# Project Title: Using Machine Learning to Detect Multiple Account Cheating and Analyze the Influence of Student and Problem Features
Project Overview
The goal of this project is to develop a robust machine learning framework to identify instances of multiple account cheating within online learning platforms. As educational environments become increasingly digital, the challenge of maintaining academic integrity has intensified. By leveraging data science techniques and machine learning algorithms, this project aims to automate the detection of cheating behaviors and analyze how various student and problem features contribute to these actions.
Objectives
1. Cheating Detection: Implement machine learning algorithms to detect patterns indicative of multiple account cheating.
2. Feature Analysis: Investigate the influence of specific student characteristics (e.g., demographics, prior performance) and problem features (e.g., question difficulty, topic) on cheating behavior.
3. Model Evaluation: Create a framework for evaluating the accuracy and efficacy of different machine learning models in detecting cheating.
4. Reporting & Visualization: Develop comprehensive reporting tools to visualize findings and assist educators in understanding cheating tendencies.
Background
With the rise of online learning platforms, the issue of multiple account cheating has become a growing concern for educational institutions. Cheating undermines the integrity of assessment processes and can warp the understanding of student performance. This project will focus on analyzing large datasets obtained from online learning platforms, where students may have created multiple accounts to gain an unfair advantage.
Methodology
1. Data Collection
– Dataset Compilation: Gather datasets from online learning platforms, including user logs, assignment submissions, exam scores, and demographic information.
– Feature Selection: Identify relevant features that may influence cheating behavior, such as:
– Student features: age, gender, prior GPA, engagement metrics.
– Problem features: difficulty level, question type, time taken for completion.
2. Data Preprocessing
– Cleaning: Remove anomalies and irrelevant features from the dataset.
– Normalization: Normalize the data to ensure uniformity, particularly for numerical features.
– Labeling: Classify instances of confirmed cheating for supervised learning.
3. Model Development
– Algorithm Selection: Explore various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Neural Networks.
– Training and Testing: Split the data into training and validation sets to train the models and evaluate performance metrics (e.g., accuracy, precision, recall).
4. Feature Importance Analysis
– Utilize techniques such as SHAP (SHapley Additive exPlanations) values and feature importance scores to determine which features most significantly influence the prediction of cheating.
– Conduct statistical analyses to explore correlations between student/problem features and cheating occurrences.
5. Reporting and Visualization
– Create dashboards and visual reports to present findings effectively.
– Utilize tools such as Tableau or matplotlib to visualize the relationship between features and cheating behavior.
Expected Outcomes
– A validated machine learning model capable of accurately detecting instances of multiple account cheating.
– Insights into the key student and problem features that correlate with cheating behavior.
– Recommendations for educational institutions on how to mitigate cheating based on data-driven evidence.
Conclusion
This project represents a significant step forward in utilizing machine learning to address integrity issues in online education. By understanding the dynamics of student behavior and problem features, we can contribute to creating a more equitable and honest learning environment. The implications of this research extend beyond cheating detection; they can influence instructional design, assessment strategies, and ultimately enhance the quality of education.
Timeline
– Month 1-2: Data collection and preprocessing.
– Month 3: Model development and initial training.
– Month 4: Model evaluation and feature analysis.
– Month 5: Reporting and visualization.
– Month 6: Final review and dissemination of findings.
Future Work
– Explore the integration of real-time detection systems in online platforms.
– Investigate additional forms of academic dishonesty beyond multiple account cheating.
– Engage with educational stakeholders to implement findings and improve academic integrity policies.
This project has the potential to provide comprehensive insights into cheating behavior in online learning environments, paving the way for future innovations in academic integrity enforcement.
