Data-Based Decision Making in Online Secondary Courses: A Multi-Level Examination of Practices, Determinants, and Needs.

Abstract

Data-based decision-making (DBDM) has become a cornerstone of educational policy and practice, aiming to enhance student learning through conducting informed instructional and pedagogical decisions (Curry et al., 2016; Hebbecker et al., 2022; Peters et al., 2021; van der Scheer et al., 2016). By leveraging various forms of student data, such as assessment results, engagement metrics, and learning analytics, educators are expected to tailor instruction, identify struggling students, and improve overall academic outcomes (Faber et al., 2018; Voithofer & Golan, 2019). However, despite its potential, the effective implementation of DBDM remains inconsistent, particularly in secondary and online education, where research is limited. This thesis, structured as a series of three articles, explores global trends in teacher engagement with DBDM, the factors shaping its use among Ontario secondary school teachers in online courses, and the professional learning opportunities needed to strengthen their DBDM practices. The first article presents a scoping review of international research on teacher DBDM, which reveals geographical and temporal patterns and highlights gaps in secondary and online education, underscoring the need for more sustainable DBDM support. The second article is a mixed methods study that investigates Ontario secondary teachers’ engagement with DBDM in online courses and identifies key influencing factors such as collaboration, leadership, data quality, and teacher efficacy. The findings suggest that while collaboration and efficacy promote data use, concerns regarding data accessibility and DBDM-related anxiety pose barriers to effective DBDM use. The third article investigates Ontario secondary teachers’ needs to be able to use data effectively by examining gaps in their data competencies and proposes targeted professional learning opportunities. Findings emphasize the necessity of training in AI-driven analytics, subject-specific DBDM applications, and scenario-based learning to support teachers in effectively integrating data into their instructional practices. Together, these studies contribute to a deeper understanding of teacher engagement with DBDM and offer empirical insights into the landscape that shape data use in education. By examining both enablers and barriers to data use within the online teaching environment, this research provides actionable recommendations for professional development, and future research to enhance data-informed teaching and improve student outcomes.