • Lagstedt, A., Lindstedt, J. P., & Kauppinen, R. (2020). An outcome of expert-oriented digitalization of university processes. Education and Information Technologies, 25, 5853–5871. https://doi.org/10.1007/s10639-020-10252-x

    Article  Google Scholar 

  • Long Y., & Aleven V. (2013). Supporting Students’ Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor. In: H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science, vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_23

  • Lu, O. H. T., Huang, A. Y. Q., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Educational Technology & Society, 21(2), 220–232.

    Google Scholar 

  • MacLeod, J., Yang, H., Zhu, S., & Li, Y. (2018). Understanding students’ preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122, 80–91. https://doi.org/10.1016/j.compedu.2018.03.015

    Article  Google Scholar 

  • Marcelo-García, C., Yot-Domínguez, C., & Mayor-Ruiz, C. (2015). University teaching with digital technologies. . Comunicar, 23(45), 117–124. https://doi.org/10.3916/C45-2015-12

    Article  Google Scholar 

  • Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173

    Article  Google Scholar 

  • Mubin, O., Stevens, C. J., Shahid, S., Mahmud, A. A., & Dong, J.-J. (2013). A review of the applicability of robots in education. Technology for Education and Learning, 1, 209–215. https://doi.org/10.2316/Journal.209.2013.1.209-0015

    Article  Google Scholar 

  • Newman, T., & Beetham, H. (2017). Student digital experience tracker 2017: the voice of 22,000 UK learners. Retrieved from https://repository.jisc.ac.uk/6662/1/ Jiscdigitalstudenttracker2017.pdf

  • OECD. (2018). Going digital in a multilateral world. Retrieved from https://www.oecd.org/going-digital/C-MIN-2018-6-EN.pdf

  • Omolewa, O. T., Oladele, A. T., Adeyinka, A. A., & Oluwaseun, O. R. (2019). Prediction of student’s academic performance using k-means clustering and multiple linear regressions. Journal of Engineering and Applied Sciences, 14(22), 8254–5260. https://doi.org/10.36478/jeasci.2019.8254.8260

  • Phillips-Wren, G. (2014). Intelligent systems to support human decision making. In J. Wang (Ed.), Encyclopedia of Business Analytics and Optimization (pp. 1297–1309). IGI Publishing.

    Chapter  Google Scholar 

  • Phua, P. L., Wong, S. L., & Abu, R. (2012). Factors influencing the Behavioural intention to use the internet as a teaching-learning tool in home economics. Procedia - Social and Behavioral Sciences, 59, 180–187. https://doi.org/10.1016/j.sbspro.2012.09.263

    Article  Google Scholar 

  • Preston, A., Lazem, S., Kharrufa, A., Pursglove, B., & Olivier, P. (2018). Supporting the smart teacher: an agenda for the use of embedded sensing in novel learning spaces. Smart Learning Environments, 5(19). https://doi.org/10.1186/s40561-018-0068-8

  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/

  • Rahmana, M. M., Lesch, M. F., Horrey, W. J., & Strawdermana, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident Analysis & Prevention, 108, 361–373. https://doi.org/10.1016/j.aap.2017.09.01110.1016/j.aap.2017.09.011

    Article  Google Scholar 

  • Ribbe, E., & Bezenilla, M. J. (2013). Scaffolding learner autonomy in online university courses. Digital Education Review, 24, 98–113. https://doi.org/10.1344/der.2013.24.98-112

    Article  Google Scholar 

  • Saqr, M., & Alamro, A. (2019). The role of social network analysis as a learning analytics tool in online problem based learning. BMC Medical Education, 19, 160. https://doi.org/10.1186/s12909-019-1599-6

    Article  Google Scholar 

  • Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. (2018). What do we talk about when we talk about dashboards? IEEE Transactions on Visualization and Computer Graphics, 25(1), 682–692. https://doi.org/10.1109/TVCG.2018.2864903

    Article  Google Scholar 

  • Sathye, S., Prasad, B., Sharma, D., Sharma, P., & Sathye, M. (2018). Factors influencing the intention to use of mobile value-added services by women-owned microenterprises in Fiji. The Electronic Journal of Information Systems in Developing Countries, 84(2), e12016. https://doi.org/10.1002/isd2.12016

    Article  Google Scholar 

  • Shih, Y. Y., & Fang, K. (2004). The use of a Decomposed theory of planned behavior to study internet banking in Taiwan. Internet Research, 14(3), 213–223. https://doi.org/10.1108/10662240410542643

    Article  Google Scholar 

  • Slepcevic-Zach, P., & Stock, M. (2018). ePortfolio as a tool for reflection and self-reflection. Reflective Practice, 19(3), 291–307. https://doi.org/10.1080/14623943.2018.1437399

    Article  Google Scholar 

  • Slotta, J. (2010). Evolving the classrooms of the future: The interplay of pedagogy, technology and community. In: K. Makitalo-Siegl, J. Zottmann, F. Kaplan, & F. Fischer (Eds.), Classroom of the future: Orchestrating collaborative spaces (pp. 215–242). Rotterdam: Sense

  • Southgate, E., Blackmore, K., Pieschl, S., Grimes, S., McGuire, J., & Smithers, K. (2018). Artificial intelligence and emerging technologies (virtual, augmented and mixed reality) in schools: A research report. University of Newcastle, Australia.

