Identifying cheating users in online courses

Máster Universitario en en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosStudents interact with online courses mainly in two ways: by reviewing the course materials and by solving exercises. However, there are cases in which student behaviour differs and tends to become more focused on solving exercises without looking at course materials. This type of interaction could be an indicative of unethical behavior, such as students who collaborate by sharing answers with one another or fake accounts that are used by students to obtain the correct answers for exercises. In this work, we propose several metrics to identify these two types of cheating based on cooccurring events and measures of interaction with the course. From the pool of accounts in the course, the pairs of accounts that solve exercises very close in time are considered to be potential collaborating accounts. The proposed metrics are computed for these pairs of accounts and K-means clustering is used to separate pairs of real students who collaborate with respect to students who use fake accounts to harvest the correct answers to exercises. A generalization accuracy over 95% to classify these types of cheating is achieved by using a Support Vector Machine (SVM

CHEATING DETECTION IN ONLINE EXAMS BASED ON CAPTURED VIDEO USING DEEP LEARNING

Today, e-learning has become a reality and a global trend imposed and accelerated by the COVID-19 pandemic. However, there are many risks and challenges related to the credibility of online exams which are of widespread concern to educational institutions around the world. Online exam system continues to gain popularity, particularly during the pandemic, due to the rapid expansion of digitalization and globalization. To protect the integrity of the examination and provide objective and fair results, cheating detection and prevention in examination systems is a must. Therefore, the main objective of this thesis is to develop an effective way of detection of cheating in online exams. In this work, a system to track and prevent attempts to cheat on online exams is developed using artificial intelligence techniques. The suggested solution uses the webcam that is already connected to the computer to record videos of the examinee in real time and afterwards analyze them using different deep learning methods to find best combinations of models for face detection and classification if cheating/not cheating occurred. To evaluate the system, we use a benchmark dataset of exam videos from 24 participants who represented examinees in online exam. An object detection technique is used to detect face appeared in the image and crop the face portion, and then a deep learning based classification model is trained from the images to classify a face as cheating or not cheating. We have proposed an effective combination of data preprocessing, object detection, and classification models to obtain high detection accuracy. We believe that the suggested invigilation methodology can be used in colleges, institutions, and schools to look for and keep an eye on suspicious student behavior. Hopefully, by putting the proposed invigilation method into place, we can aid in eliminating and reducing cheating incidences as it undermines the integrity and fairness of the educational system

Program and Abstracts Celebration of Student Scholarship, 2010

Program and Abstracts for the Celebration of Student Scholarship on April 21, 2010

Social media mining to investigate the impacts of the COVID-19 pandemic

The COVID-19 pandemic created a global crisis with devastating social and economic impacts. Firstly, public health measures for COVID-19, such as social distancing affected how we work and study. Secondly, this crisis caused mobility restrictions and shutdowns that impacted our economy. In this thesis, we aim to obtain a better understanding of how these socio-economic impacts have affected people. We, therefore, choose one problem from each of these two areas of impact for further study. The social distancing mandates shifted working environments and education online. Due to cheating being more prevalent in online education, serious issues may arise during the pandemic when classes and examinations are online. In order to understand these issues and their impacts on college students, we ask: how do college students feel about online cheating? Fuel consumption and carbon emissions declined due to mobility restrictions and economic shutdowns. As a result, air quality improved. Economic shutdowns, however, impacted countries' ability to fight climate change. We are interested in understanding how people's perspectives have changed due to both the positive and negative effects of the pandemic on climate change. To do so, we ask: What is the public's attitude towards climate action during the COVID-19 recovery and beyond? We answer these questions by analyzing discussions on Twitter and Reddit social media platforms. These online social media platforms are considered essential tools for reflecting and forecasting public opinion on a wide range of topics. Therefore, we answer our questions by mining text messages that were posted during the COVID-19 crisis. We begin by collecting the necessary posts and comments. We then prepare the documents for text mining by using standard pre-processing techniques. As a result, we are able to construct an understanding of the discussions by using these methods. While investigating the discussions about academic dishonesty on Reddit, we found more discussions related to cheating in 2020 than in 2019. The topics have expanded from plagiarism in programming assignments to online assessments in general. Topic modelling of the Fall 2020 discussions revealed three concerns raised by students: that cheating inflates grades and forces instructors to increase the difficulty of assessments; that witnessing cheating go unpunished is demotivating; and that academic integrity policies are not always communicated clearly. Investigating the discussions about the climate change and the pandemic on Twitter revealed that most tweets support climate action and point out lessons learned during the pandemic response that can shape future climate policy, although skeptics continue to have a presence. Additionally, concerns arise in the context of climate action during the pandemic, such as mitigating the risk of COVID-19 transmission on public transit

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc