18 research outputs found

    Using Multiple Accounts for Harvesting Solutions in MOOCs

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    The study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, ~2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and ~10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.Madrid (Spain: Region) (eMadrid Grant S2013/ICE-2715)Spain. Ministerio de Economia y Competitividad (Grant RESET TIN2014-53199-C3-1-R

    Identifying cheating users in online courses

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    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

    Security Mechanisms on Web-Based Exams in Introductory Statistics Community College Courses

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    The credibility of unsupervised online exams is an ongoing concern in higher education. Proctoring, in the form of physical or remote supervision, has been the main mechanism for maintaining academic integrity. However, both forms of proctoring are expensive and inconvenient. Several researchers have examined security mechanisms as a substitute for proctoring and obtained mixed results. This article describes a quasi-experimental study, the main goal of which was to examine the effectiveness of nonbiometric security mechanisms. The security mechanisms were selected based on the taxonomy of cheating reduction techniques rooted in the fraud triangle theory. The security mechanisms were considered effective if the scores were equivalent or lower on the unproctored exams. Two one-sided dependent t tests were used to test for equivalence of scores on two sets of proctored and unproctored exams in face-to-face (N = 704), hybrid (N = 91), and online (N = 55) introductory statistics community college courses. In the first set, the proctored exam was followed by the unproctored exam; in the second set, the order was reversed. In the first set, the scores on proctored and unproctored exams were equivalent in face-to-face and online groups, but students in the hybrid group had significantly lower scores on the unproctored exam. In the second set, the students’ scores were lower on the unproctored exam in all groups. The study’s results suggest that the used security mechanisms were effective

    Evaluating the Robustness of Learning Analytics Results Against Fake Learners

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    Massive Open Online Courses (MOOCs) collect large amounts of rich data. A primary objective of Learning Analytics (LA) research is studying these data in order to improve the pedagogy of interactive learning environments. Most studies make the underlying assumption that the data represent truthful and honest learning activity. However, previous studies showed that MOOCs can have large cohorts of users that break this assumption and achieve high performance through behaviors such as Cheating Using Multiple Accounts or unauthorized collaboration, and we therefore denote them fake learners. Because of their aberrant behavior, fake learners can bias the results of Learning Analytics (LA) models. The goal of this study is to evaluate the robustness of LA results when the data contain a considerable number of fake learners. Our methodology follows the rationale of ‘replication research’. We challenge the results reported in a well-known, and one of the first LA/PedagogicEfficacy MOOC papers, by replicating its results with and without the fake learners (identified using machine learning algorithms). The results show that fake learners exhibit very different behavior compared to true learners. However, even though they are a significant portion of the student population (∼15%), their effect on the results is not dramatic (does not change trends). We conclude that the LA study that we challenged was robust against fake learners. While these results carry an optimistic message on the trustworthiness of LA research, they rely on data from one MOOC. We believe that this issue should receive more attention within the LA research community, and can explain some ‘surprising’ research results in MOOCs. Keywords: Learning Analytics, Educational Data Mining, MOOCs, Fake Learners, Reliability, IR

    Lying, Cheating, & Stealing: Strategies for Mitigating Technology-Driven Academic Dishonesty in Collegiate Schools of Business

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    We summarize contemporary issues related to academic dishonesty and draw from relevant organizational ethics program research to present a dual framework that business educators can use to mitigate technology-driven cheating among their students. Based on a review of the relevant literature, we develop a rationale which identifies three key observations: 1) technology-driven academic dishonesty is pervasive among college business students, 2) there are proactive steps that can be taken to address this problem, and 3) faculty, staff, and administrators in collegiate schools of business can and should do more to mitigate cheating among their students. We first provide an overview concerning the evolution of academic dishonesty and the technological advances that simplify cheating. Next, we propose a conceptual framework and list recommendations for business educators, using both compliance-based and values-based strategies, to reduce the frequency and severity of cheating

    Analyzing the behavior of students regarding learning activities, badges, and academic dishonesty in MOOC environment

