Abstract:

Self-regulated learning (SRL) is a cyclical process aimed at attaining personal goals and organized in three phases: forethought, performance and self-reflection (Zimmerman, 2000). It is widely considered to be a factor of academic success (Pintrich & De Groot, 1990).

SRL is usually measured indirectly, by deconstructing it into separate skills involved in the process of SRL. These skills are then measured by observing how students use corresponding SRL strategies, such as memory strategy, goal setting, self-evaluation, seeking assistance, environmental structuring, learning responsibility and organizing. There are a number of scales which measure SRL strategies, this study used the Academic Self-Regulated Learning scale (A-SRL-S) (Magno, 2010).

Traditionally SRL was studied in a classroom setting using think-aloud protocols or self-reporting. Widespread usage of online courses led to the emergence of SRL research based on digital traces, such as clicks on course modules and assignment completion time, which offered a more objective and authentic measurement of SRL strategies (e.g. Saint et al., 2022).

Nowadays, online elements have found their way to traditional classrooms through adoption of learning management systems (LMS). Consequently, advances in the field of online SRL research may be applied in offline or blended settings. For example, (Du et al., 2023) systematized groups of digital traces in correspondence to SRL strategies used by online students, and we assume this approach may be utilized in a blended setting.

Thus, the aim of this study is to investigate the possibility of using digital traces as an instrument to measure blended course students SRL.

To do that, we applied a mixed-methods strategy to the sample of 1st year masters students of a blended introductory course on data analysis in social sciences (n=16). Firstly, their digital traces were collected from university LMS. We obtained such data as clicks on lecture recordings and additional literature, visits to the course syllabus, the number of test attempts, etc. Digital traces were grouped corresponding to different types of SRL strategies based on previous literature (Du et al., 2023).

Secondly, we conducted a survey of the students' SRL strategies, using the A-SRL-S scale. Finally, we interviewed 9 students who volunteered. The interview was focused on how they applied SRL strategies during this course and their LMS usage.

Data analysis strategy consisted of two parts: quantitative and qualitative. We used correlation analysis to establish relationships between groups of digital traces and SRL strategies according to the questionnaire. The interviews aimed to explain some of the relationships found in the correlation analysis, as well as to obtain more information about student learning and perceptions to support the interpretation of the results.

We expected digital trace groups derived from literature to highly correlate with corresponding SRL strategies. However, only memory strategy and environmental structuring were correlated with digital traces, but not in an expected way. Despite possible insufficiency of the sample, these contradictions provided us information for the qualitative part of the research. Preliminary results have shown that substantial limitation of using digital traces to monitor STL strategies was the blended nature of the course. Nevertheless, we found out that digital traces can act as a good predictor of memory strategy use.

Keywords:

Self-regulated learning, SRL, digital traces.