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SAES- Expert System for Advising Academic Major An Indian Perspective

机译:萨斯 - 建议学术专业印度观点的专家制度

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Most students in India choose their undergraduate major solely on the basis of persisting trends in the society. Due to the lack of a holistic guidance system, students often end up making choices solely on the basis of the above parameter, which in eventuality, may fail to align with the student's actual interest and inherent aptitude towards a particular major. In this paper we propose an expert system-SAES which aims to provide intelligent advice to the student as to which major he/she should opt. SAES acquires knowledge of academic performances as well as explicit and implicit interests of the candidate. Knowledge representation in SAES is done by the use of a combination of case based and rule based reasoning. SAES draws inferences on the basis of acquired knowledge and also takes into account the degree of dilemma faced by the candidate and the time he/she takes to decide the interest areas. SAES then recommends the most suitable majors for each candidate, which are further classified as strong, mild and weak on the basis of calculated relative probabilities of success. At the end, we analyze results of the test conducted on a working prototype of SAES.
机译:印度大多数学生只在社会持续趋势的基础上选择了他们本科专业。由于缺乏整体指导系统,学生往往是基于上述参数的基础上的学生,这可能无法与学生的实际兴趣和对特定专业的固有能力保持一致。在本文中,我们提出了一个专家系统 - Saes,旨在向学生提供智能建议,以至于他/她应该选择哪个主要的专业。 Saes获得了学术表演的知识,以及候选人的明确和隐含的利益。 Saes中的知识表示是通过使用基于案例和规则的推理的组合来完成的。 Saes在获得的知识的基础上汲取推论,并考虑了候选人所面临的困境以及他/她所采取的时间来决定兴趣区。然后,Saes为每个候选人推荐最合适的专业,这在计算出的相对成功概率的基础上进一步归类为强劲,轻度和弱势。最后,我们分析了在SAE的工作原型进行的测试结果。

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