Adaptive Network Based Fuzzy Inference System and the Future of Employability

Educational data is considered by researchers and data scientists as an indicator for the future predictions. The current research study aims for classifying IT alumni students into employed and unemployed. The data collected from two universities in Jordan. 781 of IT alumni students in two universities in Jordan participate in the current study. Three classifiers are compared to determine the most suitable one for predicting the future of IT students’ employability. The results show that Adaptive Network Based Fuzzy Inference System came as a suitable classifier for predicting IT students’ employment in Jordan. As gender, programming skills, and communication skills came as the most effective factors affecting IT recruitment field, a set of recommendations is presented to the ministry of higher education based on the significant factors affecting IT graduates employment. Keywords: employability, ANFIS, classification, data mining DOI: 10.7176/NCS/12-04 Publication date: January 31st 202

Comportamiento de indicadores de empleabilidad en egresados universitarios

Introduction: This article is the result of research entitled the behavior of employability indicators in university graduates, developed at the Universidad Distrital Francisco José de Caldas in 2019. Problem: The Emple-AP project promotes the creation of an observatory for labor insertion and the strengthening of employability in countries of the Pacific Alliance (PA), which particularly benefits Colombia, because one of its objectives with the PA is to overcome the socioeconomic inequality that exists among its inhabitants. Objective: To identify the relationship between employability indicators through classification methods used in Artificial Intelligence. Methodology:The indicators’ behavior description involves data pre-processing, a formal global study in statistics and a specific formal study through comparison of classification methods. Results: Descriptions of these employability indicators show characteristics of the situation in the studied population. Conclusion:Given the analysis of the classification model, it is determined that the diversity and disparity of the dataset makes the RandomTree model the most accurate in this research, finding that the system has characteristic behaviors of an adaptative complex system. Originality:Through this research, employability indicators were analyzed through data mining tools, additionally the analysis presented in this article could be replicated under particular conditions in other countries of the PA. Limitations:The information comes from the Universidad Distrital Francisco José de Caldas graduate’s office. A single source generates a limitation in the data and in the population studied.Introducción: El presente artículo es resultado del proyecto de investigación comportamiento de los indicadores de empleabilidad de egresados universitarios desarrollado en la Universidad Distrital Francisco José de Caldas en 2019 Problema: El proyecto Emple-AP promueve la creación de un observatorio para la inserción laboral y el fortalecimiento de la empleabilidad en países de la Alianza del Pacífico (AP), lo cual beneficia particularmente a Colombia, debido a que uno de sus objetivos con la AP es superar la desigualdad socioeconómica que existe entre sus habitantes. Objetivo: Identificar la relación existente entre los indicadores de empleabilidad mediante métodos de clasificación empleados en Inteligencia Artificial. Metodología:La descripción del comportamiento de los indicadores implica el pre- procesamiento de datos, el estudio formal global en la estadística y el estudio formal especifico a través de comparación de métodos de clasificación. Resultados:Las descripciones de estos indicadores de empleabilidad evidencian características de la situación en la población estudiada. Conclusión:Dado el análisis de modelos de clasificación se determina que la diversidad y disparidad del conjunto de datos hace que el modelo RandomTree sea el que tiene mayor precisión en esta investigación, encontrando que el sistema tiene comportamientos característicos de un sistema complejo adaptativo. Originalidad:A través de esta investigación se analizó el comportamiento de la empleabilidad por medio de herramientas de minería de datos, adicionalmente el análisis presentado en este artículo podría ser replicado bajo condiciones particulares en los demás países de la AP. Limitaciones:La información proviene de la oficina de egresados de la Universidad Distrital Francisco José de Caldas, siendo una sola fuente, loque genera una limitante en los datos y en la población estudiada

Relationship between employability and graduate’s competencies based on programme learning outcomes analysis

Graduate employability pertains to issues that are related to the person’s character or quality of being employable such as the knowledge and skills they possess to the labour market is a crucial issue in higher education institutions (HEIs). Accordingly, in line with the aspirations and mission of the National Graduate Employability Blueprint Malaysia 2012-2017, they expect to produce competent graduates with 75% of the graduates working in their related fields within six months after graduation. Throughout the literature, tracer study is commonly used and has been adopted by the Ministry of Higher Education to trace graduate employability (GE) information and evaluation on study programmes have helped in improving the transition of graduates from education to the labour market. However, two issues arise;- (1) a lack in predictive capability, and (2) the lack of inclusive graduate data where data and analysis from a tracer study have not been well communicate to other stakeholders. Furthermore, there have been little discussion on predicting the duration of a graduate’s employment after graduation. Therefore, this study intends to investigate the relationship between employability duration and the graduate’s competencies based on programme learning outcomes (PLO) among Computer Science or IT engineering domain. The outcome-based education (OBE) contributes to the learning outcomes attainment which is the PLO that helps the learners to succeed especially in professional life and education. Thus, this study used a modified version of the predictive analytic process that started with problem definition and obtained a clean dataset before the model formulation and evaluation took place. There are two data sources that have been used in this study, institutional academic database (PTMK UMP) and an online feedback from the graduates. This study received 47 responses out of 164 graduates from 2014/2015 Faculty of Computing (FK) batch, with a response rate of 29%. A simple linear regression was used to measure the correlation between the category of PLO and the duration of graduate to get employed as well as to formulate the prediction model. The findings from this study found that PLO6 (problem solving and scientific skills) was the most sensitive PLO on the duration for a graduate to get employed (r = -0.2515, p = 0.0882, p < 0.25, N = 47). Thus, the model was formulated based on the linear equation of PLO6 which is Duration = -9.549x + 73.497. This prediction model was validated through error rate analysis with acceptable result and evaluated by error rate frequency analysis. The evaluation through ranking method based on the frequency analysis of error rate also found that PLO6 was at the first rank followed by PLO3, PLO1, PLO4, PLO5, PLO2, PLO8, PLO7. This study reported the potential of outcome-based education data to predict graduate employability performance within the time frame (six months) as determined by the Ministry of Higher Education. With prediction capacity from the formulated model, more intervention programme can be strategically planned to assure that graduates can be employed in time and in-field

Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia

The performance of variable selection is essential to build an effective logistic regression model. Generally, p-values are used to identify significant variables or factors in the model. However, when dealing with real tracer study data for a country, the size of the data is typically large of which causes the p-values to be deflated and affect the variable selection performance. Therefore, it is crucial to have an appropriate sample size and sampling ratio for this purpose. In this study, the appropriate sample size has been proposed based on simulated correlation tests and significant variables in order to improve the accuracy of variable selection. In addition, the sampling ratio in the response variable shows its best when it reflects the population ratio. Based on the proposed samples, the logistic regression model for graduate employability factor is subsequently proposed. It has been found that age, Cumulative Grade Point Average (CGPA), discipline of study, gender, state, and type of universities are the factors that significantly affect graduate employability among public universities in Malaysia. The results show that the proposed model has successfully improved the variable selection, model fitting, and classification accuracy as compared to the full model. Thus, by using a smaller sample size, the proposed model is able to maintain its statistical power in real data scenario by accurately selecting the significant factors

Supervised and Unsupervised Learning in Data Mining for Employment Prediction of Fresh Graduate Students

Data mining techniques are widely used in engineering, medicine, industry, agriculture and even used in education to predict a future situation. In this paper, the used of data mining techniques applied in features selection and determine the best model that can be used to predict the employment status of fresh graduate Public Institutions either employed or unemployed, six months after graduation. In CRISP-DM methodology, six phases were adopted. The algorithm in supervised and unsupervised learning; K-Nearest Neighbor, Naive Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machines were compared using the training data set from Tracer Study to determine the highest accuracy in turn is used as a predictive model. Rapid Miner as a data mining tool was used for data analysis algorith

The impact of entrepreneurship educations on entrepreneurial capacity and self-employment

Real Estate Management (REM) practice is being increasingly challenged, as a result of the economic recession, encroachment of quacks and allied professionals as well as the technological revolution of the contemporary knowledge-based economy. This implies that conformist approach to the REM training might not be enough to guarantee REM students employment prospect. More so, the issue of graduates’ employability in the job market is becoming more competitive in Malaysia. However, entrepreneurship education (EE) introduced in the Malaysian Higher learning institutions with the intention of boosting the employability of the graduates. In spite of the above initiative, impact assessment of the EE is still ambiguous, particularly, in the REM discipline. Hence, an absence of a common assessment framework to evaluate diverse EEs registered in the literature. Therefore, the research aimed to assess perception of the REM students on the impact of entrepreneurship education on the entrepreneurial capacity and self-employment intention and to propose an Objective-based Entrepreneurship Education Assessment Model (OBEEAM). The research employed quantitative research approach and ex-post research design. Hence, purposive sample technique applied to collect data on the sample size of 437 REM students through a cross-sectional survey in the four Malaysian public universities. SPSS 22.0 and Structural Equation Modeling tools of analysis were used to analyse, data collected, proposed OBEEAM and test of the nine research hypotheses empirically. The findings indicated a positive impact of entrepreneurship education on the perception of REM students’ entrepreneurial capacity and self-employment intention as a career option. Despite, the skills of creativity and innovation in the idea development, risk taking proficiency and practical workshop practice were somewhat weak. Therefore, the need for more practical initiative exercises such as extended entrepreneurship teaching in the core courses and across the years of REM programmes recommended. This could provide the innovation required for the development of dynamic future real estate-entrepreneurs in Malaysia. The research’s novelty is the proposed multidirectional OBEEAM that had integrated the core values and drivers of entrepreneurship teaching and self-employment intention; it could be adopted, adapted and implemented for the assessment of EEs in any academic field of studies

Estimating a prediction model for the early identification of low employability graduates in Malaysia

This paper describes the development of a prediction model for the early identification of low employability graduates in Malaysia. A total of five proportional hazard models are estimated and using the criteria of percentage correctly and wrongly predicted, a prediction model is selected based on the percentage correctly predicted. The percentile of the predicted hazard rate is used as the employability index (EI). In the context of Malaysia, it is recommended that the 5th percentile graduates be considered as low employability graduates. With this early identification tool, specific intervention programs can be tailored for the right target groups

Bridging the Gap between Higher Education and the Logistic Sector Needs in Oman: Designing a Needs-based Curriculum

The purpose of the study is to evaluate and measure the curriculum as an academic plan that provides expected knowledge, skills and attitudes for the learner through understanding curriculum theories to build “a more practice-based curriculum” to achieve the stated learning outcomes to enhance job performance and predict job success of the logistics sector in Oman and identify the best processes and tools for designing the curriculum to fit the needs of the sector and bridge the bridge any gaps found in the curriculum gap using mixed-method through three samples (Curriculum Designers within HEIs, Middle-Managers and employees within logistics companies)

what the employer think and graduates have?

Thesis(Master) --KDI School:Master of Development Policy,2013masterpublishedNoor Shuhailie M Mohamed Noor