V. Conclusions and Suggestions

Based on the research and discussions mentioned above, a summary of the factors influencing the learning behaviours of the students of OUC online courses is made and suggestions are given for the course development, instruction and learner support, and management mechanism of the OUC’s MPOCs so as to improve course development and to improve the effectiveness of online teaching.

(i) Conclusions

1. The time spent learning is concentrated and the level of interaction needs improving

The online learning achievements of OUC students are directly related to whether they can get credits andcertificates. Therefore students have very clear online learning objectives: to complete scored assignments and activities. Therefore, most students complete scored assignments and tests online across several concentrated days without learning persistence and with low behavioural data access;The interactive tools and functions of the teaching platform are not fully used, and the learning guidance and promotion role of the tutors’ and class tutors’ is not given full play. Neither human-machine interactions nor interpersonal interactions are able to meet expectations yet.

2. Assessment learning is emphasised but the resource category of learning is neglected

As seen from the analysis mentioned above, it is rather low either in behaviour of browsing resources or resource utilisation, the completion rate of assessed assignments and quizzes tightly related to the final examination is good. Since at present the statistics for resource browsing and the duration of resource usage are not collectable in the learning platform, these parts of most courses are not included in the assessment, which leads to low clicks and poor browsing rates.

3. There is a significant relationship between the behaviour data of teachers and students. Timely support from teaching teams is a key factor in ensuring MPOC teaching quality

As seen from the correlation analysis, the behaviour data items of the teachers and students are highly correlated. Online teaching behaviours such as forum posts, assignment revision, and internal memos are significantly related to resource browsing, interpersonal interaction, human-machine interaction, and course achievements among the students. Guidance and promotion from teachers enhances the students’ online learning. The teaching teams have a clear influence on student learning behaviours. Timely tutorials and learning promotion support by the members of the teaching team effectively improve online learning engagement, course interaction frequency, resource browsing, and learning results, and ensure the teaching quality of the MPOCs.

In the pilot projects, all the course teams have organised academic and non-academic teams the meet the needs of course teaching with clear division of responsibilities and job mechanisms in order to offer the students online teaching, tutorials, and learner support that has remarkable effect.Teaching effects of the pilot courses are greatly improved in terms of course interactivity, procedural learning behaviours such as resource use, and the learning results.

4. Good management mechanisms ensure the smooth operation of online teaching

An analysis of the influence of course operation areas over learning activities shows that educational units with solid organisation, good teaching policies and systems, and sufficient teaching staff are of great importance in promoting online teaching and learning, and thus increasing their effectiveness remarkably.

5. Course design directs learning behaviours and concisely designed courses are generally popular with students.

Course teaching and page design do have an effect on learning behaviours. If the design of courses are inclined towards exploration and practice, the average value of interpersonal interaction category of behaviours among students is greater. If the courses are inclined towards lecturing, then the value of the resource browsing category of behaviours increases. In Figure 2, the average value of students’ log in, navigation, and other categories of behaviours is the highest. Further analysis shows that problems such as mixed course page designs, unclear navigation, and complicated learning paths disorient the students. The students have to return to the previous browsing page by relying on the navigation columns or click the navigation columns several times before they can find the relevant contents. By integrating the analysis on the influence of course designs on learning behaviour with practical teaching and interviews with teachers and students, we can see that tutors and students prefer simply designed courses. They generally don’t get lost in such courses, which is more favourable for tutors to organise teaching, and for students to look up resources and complete assignments. The data value of all students’ behaviours for these courses is rather high.

(ii) Suggestions

1. On course development

(1) Strengthen process assessment and ensure the consistency of assessments and course contents

Methods of online course assessment are to be ameliorated in order to improve the proportion of process assessment. Students can be guided to learn at regular intervals by way of increasing assessment on usual learning behaviours and learning processes, and increasing the proportion of interpersonal interaction assignments. The consistency of learning resources and assessment and teaching objective design should be ensured in order to strengthen the correlation between resources and assessments, so that students can use learning resources to complete learning objectives and assessments and thus increase the students’ resource use rate. At the same time, attention should be paid to designing courses that are concise, lively, and interesting, and strengthen the design of learning guidance content in order to arouse the students’ interest in course contents, improve the rate of resource access, and establish the teaching process.

(2) Optimise course design and improve platform function

The course designs should be further optimised based on learning behaviour data in order to unify and simplify the course page and layout. Course knowledge maps can be added in order to reduce the time taken looking up course contents and provide the students with good course experiences through column navigation with clear design and streamlined course learning paths. The course development process should be standardised in order to establish course development and resource upload criteria and facilitate the ability of the platform to record data on learning behaviours. Functions or tools such as report forms, push notifications, and real-time communication can be added to make it possible to achieve real-time learning query, teaching intervention, online teaching research, and real-time question and answer sessions.

