Learning Behaviours in Massive Private Online Courses and Their Influencing Factors¹:
Data from the Open University of China
Shi Lei, Cheng Gang, Li Chao, Wei Shunping
The Open University of China
Abstract: This study collects data from 54,228 distance learners enrolled in 57 Massive Private Online Courses (MPOCs) at the Open University of China (OUC) in the autumn term of 2015. Descriptive and correlation analyses were conducted based on more than 56 million learning behaviour logs created by these students during their online learning. Drawing further upon data from other sources, including tracking teaching processes, interviews with both staff and students, and the online learning status of OUC students as well as their interactivity with the courses , the study sets out to identify the features of student learning behaviours and the factors that influence them. The findings show that MPOC learners vary considerably in their engagement. Most students only care about assignments and texts that are directly related to course assessment and often rush through learning activities. In contrast, students of well-organised and well-supported courses tend to spend more time online and be more engaged. The findings also suggest that instruction from teachers and learner support effectively facilitate interpersonal and human-machine interaction in terms of assignment submission, test completion, and forum participation but negligibly increase the use of learning resources. Effective management mechanisms and adequate course design are also found to be influencing factors. The implications of these findings for the OUC are discussed in relation to course development, instruction and learner support, and management.
Keywords: Massive Private Online Courses; Massive Open Online Courses; learning behaviour; learning analytics; Open University of China; online courses
I. Introduction
With the increasingly wide use of MOOCs (Massive Open Online Courses), their shortcomings in terms of learner support, tutoring, teaching design, and teaching-learning interaction have become gradually apparent. In response to the limitations of MOOCs, MOOC-based DLMOOC (Deeper Learning MOOCs), SPOCs (Small Private Online Courses), MPOC s(Massive Private Online Courses), and other new teaching models have gradually come into being. Of them, MPOCs have become one of the leading formats among current distance educational and training institutions due to features such as private ownership, fee payment, class grouping, and matched tutoring teams, and their capacity to offer targeted teaching contents, organised teaching processes, and individualised learner support to online learners.
Since its beginning in 2013, the OUC has developed a number of MPOC courses that incorporate teaching support, learning, testing, and assessment based on the Moodle learning platform. There is a significant difference between MOOCs and the OUC online courses in terms of the students’ choice of courses, course development, teaching model, and learner support. Firstly, all the learners on the OUC online courses are students registered for degree education at the OUC. They are of a similar educational background and level, and the objectives of course teaching are compatible. Secondly, the OUC online courses are designed for OUC students, incorporating resources, learning activities, learning evaluation, and learner support. They are online degree education courses for effective completion of the entire process of learning, activities, and assessments. As far as the specific design is concerned, choices can be made on the types of activities, and methods and proportions of examinations according to the course characteristics. Thirdly, with regard to teaching and learner support, the OUC offers “multi-process, blended, and team-based” learning guidance, support, and promotion by relying on the teachers in its headquarters, branches, and branch schools. In other words, the teachers on the teaching team offer online and offline blended teaching; online teaching mainly represents the organisation and implementation of online teaching assessment, the completion of course assignments and tests based on online courses, and offline teaching mainly represents face-to-face tutorship and the final term examination, depending on the course. In the autumn term of 2015, there were a total of 57 courses, 169 classes, and 1,871 teaching groups on the OUC learning platform. Each class is divided according to each course offered in each branch and the teaching teams are divided according to the different learning centres for each class. A total of 54,228 students are involved in blended learning based on online courses.
This paper studies data from the online course behaviours of students of the Open University of China (OUC) in the autumn term of 2015. Analysis is made based on the characteristics of the online learning behaviour of OUC students, the students’ learning results, and the factors influencing them by collecting data on the students’ learning behaviours as recorded in the platform logs. The paper gives further explanations about the learning behaviours and offers relevant teaching and learner support suggestions in order to improve course teaching design, optimise the teaching process, and promote the teaching and learning of MPOC courses by tracking teaching processes and interviews with both teachers and students.
II. Literature Review
“Online learning behaviour” refers to a range of traceable activities recorded when using online learning platforms, including logging in, browsing, interaction, and retrieval. Compared to research materials such as self-administered questionnaires and interviews, these activities truly reflect the entire online learning process. As a result, great attention has been paid to them by researchers in the relevant fields. In the early 20th century, researchers tried to analyse learners’ online learning behaviours from the original Web access logs. In their research, Zaïane＆Luo (2001) conducted an analysis of 420,000 original Web access logs and created generalised laws of the students’ behaviours, such as the most-visited modules and the obvious links between the visited modules. However, the mined information from the Web logs had its limitations. With the improvement of the online learning platform, more researchers began to consider how to obtain and analyse richer learning behaviour information from the structured background data. For example, researchers from Taiwan attempted to analyse the learning archives from structured courses in line with the SCORM norms (Su, et al., 2001). American scholars Hung and Zhang (2008) tried capturing behaviour log information from the LMS’ backstage data base, classified the students’ characteristics, and summarised their daily learning behaviour modes. Most of the analyses of learning behaviours in this period fall into the category of small sample research of a small number of courses and classes. Research on massive online courses with larger sample sizes was rather rare.
After the year 2012, educational research institutions in China and abroad began to turn to the field of massive open online courses (MOOCs). Overseas research includes analyses and forecasts of MOOC learners’ behaviour characteristics, interactions, and course completion rate, and course development improvement, teaching process intervention, and other aspects in accordance with the learners’ behavioural characteristics. In particular, foreign universities including Harvard and MIT have collected and analysed course behaviours from platforms such as Coursera and edX. Chinese research is mainly focused on online learning engagement, learning behaviour analysis and achievement forecasting, learner classification, and exploration of online learning models. For example, Li Shuang and others put forward six dimensions for a learning engagement analysis framework;Jia Ji and others conducted a comparative study on approximately twenty data indexes of the Peking University courses offered on Coursera, and they indicated a positive correlation between learners’ achievements and the time they spent online, the frequency with which they watched videos, and browsed and downloaded resources, and their forum participation;Zheng Qinhua et al (2016) conducted an overall analysis of 14 mainstream Chinese MOOC platforms and pointed out the current problems with MOOCs, such as the single teaching mode, delayed learner support and tutorials, unsatisfactory interaction, deep learning deficiency, and low learning quality and completion;Ma Xiulin et al (2016) conducted empirical studies on the teaching effects of MOOCs and SPOCs, and indicated that SPOCs were superior to MOOCs with regards to resource construction and customised support. Guo Wenge et al (2015) put forward design and operation methods for MPOCs, and indicated that it was highly probable that MPOCs would represent a future development trend for MOOCs. Wei Shunping (2012) conducted a learning behaviour analysis of one of the OUC’s MPOCs in an attempt to discover more learning behaviour characteristics from the perspectives of learning time, learning activities, teacher-student interaction, learning resources, and test results.
Based on the above research, this paper focuses on an analysis of learning behaviour in the MPOC teaching model. It studies learning behaviours from more than 56 million data instances at the OUC in the autumn term of 2015 in the hope of identifying new features and the factors influencing MPOC learning behaviours through interdisciplinary and trans-regional data analysis. The aim is to provide evidence for all educational institutions to optimise the design of MPOCs, improve teaching implementation, and upgrade the teaching level.