Competency mapping in academic environment: A multi objective approach

S. S. Rout, B. Misra, S. Samanta
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引用次数: 2

Abstract

Mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach.
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学术环境中的胜任力映射:一种多目标方法
绘制专业领域中具有不同水平的专业知识、技能和经验的人员的能力图是一项艰巨、复杂但必不可少的任务。在这项工作中,我们考虑了一所工程学院,其师资数量适中,经验、专业知识和研究水平各不相同。在这里,我们把在学期开始时分配给教员的负荷看作是能力映射任务。每个有能力教授不同学科的教师需要写两篇理论论文,包括或不包括实验部分。课题分配的决定因素可能是知识深度、诚意、班级管理、对研究的贡献、教科书出版。除非有一些有效的限制,否则应优先考虑教师的进一步偏好。同样,一个部门的教学人员有不同的名称和不同的行政责任,因此每个人都不能被分配相同的教学负荷。将决定因素作为优化目标,采用多目标粒子群算法(MOPSO)进行能力映射。仿真结果表明了该方法的有效性。
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