Source: Innovations in Education and Teaching International, Vol. 51, No. 3, 338–351, 2014
(Reviewed by the Portal Team)
Based on new cloud technology and related learning theories, this article presents a new e-learning model called the collaborative learning cloud to solve the problem of instructor–student imbalance in current e-learning applications, especially in China.
A prototype system has been developed:
Learners sending their requests for instructional support service to the collaborative learning cloud.
The cloud collects information about service demand and then lists service providers who are most likely to match the demand.
Learners then select their preferred service providers according to the provider’s knowledge status, service quality and service price.
After consuming the services, learners make the payment using a certain amount of virtual money (credit score) to the provider, and evaluate the service quality.
The authors evaluated the effectiveness of the proposed collaborative learning cloud model.
Two hundreds and fifty seven students, who majoring in computer science from the e-learning school of South West University of China, used the prototype for three months.
At the end of the third month, they responded an online survey.
In addition, 10 students were randomly selected for the purpose of interview.
The findings from the pilot evaluation showed an obvious positive attitude towards the prototype.
About 60.7% of the students felt that learning support from students with good academic performance had the same effect as the support from the instructor in the prototype, although they preferred to be tutored by the instructor.
Interview data indicated that although students preferred instructors’ tutoring when they needed help in studying, if they could obtain similar help from students with better academic performance, they would also be satisfied.
Furthermore, when learning with their peers, students felt more relaxed, which could facilitate deep discussion.
However, in some situations, the instructor was required.
Furthermore, the results showed that about36.8% of the students said the prototype recommended the right service providers they needed.
About 51.4% of the students said the recommendation was acceptable, but it might not be the best choice.
About 11.8% of the students did not think the recommendations made by the prototype were helpful.
Finally, most of the users had positive attitudes towards the prototype.
About 55.7% of the students thought virtual money and marketplace rules would be very helpful in stimulating users’ participation and in improving the quality of service about 26.5% of the students thought that this model might be useful.
Only 17.8% of the students considered it useless.
In the interviews, some students said that people would be embarrassed when collecting money or paying for the service from someone with whom they were familiar.
Sometimes, it would be better to change virtual currency to virtual gifts as the service fee. Another comment was that novices were incapable of playing the role of service provider. Most of the time, they would need to get services from others.
So, in economic terms, they would not have any income in the early stage and might need initial virtual capital to overcome this period.
The authors conclude that students can receive learning support services according to their needs from the collaborative learning cloud in which other students and instructors are connected with each other as a kind of virtual learning resources.
By applying the knowledge modelling technique and the economic model of free market in the collaborative learning cloud, virtual resources can be dispatched in the most reasonable and effective way.
This design alleviates the tension between limited instructional resources and too many learning support demands.