Trusting the data: an updated framework for teachers’ data-driven decision-making (DDDM) in higher education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".