Parallel Session #1, Subtheme 6: The Regulatory Environment and Strategies for Innovative Open and Distance Learning. ICDE Conference 2013.
Blogging my learning. One sidenote on the format. With English being the second language for so many participants, it was very important to have much of the text written out on the PowerPoint slides so that the audience could follow along.
On How TV University Academic Supervisors Can Inspire Their Students with Innovative Ideology
By Wang Jinpeng, Tianjin Open University, Dongli Branch, China.
Summary: Academic supervisors should teach with our hearts, our love, our wisdom, and our encouragement. The old style of teaching doesn’t meet the needs of the students. Academic supervisors need to provide the students with necessary resources and supports, particularly students who are older and have a heavier burden with living or who travel a long distance to attend classes. More flexible ways can help them finish their academic tasks. Teachers should learn to use computers to communicate with students online. We need to be improving our qualifications and being true friends to the students. Innovative ideology is to be flexible and to try different methods of teaching students.
Management Guidelines for Implementing M-Learning in Distance Education
By Johan Redelinghuys and Hermanus Jacobus van Vuuren.
Interaction and communication between a student and institution regarding administrative and student support are possible via m-learning. Unit for Open Distance Learning at North-West University. 55 centres and programs spread over Southern Africa. There is a library and computer access at each center. Two interactive whiteboards at each centre. Communication and reminders of classes and dates of contact sessions. They are providing learning to teachers, School of Nursing just came on board as well as Theology. They are doing Lecture Capture also and share through a dropbox. Mobile learning started with an SMS session. Administrative or academic questions can be sent to a 5 digit code. They are sending SMSes about when assignments are due. They see m-learning as the middle that connects them and the students. M-learning can add flexibility in dealing with distance learning. Computers are still used, but m-learning is at the intersection of mobile computing and e-learning (MacLean, 2003). Mobile learning allows them to communicate at any time with their institution. One of his study results was that the students wanted the mobile learning used for teaching and learning; but the facilitators weren’t as sure. Text messaging is a preferred method of communication by the students. SNS (social network sites) enable students to connect with each other. They use Facebook for lesson plans, dropbox items, and more. Their students and facilitators need skills training on using the mobile devices so they can interact more. Some of the recommendations:
- Continuous administration and academic support
- Improving quality of teaching and learning using midterm communication technologies
- Increase the available of mobile technologies
- Assistance to enable the affordability of mobile technologies (cell phone service providers – at lunch I met Dr. Tae Rim Lee from the Korean Open University and they negotiated with the cell phone providers for unlimited download for their students)
- Enhancing accessibility to resources for teaching and learning
- Contributing to the flexibility of the learning
- Need an affordable method for downloading relevant information
- Effective academic and administrative support
In this program, they see the students at least 5 times in six months at the various centres.
Reflection: This paper was fascinating to me. The model is a combination of face to face (at centres), online delivery through Facebook and dropbox, and administrative communication through text messaging. The blend of various tools seems to make for an effective delivery system.
Research on Online Learning Diagnosis Based on Data Mining
By Sun Xin, Feng Xia from Renmin University, and the China Data Mining Project.
In this study, she used data mining methods to analyze learners’ behaviors on multiple aspects, and examined the key characteristics of dropouts and low-efficiency and high-efficiency learners. Online learning requires successful learners to have strong self-direction and self-motivation. Data mining can be used to find learners who lag behind.
New ideas in Big Data: Analyze collective data rather than sample data. Seek for efficiency rather than absolute accuracy. Care for the correlation rather than causality. Data mining technology makes use of massive data and predict things by mathematical and statistical algorithms. Statisticians, database administrators, and the [online learning] expert need to work together to make this work.
Their study had all the data recorded in the system since 2001: Registration, admission test, course selection, payment, exam, etc. Students were classified by speed: high efficiency, low efficiency, and ordinary learners. The learning cycle was divided into adaptation period, critical period, and graduation acceleration period. They calculated the probability that learners will become a low-efficiency learner or dropout according to their overall performance. The model measures the learning effect of the learners and judges based on reviewing the behaviors and states of learners in the adaptation period and the critical period respectively.
They have an admissions diagnosis model and an in-progress diagnosis model. They use the data to provide learning diagnosis and analysis of each learning cycle for each learner and learning manager, remind learners to adjust the learning plan, and seek learning guidance.
Reflection: It is interesting to see the various ways that different groups are investigating the data on distance learners to determine what they are doing and how to assist them further.