Methodological attempts to accommodate data from educational technology (EdTech) systems focus, to a significant extent, on the quality of information over the quality of the raw data. Popular data-driven methodologies, such as learning analytics and educational data mining rely on data collected from EdTech systems as their point of departure. However, contemporary research argues that high-quality analytics systems for educational decision-making should begin by assessing the quality of raw data, including efforts to identify - or “datafy” - previously unquantified aspects of the learners and the environments. This paper argues the need to datafy educational processes to optimise the reliance of data for enhancing education. With the assumption that high-quality data leads to high-quality analytics and that the quality of raw educational data can be improved through careful datafication, this scoping review explores three research questions: How is datafication defined in the education domain? What is its purpose? How has datafication been implemented in the literature?

The methodological approach is a scoping review using the PRISMA framework, yielding a selection of 153 articles for analysis. Three researchers conducted the selection process: two independently screened articles, categorizing them as “yes,” “no,” or “maybe,” while the third resolved conflicts. The articles were analyzed using a quantitative approach focussing on the terminology and semantics of the description of datafication in the selected articles.

Findings reveal that articles positions or mostly present, datafication, do not necessarily with the same underlying meaning. Although the terminological point of reference for datafication is Big Data by Schoenberger, et. al., most articles presented the concept closer to the autonomous harbouring of data in EdTech systems instead. An interesting outcome was how little the original description of datafication has changed throughout time. The meaning of the definition was consistence throughout the literature, irrespective of the time, however, the articles heavily focussing on datafication belong to the past 7 years, most of them are after 2023, a decade after its original definition. Thus, this study mapped different interpretations (or points of view) as a summary rather than finding a unified definition. Furthermore, there is no evidence for the validity and completeness of the term qualifying as a concrete definition. However, due to the advancement of technology, where almost any human physical action can be measurable, and recorded through technology, datafication is tapping the ethical boundaries, which may either be considered or included in the definition.

Based on the outcomes, this study brings into the spotlight a gap in the literature regarding the quality of raw data and the role of datafication towards a sustainable and data-driven educational environment. It underscores the potential of datafication to enhance insights into teaching, learning, and education more broadly. Additionally, the outcomes call for deeper investigation into datafication’s impact on user-centricity, privacy, and ethical considerations for preserving data subject privacy when optimising processes through datafication.