Trusting the data: an updated framework for teachers’ data-driven decision-making (DDDM) in higher education

Abstract This study presents an enhanced framework for Data-Driven Decision-Making (DDDM) in higher education, explicitly designed to address the complexities of contemporary teaching and learning environments. Expanding on Mandinach, Honey, and Light’s foundational 2006 framework, this study integrates pedagogical and contextual data with traditional technological inputs, creating a holistic approach to faculty decision-making. The study explores two primary questions: (1) How does the integration of pedagogical, contextual, and technological data sources influence iterative faculty decision-making over time, and (2) How do these integrated data sources guide faculty responses to real-time challenges such as COVID-19 and Generative Artificial Intelligence (GenAI)? Through a five-year longitudinal case study of an elective undergraduate course utilizing Wikipedia, Wikidata, Learning Management Systems (LMS), and real-time communication tools, this research illustrates how diverse data sources informed iterative decisions, course adaptations, and instructional strategies. Findings reveal that effective DDDM requires integrating quantitative technological data with qualitative pedagogical insights and contextual factors such as external disruptions. The proposed framework demonstrates adaptability, addressing emergent educational challenges including the COVID-19 pandemic and the integration of GenAI into teaching practices. This study uniquely contributes to the existing discourse by articulating how the updated DDDM framework builds upon and enhances Mandinach et al.‘s original model. The framework offers educators an empirically-informed model for integrating diverse data sources to enhance student engagement, course design, and learning outcomes. By bridging existing gaps, this updated framework significantly advances data-driven practices in higher education.