Understanding students’ effective use of data in the age of big data in higher education

Wanli Xing and Xianhui Wang

Behaviour and Information Technology, 2022, vol. 41, issue 12, 2560-2577

Abstract: With the advancement of digital technologies, big data and learning analytics have become prevalent in the higher education. Various student-facing systems increased the amount of data available to students, and whether students can use big data and learning analytics effectively will affect their academic success. Most studies, however, have focused on how teachers and administrative personnel use student data to make data-driven instruction and management decisions. As a result, little attention has been given to students' use of relevant data that generated by big data and learning analytics to promote their own learning and growth. This study explored using social cognitive theory to identify possible environmental, personal, and behavioural factors that affect students' data use. We used an online questionnaire that collected 242 completed surveys from Chinese university students. Partial Least Squares (PLS) path modelling was used to analyse the data. The initial findings support the conclusion that university students could be encouraged to effectively use data in three ways: (1) through the promotion of university-wide cultures of data use and sustained improvements in data quality, (2) through the professional development of student data literacy, and (3) through the support of student data autonomy, student data reflectiveness, and students' digital identities.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0144929X.2021.1936176 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tbitxx:v:41:y:2022:i:12:p:2560-2577

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tbit20

DOI: 10.1080/0144929X.2021.1936176

Access Statistics for this article

Behaviour and Information Technology is currently edited by Dr Panos P Markopoulos

More articles in Behaviour and Information Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().