{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:38:13Z","timestamp":1760402293563,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students\u2019 dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student\u2019s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student\u2019s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.<\/jats:p>","DOI":"10.3390\/s22020654","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Method for Cheating Indication in Unproctored On-Line Exams"],"prefix":"10.3390","volume":"22","author":[{"given":"Dan","family":"Komosny","sequence":"first","affiliation":[{"name":"Department of Telecommunications, Brno University of Technology, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2159-865X","authenticated-orcid":false,"given":"Saeed Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Flinders University, Adelaide 5042, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"ref_1","unstructured":"Callejo, P., Gramaglia, M., Cuevas, R., and Cuevas, \u00c1. (2021). A deep dive into the accuracy of IP Geolocation Databases and its impact on online advertising. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Wang, F., Huang, C., and Yu, C. (November, January 30). Improving IP geolocation databases based on multi-method classification. Proceedings of the 2020 IEEE 14th International Conference on Anti-Counterfeiting, Security, and Identification (ASID), Xiamen, China.","DOI":"10.1109\/ASID50160.2020.9271694"},{"key":"ref_3","unstructured":"Canvas LMS (2021, December 29). API Documentation. Available online: canvas.instructure.com\/doc\/api\/."},{"key":"ref_4","unstructured":"Blackboard Learn (2021, December 29). Help for Administrators and Super Users. Available online: help.blackboard.com\/Filter\/Administrator."},{"key":"ref_5","unstructured":"D2L Brightspace (2021, December 29). Brightspace Help. Available online: documentation.brightspace.com\/EN\/administrators\/administrators.htm."},{"key":"ref_6","unstructured":"Schoology (2021, December 29). System Administrators. Available online: support.schoology.com\/hc\/en-us\/categories\/200077693-Help-Center."},{"key":"ref_7","unstructured":"Moodle (2021, December 29). Managing a Moodle Site. Available online: docs.moodle.org\/311\/en\/Managing_a_Moodle_site."},{"key":"ref_8","unstructured":"Safe Exam Browser (2021, December 29). ETH Zurich, Educational Development and Technology (LET), 2021. Available online: safeexambrowser.org\/about_overview_en.html."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"M\u00fcller, A.M., Goh, C., Lim, L.Z., and Gao, X. (2021). COVID-19 Emergency eLearning and Beyond: Experiences and Perspectives of University Educators. Educ. Sci., 11.","DOI":"10.3390\/educsci11010019"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Pe\u00f1alvo, F.J., Corell, A., Abella-Garc\u00eda, V., and Grande-de-Prado, M. (2020). Recommendations for Mandatory Online Assessment in Higher Education During the COVID-19 Pandemic. Radical Solutions for Education in a Crisis Context. Lecture Notes in Educational Technology, Springer.","DOI":"10.1007\/978-981-15-7869-4_6"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1002\/cae.22334","article-title":"Reflections on the last decade of MOOC research","volume":"29","author":"Yousef","year":"2021","journal-title":"Comput. Appl. Eng. Educ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cleophas, C., Hoennige, C., Meisel, F., and Meyer, P. (2021). Who\u2019s Cheating? Mining Patterns of Collusion from Text and Events in Online Exams. Informs Trans. Educ., 1\u201311.","DOI":"10.2139\/ssrn.3824821"},{"key":"ref_13","unstructured":"Niwattanakul, S., Singthongchai, J., Naenudorn, E., and Wanapu, S. (2013, January 13\u201315). Using of Jaccard Coefficient for Keywords Similarity. Proceedings of the International MultiConference of Engineers and Computer Scientists 2013, London, UK."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41539-020-00083-3","article-title":"Optimized collusion prevention for online exams during social distancing","volume":"6","author":"Li","year":"2021","journal-title":"NPJ Sci. Learn."},{"key":"ref_15","unstructured":"Alaoui, M.M., and Vorsatz, M. (2021). Academic Integrity in On-line Exams: Evidence from a Randomized Field Experiment, Graduate School of Economics."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/JSYST.2015.2389518","article-title":"Using Geolocation for the Strategic Preincident Preparation of an IT Forensics Analysis","volume":"10","author":"Koch","year":"2016","journal-title":"IEEE Syst. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/tgis.12305","article-title":"A geoprivacy manifesto","volume":"22","author":"McKenzie","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"17606","DOI":"10.1109\/ACCESS.2018.2822260","article-title":"Location Privacy and Its Applications: A Systematic Study","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.dss.2018.12.004","article-title":"Enhancing geotargeting with temporal targeting, behavioral targeting and promotion for comprehensive contextual targeting","volume":"117","author":"Lian","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gulati, A., Dubey, P., MdFuzail, C., Norman, J., and Mangayarkarasi, R. (2017). Credit card fraud detection using neural network and geolocation. IOP Conference Series: Materials Science and Engineering, IOP Publishing.","DOI":"10.1088\/1757-899X\/263\/4\/042039"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/JSAC.2011.111214","article-title":"A Geolocation Databases Study","volume":"2","author":"Shavitt","year":"2011","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/3402413.3402415","article-title":"RIPE IPmap active geolocation: Mechanism and performance evaluation","volume":"50","author":"Du","year":"2020","journal-title":"ACM Sigcomm Comput. Commun. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gharaibeh, M., Shah, A., Huffaker, B., Zhang, H., Ensafi, R., and Papadopoulos, C. (2017, January 1\u20133). A look at router geolocation in public and commercial databases. Proceedings of the IMC \u201917: The 2017 Internet Measurement Conference, London, UK.","DOI":"10.1145\/3131365.3131380"},{"key":"ref_24","unstructured":"Huffaker, B., Fomenkov, M., Claffy, K., and Geocompare: A Comparison of Public and Commercial Geolocation Databases (2021, December 09). Technical Report, Cooperative Association for Internet Data Analysis (CAIDA). Available online: catalog.caida.org\/details\/paper\/2011_geocompare_tr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Scheitle, Q., Gasser, O., Sattler, P., and Carle, G. (2017, January 21\u201323). HLOC: Hints-based geolocation leveraging multiple measurement frameworks. Proceedings of the 2017 Network Traffic Measurement and Analysis Conference (TMA), Dublin, Ireland.","DOI":"10.23919\/TMA.2017.8002903"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48816","DOI":"10.1109\/ACCESS.2019.2909691","article-title":"Using RIPE Atlas for Geolocating IP Infrastructure","volume":"7","author":"Candela","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"GeoLite2 Free Geolocation Data (2021, December 10). MaxMind, 2021. Available online: dev.maxmind.com\/geoip\/geolite2-free-geolocation-data."},{"key":"ref_28","unstructured":"Moodle (2021, December 29). Student Activity Report. Available online: docs.moodle.org\/23\/en\/Logs."},{"key":"ref_29","first-page":"1","article-title":"lifelines: Survival analysis in Python","volume":"4","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Padmanabhan, R., Dhamdhere, A., Aben, E., Claffy, K.C., and Spring, N. (2016, January 14\u201316). Reasons Dynamic Addresses Change. Proceedings of the 2016 Internet Measurement Conference, Santa Monica, CA, USA.","DOI":"10.1145\/2987443.2987461"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:28Z","timestamp":1760362048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,15]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020654"],"URL":"https:\/\/doi.org\/10.3390\/s22020654","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,1,15]]}}}