TY - GEN
T1 - Restructuring iCheating Model with Cluster Analysis on Affecting Factors of Academic Cheating Behavior
AU - Muqtadiroh, Feby Artwodini
AU - Herdiyanti Prabowo, Anisah
AU - Tarigan, Raihan Natigor
AU - Purwitasari, Diana
AU - Purnomo, Mauridhi Hery
AU - Pribadi Subriadi, Apol
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/18
Y1 - 2020/12/18
N2 - The rapid ICT advancement entails significant impacts to human life specifically in education. One of susceptible drawback of ICT is any attempt to cheat dropping the academic integrity. One of the most commonly frauds found is deceiving by using iPhone exploitable as a media to cheat in exams called as iCheating. iCheating is a form of academic cheating using iPhone. In order to properly cope with the iCheating, the education institutions need to identify factors affecting the iCheating behavior among students to anticipate earlier and to maintain the academic integrity. The objective of this research was to grant recommendations to the education institutions to minimize iCheating. The research was based on iCheating Model developed by Elodie Gentina. Data collected was to 170 students using iPhone based on three main factors observed: emotional intelligence, nomophobia and academic iCheating. Having obtained the data calculation, model restructuring was performed on clustering method using DBSCAN and K-Means algorithm to reveal broaden observations what real characteristics represent the students commit iCheating.
AB - The rapid ICT advancement entails significant impacts to human life specifically in education. One of susceptible drawback of ICT is any attempt to cheat dropping the academic integrity. One of the most commonly frauds found is deceiving by using iPhone exploitable as a media to cheat in exams called as iCheating. iCheating is a form of academic cheating using iPhone. In order to properly cope with the iCheating, the education institutions need to identify factors affecting the iCheating behavior among students to anticipate earlier and to maintain the academic integrity. The objective of this research was to grant recommendations to the education institutions to minimize iCheating. The research was based on iCheating Model developed by Elodie Gentina. Data collected was to 170 students using iPhone based on three main factors observed: emotional intelligence, nomophobia and academic iCheating. Having obtained the data calculation, model restructuring was performed on clustering method using DBSCAN and K-Means algorithm to reveal broaden observations what real characteristics represent the students commit iCheating.
KW - Academic Behavior
KW - Cheating
KW - Clustering
KW - DBSCAN
KW - K-Means
KW - iCheating Model
UR - http://www.scopus.com/inward/record.url?scp=85113507613&partnerID=8YFLogxK
U2 - 10.1109/ICETAS51660.2020.9484264
DO - 10.1109/ICETAS51660.2020.9484264
M3 - Conference contribution
AN - SCOPUS:85113507613
T3 - 7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020
BT - 7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2020
Y2 - 18 December 2020 through 20 December 2020
ER -