Teachers and Their Critical Data Literacy Teaching Practices: The Role of Disciplines, Research Experience and Organizational Context

Abstract In recent years, data has fed a growing industry of automation and artificial intelligence (AI). Also, the COVID-19 pandemic forced and accelerated the “digitalization” of education, raising issues about data collection and usage, privacy, and ethics. This paper explores a survey about data literacy teaching practices in two, large-sized, Euro-Mediterranean universities. The aim was, among others, to identify which variables influence critical data teaching practices (namely, disciplinary area, research experience and organizational setting). After polishing the dataset, 562 over 1174 records remained. The results indicate statistically significant differences between social sciences and STEM academics in selected dimensions of data literacy practices. Social sciences teachers reported higher engagement in addressing ethical issues related to data collection and use in learning (SDL4, M = 2.49, + 0,63, p = .002) and in educational and research contexts (SDL5, M = 2.66, + 0,73, p < .001). They also reported more frequent attention to the social conditions shaping data and AI use (SDL3, M = 2.35, + 0,60, p = .001), suggesting a stronger tendency to frame data practices within broader societal and institutional constraints. However, the role of teachers’ research experience and organisational settings remains less clearly patterned, pointing to heterogeneous configurations of data practices that warrant further investigation.

Authors

Institutions

Publication Details

Journal
Technology Knowledge and Learning
Published
2026-05-16
DOI
https://doi.org/10.1007/s10758-026-09987-2
Primary Topic
Educational Assessment and Improvement
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Teachers and Their Critical Data Literacy Teaching Practices: The Role of Disciplines, Research Experience and Organizational Context

Flaviano Battistig, Juliana E. Raffaghelli
Technology Knowledge and Learning
Educational Assessment and Improvement
article

Teachers and Their Critical Data Literacy Teaching Practices: The Role of Disciplines, Research Experience and Organizational Context

Flaviano Battistig, Juliana E. Raffaghelli
article en

Abstract

Abstract In recent years, data has fed a growing industry of automation and artificial intelligence (AI). Also, the COVID-19 pandemic forced and accelerated the “digitalization” of education, raising issues about data collection and usage, privacy, and ethics. This paper explores a survey about data literacy teaching practices in two, large-sized, Euro-Mediterranean universities. The aim was, among others, to identify which variables influence critical data teaching practices (namely, disciplinary area, research experience and organizational setting). After polishing the dataset, 562 over 1174 records remained. The results indicate statistically significant differences between social sciences and STEM academics in selected dimensions of data literacy practices. Social sciences teachers reported higher engagement in addressing ethical issues related to data collection and use in learning (SDL4, M = 2.49, + 0,63, p = .002) and in educational and research contexts (SDL5, M = 2.66, + 0,73, p < .001). They also reported more frequent attention to the social conditions shaping data and AI use (SDL3, M = 2.35, + 0,60, p = .001), suggesting a stronger tendency to frame data practices within broader societal and institutional constraints. However, the role of teachers’ research experience and organisational settings remains less clearly patterned, pointing to heterogeneous configurations of data practices that warrant further investigation.

Technology Knowledge and Learning
University of Padua (IT)
Università degli Studi di Padova
Quality Education
Openalex Percentile: Top 2%
Educational Assessment and Improvement
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.