Abstract:

Online education gives adult students an opportunity to enhance their position on the labor market, gain new skills, and pursue a modified career path. However, not all students are able to benefit from such a learning environment and achieve their educational goals. Commonly cited reasons for this include dropout, which can be attributed to learner-related, course-related or external factors. Despite the substantial growth of online education and considerable amount of research in this area, there is still a gap in understanding the relationship between students’ background and the likelihood of achieving their learning objectives.

Student engagement is a critical factor that has an impact on educational outcomes, such as a possibility of dropping out and, most significantly, academic achievement. Although, there is still a lack of universal understanding of the construct, student engagement is seen as a multidimensional construct which comprises behavioral, cognitive, and emotional engagement.

One of the key features of online learning is a high level of autonomy for students. Namely, they can determine the place, time, and the pace of their learning. Moreover, students are not limited in their choice of educational resources they can access. Based on modern research, we can assume that the increasing use of smartphones and the growing popularity of “free” platforms with educational content, such as YouTube and MOOCs, will further promote the concept of lifelong learning. This, in turn, might provide equal opportunities and benefits for adult learners. However, while some of the students are able to use this potential for professional growth, others may fail to reach their goals. Unlike in traditional academic settings, learners in online education have their own, various objectives. Therefore,we can expect this heterogeneous group with different ambitions to have varying levels and patterns of engagement.

In this research, we aim to find out whether the socio-demographic characteristics of students are associated with their level of engagement. For the research purposes, we used data from a Russian online educational platform. The data was collected from a survey of 2000 online learners aged 22 years and above. In order to examine the relation between variables and test our hypothesis, we plan to use linear regression analysis.

Our findings can benefit teachers and instructional designers who are interested in making education more effective. By understanding these relationships, we can consider making appropriate adjustments to online educational environments in order to tailor them to the needs of adult learners, and ultimately increase the payoff from online learning.

Keywords:

Lifelong learning, online education, engagement.