Personality predictors of self-regulation strategies for independent work of students in distance learning

Context and relevance. With the development of distance education, the role of various forms of students' psychological self-regulation is increasing, significantly contributing to their academic performance and well-being. Furthermore, distance learning technologies offer a unique opportunity to study students' self-regulation strategies by analyzing the “digital traces” of their work in an online learning environment. Objective: to study students' self-regulation strategies in learning using distance learning technologies, as well as their personality predictors, based on their “digital traces” in an online learning environment. Hypothesis. Two hypotheses were tested. 1. Based on students' “digital traces”, general self-regulation strategies in learning can be identified. 2. Personality variables, such as motivational, characterological, and volitional, can act as predictors of self-regulation strategies in learning. Methods and materials. The study involved 506 university students studying using distance learning technologies (average age 35.4 years, 465 women and 41 men). The digital traces were recorded over 100 days of theoretical instruction in the spring semester and then analyzed using latent profile analysis. The following measures were used to assess personality predictors: the Brief Academic Motivation Scale, the General Self-Efficacy Scale, the Big Five Personality Inventory (B5-10), the PIL test, the Action Control Scale, the Questionnaire for Identifying the Expression of Self-Control in Emotions, Activities, and Behavior, and the Self-Assessment of Volitional Traits. Linear discriminant analysis was used to identify significant predictors. Results. Four latent classes of students and their corresponding self-regulation strategies in learning were identified: balanced active, passive, overactive chaotic, and ultra-low fading. The main predictors of strategy choice were: course of study, conscientiousness, motivation, and social impulsivity. Conclusions. The obtained results indicate that “digital traces” can be an important source of information about students' self-regulation strategies.

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