TY - JOUR
T1 - Lessons learned from comprehensive deployments of multiagent CSCL applications I-MINDS and ClassroomWiki
AU - Khandaker, Nobel
AU - Soh, Leen Kiat
AU - Miller, Lee Dee
AU - Eck, Adam
AU - Jiang, Hong
N1 - Funding Information:
The I-MINDS project was seed-funded by a National Center for Information Technology in Education grant in 2002 and 2003. Further development on I-MINDS was subsequently funded with US National Science Foundation (NSF) grant DMI-0441249 in 2005. Extension to ConferenceXP was supported by two separate grants from Microsoft Research from 2005 to 2007, as well as three undergraduate research UCARE grants from UNL and Pepsi. ClassroomWiki was supported in part by NSF grant CNS-0829647. The authors thank I-MINDS and ClassroomWiki team members: Jameela Al-Jaroodi, Xuli Liu, Suresh Namala, Phanivas Vemuri, and Xuesong Zhang. They also thank instructors and faculty who adopted I-MINDS/ClassroomWiki or facilitated their deployments and studies: Charles Ansorge, faculty from Bellevue University, Will Thomas, and June Griffin.
Funding Information:
Initially, from a seed grant sponsored by the University of Nebraska’s National Center for Information Technology in Education (NCITE), I-MINDS (see Fig. 1) was developed [11], [17]. The development of this initial prototype was driven by the usefulness of collaborative learning for improving college education of students as reported in [28] and the ability of intelligent agents to work together to solve difficult problems through tracking and modeling the environment, communication, and collaboration [8], [17]. We also used the Jigsaw method [29], [30] to implement a structured computer-supported collaborative learning classroom. The Jigsaw is a specific process of collaboration, which works as follows: In the Jigsaw model, after the teacher introduces the topic, the students are divided into their original groups. Each group then decides which member would be responsible for which subtask. After this task allocation, each member then joins members from other
Funding Information:
Our I-MINDS work was further supported by an NSF SBIR grant DMI-0441249 to enhance the software with distributed computing infrastructure. Though that venture did not turn fruitful in terms of solving the distributedness problem with I-MINDS [5], it led us to further investigate the underlying communication and coordination infrastructure for supporting the student agents online through automated group formation. In [6] and [7], we described an innovative infrastructure to support student participation and collaboration and help the instructor manage large or distance classrooms using multiagent system intelligence. The upgraded I-MINDS contained a host of intelligent agents for each classroom: a teacher agent ranked and categorized real-time questions from the students and collected statistics on student participation, a number of group agents that each maintained a collaborative group and facilitated student discussions, and a student agent for each student that profiled a student and found other compatible students to form the student’s “buddy group.” Each agent was capable of machine learning, thus improving its performance and services over time. This improved I-MINDS supported student participation and collaboration and helped the instructor manage large distance classrooms. We the developed a multiagent-based learning-enabled algorithm called VALCAM to form student groups in a structured cooperative learning setting. As reported by the collaborative learning researchers, two critical components that impact the students’ learning and collaboration in a student group are their prior knowledge (i.e., competence) [32], [33] and their social relationship (e.g., compatibility) [34], [35]. So, we designed the VALCAM algorithm to form student groups by balancing the competence and compatibility of members.
PY - 2011
Y1 - 2011
N2 - Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms.
AB - Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms.
KW - Collaborative learning
KW - education
KW - multiagent systems
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U2 - 10.1109/TLT.2010.28
DO - 10.1109/TLT.2010.28
M3 - Article
AN - SCOPUS:84857706178
SN - 1939-1382
VL - 4
SP - 47
EP - 58
JO - IEEE Transactions on Learning Technologies
JF - IEEE Transactions on Learning Technologies
IS - 1
M1 - 5557841
ER -