TY  - CONF
SN  - 9781538631454
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2018///
EP  - 48
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050774001&doi=10.1109%2fIC3e.2017.8409236&partnerID=40&md5=b43025c025d2b72f42ea697787fd0483
A1  - Norizan, N.A.M.
A1  - Taib, S.M.
A1  - Zakaria, N.
AV  - none
KW  - E-learning; Students; Supervisory personnel
KW  -  Automated assignments; Centralized systems; internship coordination; Internship programs; Learning management system; Student assignments; University students; Unstructured methods
KW  -  Genetic algorithms
SP  - 44
TI  - E-internship system with genetic algorithm
ID  - scholars10130
N1  - cited By 2; Conference of 2017 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2017 ; Conference Date: 16 October 2017 Through 17 October 2017; Conference Code:137885
N2  - Internship program provides a hands-on opportunity for university students to gain experience in specific industries. Unstructured method in coordinating internship students lead to poor cost and manpower optimization, and poor compatibility between students and the assigned supervisors. The idea of augmenting an E-Internship system with Genetic Algorithm (GA) is to accommodate the need for a systematic and centralized system for internship coordination. E-Internship system is implemented as a Moodle function. The GA complements the system by automatically assigning a supervisor for each internship student based on two important criteria: distance between student location and number of students assigned to a lecturer or supervisor. An illustrative example of using GA for the Supervisor-Student Assignment (SSA) is provided. For evaluating the performance of the GA, an actual case was solved and the results show that the proposed GA is able to find nearly optimal solutions. © 2017 IEEE.
ER  -