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ORIGINAL RESEARCH article

Front. Educ.
Sec. Digital Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1406699

Analyzing Factors Promoting Teachers' Use of Lirmi: A Digital Monitoring System in Chile Using the Technology Acceptance Model

Provisionally accepted
Lucas Silva Lucas Silva *Maritté Tagle Maritté Tagle Isabel Loncomil Isabel Loncomil
  • Other, Santiago, Chile

The final, formatted version of the article will be published soon.

    Digital assessment data's increasing prevalence in schools offers a powerful tool to enhance educational outcomes, yet its adoption by teachers varies widely. For the first time, an extended version of the Technology Acceptance Model (TAM) was used in a Chilean sample to explore the factors that explain teachers' use of digital assessment data. An online survey was conducted with 319 teacher users of Lirmi, a consolidated online educational platform with a student assessment module. Using Structural Equation Modeling (SEM), we found that TAM remains a robust and parsimonious framework for explaining teachers' adoption of digital monitoring systems in a global southern country. While attitudes toward using the system, intention to use it, and perceived usefulness emerged as key predictors (consistent with prior TAM studies) the study provides novel insights by validating TAM in a real-world K-12 educational setting with a commercial product. Importantly, facilitating conditions were found to explain perceived ease of use, and subjective norms significantly influenced perceived usefulness, underscoring the role of infrastructure and peer influence. The results highlight the need for policymakers, school administrators, and ed-tech providers to ensure proper support to foster teachers' adoption of digital monitoring systems. By extending TAM's applicability to the Chilean context, this study contributes to understanding how teachers adopt digital tools for assessment in a specific national setting that remains underexplored in the literature.

    Keywords: data-informed decision-making, digital monitoring systems, Learning analytics, Technology acceptance model (TAM), Structural Equation Modeling

    Received: 25 Mar 2024; Accepted: 07 Feb 2025.

    Copyright: © 2025 Silva, Tagle and Loncomil. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lucas Silva, Other, Santiago, Chile

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.