2. On course teaching and support

(1) Provide a one-stop teaching team that meets the course teaching needs and ensures teaching quality

In order to meet the course teaching needs, full-time tutors and class tutors for each teaching class should be established in order to offer students individualized learner support and to solve their problems related to discipline, teaching affairs, and teaching techniques. A scientific, effective team work mechanism should be established in order to guide online learning and interaction, ensure teaching quality, and improve the effectiveness of teaching. The OUC has realised from the pilot work the importance of a diversified one-stop online teaching team to online teaching in this respect. Efforts should be made to promote and encourage the construction of a teaching team for each course and major.

(2) Provide timely teaching support and encourage the students’ learning process

Targeted teaching plans and support mechanisms should be formulated in accordance with the students’ behavioural characteristics, to arouse the students’ learning interest, and increase investment and engagement in the resource and activity categories of the learning process to create a balanced learning environment. The role of the teaching and mining team is to be brought into full play to guide and urge the students’ participation in process learning. For example, tutors should push the students to take an active part in posting on the forums and the class tutors should use internal memos, forums, emails, and phone calls to further encourage the students to complete their assignments and log on to learn. At the same time, platform functions such as badges should be developed in order to increase learning rewards and encourage the students’ learning process.

3. On management mechanism

(1) Transform the function and assessment mechanisms used by the teachers and strengthen teaching team construction

In the MPOC teaching model, course organisation and support are covered by the teaching team. This paper introduces the teaching team used by the seven pilot courses, made up of 1 + N members (one course leader and several tutors) and the course project system teaching operation method. To the courses with a huge number of students, the teaching team usually operates and implements teaching in the organisation form of N* (1 + N). The teachers’ assessment mechanism should change in accordance with the transformation of job responsibilities of various kinds of teachers in the team based on the the conditions of the MPOC. Assessment is no longer made based on individual teachers but on the overall teaching team. Within the teaching team, all of the team members will be assessed and rewarded or punished by the course leader according to their teaching and feedback from the students etc. In order to ensure that the needs of the students for teaching are covered, educational institutions should encourage the construction of one-stop diversified teaching teams that are in line with the features of each course in order to provide the students with individualised learning support. Meanwhile, the teaching units should study teaching systems and management mechanisms that may be suitable for the MPOC teaching model and provide policy support, and human, financial, and material guarantees for teaching implementation in the MPOC model.

(2) Strengthen teacher training and improve teachers’ capacity for online teaching and service

Teaching capabilities such as course teaching design, organisation, learning guidance, learning promotion, and technical support can all be improved through a range of training scheme organized for course leaders, tutors, class tutors, administrators, and technicians in order to offer the students a quality course experience and strong learner support.

(3) Share cases studies and guide the teaching processes with data analysis

Cases studies on outstanding course designs and team operation mechanism should be shared through meetings and workshops in order to learn from the experiences of others, upgrade the overall teaching level of the MPOC team, and improve the operation mechanism. The results of any behaviour data analysis should be provided to the course team in order to present teaching in an objective and all-round way, guide course teaching and operation, and ensure teaching quality.

This paper provides an overall, preliminary analysis of massive data from MPOCs, which does not make further explorations to the in-depth reasons of teaching, learning behaviours and phenomena. In the next stage, an analysis of teaching behaviour data shall be conducted from multiple perspectives and levels in combination with qualitative research. For example, further research should cover the behavioural features of various types of students; the influence of mutual assistance among the students over learning behaviours; the influence of the supervision and guiding functions of teaching behaviours on learning; and the role of teaching intervention by course leaders in the promotion of process learning, in the hope to continuously improving the MPOC operation mechanism and learning support model.

Works Cited:

Chen Kan, Zhou Yaqian, Ding Yan, et al. 2016. Research on Learning Engagement for Online Video: Big Data Analysis of the Relationship between the Features of MOOCs Videos and Skipping Behaviour While Watching [J] Distance Education in China (4):35-42.

Guo Wenge, Shen Xudong. 2015. MPOCs：The Design and Implementation of Massive Private Online Courses [J]. Modern Distance Education Research (1): 22-32.

Jia Jiyou,, Liao Jingmin, and Wang Qiong. 2014. Big Data Analysis of the Learning Behaviour and Effect of MOOCs: Study of Six Peking University MOOCs [J]. Industry and Information Technology Education (9): 23-29.

Jiang Zhuoxuan,, Zhang Yan, and Li Xiaoming. 2015. Learning Behaviour Analysis and Predictions Based on MOOC Data [J]. Journal of Computer Research and Development, 52(3): 614-626.

Li Shuang, Wang Zengxian, Yu Chen, et al. 2016. Mining LMS Data for Behavioural Engagement Indicators in the Online Learning Environment [J]. Open Education Research (2): 77-88.Ma Xiulin,Mao He, Wang Cuixia. From MOOCs to SPOCs: An Empirical Study on the Effect of Two Kinds of Online Learning Models [J]. Journal of Distance Education (4): 43-50.

