Proposed Recommended System for Employability Skilling using Neural Network

Abstract

Higher educational institutions are apprehensive with using approaches to enhance the development of competences for post graduate employability. Subsequently witnessing, rate of students regarding unemployability are gradually increasing after post-graduation, we are proposing a framework to improve student’s skillset for getting placed in preferred career profile. This paper aims to propose a framework for post graduate students to increase employability and proficiency enhancement. The proposed framework based on artificial neural network will produce desired skillsets. This skillsets will map to their determined career possibilities. This Proposed recommended system will be helpful to not only higher educational institutes for mentoring students towards employability rate but also to post graduate students towards attainment of expected career path by improving their skillsets.

Applying Data Mining in Graduates’ Employability

“…Author Techniques Knowledge Gap Bañeres and Conesa [37] Not clear from the literature Limited sample comprising a single university. Qamhieh, Sammaneh, and Demaidi [50] Fuzzy intelligence Proposed a recommender system for higher education in Palestine, but socio-economic drivers were not considered in the study Suryawanshi, Patil, and Choudhari [51] The study proposed to use ANN The study did not implement the recommender system. It proposed only a graphical framework Purkar, et al [52] Content based The model was limited to informal jobs.…”

Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability

“…The technique enhances repeatability of a research results since it offers reliable outcomes when analyzing real-world phenomena [31]. Therefore, with the lack of consistent factors that influence employability in developing countries in the existing literature [21], EFA assisted the authors in the determination of relevant factors that can be examined in the DRC and generalized to similar developing countries that are faced with socio-political and economic instability [32]. Despite the difficulty of capturing factors that predict employability of youth in the DRC [24], EFA enabled investigators to develop a relevant questionnaire to reflect the understanding of social issues through a set of variables [33].…”

“…Furthermore, the non-uniform nature of employability factors makes it difficult to predict uniformly for such countries. Many researchers have applied data mining techniques to predict employability by using secondary data and applying either feature selection to extract factors that predict this social fact [18] or feature engineering to constitute factors in order to provide appropriate factors that predict graduates' employability [19] and then try to mitigate the unemployment problem by developing prescriptive models [20][21][22]. Nevertheless, the use of secondary data and the application of feature selection and feature engineering techniques to obtain factors constitute several challenges to capture relevantly graduates' employability reality in developing countries, especially in the presence of socio-political and economic crises.…”

“…EFA, which is one of the techniques of factor analysis, plays a preponderant role in data mining as it detects factors to be retained in an observed set of variables [25]. Qamhieh, Sammaneh, and Demaidi [21] observed that the best way of predicting and prescribing employability in developing countries is to consider contextual factors such as the prevailing socio-political and economic factors. It however appears that no study has proposed a consistent set of consistent factors from the EFA technique which can effectively predict the employability of IT graduates in sub-Saharan countries and allow the formulation of a generalizable predictive model.…”