Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 3 |
Descriptor
Computer Assisted Testing | 3 |
Pattern Recognition | 3 |
Cheating | 2 |
Electronic Learning | 2 |
Supervision | 2 |
COVID-19 | 1 |
College Students | 1 |
Data Analysis | 1 |
Demography | 1 |
Educational Environment | 1 |
Foreign Countries | 1 |
More ▼ |
Author
Becker, Kirk | 1 |
David Beard | 1 |
Goedicke, Michael | 1 |
Hanck, Christoph | 1 |
Jonathan Godbey | 1 |
Klenke, Jens | 1 |
Langerbein, Janine | 1 |
Massing, Till | 1 |
Meng, Huijuan | 1 |
Richard Fendler | 1 |
Striewe, Michael | 1 |
More ▼ |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Audience
Location
Germany | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Becker, Kirk; Meng, Huijuan – Journal of Applied Testing Technology, 2022
The rise of online proctoring potentially provides more opportunities for item harvesting and consequent brain dumping and shared "study guides" based on stolen content. This has increased the need for rapid approaches for evaluating and acting on suspicious test responses in every delivery modality. Both hiring proxy test takers and…
Descriptors: Identification, Cheating, Computer Assisted Testing, Observation
Richard Fendler; David Beard; Jonathan Godbey – Electronic Journal of e-Learning, 2024
The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning,…
Descriptors: Cheating, Computer Assisted Testing, Supervision, Student Behavior
Langerbein, Janine; Massing, Till; Klenke, Jens; Striewe, Michael; Goedicke, Michael; Hanck, Christoph – International Educational Data Mining Society, 2023
Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we…
Descriptors: Information Retrieval, Pattern Recognition, Data Analysis, Information Technology