Semantic Relatedness: A Strategy for Plagiarism Detection in SQL Assignments

Obionwu, Chukwuka Victor; Kumar, Rahul; Shantharam, Suhas; Broneske, David; Saake, Gunter

doi:10.1109/wccct56755.2023.10052438

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Creating a real-time cheating detection system to replace human efforts is now feasible because of scientific and technical breakthroughs, notably with various deep learning algorithms [7,8]. The numerous movements made throughout the exam will be able to be detected by the automated system [9][10][11]. In addition, it will be able to identify numerous illegal items throughout the tests and record the timing of the sequence for a supervisory review at a later time.…”

Section: Introductionmentioning

confidence: 99%

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Alsabhan

2023

Sensors

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Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance.

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Section: Introductionmentioning

confidence: 99%

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Alsabhan

2023

Sensors

View full text Add to dashboard Cite

show abstract

Semantic Relatedness: A Strategy for Plagiarism Detection in SQL Assignments

Cited by 1 publication

References 8 publications

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques

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