A MODEL FOR AUTOMATED MATCHING BETWEEN JOB MARKET DEMAND AND UNIVERSITY CURRICULA OFFER

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Abstract

Technology plays a very important role in virtually all areas, and has become an inseparable part of the industry. Currently, industry and technology are at a high point of development and research, but there is an ever increasing gap between the market needs and the skills that universities deliver to students. There is an increasing need for consolidation between university curricula and the industry needs in terms of qualifications. In this paper we will present a description of the current state of the labor market in the field of technology, including the needs that arise in improving the existing curricula of the Universities. We review the different technologies that can be used, in order to automatically gather information about the market needs in terms of job offers, and how they can be compared against University curricula. We will also present the latest achievements on these methods, and the suggestions that the researchers provide.

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  • [1] M. Agaoglu “Predicting Instructor Performance Using Data Mining Techniques in Higher Education”. In IEEE Access May 2016.

  • [2] T. Xie Q. Zheng W. Zhang H Qu “Modeling and Predicting the Active Video - Viewing Time in a Large - Scale E - Learning System”.In IEEE Access June 2017.

  • [3] A. M. Njeru M. S. Omar S. Yi “IoTs for Capturing and Mastering Massive Data Online Learning Courses”. In IEEE Computer Society ICIS Wuhan China May 2017.

  • [4] R. Heartfield G. Loukas D. Gan “You are probably not the weakest link: Towards Practical Prediction of Susceptibility to Semantic Social Engineering Attacks”. In IEEE Access October 2016.

  • [5] E. J. Fortuny D. Martens “Active Learning - Based Pedagogical Rule Extraction”. In IEEE Transaction on Neural Network and Learning Systems Vol. 26 No. 11 November 2015.

  • [6] A. Mukhopadhyay S. Bandyopadhyay “A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I”. In IEEE Transaction on Evolutionary Computation Vol. 18 No. 1 February 2014.

  • [7] Zh. Song A. Kusiak “Optimization of Temporal Processess: A Model Predictive Control Approach”. In IEEE Transaction on Evolutionary Computation Vol. 13 No. 1 February 2009.

  • [8] S. Malgaonkar S. Soral Sh. Sumeet T. Parekhji “Study on Big Data Analytics Research Domain”. In International Conference on Reliability Infocom Technologies and Optimization ICRITONoida India September 2016.

  • [9]K. P. Anicic B. Divjak K. Arbanas “Preparint ICT Graduates for Real - World Challenges: Results of a Meta - Analysis”. In IEEE Transactions on Education Vol 60 No. 3 August 2017.

  • [10] A. Haskova D. V. Merode “Professional Training in Embedded Systems and its Promotion”. In IEEE Transacions on Education 2016.

  • [11] S.C. Smith W. K. Al-Assadi J. Di “Integrating Asynchronous Digital Design into the Computer Engineering Curriculum”. In IEEE Transactions on Education Vol. 53 No. 3 August 2010.

  • [12] M. D. Koretsky D. Amatore C. Barnes Sh. Kimura “Enhancement of Student Learning in Experimental Design Using a Virtual Laboratory”. In IEEE Transactions on Education Vol. 51 No. 1 February 2008.

  • [13] B. G. Member V. S. Sheng K. Y. Tay W. Romano Sh. Li “Incremental Support Vector Learning for Ordinal Regression”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 26 No. 7 July 2015.

  • [14] J. Li T. Zhang W. Luo J. Yang X. T. Yuan J. Zhang “Sparseness Analysis in the Pretraining of Deep Neural Networks”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 28 No. 6 June 2017.

  • [15] Y. Qian F. Li J. Liang B. Liu Ch. Dang “Space Structure and Clustering of Categorical Data”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 27 No. 10 October 2016.

  • [16] Y. Xiao B. Liu Zh. Hao “A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 27 No. 5 May 2016.

  • [17] B. Gu V. S. Sheng K. Y. Tay W. Romano Sh. Li “Incremental Support Vector Learning for Ordinal Regression”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 26 No. 7 July 2015.

