This study examines factors influencing Learning Analytics Technology (LAT) acceptance among secondary school teachers in Chongqing, China, and investigates its relationship with teaching decision-making. Through a quantitative cross-sectional design, data were collected from 341 teachers (197 urban, 144 rural) using a structured questionnaire. Descriptive statistics revealed moderately high overall LAT acceptance (M=3.68, SD=0.79), with significant differences between urban (M=3.92, SD=0.65) and rural teachers (M=3.44, SD=0.78). Correlation analysis identified strong associations between LAT acceptance and teacher training (r=0.624, p<0.001), management support (r=0.581, p<0.001), and technology availability (r=0.537, p<0.001), with peer influence showing a moderate relationship (r=0.429, p<0.001). LAT usage demonstrated a significant positive correlation with teaching decision-making efficacy (r=0.578, p<0.001), particularly with decision quality (r=0.594, p<0.001). This relationship was stronger among urban teachers (r=0.612, p<0.001) than rural counterparts (r=0.524, p<0.001), highlighting an urban-rural divide in educational technology benefits. The findings provide empirical evidence for educational administrators and policymakers seeking to enhance LAT implementation in secondary schools, suggesting that comprehensive approaches addressing professional development, institutional support, and technological infrastructure are essential for effective LAT adoption, with particular attention needed to reduce urban-rural disparities.

Keywords: Learning analytics technology; technology acceptance; secondary school teachers; teaching decision-making; urban-rural divide.

References

Casey, J. E., Kirk, J., Kuklies, K., & Mireles, S. V. (2023). Using the technology acceptance model to assess how preservice teachers' view educational technology in middle and high school classrooms. Education and Information Technologies, 28(2), 2361-2382. https://doi.org/10.1007/s10639-022-11263-6

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Davis, F. D. (1989). Technology acceptance model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption (pp. 205-219). IGI Global.

Du, J., Li, W., & Li, Q. (2022). Who are we, what can we do, and what do we think? Review on EAP teacher development. Chinese Journal of Applied Linguistics, 45(4), 532-550. https://doi.org/10.1515/cjal-2022-0403

Frøsig, T. B. (2023). Expanding the Technology Acceptance Model (TAM) to consider teachers' needs and concerns in the design of educational technology (EdTAM). International Journal of Emerging Technologies in Learning, 18(16), 130-140. https://doi.org/10.3991/ijet.v18i16.42319

Gasevic, D., Tsai, Y. S., Dawson, S., & Pardo, A. (2019). How do we start? An approach to learning analytics adoption in higher education. The International Journal of Information and Learning Technology, 36(4), 342-353. https://doi.org/10.1108/IJILT-02-2019-0024

Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2020). Learning analytics in education: Literature review and case examples from vocational education. Scandinavian Journal of Educational Research, 64(7), 1105-1119. https://doi.org/10.1080/ 00313831.2019.1649718

Granić, A. (2023). Technology acceptance and adoption in education. In R. Huang, J. M. Spector, & J. Yang (Eds.), Handbook of open, distance and digital education (pp. 183-197). Springer Nature Singapore. https://doi.org/10.1007/ 978-981-19-2080-6_11

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. https://doi.org/10.1111/bjet.12864

Guo, Y., & Li, X. (2024). Regional inequality in China's educational development: An urban-rural comparison. Heliyon, 10(4), Article e26087. https://doi.org/10.1016/j.heliyon.2024.e26249

Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18, Article 23. https://doi.org/10.1186/s41239-021-00258-x

Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: State of the art. International Journal on Interactive Design and Manufacturing, 16(3), 1209-1230. https://doi.org/10.1007/s12008-022-00930-0

Hong, X., Zhang, M., & Liu, Q. (2021). Preschool teachers' technology acceptance during the COVID-19: An adapted technology acceptance model. Frontiers in Psychology, 12, Article 691492. https://doi.org/10.3389/fpsyg.2021.691492

Huang, F., & Teo, T. (2021). Examining the role of technology‐related policy and constructivist teaching belief on English teachers' technology acceptance: A study in Chinese universities. British Journal of Educational Technology, 52(1), 441-460. https:// doi.org/10.1111/bjet.13027

Ifenthaler, D., Gibson, D., Prasse, D., Shimada, A., & Yamada, M. (2021). Putting learning back into learning analytics: Actions for policy makers, researchers, and practitioners. Educational Technology Research and Development, 69, 2131-2150. https://doi.org/10.1007/s11423-020-09909-8

Jiang, M., Ahmad, A. L., & Aziz, J. (2024). New media and cross-cultural adaptation: A bibliometric analysis using VOSviewer. e-BANGI Journal, 21(1), 139-163. https://doi.org/10.17576/ebangi.2024.2101.24

Karsh, S. A. (2018). New technology adoption by business faculty in teaching: Analyzing faculty technology adoption patterns. International Journal of Technology in Education and Science, 2(1), 17-30. https://doi.org/10.11648/j.edu.20180701.12

Kew, S. N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405-427. https://doi.org/10.1007/s10758-021-09541-2

Kotrlik, J. W., & Redmann, D. H. (2009). Technology adoption for use in instruction by secondary technology education teachers. Journal of Technology Education, 21(1), 44-59. https://doi.org/10.21061/jte.v21i1.a.3

Krejcie, R. V., & Morgan, D. W. (1970). Sample size determination table. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/001316447003000308

Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: Learning analytics and decision making. Behaviour & Information Technology, 38(4), 410-421. https://doi.org/10.1080/0144929X.2018.1539517

Lee, L. K., Cheung, S. K., & Kwok, L. F. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7, 1-6. https://doi.org/10.1007/s40692-020-00155-8

Leitner, P., Khalil, M., & Ebner, M. (2017). Learning analytics in higher education—A literature review. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends (pp. 1-23). Springer. https://doi.org/10.1007/978-3-319-52977-6_1

Li, Y., & Ranieri, M. (2013). Educational and social correlates of the digital divide for rural and urban children: A study on primary school students in a provincial city of China. Computers & Education, 60(1), 197-209. https://doi.org/10.1016/j.compedu.2012.08.001

Liu, H., Wang, L., & Koehler, M. J. (2019). Exploring the intention‐behavior gap in the technology acceptance model: A mixed‐methods study in the context of foreign‐language teaching in China. British Journal of Educational Technology, 50(5), 2536-2556. https://doi.org/10.1111/bjet.12824

Liu, Z., Ren, Y., Kong, X., & Liu, S. (2022). Learning analytics based on wearable devices: A systematic literature review from 2011 to 2021. Journal of Educational Computing Research, 60(6), 1514-1557. https://doi.org/10.1177/07356331211064780

Luo, H., Zuo, M., & Wang, J. (2022). Promise and reality: Using ICTs to bridge China's rural–urban divide in education. Educational Technology Research and Development, 70(3), 1125-1147. https://doi.org/10.1007/s11423-022-10118-8

Lytras, M. D., Aljohani, N. R., Visvizi, A., Ordonez De Pablos, P., & Gasevic, D. (2018). Advanced decision-making in higher education: Learning analytics research and key performance indicators. Behaviour & Information Technology, 37(10-11), 937-940. https://doi.org/10.1080/0144929X.2018.1512940

Meng, J., Zheng, X., Zhang, Z., & Zhang, L. (2024). Strategic decision-making in higher education learning management system adoption using hybrid intuitionistic fuzzy method. Expert Systems with Applications, 238, Article 121746. https://doi.org/10.21203/rs.3.rs-5231849/v1

Mukred, M., Asma'Mokhtar, U., Hawash, B., AlSalman, H., & Zohaib, M. (2024). The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model. Heliyon, 10(4), Article e26230. https://doi.org/10.1016/j.heliyon.2024.e26315

Niet, Y. V., Díaz, V. G., & Montenegro, C. E. (2016, September). Academic decision making model for higher education institutions using learning analytics. In 2016 4th International Symposium on Computational and Business Intelligence (ISCBI) (pp. 27-32). IEEE. https://doi.org/10.1109/ISCBI.2016.7743255

Ouyang, F., & Zhang, L. (2024). AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Research Review, 44, Article 100616. https://doi.org/10.1016/j.edurev.2024.100616

Peña-Ayala, A. (2017). Learning analytics: Fundaments, applications, and trends. A view of the current state of the art to enhance e-learning. Springer. https://doi.org/10.1007/978-3-319-52977-6

Song, Z. (2023). Disparity in educational resources between urban and rural areas in China. Journal of Advanced Research in Education, 2(5), 64-69. https://doi.org/10.56397/JARE.2023.09.06

Sun, P. P., & Mei, B. (2022). Modeling preservice Chinese-as-a-second/foreign-language teachers' adoption of educational technology: A technology acceptance perspective. Computer Assisted Language Learning, 35(4), 816-839. https://doi.org/10.1080/09588221.2020.1750430

Teo, T. (Ed.). (2011). Technology acceptance in education. Springer Science & Business Media. https://doi.org/10.1007/978-94-6091-487-4

Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers' acceptance of Web 2.0 technologies in their future teaching: A Chinese perspective. Interactive Learning Environments, 27(4), 530-546. https://doi.org/10.1080/10494820.2018.1489290

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

Wang, D., Zhou, T., & Wang, M. (2021). Information and communication technology (ICT), digital divide and urbanization: Evidence from Chinese cities. Technology in Society, 64, Article 101516. https://doi.org/10.1016/j.techsoc.2020.101516

Wei, N. (2022). Decreasing land use and increasing information infrastructure: Big data analytics driven integrated online learning framework in rural education. Frontiers in Environmental Science, 10, Article 1025646. https://doi.org/10.3389/fenvs.2022.1025646

Wu, N., & Mustafa, S. E. (2023). The current situation and influential factors of bottom-up technology transmission in Chinese rural families. e-BANGI Journal, 20(2), 78-95. https://doi.org/10.17576/ebangi.2023.2002.05

Yang, J., Wang, Q., Wang, J., Huang, M., & Ma, Y. (2021). A study of K-12 teachers' TPACK on the technology acceptance of E-schoolbag. Interactive Learning Environments, 29(7), 1062-1075. https://doi.org/10.1080/10494820.2019.1627560

Zhang, L., Wu, M., & Ouyang, F. (2024). The design and implementation of a teaching and learning analytics tool in a face-to-face, small-sized course in China's higher education. Education and Information Technologies, 29(3), 2697-2720. https://doi.org/10.1007/s10639-023-11940-0

Zilvinskis, J., Willis III, J., & Borden, V. M. (2017). An overview of learning analytics. New Directions for Higher Education, 2017(179), 9-17. https://doi.org/10.1002/he.20239