SAES- expert system for advising academic major

Sourabh Deorah, Srivatsan Sridharan, S. Goel
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引用次数: 22

Abstract

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.
学术专业咨询专家系统
在印度,大多数学生选择本科专业完全是基于社会的持续趋势。由于缺乏全面的指导体系,学生往往只根据上述参数进行选择,最终可能与学生对特定专业的实际兴趣和内在天赋不符。在本文中,我们提出了一个专家系统-SAES,旨在为学生提供他/她应该选择哪个专业的智能建议。SAES获取学习成绩以及候选人的显性和隐性兴趣的知识。sae中的知识表示是通过结合使用基于案例和基于规则的推理来完成的。SAES在获得知识的基础上进行推断,也考虑到候选人面临的困境程度以及他/她决定感兴趣领域所花费的时间。然后,sae为每位候选人推荐最合适的专业,并根据计算出的相对成功概率,将这些专业进一步分为强、中、弱三个等级。最后,对系统工作样机的测试结果进行了分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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