  • [18] P. Navrat L. Molnar “Curricula Transformation in the Countries in Transition: An Experience from Slovakia”. In IEEE Transactions on Education Vol. 41 No. 2 May 1998.

  • [19] S. Nalintippayawong K. Atchariyachanvanich “IT Management Status in Public Higher Education Institutions in Thailand”. In IEEE ICIS 2016 June 26-29 2016 Okayama Japan.

  • [20] J. I. Godino - Llorente R. Fraile J. C. Gonzales de Sante V. Osma - Ruiz N. Saenz - Lechon ““Design for All in the Context of the Information Society”: Integration of a Specialist Course in a Generalist M.Sc. Program in Electrical and Electronics Engineering”. In IEEE Transactions on Education Vol. 55 No. 1 February 2012.

  • [21] M. Dolores Cano “Students’ Involvement in Continuous Assessment Methodologies: A Case Study for a Distributed Information Systems Course”. In IEEE Transactions on Education Vol. 54 No. 3 August 2011.

  • [22] Y. He Ch. Wang Ch. Jiang “Mining Coherent Topics with Pre-Learned Interest Knowledge in Twitter”. In IEEE Access June 2017.

  • [23] H. Pirkkalainen J. P. P. Jokinen J. M. Pawlowski “Understanding Social OER Environments-A Quantitative Study on Factors Influencing the Motivation to Share and Collaborate”. In IEEE Transactions on Learning Technologies Vol. 7 No. 4 October-December 2014.

  • [24] G. Goth “Network-Enabled Compulsory Education Getting Big Push”. In IEEE Computer Society February 2009.

  • [25] R. Mehmood F. Alam N. N. Albogami I. Katib A. Albeshri S. M. Altowaijri “UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies”. In IEEE Access February 2017.

  • [26] J. J. Guerrero L. A. Guerrero “A Virtual Repository of Learning Objects to Support Literacy of SEN Children”. In IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje Vol. 10 No. 3 August 2015.

  • [27] A. A. Choudhury J. Rodriguez “A New Curriculum in Fluid Mechanics for the Millennial Generation”. In IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje Vol. 12 No. 1 February 2017.

  • [28] A. Sethi “Factors Responsible for Mismatch between Demand and Supply of Requisite Skill in India”. In IJARIIE-ISSN (O)-2395-4396 Vol. 3 Issue 3 2017.

  • [29] L. Anastasiu A. Anastasiu M. Dumitran C. Crizboi A. Homaghi M. N. Roman “How to Align the University Curricula with the Market Demands by Developing Employability Skills in the Civil Engineering Sector”. In Education Sciences doi:

    • Crossref
    • Export Citation
  • [30] K. P. Anicic B. Divjak K. Arbanas “Preparing ICT Graduates for Real - World Challenges: Results of Meta - Analysis”. In IEEE TRANSACTIONS ON EDUCATION Vol. 60 No. 3 August 2017.

  • [31] M. T. R. A. Aziz Y. Yusof “Graduates Employment Classification using Data Mining Approach”. In Proceedings of the International Conference on Applied Science and Technology ICAST 2016.

  • [32] S. Sahu M. Bhatt “Big Data Classification of Student Result Prediction”. In International Journal of Research In Science & Engineering Volume: 3 Issue: 2 March-April 2017.

  • [33] V. Bharanipriya V. Kamakshi Prasad “Web Content Mining Tools: A Comparative Study”. In International Journal of Information Technology and Knowledge Management Volume 4 No. 1 pp. 211-215 2011.

  • [34] P. Thakar A. Mehta Manisha “Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue”. In International Journal of Computer Applications (0975 - 8887) Volume 110 - No. 15 January 2015.

  • [35] G. Grasso T. Furche Ch. Schallhart “Effective Web Scraping with OXPath”. In WWW 2013 Companion Rio de Janeiro Brazil. ACM 978-1-4503-2038-2/13/05 2013.

  • [36] M. Thelwall “A Web Crawler Design for Data Mining”. In Journal of Information Science pp. 319-325 2001.

