Learning Analytics in Schools

Is Digital Data Use Influenced by Teacher-Level or School-Level Factors?

Authors

DOI:

https://doi.org/10.18608/jla.2025.8567

Keywords:

schools, teachers, learning analytics, digital data use, digital transformation, research paper

Abstract

Digital transformation in schools involves the use of digital data to inform teachers’ pedagogical decisions. Previous research indicates that a deeper understanding of the factors influencing teacher utilization of learning analytics and a comprehensive school context analysis is required. In this article, we conducted a survey study with N = 2,247 teachers in 112 upper secondary schools in Switzerland to examine teacher characteristics and school-related factors that impact teacher use of digital data for pedagogical purposes. The results show that teacher characteristics including their positive beliefs about technologies, competency with digital data, and availability of data technologies significantly predict their digital data use, with differences identified between subject matter teachers and schools. School-related factors about digitalization, such as formal and informal collaboration between colleagues and support from school principals, indirectly influence teachers’ digital data use, mediated by teacher characteristics. Based on these results, personalized support can be formulated for teacher utilization of learning analytics according to their characteristics and the supportive school environment.

References

Aguerrebere, C., He, H., Kwet, M., Laakso, M.-J., Lang, C., Marconi, C., Price-Dennis, D., & Zhang, H. (2022). Global perspectives on learning analytics in K–12 education. In C. Lang, A. F. Wise, A. Merceron, D. Gašević, & G. Siemens (Eds.), The handbook of learning analytics (2nd ed., pp. 223–231). Society for Learning Analytics Research. https://doi.org/10.18608/hla22.022

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

Beardsley, M., Santos, P., Hernández‐Leo, D., & Michos, K. (2019). Ethics in educational technology research: Informing participants on data sharing risks. British Journal of Educational Technology, 50(3), 1019–1034. https://doi.org/10.1111/bjet.12781

Bond, M., Viberg, O., & Bergdahl, N. (2023). The current state of using learning analytics to measure and support K–12 student engagement: A scoping review. LAK2023: 13th international learning analytics and knowledge conference (pp. 240–249). ACM Press. https://doi.org/10.1145/3576050.3576085

Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1

Cattaneo, A., Schmitz, M.-L., Gonon, P., Antonietti, C., Consoli, T., & Petko, D. (2025). The role of personal and contextual factors when investigating technology integration in general and vocational education. Computers in Human Behavior, 163, Article 108475. https://doi.org/10.1016/j.chb.2024.108475

Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017, June 5). Effective teacher professional development [Report]. Learning Policy Institute. https://learningpolicyinstitute.org/product/effective-teacher-professional-development-report

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

de Sousa, E. B. G., Alexandre, B., Ferreira Mello, R., Pontual Falcão, T., Vesin, B., & Gašević, D. (2021). Applications of learning analytics in high schools: A systematic literature review. Frontiers in Artificial Intelligence, 4, Article 737891. https://doi.org/10.3389/frai.2021.737891

Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138. https://doi.org/10.1037/1082-989X.12.2.121

Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551

Fang, G., Li, X., Chan, P. W. K., & Kalogeropoulos, P. (2024). A multilevel investigation into teacher-supported student use of technology in East Asian classroom: Examining teacher and school characteristics. Computers & Education, 218, Article 105092. https://doi.org/10.1016/j.compedu.2024.105092

Field, A. (2013). Discovering statistics using IBM SPSS statistics. SAGE Publications.