Sun Hongtao, Li Qiujie, Zheng Qinhua. 2016. A Cluster Analysis of MOOC Interaction Patterns [J]. Distance Education in China (3): 33-38.

Wang Ping. 2015. Analysis of Learners Based on edX Open Data [J]. Modern Educational Technology (4): 86-93.

Wei Shunping. 2012. An Analysis of Learning Behaviours and Their Influential Factors: A Case Study of Students’ Learning Processes in the Open University of China Online Course “Open Education Learning Guide” [J]. Open Education Research (4): 81-90.

Wei Shunping. 2013. Learning Analytics: Mining the Value of Education Data in the Big Data Era [J]. Modern Educational Technology (2): 6-11.

Wei Shunping, Zhao Xuan, Cheng Gang. 2015. Research on the Construction of a Data-Driven Online Learning Performance Model [J]. Journal of Tianjin R & TV University (03): 35-41.

Wei Shunping, Cheng Gang, Wang Lina, et al. 2016.Research on the Construction of an Evaluation Index System for Data Driven Online Course Implementation Processes [J]. Journal of Beijing Radio and TV University (2): 42-48.

Wei Shunping. 2016. Research on Data Models and Data Processing Methods in Learning Analytics [J]. China Educational Technology (2): 8-16.

Wu Jiang, Ma Panhao. 2015. A behavioural Analysis of MOOC learners: Case Study of an MOOC on the iCourse Platform [J]. Knowledge Management Forum (3): 52-61.

Zheng Qinhua, Chen Li, Lin Shiyuan, 2016. The Construction and Development of MOOCs in China [M]. Beijing: Publishing House of Electronics Industry.

JiaweiHan, MiehelineKamber, Jian Pei. 2012. Jiawei Han, Mieheline Kamber, Jian Pei. 2012. Data Mining Concepts and Techniques [M]. Fan Ming, Ma Xiaofeng, translators.Third Edition. Beijing: China Machine Press.

Williams, B., (2015, August 25).Rollcall: taking a census of MOOC students. Retrieved September 21, 2016, fromhttp://www.ceur-ws.org/Vol-1009/0103.pdf.

Gašević, D.,Kovanović,V.,Joksimović, S.,&Siemens, G. (2014). Where is Research on Massive Open Online Courses Headed? A Data Analysis of the MOOC Research Initiative [Electronic version]. The International Review of Research in Open and Distributed Learning, 15 (5), 134-176.

Hung, J. L.,&Zhang, K. (2008). Revealing Online Learning Behaviours and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching [Electronic version].Journal of Online Learning & Teaching, 4 (4), 426-436.

Su, J. M., Tseng, S. S., Wang, W., Weng, J. F., Yang, J. T. D., &Tsai, W. N. (2006). Learning Portfolio Analysis and Mining for SCORM Compliant Environment [Electronic version]. Journal of Educational Technology & Society, 9 (1), 262-275.

Haggard, S. (2013, September).The Maturing of the MOOC.Retrieved April 20, 2016, from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/240193/13-1173-maturing-of-the-mooc.pdf.

Zaïane,O. R., &Luo, J. (2001). Towards Evaluating Learners’ Behaviour in a Web-Based Distance Learning Environment [Electronic version].Advanced Learning Technologies, 8, 357-360.

Footnote:

1. This paper is the research achievement of the 2015 key research project of the Twelfth Five-Year Plan of Beijing Educational Science “Research on Teaching Performance Evaluation System and Its Application in Massive Private Online Courses Based on Education Big Data” (project approval No.: AJA15233) and the 2014-2015 Youth Project of the Twelfth Five-Year Plan of the Open University of China “Learning Behavious in Massive Private Online Courses and Their Influencing Factors: Data from the Open University of China” (project approval No.: G14A0031Q).

About the Authors:

Shi Lei: master’s degree holder, assistant research fellow, Student Affairs and Teacher Development Centre, Open University of China
Add: #75 Fuxing Road, Haidian District, Beijing100039, P.R.China
Tel: 13311471462
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Cheng Gang: Ph.D holder, associate professor, deputy director, Student Affairs and Teacher Development Centre, Open University of China
Add: #75 Fuxing Road, Haidian District, Beijing100039, P.R.China
Tel: 57519546
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Li Chao: master’s degree holder, engineer, Student Affairs and Teacher Development Centre, Open University of China
Add: #75 Fuxing Road, Haidian District, Beijing100039, P.R.China
Tel: 57519377
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Wei Shunping: Ph. D holder, associate research fellow, deputy director of the OUC Engineering Research Centre for Technology Integration and Application of E-Learning, Ministry of Education
Add: #75 Fuxing Road, Haidian District, Beijing100039, P.R.China
Tel: 57519371
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

*This paper is published in the issue No. 4 2017 (Serial No. 507) of the journal of Distance Education in China