  • [37] T. V. Adapure R. D. Kale R. C. Dharmik “Study of Web Crawler and its Different Types”. In IOSR Journal of Computer Engineering Volume 16 Issue 1 PP 01-05 2014.

  • [38] F. Ahmad N. H. Ismail A. A. Aziz “Using Classification Data Mining Techniques”. In Applied Mathematical Sciences Vol. 9 pp. 6415 - 6426 no. 129 2015.

  • [39] D. Garcia-Saiz M. Zorilla “Comparing Classification Methods for Predicting Distance Students Performance”. In JMLR: Workshop and Conference Proceedings 17 pp. 26-32 2011.

  • [40] N. R. Haddaway “The Use of Web-scraping Software in Searching for Grey Literature”. In The Grey Journal Volume 11 2015.

  • [41] T. Furche G. Gottlob G. Grasso Ch. Schallhart A. Sellers “OXPath: A language for scalable data extraction automation and crawling on the deep web”. In The VLDB Journal 2012.

  • [42] L. Auria R. A. Moro “Support Vector Machines (SVM) as a Technique for Solvency Analysis”. In German Institute for Economic Research 2008.

  • [43] M. Awad L. Khan F. Bastani “An Effective Support Vector (SVM) Performance Using Hierarchical Clustering”. In IEEE 24th International Conference on Tools with Artificial Intelligence 2004.

  • [44] A Brief Introduction to Support Vector Machine (SVM). January 25 2011.

  • [45] ACM Recommendations for Computer Science Curricula Volume I 1983.

  • [46] Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering A Volume of the Computing Curricula Series IEEE 2014.

  • [47] Th. Iliou Ch. N. Anagnostopoulos M. Nerantzaki “A Novel Machine Learning Data Preprocessing Method for Enhancing Classification Algorithms Performance”. In 16th EANN workshops ACM Rhodes Island Greece 2015.

  • [48] M. M. Yusof R. Mohamed N. Wahid “Benchmark of Feature Selection Techniques with Machine Learning Algorithms for Cancer Datasets”. In ICAIR and CACRE '16 ACM Kitakyushu Japan 2016.

  • [49] D. Brandon “TEACHING DATA ANALYTICS ACROSS THE COMPUTING CURRICULA *”. In CCSC: Mid-South Conference 2015.

  • [50] H. Hu J. Li A. Plank H. Wang G. Daggard “A Comparative Study of Classification Methods for Microarray Data Analysis”. In Proc. Fifth Australasian Data Mining Conference 2006.

  • [51] H. Liu X. Yin J. Han “An Efficient Multi-relational Naïve Bayesian Classifier Based on Semantic Relationship Graph”. In MRDM’05 ACM Chicago Illinois USA 2005.

  • [52] M. HooshSadat H. W. Samuel S. Patel “Fastest Association Rule Mining Algorithm Predictor (FARM-AP)”. In C3S2E 11 Montreal QC Canada 2011.

  • [53] H. Hu J. Li “Using Association Rules to Make Rule-based Classifiers Robust”. In Australian Computer Society Inc. ACM 2005.

  • [54] A. Sun E. Lim W. Ng “Web Classification Using Support Vector Machine∗”. In WIDM’02 ACM Virginia USA 2002.

  • [55] L. Borges V. Marques J. Bernardino “Comparison of Data Mining techniques and tools for data classification”. In C3S2E-13 ACM Porto Portugal 2013.

  • [56] Y. N. Silva S. W. Dietrich J. M. Reed “Integrating Big Data into the Computing Curricula”. In SIGCSE’14 ACM Atlanta GA USA 2014.

  • [57] E. Trandafili A. Allkoci Elinda Kajo A. Xhuvani “Discovery and Evaluation of Student’s Profiles with Machine Learning”. In BCI’12 ACM 978-1-4503-1240-0/12/09 Novi Sad Serbia 2012.

  • [58] B. Edwards M. Zatorsky R. Nayak “Clustering and Classification of Maintenance Logs using Text Data Mining”. In Proc. 7th Australasian Data Mining Conference (AusDM'08) Glenelg South Australia 2008.