Gelman, A. (2007). Struggles with survey weighting and regression modeling. Statistical Science, 22(2), 153–164. https://doi.org/10.1214/088342306000000691

Håkansson Lindqvist, M. (2019). School leaders’ practices for innovative use of digital technologies in schools. British Journal of Educational Technology, 50(3), 1226–1240. https://doi.org/10.1111/bjet.12782

Hase, A., Kahnbach, L., Kuhl, P., & Lehr, D. (2022). To use or not to use learning data: A survey study to explain German primary school teachers’ usage of data from digital learning platforms for purposes of individualization. Frontiers in Education, 7, Article 920498. https://doi.org/10.3389/feduc.2022.920498

Hase, A., & Kuhl, P. (2024). Teachers’ use of data from digital learning platforms for instructional design: A systematic review. Educational Technology Research and Development, 72(4), 1925–1945. https://doi.org/10.1007/s11423-024-10356-y

Hill, H. C., Lynch, K., Gonzalez, K. E., & Pollard, C. (2020). Professional development that improves STEM outcomes. Phi Delta Kappan, 101(5), 50–56. https://doi.org/10.1177/0031721720903829

Hirsto, L., Saqr, M., López-Pernas, S., & Valtonen, T. (2022). A systematic narrative review of learning analytics research in K–12 and schools. In L. Hirsto, S. López-Pernas, M. Saqr, E. Sointu, T. Valtonen, & S. Väisänen (Eds.), Proceedings of the 1st Finnish learning analytics and artificial intelligence in education conference (FLAIEC 2022) (Article 9536). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-3383/FLAIEC22_paper_9536.pdf

Howard, S. K., Swist, T., Gašević, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N., & Selwyn, N. (2022). Educational data journeys: Where are we going, what are we taking and making for AI? Computers and Education: Artificial Intelligence, 3, Article 100073. https://doi.org/10.1016/j.caeai.2022.100073

Hung, J.-L., Shelton, B. E., Yang, J., & Du, X. (2019). Improving predictive modeling for at-risk student identification: A multistage approach. IEEE Transactions on Learning Technologies, 12(2), 148–157. https://doi.org/10.1109/TLT.2019.2911072

Ifenthaler, D. (2021). Learning analytics for school and system management. In OECD digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots (pp. 161–172). OECD Publishing.

Inan, F. A., & Lowther, D. L. (2010). Factors affecting technology integration in K–12 classrooms: A path model. Educational Technology Research and Development, 58(2), 137–154. https://doi.org/10.1007/s11423-009-9132-y

Jarke, J., & Breiter, A. (2019). Editorial: The datafication of education. Learning, Media and Technology, 44(1), 1–6. https://doi.org/10.1080/17439884.2019.1573833

Jimerson, J. B., & Childs, J. (2017). Signal and symbol: How state and local policies address data-informed practice. Educational Policy, 31(5), 584–614. https://doi.org/10.1177/0895904815613444

Karademir, O., Di Mitri, D., Schneider, J., Jivet, I., Allmang, J., Gombert, S., Kubsch, M., Neumann, K., & Drachsler, H. (2024). I don’t have time! But keep me in the loop: Co-designing requirements for a learning analytics cockpit with teachers. Journal of Computer Assisted Learning, 40(6), 2681–2699. https://doi.org/10.1111/jcal.12997

Knezek, G., & Christensen, R. (2016). Extending the will, skill, tool model of technology integration: Adding pedagogy as a new model construct. Journal of Computing in Higher Education, 28(3), 307–325. https://doi.org/10.1007/s12528-016-9120-2

Kovanovic, V., Mazziotti, C., & Lodge, J. (2021). Learning analytics for primary and secondary schools. Journal of Learning Analytics, 8(2), 1–5. https://doi.org/10.18608/JLA.2021.7543

Krein, U., & Schiefner-Rohs, M. (2021). Data in schools: (Changing) practices and blind spots at a glance. Frontiers in Education, 6, Article 672666. https://doi.org/10.3389/feduc.2021.672666

Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. Routledge.

Lang, C., Siemens, G., Wise, A., & Gašević, D. (Eds.). (2017). The handbook of learning analytics. Society for Learning Analytics and Research.

