The article presents the results of the study on the patterns of behavior of massive open online courses (MOOCs) learners in the context of motives to the learning, set criteria of quality, and also the engagement rate. Within the research the data collected on learners enrolled in online courses of Tomsk State University on the online platforms Courcera, Lectorium and National Open Education Platform was classified and analyzed. It allowed us to define the students' patterns of behavior, to reveal the goals of learning on MOOC, social and demographic characteristics of the students, and also the quality criteria of the courses set by the students. As a result of data processing, a correlation between the above-mentioned parameters was found.
The study aims to identify, test, and evaluate the effectiveness of current learner support models of online courses based on the analysis of online courses from the leading platforms, and to identify relevant support tools both built into online learning platforms and outside platforms. The study is based on the hypothesis that it is possible to increase students’ engagement rate in online learning using the platform data, the patterns of student behavior on MOOCs, and various user support tools. The study employed the following methods: questionnaire survey, data analysis, descriptive statistics, cluster analysis, visualization, and comparative analysis. The study was carried out in four stages. At the first stage, the authors of the article subscribed to MOOCs on 9 platforms and observed the tools used to support listeners. Using the collected data, they constructed a matrix of correspondence of courses and used support tools and made a list of the most common listener support tools in MOOCs. At the second stage, the authors conducted a survey to identify students’ perceptions of and expectations from various support tools in the MOOC on the Coursera and Lectorium platforms. The confidence level was 97%, the confidence interval was 8%. The authors learned that there was a request from learners for new tools that would make communication more efficient and support more individualized. At the third stage, the authors used the principles of electronic moderation by Gilly Salmon and the result of the analysis of the most frequently used and most demanded support tools on platforms with a view to design three models of MOOC listener support. The platform, off-platform and mixed models were developed. At the fourth stage, the authors conducted a pedagogical experiment to test the developed support models. The cluster analysis method revealed patterns of listeners’ behavior before using the three support models (control group) and during the application of these models (experimental group). The behavior patterns of students of each of the six MOOCs were considered in the context of one of the support models. The emphasis was on the behavior of students in completing assignments and in viewing video lectures as key characteristics of the educational activities of students of online courses. The experiment showed the nonviability of the off-platform tracking model, the low efficiency of the platform model. A comparative analysis of the behavior patterns of the control and experimental groups showed that maximum efficiency was achieved with a mixed support model: the engagement rate of students increased, which is manifested in the number of students who completed more than 10% of the tasks and/or completed the course. The increase averaged over 2%. It is necessary to build learner support on a mixed basis, combining the resources of online platforms and ecosystem agents that can be integrated into online platforms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.