RETRACTED: A practical and efficient multi-assessment system for vocational teaching based on machine learning (Retracted Article)

被引:7
作者
Xiao, Meng [1 ]
Yi, Haibo [2 ]
机构
[1] Shenzhen Polytech, Sch Management, Shenzhen, Peoples R China
[2] Shenzhen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China
关键词
Multi-assessment system; vocational teaching; machine learning; higher vocational education; teaching assessment; EDUCATION;
D O I
10.1177/0020720920940573
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Higher vocational education is a self-consistent system for higher education appropriate to the development of productivity and economy in the world. It aims at training skilled talents, which has made great contribution to the economy and industry. Generally, designing courses in high vocational education includes teaching analysis, teaching strategy, teaching practice and teaching assessment. Among the teaching steps, teaching assessment is one of the most important method to improve the quality of course teaching. However, in most high vocational education courses, traditional written exam is still the primary tools of assessments, which can not fulfill the development of high vocational education. In order to improve the quality of high vocational education, it is very urgent to design a practical and efficient system with multiple assessments. We exploit machine learning techniques to design assessment system for high vocation education. Machine learning is a very powerful tool for data analysis and it has been used for education tools in recent years. First, we improve the teaching organization for training skilled talents. Second, we propose a feature selection model based on the improved teaching organization. Third, we propose a machine learning model for teaching assessment. With the main contributions and other improvements, we design a multi-assessment system for vocational teaching based on machine learning. We implement the multi-assessment system by using Python and TensorFlow, which shows that the system can provide practical and efficient multiple assessments for vocational teaching based on training machine learning model. Compared with other assessment methods, machine learning based multi-assessment is more intelligent and automatic. Besides, it can be extended to other fields of education with slight modifications.
引用
收藏
页数:13
相关论文
共 27 条
[1]   Integrating internet of things in electrical engineering education [J].
Alharbi, Fahd .
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION, 2020, 61 (02) :258-275
[2]   Machine learning in the string landscape [J].
Carifio, Jonathan ;
Halverson, James ;
Krioukov, Dmitri ;
Nelson, Brent D. .
JOURNAL OF HIGH ENERGY PHYSICS, 2017, (09)
[3]   Students' Use of Optional Online Reviews and Its Relationship to Summative Assessment Outcomes in Introductory Biology [J].
Carpenter, Shana K. ;
Rahman, Shuhebur ;
Lund, Terry J. S. ;
Armstrong, Patrick I. ;
Lamm, Monica H. ;
Reason, Robert D. ;
Coffman, Clark R. .
CBE-LIFE SCIENCES EDUCATION, 2017, 16 (02)
[4]   CAEP 2016 Academic Symposium: A Writer's Guide to Key Steps in Producing Quality Education Scholarship [J].
Chan, Teresa M. ;
Thoma, Brent ;
Hall, Andrew Koch ;
Murnaghan, Aleisha ;
Ting, Daniel K. ;
Hagel, Carly ;
Weersink, Kristen ;
Camorlinga, Paola ;
McEwen, Jill ;
Bhanji, Farhan ;
Sherbino, Jonathan .
CANADIAN JOURNAL OF EMERGENCY MEDICINE, 2017, 19 :S9-S15
[5]   Overcoming vocational prejudice: how can skills competitions improve the attractiveness of vocational education and training in the UK? [J].
Chankseliani, Maia ;
Relly, Susan James ;
Laczik, Andrea .
BRITISH EDUCATIONAL RESEARCH JOURNAL, 2016, 42 (04) :582-599
[6]   Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study [J].
Dawes, Timothy J. W. ;
de Marvao, Antonio ;
Shi, Wenzhe ;
Fletcher, Tristan ;
Watson, Geoffrey M. J. ;
Wharton, John ;
Rhodes, Christopher J. ;
Howard, Luke S. G. E. ;
Gibbs, J. Simon R. ;
Rueckert, Daniel ;
Cook, Stuart A. ;
Wilkins, Martin R. ;
O'Regan, Declan P. .
RADIOLOGY, 2017, 283 (02) :381-390
[7]  
Deissinger T., 1997, EDUC TRAIN, V39, P297, DOI DOI 10.1108/00400919710190090
[8]   Making Machine Learning Robust Against Adversarial Inputs [J].
Goodfellow, Ian ;
McDaniel, Patrick ;
Papernot, Nicolas .
COMMUNICATIONS OF THE ACM, 2018, 61 (07) :56-66
[9]   A comparative study of machine learning classifiers for modeling travel mode choice [J].
Hagenauer, Julian ;
Helbich, Marco .
EXPERT SYSTEMS WITH APPLICATIONS, 2017, 78 :273-282
[10]   General Education, Vocational Education, and Labor-Market Outcomes over the Lifecycle [J].
Hanushek, Eric A. ;
Schwerdt, Guido ;
Woessmann, Ludger ;
Zhang, Lei .
JOURNAL OF HUMAN RESOURCES, 2017, 52 (01) :48-87