  • [59] L. Merschmann A. Plastino “A Bayesian Approach for Protein Classification”. In SAC’06 ACM 1-59593-108-2/06/0004 Dijon France 2006.

  • [60] A. Veloso W. Meira Jr. M. Cristo M. Goncalves M. Zaki “Multi-Evidence Multi-Criteria Lazy Associative Document Classification”. In CIKM’06 ACM 1-59593-433-2/06/0011 Virginia USA 2006.

  • [61] R. Frank M. Ester A. Knobbe “A Multi-Relational Approach to Spatial Classification”. In KDD’09 978-1-60558-495-9/09/06 Paris France 2009.

  • [62] M. Ericsson A. Wingkvist “Mining Job Ads to Find What Skills are Sought After from an Employers’ Perspective on IT Graduates”. In ITiCSE’14 ACM 978-1-4503-2833-3/14/06 Uppsala Sweden 2014.

  • [63] Q. Ding Q. Ding W. Perrizo “Decision Tree Classification of Spatial Data Streams Using Peano Count Trees”. In SAC 2002 ACM 1-58113-445-2/02/03 Madrid Spain 2002.

  • [64] Ch. C. Aggarwal “The Setwise Stream Classification Problem”. In KDD’14 ACM 978-1-4503-2956-9/14/08 New York NY USA 2014.

  • [65] Ch. C. Aggarwal J. Han Ph. S. Yu “On Demand Classification of Data Streams”. In KDD’04 ACM 1-58113-888-1/04/0008 Seattle Washington USA 2004.

  • [66] Ch. C. Aggarwal “Towards Exploratory Test Instance Specific Algorithms for High Dimensional Classification”. In KDD’05 ACM 1-59593-135-X/05/0008 Chicago Illinois USA 2005.

  • [67] J. Li R. Topor H. Shen “Construct robust rule sets for classification”. In SIGKDD’02 ACM 1-58813-567-X/02/0007 Alberta Canada 2002.

  • [68] N. Jin C. Young W. Wang “GAIA: Graph Classification Using Evolutionary Computation”. In SIGMOD ’10 ACM 978-1-4503-0032-2/10/06 Indiana USA 2010.

  • [69] H. Fei J. Huan “Structure Feature Selection for Graph Classification”. In CIKM ’08 ACM 978-1-59593-991-3/08/10 California USA 2008.

  • [70] A. Wegmann “Theory and Practice behind the Course Designing Enterprisewide IT Systems”. In IEEE Transactions on Education Vol. 47 No. 4 November 2004.

  • [71] J. E. Froyd Ph C. Wankat K. A. Smith “Five Major Shifts in 100 Years of Engineering Education”. In Proceedings of the IEEE 0018-9219 Vol. 100 May 13th 2012.

  • [72] Z. Shiller “A Bottom-Up Approach to Teaching Robotics and Mechatronics to Mechanical Engineers”. In IEEE Transactions on Education Vol. 56 No. 1 February 2013.

  • [73] M. Marques M. C. Viegas M. C. Costa - Lobo A. V. Fidalgo G. R. Alves J. S. Rocha I. Gustavsson. In IEEE Transactions on Education Vol. 57 No. 3 August 2014.

  • [74] A. K. Kakar “Teaching Theories underlying Agile Methods in a Systems Development Course”. In 47th Hawaii International Conference on System Science IEEE 978-1-4799-2504-9/14 2014.

  • [75] M. G. Soto R. Dzwonczyk “Maximizing Service and Learning in an International Engineering Service Learning Program”. In IEEE 2015 Global Humanitarian Technology Conference 978-1-4673-6561-1/15 2015.

  • [76] W. He J. T. Kwok J. Zhu Y. Liu “A Note on the Unification of Adaptive Online Learning”. In IEEE Transactions on Neural Networks and Learning Systems Vol. 28 No. 5 May 2017.

  • [77] D. Klosters “Matching Skills and Labor Market Needs”. In World Economic Forum January 2014.

  • [78] A. Ghani Kanesan Bin Abdullah “Bridging the Gap between Industry and Higher Education Demands on Electronic Graduates’ Competencies”. In IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) Volume 8 Issue 1 2013.

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