• Journal of The Korean Association For Science Education
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• v.32 no.4
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• pp.555-569
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• 2012

Many science education research and practices are recently emphasizing the importance of collaborative learning. This study also understands learning in aspects of socio-cultural context, and regarded the creation of meaning in a same-age group as an important learning process. This is most especially true in the premise that the formation of roles in a collaborative learning is important for successful interactive learning. This study aims to find out how roles form in a group. For this purpose, university students participating in a group discussion activity about energy flow and circulation of material were selected as research participants. Discussions among the nine students in one group consisted of cognitive conversations on the topic and operational conversations for preparing a presentation. Video-clips of the discussions were made and transcribed. For the analysis, we developed a framework that includes four interaction functions (cognitive, organizational, meta-cognitive, operational), four action elements (question, simple answer, providing opinion, response to opinion), and two to four intention elements by each action elements. As a result, a total of nine roles were revealed through the interaction function and element; cognitive questioner, operational questioner, simple answerer, operational suggester, organizational commander, operational commander, cognitive explainer, terminator, reflective thinker. These roles are re-classified into seven utterance patterns by the utterance order and object, and they were categorized into three role groups (facilitating interaction, sustaining interaction, finishing interaction). The result means that role formation and function can have influence on learning and interaction. This study is meaningful to the suggestion to collaborative learning including project-based learning, investigation, club activity, and for the re-illumination of the role in an aspect of the interaction.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.