# Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors
## 4 Analysis of Affecting Factors
### Methods and Mechanisms in Collaborative Learning  
Collaborative learning, in a broad sense, is an educational approach for learning and teaching in which learners create a group (two or more people) working together by face-to-face conversation or over the Internet, with the purpose of solving a problem, completing a project, creating something new, and/or maximizing their own and each other's learning. Various Collaborative Learning Techniques (CoLTs) have been developed to support this process. These techniques can provide good directions for improving the quality of teaching and learning. Furthermore, they can deliver helpful guidelines to teachers and learners for being more productive. Each of these techniques is designed for adoption in a different educational context [54]. These techniques have succeeded to provide a number of social and psychological benefits: having more positive attitudes toward ourselves, our group and collective learning; being exposed to different viewpoints; learning how to collaborate in group; and how to enhance group productivity, to name a few [55]. From a cognitive perspective also, these techniques can ensure knowledge is jointly built, not just shared [56]. Literature has documented ample positive yield regarding to effectiveness of CoLTs that could turn learners into critical thinkers and reflective learners, and how well they could boost learners' capability in decision-making and problem-solving [57]. Collaborative Learning Techniques and Mass Collaboration  
The conditions for applying CoLTs in general collective learning are relatively distinguished from mass collaborative learning due to some specific characteristics. For instance, group size (the prominent feature of mass collaboration) shifts from small or medium to a large number of learners; learning shifts from knowledge acquisition (at individual level) to knowledge building (at group level); communication and collaboration for distributed learners could be facilitated by applying digital technologies; the co-created product in a mass collaborative learning project is jointly produced in the form of a virtual artifact. Lastly, community satisfaction is the ultimate aim of learners' participation instead of just being satisfied personally [1]. In recent years, several research works have studied some contexts in which mass collaborative learning can take place (e.g., Wikipedia [27], Innovative Socio-Technical environments [36], MOOCs [1], Yahoo! Answers [10], etc.). Different aspects are also evaluated (e.g., the role of learners and the way of organizing them in mass collaboration process [1], qualifying and measuring the learning output [58], and the role of collaborative methods [1], etc.). However, very few researchers thus far have studied the role of CoLTs in mass collaborative learning projects; nor has a comprehensive list of adopted CoLTs in this context has been presented. Therefore, not enough evidence for this purpose exists in the literature to clarify what kinds of techniques are exactly required and how they can support learners effectively in mass collaborative projects. In spite of such limitations, in order to identify the most promising techniques for the purpose of learning in mass collaboration, the taxonomy recommended in [54] was selected. This taxonomy, among existing classifications, was chosen as it is more comprehensive than the others in terms of number of proposed categories and contained techniques, and it covers all the techniques that are addressed in the collected papers. This taxonomy encompasses five main categories of general learning activities namely, (1) discussion, (2) reciprocal teaching, (3) problem solving, (4) graphical information organizing, and (5) collaborative writing. Each category can be used for different purposes and it includes a set of techniques. Each technique is defined for a specific group size and period, and amount of time to be devoted to the task.