Group Composition for Collaborative Learning With Distributed Leadership in MOOCs Using Particle Swarm optimization

A Framework of Students’ Regulation Processes for Flipped Learning

“…Manage various conflicting objectives and objectives, trying to agree on which objective will be accomplished first. Or when a person's goals do not match those of the group, they should try to reconcile these conflicting goals (Ullmann, 2016).…”

“…The relations between the group members must be monitored, in order to facilitate the promotion of good interpersonal relationships. It is important to monitor connectivity within the group, that is, to try to understand if people are happy with the group's behavior, in order to get around a problem if it exists (Ullmann, 2016). Students should understand what the group's objectives are and, with them in hand, try to integrate them so that everyone benefits.…”

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

“…Ullmann et al [26] in their work propose an adaptation of the particle swarm optimization algorithm based on three criteria: level of knowledge, interests and leadership profiles; forming groups with different levels of knowledge, similar interests and distributed leadership, providing better interaction and knowledge construction.…”

Supporting teachers in the design and implementation of group formation policies to carry out group learning activities in massive and variable scale on-line learning contexts

“…Ullaman, Fjames, Camilo-Junior and Nogueira [141] proposed, for the formation of the groups, an adaptation of the Particle Swarm Optimization algorithm [70] on the basis of three criteria: level of knowledge, interests and leadership profiles. They formed groups with different levels of knowledge, similar interests and distributed leadership, providing a better interaction and construction of knowledge.…”

“…Wen tested the effectiveness of giving the students the opportunity to interact meaningfully with the community before they are assigned to teams, in order to extract evidence of which students would work well together [148] and [149]. Bahargam [7], [8] and Ullman [141] addressed the problem from a more mathematical point of view, solving algorithmically a problem of optimization. However, the parameters they used for such optimizations were numeric variables (such as total number of students, time interval, number of different activities required, and desired number of groups) or static factors taken mostly from the students' profiles, and they did not take into account any of the student dynamics to form the groups.…”