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Autonomous Unbiased Study Group Formation Algorithm for Rapid knowledge propagation

Computer Systems Science & Engineering 2022年第7期42卷 15-31页

作者： Monday Eze Charles Okunbor Solomon Esomu Nneka Richard-Nnabu Kayode Oladapo Oghenetega Avwokuruaye Abisola Olayiwola Akpovi Ominike Godwin Odulaja Oluwatobi Akinmerese Department of Computer Science Babcock UniversityOgunNigeria Department of Computer Science Admiralty UniversityDeltaNigeria Department of Computer Science National Open UniversityNigeria Department of Computer Science and Informatics Alex-Ekwueme Federal UniversityEbonyiNigeria Department of Cyber Security Admiralty UniversityDeltaNigeria Department of Computer Science Caleb UniversityImotaLagosNigeria Department of Computer Science and Information Technology Petroleum Training InstituteDeltaNigeria Department of Computer Science Tai Solarin University of EducationOgunNigeria

knowledge propagation is a necessity,both in academics and in the industry.The focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study groups.The practice of knowledge sharing in study groupsﬁnds relevance in conferences,workshops,and class rooms.Unfortu-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge propagation.This work bridges this gap by presenting a workﬂow driven compu-tational algorithm for autonomous and unbiased formation of study groups.The system workﬂow consists of a chronology of stages,each made of distinct steps.Two of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative pairs.This work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among others.The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external process.As part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 participants.The quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefﬁcients,which resulted in values higher than 0.9 in all cases.Real life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May 2021.Empirical result showed that the per-formance of the learners was impro

关键词： knowledge propagation collaborative study group unbiased matching twisted round robin exhaustive multiplication

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Modeling and Simulation of the knowledge propagation of Group Nonlinear Learning Based on the Complex Network

Modeling and Simulation of the Knowledge Propagation of Grou...

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2010 Chinese Control and Decision Conference

作者： Shaocheng Qu,Wenhui Tian,Sha Li Dept.of Information and Technology,Huazhong Normal University,Wuhan,430079,China

＜正＞A knowledge propagation model of group nonlinear learning on complex network is discussed.According to the characteristics of knowledge propagation in the general group learning network,the Cobb-Dauglas generator function is introduced to establish the knowledge propagation model of group nonlinear learning.Through considering self-motivation of excellent individual and repellency of laggard individual,an improved knowledge propagation model is proposed.Simulation results show that propagation speed and distribution of knowledge will increase with the stochastic degree of networks under the same conditions.

关键词： Complex Network Nonlinear Learning knowledge propagation Numerical Simulation

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