Lee, J., Alonzo, D., Beswick, K., Abril, J. M. V., Chew, A. W., & Oo, C. Z. (2024). Dimensions of teachers’ data literacy: A systematic review of literature from 1990 to 2021. Educational Assessment, Evaluation and Accountability, 36(2), 145–200. https://doi.org/10.1007/s11092-024-09435-8

Lockton, M., Weddle, H., & Datnow, A. (2020). When data don’t drive: Teacher agency in data use efforts in low-performing schools. School Effectiveness and School Improvement, 31(2), 243–265. https://doi.org/10.1080/09243453.2019.1647442

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. https://er.educause.edu/-/media/files/article-downloads/erm1151.pdf

Martens, M., De Wolf, R., & De Marez, L. (2024). Datafication and algorithmization of education: How do parents and students evaluate the appropriateness of learning analytics? Education and Information Technologies, 29(7), 8151–8177. https://doi.org/10.1007/s10639-023-12124-6

Mavroudi, A., Papadakis, S., & Ioannou, I. (2021). Teachers’ views regarding learning analytics usage based on the technology acceptance model. TechTrends, 65(3), 278–287. https://doi.org/10.1007/s11528-020-00580-7

Mandinach, E. B., & Abrams, L. M. (2022). Data literacy and learning analytics. In C. Lang, A. F. Wise, A. Merceron, D. Gašević, & G. Siemens (Eds.), Handbook of learning analytics (pp. 196–203). Society for Learning Analytics Research. https://doi.org/10.18608/hla22.019

Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011

McNeish, D. (2017). Multilevel mediation with small samples: A cautionary note on the multilevel structural equation modeling framework. Structural Equation Modeling: A Multidisciplinary Journal, 24(4), 609–625. https://doi.org/10.1080/10705511.2017.1280797

Meinck, S. (2015). Computing sampling weights in large-scale assessments in education. Survey methods: Insights from the field. https://doi.org/10.13094/SMIF-2015-00004

Michos, K., Schmitz, M.-L., & Petko, D. (2023). Teachers’ data literacy for learning analytics: A central predictor for digital data use in upper secondary schools. Education and Information Technologies, 28(11), 14453–14471. https://doi.org/10.1007/s10639-023-11772-y

Michos, K., Lang, C., Hernández-Leo, D., & Price-Dennis, D. (2020). Involving teachers in learning analytics design: Lessons learned from two case studies. In C. Rensing, H. Drachsler, V. Kovanović, N. Pinkwart, M. Scheffel, & K. Verbert (Eds.), LAK ’20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 94–99). ACM Press. https://doi.org/10.1145/3375462.3375507

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Molenaar, I., & Knoop-van Campen, C. A. N. (2019). How teachers make dashboard information actionable. IEEE Transactions on Learning Technologies, 12(3), 347–355. https://doi.org/10.1109/TLT.2018.2851585

Monroy, C., Rangel, V. S., & Whitaker, R. (2014). A strategy for incorporating learning analytics into the design and evaluation of a K–12 science curriculum. Journal of Learning Analytics, 1(2), 94–125. https://doi.org/10.18608/jla.2014.12.6

Oberski, D. (2014). lavaan. survey: An R package for complex survey analysis of structural equation models. Journal of Statistical Software, 57(1), 1–27. https://doi.org/10.18637/jss.v057.i01

Petko, D. (2012). Teachers’ pedagogical beliefs and their use of digital media in classrooms: Sharpening the focus of the “will, skill, tool” model and integrating teachers’ constructivist orientations. Computers & Education, 58(4), 1351–1359. https://doi.org/10.1016/j.compedu.2011.12.013.

Petko, D., Prasse, D., & Cantieni, A. (2018). The interplay of school readiness and teacher readiness for educational technology integration: A structural equation model. Computers in the Schools, 35(1), 1–18. https://doi.org/10.1080/07380569.2018.1428007

Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48(1), 85–112. https://doi.org/10.1016/j.jsp.2009.09.002

Prasse, D. (2012). Bedingungen innovativen Handelns in Schulen: Funktion und Interaktion von Innovationsbereitschaft. Innovationsklima und Akteursnetzwerken am Beispiel der IKT Integration an Schulen [Conditions of innovative action in schools: Function and interaction of willingness to innovate. Innovations climate and networks of actors using the example of ICT integration in schools]. Waxmann.

