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Abstract
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.
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