Test Cheating Detection Method Based on Random Forest

Hu, Haiyang; Li, Zhizhuang; Wang, Zisihan

doi:10.1109/cste53634.2021.00017

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…This is necessary for recent online classes, different from traditional classrooms, as the possibility of cheating increases without the face-to-face monitoring of instructors [ 4 , 5 , 6 , 7 ]. Previous studies on such topics (i.e., detection of cheating on programming assignments) have mostly focused on detecting plagiarism in the students’ source code [ 12 , 13 , 14 , 18 , 19 ]. This is because the common approach to detect plagiarism in programming assignments is to compare the submitted assignments of students in the form of a hard copy in traditional classrooms.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, there was an attempt to adopt machine learning techniques to detect cheating of students in programming tests [ 14 ]. The proposed technique utilizes a random forest model to predict each student’s score based on the previous results from the exercises given in the classes.…”

Section: Related Workmentioning

confidence: 99%

“…The major approaches to the automatic assessment with a fair evaluation can be divided into plagiarism detection on the submitted source code and the activity tracking of the students writing the source code. Different from the plagiarism detection [ 11 , 12 , 13 , 14 ] that has been widely studied for many years, monitoring the coding activity [ 15 , 16 , 17 ] of students has not been sufficiently investigated.…”

Section: Introductionmentioning

confidence: 99%

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Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments

Kim

Lee

Park

2022

Sensors

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Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students’ hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments

Kim

Lee

Park

2022

Sensors

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“…The COVID-19 pandemic converted this possibility into a necessity [7], [8], [9]. However, with the remote way of teaching comes the challenge of unsupervised online testing, shown to yield a higher possibility of fraud [9], [10], [11], challenging the fair principle of evaluation [12].…”

Section: Introductionmentioning

confidence: 99%

Detecting Fraudulent Student Communication in a Multiple Choice Online Test Environment

Carrasco

Silva

Henriques

2024

IEEE Trans. Comput. Soc. Syst.

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Online evaluation systems, pervasive nowadays, are known to be susceptible to higher fraud risks. This work proposes a novel and robust method to detect potential fraud acts in online multiple-choice question (MCQ) exams. For the first time, the communication probability between the examinees is statistically assessed based on the concordance of responses and answer time against null expectations and is subsequently used to identify potential fraud behavior. The model is sensitive to the direction of communication acts, distinguishing content consumption from production, as well as multiwise communication channels. Online remote tests from engineering courses at Técnico Lisboa are used as a case study. We show that the cumulative contribution of concordant responses between students, when recurrent, offers a way of signaling fraud behavior. Separating content production from consumption reveals the underlying student role played in potential fraud acts. Collusion behavior is assessed against null models of fraud and conformity, and therefore being statistically framed and offering a solid criterion to guide tutors in ascertaining fraud and discouraging communication.

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Test Cheating Detection Method Based on Random Forest

Cited by 2 publications

References 9 publications

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments

Detecting Fraudulent Student Communication in a Multiple Choice Online Test Environment

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