relation: https://khub.utp.edu.my/scholars/10511/
title: Automated multi-factor analytics for cheat-proofing attendance-taking
creator: Tachmammedov, S.
creator: Hooi, Y.K.
creator: Kalid, K.S.
description: A potential application of smartphone is as a tool to prevent attendance cheating. This paper proposes an automated multi-factor analytics using common smartphone features to identify cheating. The first factor is using Quick Response (QR) code as a unique token for validation. The second factor checks for the phone's unique International Mobile Equipment Identity (IMEI) number. The third factor checks attendance time using server time. The fourth factor is geo-location of the student. Algorithm analyzes geolocation statistical variance after passing the first two factors. The algorithm is implemented as a proof of concept in a typical university lecture and lab attendance taking. The proposed algorithm has shown promising efficiency and feasibility of implementation. User survey has indicated reasonable acceptance and potential issues. © 2018 Association for Computing Machinery.
publisher: Association for Computing Machinery
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Tachmammedov, S. and Hooi, Y.K. and Kalid, K.S.  (2018) Automated multi-factor analytics for cheat-proofing attendance-taking.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048486620&doi=10.1145%2f3185089.3185093&partnerID=40&md5=0a4777a62816bd8cced77f731bdbbd79
relation: 10.1145/3185089.3185093
identifier: 10.1145/3185089.3185093