Data Literacy and Data Usage Amongst Teaching Staff in UK Higher Education Institutions: Current Practices, Challenges, and Aspirations.
Saved in:
| Title: | Data Literacy and Data Usage Amongst Teaching Staff in UK Higher Education Institutions: Current Practices, Challenges, and Aspirations. |
|---|---|
| Authors: | KALAITZOPOULOU, Eirini1 eirini.kalaitzopoulou@uwe.ac.uk, CHRISTOPOULOS, Athanasios2 atchri@utu.fi, MATTHEWS, Paul1 paul2.matthews@uwe.ac.uk |
| Source: | Informatics in Education. Sep2025, Vol. 24 Issue 3, p535-558. 24p. |
| Subject Terms: | *Data analysis, *Statistical literacy, *Higher education, *Professional education, *Universities & colleges, *Effective teaching, Statistical decision making, Lecturers |
| Geographic Terms: | United Kingdom |
| Abstract: | While research on Learning Analytics (LA) is plentiful, it often prioritises perspectives on LA systems over the practical ways instructors use data to analyse and refine the learning process per se. The present study addresses this inadequacy by investigating how student data is employed by educators in UK Higher Education Institutions (HEIs) and how it could be optimised. Specifically, a mixed-methods approach was employed combining survey data, mainly from one institution (N = 85) with insights gleaned from interviews with academics (N = 11). The findings reveal a real desire for better data capabilities and access, underscoring the need for HEIs to enhance data capture, better integrate systems and invest in professional development to enhance data literacy and foster a culture of data-driven decision-making. Importantly, a similar emphasis to that given to assessment and attendance needs to be given to data for the differentiation and personalisation of learning. [ABSTRACT FROM AUTHOR] |
| Copyright of Informatics in Education is the property of Informatics in Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
Be the first to leave a comment!
