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...idual payoffs to A and let group agents assign group payoffs to A VALCAM-U (for user agent): 1. Initialization 2. For each round of bidding for group g, bid with an amount proportional to the average of compatibility and competence of the users in g. Compatibility measures the human users’ view of one another, and competence measures the average individual performance of a human user. 4. EXPERIMENTS WITH I-MINDS We have incorporated VALCAM in a computer-supported collaborative learning (CSCL) environment called I-MINDS. Detailed description and results of our test using VALCAM can be found in [3, 8]. Our study utilized a control-treatment protocol. In the treatment section, students worked in Jigsaw cooperative learning groups [8] using I-MINDS. In the control section, students were allowed to move around in the room to join their Jigsaw groups to carry out face-to-face discussions. The results of our feasibility study shows that the performance of the students in the treatment group is comparable to the performance of the students in the control group and also the performance of the students in the treatment group improved over time. This hints that VALCAM was providing Type II scaffold...

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...n the case of an asynchronous course or lesson). In I-MINDS, each student agent serves a student, profiling the student’s behavior in his or her structured and non-structured collaborative activities =-=[12]-=-. The student agents exchange information to form peer groups that are compatible to help students collaborate [22]. The instructor is supported by a teacher agent that displays statistics of students...

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...tly discussed. In addition, [12] mentions that social relationships contribute to common understanding, an orientation toward cooperation, and the desire to remain as a group. Finally, as reported in =-=[3]-=-, the students form their view of other students due to the type and extent of collaboration they receive from their peers. Clear and Kassabova [16] further report that in collaborative learning setti...

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... learning and a classroom management applications for instructors for large classroom or distance education situations. We have previously evaluated the usefulness of I-MINDS as a CSCL environment in =-=[12]-=- [23] [24]. We further present more comprehensive results of using the iHUCOFS framework to form and scaffold human coalitions in this paper. This paper is organized as follows: Section 2 describes th...

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...ng to the principles described in iHUCOFS, we have designed the VALCAM algorithm (Soh, Khandaker, Liu and Jiang, 2006; Soh and Khandaker, 2007; Khandaker et al., 2011; Soh, Khandaker and Jiang, 2006; =-=Khandaker et al., 2006-=-; Soh et al., 2008; Khandaker and Soh, 2011b) and investigated its impact by implementing it in the asynchronous version of I-MINDS (Intelligent Multiagent Infrastructure for Distributed Systems in Ed...

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...group formation. In our research, the CSCL systems that we have developed and evaluated include the Intelligent Multiagent Infrastructure for Distributed Systems in Education (I-MINDS) [4], [5], [6], =-=[7]-=-, [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], the ConferenceXP-powered I-MINDS [18], [19], and the ClassroomWiki [20], [21]. To be specific, we divide our experience in developing and de...

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...nts such as WebCT, Blackboard, and Moodle that may be more familiar to students. These findings indicate a ―preference vs. performance paradox‖ that has also surfaced elsewhere in HCI [1, 5], in CSCL =-=[4, 8]-=-, and in online learning [6]. Whether the ―preference vs. performance paradox‖ is prevalent in CSCL or not is an open empirical373 research question; however its identification in our analyses is an ...