Machine learning based approach to exam cheating detection.
The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examin...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254340 |
| _version_ | 1818928598996746240 |
|---|---|
| author | Firuz Kamalov Hana Sulieman David Santandreu Calonge |
| author_facet | Firuz Kamalov Hana Sulieman David Santandreu Calonge |
| author_sort | Firuz Kamalov |
| collection | DOAJ |
| description | The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students' continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments. |
| first_indexed | 2024-12-20T03:31:28Z |
| format | Article |
| id | doaj.art-4058486f313243fa92443b3a5072c3a3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T03:31:28Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4058486f313243fa92443b3a5072c3a32022-12-21T19:54:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01168e025434010.1371/journal.pone.0254340Machine learning based approach to exam cheating detection.Firuz KamalovHana SuliemanDavid Santandreu CalongeThe COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students' continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.https://doi.org/10.1371/journal.pone.0254340 |
| spellingShingle | Firuz Kamalov Hana Sulieman David Santandreu Calonge Machine learning based approach to exam cheating detection. PLoS ONE |
| title | Machine learning based approach to exam cheating detection. |
| title_full | Machine learning based approach to exam cheating detection. |
| title_fullStr | Machine learning based approach to exam cheating detection. |
| title_full_unstemmed | Machine learning based approach to exam cheating detection. |
| title_short | Machine learning based approach to exam cheating detection. |
| title_sort | machine learning based approach to exam cheating detection |
| url | https://doi.org/10.1371/journal.pone.0254340 |
| work_keys_str_mv | AT firuzkamalov machinelearningbasedapproachtoexamcheatingdetection AT hanasulieman machinelearningbasedapproachtoexamcheatingdetection AT davidsantandreucalonge machinelearningbasedapproachtoexamcheatingdetection |