Career Guidance through Multilevel Expert System Using Data Mining Technique

Ansari, Gufran Ahmad

doi:10.5815/ijitcs.2017.08.03

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…This section describes the classification method and algorithm applied to build the classifier model for the career path counseling system. Ansari, 2017) is an activity that helps you to recognize and understand yourself as well as the career path in certain social circumstances, thereby making career choices, choosing personal development paths, and getting suitable careers. Career counseling activities include two purposes: shortterm and long-term.…”

Section: Methodsmentioning

confidence: 99%

A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

Ngọc¹,

Van²

2021

JCSTS

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Choosing the right career is always a big issue, an important concern for everyone. To have a job, which is suitable for you, firstly you must look at yourself, called the self, and you should be aware of what the self is then you can promote the strength of your own self and avoid your weakness. To help discover more about yourself, during researching and studying, we come up with the idea that we would propose a career counseling system based on Howard Gardner's theory. The system uses the theory of multiple intelligences (Abenti & Daradoumis, 2020) which is combined with the K-nearest neighbors (KNN) (Tang, Ying; Tang, Ying; Hare, Ryan; Wang, Fei-Yue;, 2020) algorithm to assist people and to give out a suitable suggestion about career path for them. We use the results of the eight intelligences retrieved from the KNN classification algorithm to give users the consulting for their career paths. This system is built with a dataset based on 56 multiple-choice questions. These include 48 multiple choice questions based on Howard Gardner's theory of multiple intelligences (Bravo, Leonardo Emiro Contreras; Molano, Jose Ignacio Rodriguez; Trujillo, Edwin Rivas, 2020), (businessballs, 2017) and 8 multiple choice questions which are the labels of the classifier. We divided the dataset into 8 subsets corresponding with 8 Intelligences defined by Howard Gardner with the collected dataset. In each subset, we build the KNN classifier model using KNN classification algorithm. This processing of 8 subsets come out with the results accuracy for the 8 Intelligences: linguistic intelligence (80.95%), logical-mathematical intelligence (82.14%), musical intelligence (96.43%), bodily-kinesthetic intelligence (82.14%), spatial-visual intelligence (82.14%), interpersonal intelligence (89.29%), intrapersonal intelligence (88.1%), existential intelligence (78.57%). With the outcome of 8 models, we have tested with 5 students and compared them to their actual intelligences. The comparison results tell us about the valuable potential in career path of the proposed counselling system, the advantages of this combination between Multiple Intelligence and KNN classifier.

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Section: Methodsmentioning

confidence: 99%

A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

Ngọc¹,

Van²

2021

JCSTS

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“…Three modules were created for testing and calculating different factors, report generation and augmenting the knowledge base for unknown cases. A software-based system has been developed through the nearest neighbour technique of data mining (Ansari, 2017). The system included 11 different factors, which the students rated on a fivepoint scale as per their preference.…”

Section: Case-based Expert Systemmentioning

confidence: 99%

A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL

Gunwant

2022

JL4D

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The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.

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“…This can be done through direct career guidance andinformation communication technology (ICT) solutions. Therefore, the ICT is essential in spreading information among students and as an expert system that improves student decision-making Machine learning expert systems for providing multilevel career guidance for primary, secondary, and higher-level students have been proposed [Ansari, 2017]. Various factors such as family background, socio-economic status, and students' interests were considered for developing the system.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Influential Factors Affecting Career Selection Process

2023

IJFMR

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Making careful and well-thought-out decisions regarding a career can increase one's chance of success. Choosing the right career depends on many different factors related to the individual. The scientific approach for career guidance plays a vital role in reducing the expenses and efforts of a student in determining an optimum career path. This article focuses on scientifically determining the most relevant factors affecting the career selection decision of a person. The first section deals with a literature review of the various factors considered by the researchers for designing expert systems for career selection. A detailed questionnaire was designed to extract useful information from 95 individuals from different professions. The data consisted of 63.8% male and 36.2% female individuals belonging to professions viz. agriculture, fine arts, technology, education, and media. The individuals considered in the study comprised varied education levels ranging from intermediate diploma to post-graduation. Based on the above, a logical conclusion was drawn. The most relevant factors were determined by statistical analysis of the data obtained.

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Career Guidance through Multilevel Expert System Using Data Mining Technique

Cited by 9 publications

References 19 publications

A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

A Systematic Study of the Literature on Career Guidance Expert Systems for Students: Implications for ODL

An Analysis of Influential Factors Affecting Career Selection Process

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