A framework for collective intelligence from internet Q&amp;amp;A documents

Oh, Jehwan; Jeong, Ok‐Ran; Lee, Eunseok; Kim, Won

doi:10.1504/ijwgs.2011.040445

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Our proposed research may also be applied to various domain applications, such as mobile systems 28,29,30,31,32,33,34 , RFID 35,36 , real-time systems 37 , business process 38 , web systems 39,40,41 , and fuzzy data 42 .…”

Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

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The negative association between items in databases is as important and interesting as the positive one. But, it has not been studied as much. We consider negative association in a hierarchical setting, in which we are able to generate negative association rules at different hierarchy levels. It allows to impose restrictions when we proceed to the next level and discover only most interesting negative association rules among the vast number of possible negative association rules. In this paper, we propose two algorithms for mining negative association rules by considering that items are organized in a hierarchy, and this hierarchy is reflected on the association rules we produce. In this way, we can mine for both general and specialized rules of negative association between items.

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Section: Resultsmentioning

confidence: 99%

Mining Hierarchical Negative Association Rules

Taniar¹,

Rahayu²,

Daly³

et al. 2012

IJCIS

View full text Add to dashboard Cite

show abstract

“…Think-pair-share [59] Round robin [1,60] Buzz group [61] Paired annotation [1,46,[62][63][64] Critical debates [65] Note-taking pairs [66,67] Send-a-problem [68] Group problem solving [69,70] Affinity grouping [71] Team matrix [72] Word webs [73] Dyadic essays [74][75][76] Peer editing [46,77]…”

mentioning

confidence: 99%

Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors

Zamiri

Camarinha-Matos

2019

Applied Sciences

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Learning ecosystems can benefit from mass collaboration where large numbers of minds collectively drive intellectual efforts to learn in the form of knowledge building and sharing. Mass collaborative learning represents a significant shift away from traditional teacher-centered approach towards a self-directed model in virtual communities in which contributing members take on creative roles to maximize their learning and that of their peers. In order to design, implement, and exploit such a learning approach, influencing constituents should be identified, and appropriate conditions need to be provided. This study aims to systematically review recent literature with a view to identifying relevant affecting constituents and success factors for mass collaboration and learning—namely, the type of organizational structures, collaborative learning techniques, adopted technologies, and methods for evaluating the quality of both members’ performance, and co-created knowledge. Therefore, 100 related papers are collected, and their findings are critically evaluated. The results of evaluation are then addressed and discussed.

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Social Community Based Blog Search Framework

Jeong

2012

Database Systems for Advanced Applications

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A framework for collective intelligence from internet Q&A documents

Cited by 5 publications

References 17 publications

Mining Hierarchical Negative Association Rules

Mining Hierarchical Negative Association Rules

Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors

Social Community Based Blog Search Framework

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