Fraud Detection in Statistics Education Based on the Compendium Platform and Reproducible Computing

This paper focuses on a newly developed method to detect fraud in empirical papers that are submitted by students. The proposed solution is based on the Compendium Platform and Reproducible Computing which allows the educator to build e-learning environments that are embedded in the pedagogical framework of social constructivism and which can be shown to be effective in terms of non-rote learning of statistical concepts. The paper addresses the technological aspects of the proposed fraud detection system, ways to discriminate between various types of fraud (plagiarism, free riding, data tampering, peer-review cheating), and the pedagogical issues that result from its implementation (responsibility, non-rote learning). Finally, the first experiences about the implementation of the proposed technology in an undergraduate statistics course (with a large student population) are illustrated.