    Google Scholar 

  • Spikol, D., Ruffaldi, E., & Cukurova, M. (2017). Using multimodal learning analytics to identify aspects of collaboration in project-based learning. In: B. K. Smith, M. Borge, E. Mercier, & K. Y. Lim, (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, Volume 1. Philadelphia, PA: International Society of the Learning Sciences

  • Starčič, A. I., & Vukan, M. (2019). Teachers' perception of data‐driven school ecosystem and data analytics. In: Proceedings of IC4E 2019 (IC4E 2019), January 10‐13, 2019, Tokyo, Japan. Association for Computer Machinery.

  • Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. The International Journal of Management Science, 41(8), 1328–1342. https://doi.org/10.1287/mnsc.41.8.1328

    Article  Google Scholar 

  • Sukhija, K., Jindal, M., & Aggarwal, N. (2015). The recent state of educational data mining: A survey and future visions. Presented at the 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE), (pp. 354–359). https://doi.org/10.1109/MITE.2015.7375344

  • Sullivan, F. R., & Keith, P. K. (2019). Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions. British Journal of Educational Technology, 50(6), 3047–3063. https://doi.org/10.1111/bjet.12875

    Article  Google Scholar 

  • Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007

    Article  Google Scholar 

  • Tanimoto, S. (2005). Dimensions of Transparency in Open Learner Models. In Proceedings of the AIED'05 - Workshop on Learner Modelling for Reflection, to Support Learner Control, Metacognition and Improved Communication between Teachers and Learners. AIED, IOS Press, 100 -106. Amsterdam, 2005

  • Valaei, N., & Baroto, M. B. (2017). Modelling continuance intention of citizens in government Facebook page: A complementary PLS approach. Computers in Human Behavior, 73, 224–237. https://doi.org/10.1016/j.chb.2017.03.047

    Article  Google Scholar 

  • der Heijden, V. (2003). Factors influencing the usage of websites: The case of a generic portal in the Netherlands. Information and Management, 40(6), 541–549. https://doi.org/10.1016/S0378-7206(02)00079-4

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24, 115–139. https://doi.org/10.2307/3250981

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Verina, N., & Titko, J. (2019). Digital transformation: conceptual framework. International Scientific Conference Contemporary Issues in Business, Management and Economics Engineering, VGTU Press. https://doi.org/10.3846/cibmee.2019.073

  • Vesin, B., Mangaroska, K., & Giannakos, M. (2018). Learning in smart environments: User-centered design and analytics of an adaptive learning system. Smart Learning Environment, 5, 24. https://doi.org/10.1186/s40561-018-0071-0

    Article  Google Scholar 

  • Weng, T.-C., & Lyau, N.-M. (2018). Using the technology acceptance model to analyze students’ cognitive attitude toward a head acupuncture point assisted training simulator. International Journal on Digital Learning Technology, 10(4), 59–84. https://doi.org/10.3966/2071260X2018101004003

    Article  Google Scholar 

  • Wilson, C., & Scott, B. (2017). Adaptive systems in education: A review and conceptual unification. The International Journal of Information and Learning Technology, 34(1), 2–19. https://doi.org/10.1108/IJILT-09-2016-0040

    Article  Google Scholar 

  • Yan, H., Lin, F., & Kinshuk, . (2020). Including learning analytics in the loop of self-paced online course learning design. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-020-00225-z

    Article  Google Scholar 

  • Yastibasa, A. E., & Yastibas, G. C. (2015). The Use of E-portfolio-based assessment to develop students’ self-regulated learning in english language teaching. Procedia - Social and Behavioral Sciences, 176(20), 3–13. https://doi.org/10.1016/j.sbspro.2015.01.437

    Article  Google Scholar 

  • Yoo Y., Lee H., Jo IH., Park Y. (2015) Educational dashboards for smart learning: review of case studies. In: Chen G., Kumar V., Kinshuk, Huang R., Kong S. (eds) Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_21

  • Zaharias, P., & Poylymenakou, A. (2009). Developing a usability evaluation method for e-learning applications: Beyond functional usability. International Journal of Human-Computer Interaction, 25(1), 75–98. https://doi.org/10.1080/10447310802546716

    Article  Google Scholar 

  • Zapata-Rivera, D. (2020). Open Student Modeling Research and its Connections to Educational Assessment. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-020-00206-2

  • Zhang, W., & Guterrez, O. (2007). Information technology acceptance in the social services sector context: An exploration. Social Work, 52(3), 221–231. https://doi.org/10.1093/sw/52.3.221

    Article  Google Scholar 

  • Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56(1), 513–524. https://doi.org/10.1016/j.dss.2012.11.008

    Article  Google Scholar 

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This work was supported by the MOE Teaching Practice Research Program [grant number PGE1080215].

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  1. Center for General Education, College of Future, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan

    Yung-Hsiang Hu

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Hu, YH. Effects and acceptance of precision education in an AI-supported smart learning environment. Educ Inf Technol 27, 2013–2037 (2022). https://doi.org/10.1007/s10639-021-10664-3

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