Prenger, R., & Schildkamp, K. (2018). Data-based decision making for teacher and student learning: A psychological perspective on the role of the teacher. Educational Psychology, 38(6), 734–752. https://doi.org/10.1080/01443410.2018.1426834

Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps. Educational Research, 61(3), 257–273. https://doi.org/10.1080/00131881.2019.1625716

Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482–496. https://doi.org/10.1016/j.tate.2009.06.007

Selwyn, N. (2023). “There is a danger we get too robotic”: An investigation of institutional data logics within secondary schools. Educational Review, 75(3), 377–393. https://doi.org/10.1080/00131911.2021.1931039

Sergis, S., Sampson, D. G., Rodríguez-Triana, M. J., Gillet, D., Pelliccione, L., & de Jong, T. (2019). Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education. Computers in Human Behavior, 92, 724–738. https://doi.org/10.1016/j.chb.2017.12.014

Sergis, S., & Sampson, D. G. (2016). School analytics: A framework for supporting school complexity leadership. In J. M. Spector, D. Ifenthaler, D. G. Sampson, & P. Isaias (Eds.), Competencies in teaching, learning and educational leadership in the digital age: Papers from CELDA 2014 (pp. 79–122). Springer Cham. https://doi.org/10.1007/978-3-319-30295-9_6

Stapleton, L. M., McNeish, D. M., & Yang, J. S. (2016). Multilevel and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 51(3–4), 317–330. https://doi.org/10.1080/00461520.2016.1207178

Buckingham Shum, S. (2012, November). Learning analytics [Policy brief]. UNESCO Institute for Information Technologies in Education. https://iite.unesco.org/pics/publications/en/files/3214711.pdf

Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8

Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69(2), 1101–1133. https://doi.org/10.1007/s11423-021-09977-4

Ullman, J. B., & Bentler, P. M. (2012). Structural equation modeling. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology: Research methods in psychology (2nd ed., pp. 661–690). John Wiley & Sons.

van Leeuwen, A., Knoop-van Campen, C. A. N., Molenaar, I., & Rummel, N. (2021). How teacher characteristics relate to how teachers use dashboards: Results from two case studies in K–12. Journal of Learning Analytics, 8(2), 6–21. https://doi.org/10.18608/JLA.2021.7325

van Leeuwen, A., Rummel, N., & van Gog, T. (2019). What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations? International Journal of Computer-Supported Collaborative Learning, 14(3), 261–289. https://doi.org/10.1007/s11412-019-09299-x

Vanlommel, K., & Schildkamp, K. (2019). How do teachers make sense of data in the context of high-stakes decision making? American Educational Research Journal, 56(3), 792–821. https://doi.org/10.3102/0002831218803891

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509. https://doi.org/10.1177/0002764213479363

Wayman, J. C., Wilkerson, S. B., Cho, V., Mandinach, E. B., & Supovitz, J. A. (2016, October). Guide to using the teacher data use survey. U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Appalachia. https://ies.ed.gov/rel-appalachia/2025/01/teacher-data-use-survey-guide

Wiley, K., Dimitriadis, Y., & Linn, M. (2024). A human-centred learning analytics approach for developing contextually scalable K–12 teacher dashboards. British Journal of Educational Technology, 55(3), 845–885. https://doi.org/10.1111/bjet.13383

Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871–1885. https://doi.org/10.1111/jcal.12988

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Published

2025-10-04

How to Cite

Michos, K., Schmitz, M.-L., & Petko, D. (2025). Learning Analytics in Schools: Is Digital Data Use Influenced by Teacher-Level or School-Level Factors?. Journal of Learning Analytics, 12(3), 87-101. https://doi.org/10.18608/jla.2025.8567

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Research Papers