(II) Analysis of the factors influencing learning behaviours
A comparative analysis of the students’ online learning behaviours from the course teaching team, teachers’ individual teaching behaviour, course operation area, teaching class size, specialty category, and course design was conducted. It can be seen that the teaching team and operation area have a clear influence on the students’ learning behaviours, the course design has a certain influence, and the type pf major and teaching scale do not exert an obvious influence.
1. The influence of the teaching team on learning behaviours
Of the 57 courses in operation on the OUC learning platform, seven are included in the “OUC Online Teaching Team Pilot” project (hereafter referred to as the “pilot courses”). Compared to the other 50 courses (hereafter referred to as the “non-pilot courses”), all the pilot courses are taught online with the support of funding and mechanisms. Specific team responsibilities and job assessment methods are created, and complete teaching teams made up of course leaders, tutors, class tutors, administrators, and technicians are organised in order to provide the students with all-round academic and non-academic support, including course guidance, distance or face-to-face teaching, assignment revision, and questions and answers, as well as assignment reminders, teaching affairs management, technical assistance, and consultation etc.
Figure 9 is a comparison chart of the average value of all the learning behaviour data for the pilot and non-pilot courses (the average value is the sum of the data items of each course divided by the total number of courses). It can be seen that the average value of each item for the seven pilot courses is higher than those of the non-pilot courses. Of these, the gap is greatest between the process learning behaviour data, such as the total sum of learning behaviours, the interpersonal interaction category of behaviours, other categories of behaviours, days spent learning online, resource use, activity use, students’ forum posts, tests completed, and test completion rate. The average values of the rest of the items are slightly higher; and the average values of assignments submitted, interactive assessment assignments completed, and assignments assessed by others are all very low (insufficient assignments designed for the course largely result in such situation, in particular the deficiency of assignments that require interactions and assessments) . In the course online achievements, the gap between the average values of the two kinds of courses is about 17.
Note: ① The average value is the sum of the data items of each course divided by the total number of courses. ② In order to be more intuitive, a comparison of the assignment submission percentage and test completion percentage was added.
Figure 9 Comparison Chart of the Average Data Values from the Seven Team-Based Courses and the Remaining 50 Courses
2. The influence of teachers’ individual teaching behaviours over learning behaviours
An analysis of data item correlation was conducted in order to understand the relevance and influence of various kinds of behaviour data. The influence of teaching behaviour over students’ learning can be seen from the correlation analysis of the behaviour data of teachers and students. Since the learning and interaction for the pilot courses are completed online, the seven teaching classes of the seven pilot courses (the pilot courses are offered only in designated branches with one teaching class per course) are used as the sample, in order to obtain a Pearson correlation analysis of the average value of the students’ behaviour data and that of the teachers’ teaching behaviour data. Table 3 shows the items that demonstrate extremely strong or strong correlative data.
The correlation analysis of Table 3 demonstrates the following:
➢ The teachers’ interpersonal interaction category of behaviours promotes interpersonal interaction between the students and improves the students’ rate of forum participation.
➢ Teachers provide learning support to the students and improve the assignment submission rate based on other categories of behaviours such as reading through the students learning behaviour data.
➢ Correcting assignments has a significant influence on the number of assignments submitted.
➢ When teachers post in the forum it encourages interpersonal interaction between the students and promotes the involvement in interactive assessments of assignments. Forum posts by teachers greatly encourage the students to study online for longer periods of time.
➢ Improving the teachers’ reply rate also improves the test completion rate and activity use rate, as well as the students’ online learning achievements.
➢ An increased rate of internal messages boosts interpersonal interaction between the students, including the number of words posted, and interaction with assignments and evaluations;
➢ The frequency of resource renewal correlates extremely strongly with the students’ test scores.
According to the above correlation analysis, other categories of teacher behaviours such as assignment revision and reading through the students’ learning behaviour data and learning logs have a clear utility in encouraging the students to submit assignments. Forum posts and internal memos can mobilise the students to participate in interactive assessment assignments and peer evaluation, and improve the students enthusiasm for interpersonal interaction. The frequency of replies and resource renewals influence the test category of activities and test scores, and the frequency of replies is strongly related to the students’ online achievements. Learning guidance and learning promotion behaviours effectively facilitate the students’ completion of assignments and willingness to post on the forum, and play an outstanding role in encouraging interpersonal and human-machine interactions and improving the activity completion rate. However, their role in encouraging the students to increase their browsing resource is not effective.
3. The influence of the course operation area on learning behaviours
According to an analysis of students’ learning behaviours in the course operation branches, the data from students in Chengdu and the Corps are the best. Table 4 intercepts part of the significant test results of the mean value of students’ learning behaviours for one course. It can be seen that most of the data items from Chengdu and the Corps branches are much higher than the average number and are higher than those of the other branches. Analysis of courses operated in the other areas shows the similarstatus. Further analysis and interviews show that Chengdu and the Corps branches pay great attention to online teaching and have created related teaching policies and assessment systems to promote online teaching and to increase the enthusiasm of both the teachers and learners. The smooth implementation of online teaching is ensured through training and teaching supervision in order to guarantee the quality of online teaching. We can see that the relevant policy support, systems, and faculty guarantees have an impact on learning.
4. The influence of teaching scale on learning behaviours
A number of courses were selected in order to conduct a significance analysis of the learning behaviours of students in different classes. There is no significant difference between the learning behaviour data of students in different classes. Therefore, there is no obvious correlation between the scale of the course and the students’ learning behaviours.The restrictions of various conditions may lead to a deviation in the analysis results. This is especially true with regards to the situation that the teaching classes of the course operation are distributed across different branches, and their teaching conditions, teaching staff, and polices may all cause a certain degree of deviation on the analysis results.
5. The influence of majors on learning behaviours
The 57 courses are classified into nine categories: law, education, economic management, science, engineering, humanities, biology, foreign languages, and administration. A significance test of the behaviour data from the nine courses shows that there are no major differences between the courses.
6. The influence of course design on learning behaviours
As mentioned above, courses that follow the original flow layout design on the application platform are popular with tutors and students, and exhibit strong human-machine and interpersonal interactions. In general, the behaviour data values for these courses are all relatively high. The teachers and students interviewed seem to prefer these kinds of courses.
The above analysis shows that learning guidance and promotion by teachers enhance online learning and, in particular, the support of an organised teaching team plays a large role in encouraging the students’ learning process. Great improvements have been made in terms of course interaction, the completion of tests and assignments, and course achievements. In the meantime, the teaching management level, policy support, systematic guarantees, and excellent course design of the branches where the students learn are also important factors in igniting and encouraging online learning. Therefore, teaching teams that exhibit efficient collaboration, timely learning guidance and support, good management mechanisms, and quality course design have a vital role in guaranteeing the teaching quality of MPOCs and improving the teaching level.