eprintid: 10511
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/05/11
datestamp: 2023-11-09 16:37:07
lastmod: 2023-11-09 16:37:07
status_changed: 2023-11-09 16:31:34
type: conference_item
metadata_visibility: show
creators_name: Tachmammedov, S.
creators_name: Hooi, Y.K.
creators_name: Kalid, K.S.
title: Automated multi-factor analytics for cheat-proofing attendance-taking
ispublished: pub
keywords: Application programs; Data handling; Global positioning system; Smartphones; Telephone sets, Cheat-proofing; Mobile equipments; Multi factors; Proof of concept; QR codes; Quick response code; Statistical variance; User surveys, Administrative data processing
note: cited By 4; Conference of 7th International Conference on Software and Computer Applications, ICSCA 2018 ; Conference Date: 8 February 2018 Through 10 February 2018; Conference Code:136540
abstract: 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.
date: 2018
publisher: Association for Computing Machinery
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048486620&doi=10.1145%2f3185089.3185093&partnerID=40&md5=0a4777a62816bd8cced77f731bdbbd79
id_number: 10.1145/3185089.3185093
full_text_status: none
publication: ACM International Conference Proceeding Series
pagerange: 183-188
refereed: TRUE
isbn: 9781450354141
citation:   Tachmammedov, S. and Hooi, Y.K. and Kalid, K.S.  (2018) Automated multi-factor analytics for cheat-proofing attendance-taking.  In: UNSPECIFIED.