Predicting IT Employability Using Data Mining Techniques

被引：0

作者：

Piad, Keno C. ^{[1
]}

Dumlao, Menchita ^{[2
]}

Ballera, Melvin A. ^{[3
]}

Ambat, Shaneth C. ^{[2
]}

机构：

[1] Bulacan State Univ, Sch Comp Studies, Malolos, Philippines

[2] AMA Univ, Sch Grad Studies, Quezon City, Philippines

[3] AMA Univ, Sch Comp Studies, Quezon City, Philippines

来源：

2016 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING, DATA MINING, AND WIRELESS COMMUNICATIONS (DIPDMWC) | 2016年

关键词：

decision tree; classification algorithm; employability; prediction; analytics; data; accuracy;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables. First, different classification algorithms in data mining were tested making logistic regression with accuracy of 78.4 is implemented. Based on logistic regression analysis, three academic variables directly affect; IT_Core, IT_Professional and Gender identified as significant predictors for employability. The data were collected based on the five year profiles of 515 students randomly selected at the placement office tracer study.

引用

页码：26 / 30

页数：5

共 50 条

[31] Predicting the Probability of Student's Degree Completion by Using Different Data Mining Techniques
Ahuja, Ravinder
Kankane, Yash
2017 FOURTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP), 2017, : 474 - 477
[32] Modeling and Predicting Student Academic Performance in Higher Education Using Data Mining Techniques
Chauhan, Alok Singh
INTERNATIONAL JOURNAL OF SOFTWARE INNOVATION, 2022, 10 (01)
[33] Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques
Lee, Sanghoon
Choi, Keunho
Yoo, Donghee
SUSTAINABILITY, 2020, 12 (23) : 1 - 17
[34] Using data mining and datawarehousing techniques
Forcht, KA
Cochran, K
INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 1999, 99 (5-6) : 189 - 196
[35] Using data mining and datawarehousing techniques
Forcht, Karen A.
Cochran, Kevin
Industrial Management and Data Systems, 1999, 99 (05): : 189 - 196
[36] Data Mining Techniques for Predicting Values of a Faulty Sensor at a Refinery
Saybani, Mahmoud Reza
Teh, Ying Wah
Lahsasna, Adel
Amini, Amineh
Aghabozorgi, Saeed Reza
2011 6TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND CONVERGENCE INFORMATION TECHNOLOGY (ICCIT), 2012, : 792 - 796
[37] A methodology for predicting agent behavior by the use of data mining techniques
Symeonidis, A
Mitkas, P
AUTONOMOUS INTELLIGENT SYSTEMS: AGENTS AND DATA MINING, PROCEEDINGS, 2005, 3505 : 161 - 174
[38] A Fusion of Data Mining Techniques for Predicting Movement of Mobile Users
Duong, Thuy Van T.
Tran, Dinh Que
JOURNAL OF COMMUNICATIONS AND NETWORKS, 2015, 17 (06) : 568 - 581
[39] Predicting Adolescent Deviant Behaviors through Data Mining Techniques
Liu, Yu-Chin
Hsu, Yung-Chieh
EDUCATIONAL TECHNOLOGY & SOCIETY, 2013, 16 (01): : 295 - 308
[40] Data mining techniques in predicting default rates on customer loans
Zurada, J
DATABASES AND INFORMATION SYSTEMS II, 2002, : 285 - 296

← 1 2 3 4 5 →