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    Mención Internacional en el título de doctorThe ‘big data’ scene has brought new improvement opportunities to most products and services, including education. Web-based learning has become very widespread over the last decade, which in conjunction with the Massive Open Online Course (MOOC) phenomenon, it has enabled the collection of large and rich data samples regarding the interaction of students with these educational online environments. We have detected different areas in the literature that still need improvement and more research studies. Particularly, in the context of MOOCs and Small Private Online Courses (SPOCs), where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More specifically, we are going to work towards learning analytics visualization dashboards, carrying out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and behavior of students with regular and optional activities, badges and their online academically dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory analysis providing descriptive and inferential statistics, like correlations and group comparisons, as well as numerous visualizations that facilitate conveying understandable information. Second, we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze potential adaptation of learning experiences and pedagogical implications. Third, we also provide three machine learning models, two of them to predict learning outcomes (learning gains and certificate accomplishment) and one to classify submissions as illicit or not. We also use these models to discuss about the importance of variables. Finally, we discuss our results in terms of the motivation of students, student profiling, instructional design, potential actuators and the evaluation of visual analytics dashboards providing different recommendations to improve future educational experiments.Las novedades en torno al ‘big data’ han traído nuevas oportunidades de mejorar la mayoría de productos y servicios, incluyendo la educación. El aprendizaje mediante tecnologías web se ha extendido mucho durante la última década, que conjuntamente con el fenómeno de los cursos abiertos masivos en línea (MOOCs), ha permitido que se recojan grandes y ricas muestras de datos sobre la interacción de los estudiantes con estos entornos virtuales de aprendizaje. Nosotros hemos detectado diferentes áreas en la literatura que aún necesitan de mejoras y del desarrollo de más estudios, específicamente en el contexto de MOOCs y cursos privados pequeños en línea (SPOCs). En la tesis nos hemos enfocado en el análisis de datos en las plataformas Khan Academy, Open edX y Coursera. Más específicamente, vamos a trabajar en interfaces de visualizaciones de analítica de aprendizaje, llevando a cabo la evaluación de estas herramientas de analítica visual. Además, profundizaremos en la actividad y el comportamiento de los estudiantes con actividades comunes y opcionales, medallas y sus conductas en torno a la deshonestidad académica. Este análisis de actividad y comportamiento comienza primero con análisis exploratorio proporcionando variables descriptivas y de inferencia estadística, como correlaciones y comparaciones entre grupos, así como numerosas visualizaciones que facilitan la transmisión de información inteligible. En segundo lugar aplicaremos técnicas de agrupamiento para encontrar distintos perfiles de estudiantes con diferentes propósitos, como por ejemplo para analizar posibles adaptaciones de experiencias educativas y sus implicaciones pedagógicas. También proporcionamos tres modelos de aprendizaje máquina, dos de ellos que predicen resultados finales de aprendizaje (ganancias de aprendizaje y la consecución de certificados de terminación) y uno para clasificar que ejercicios han sido entregados de forma deshonesta. También usaremos estos tres modelos para analizar la importancia de las variables. Finalmente, discutimos todos los resultados en términos de la motivación de los estudiantes, diferentes perfiles de estudiante, diseño instruccional, posibles sistemas actuadores, así como la evaluación de interfaces de analítica visual, proporcionando recomendaciones que pueden ayudar a mejorar futuras experiencias educacionales.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Davinia Hernández Leo.- Secretario: Luis Sánchez Fernández.- Vocal: Adolfo Ruiz Callej

    Blockchain Applications in Lifelong Learning and the Role of the Semantic Blockchain

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    The emergence of the blockchain promises to revolutionise not only the financial world but also lifelong learning in various ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and tutoring services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This chapter explores the different aspects of lifelong learning that are affected by this new paradigm and describes an ecosystem that places the learner at the centre of the learning process and its associated data. This chapter also discusses the possibilities that will be afforded by the combination of trustworthy educational data enhanced with meaningful web-accessible linked data, and what these developments will mean for learners, educators, and the employment market

    The Art of Cheating in the 21st Millennium: Innovative Mechanisms and Insidious Ploys in Academic Deceit

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    Cheating is rampant throughout academia, with no hard evidence suggesting that such pedagogic deceit will wane. Cheating is most insidious on the college level, where such academic deceit has evolved from perhaps its basic pattern of merely peeking at another student’s examination, to planned deceit employing sophisticated subterfuges and interplay between two or more co-conspirators. Importantly, cheating per se may not necessarily be student initiated, but fostered by college/university staff for purposes of institutional or personal financial gain. Statistical studies (e.g., demographics) in complement with sociological and psychological factors associated with cheating have been previously described. This review does not attempt to embellish the plethora of earlier reviews or research on the subject, but stands unique in that specific case reports and recent findings are presented describing techniques or mechanisms used in the performance of academic deceit to by-pass university codes of ethics. The purpose of this work is to acquaint adjunct staff, tenure track, and perhaps senior faculty in the biological sciences and other disciplines to those mechanistic approaches used by students and college staff as well, in the commission of academic fraud. Suggestions are proposed to help detect and reduce academic deceit

    The Convergence of Online Teaching and Problem Based Learning Modules amid the COVID-19 Pandemic

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    There is a convergence unfolding between two formerly unique and separate areas of teaching research methodology: distance education and problem-based learning (PBL) environments. Much has been published on each field independently, however, in the modern-era of online, distance, and hybrid educational programs there is a need for more case and experiential-based learning activities which can effectively measure stated learning objectives. Trends in education have led to the development of various methods to instruct courses and conduct research online. Teaching research methodology and pedagogy have evolved to include video capture, remote conferencing, and other real-time communications techniques allowing faculty and students to collaborate across great distances. Meanwhile, PBL environments have been used extensively in teaching medicine, clinical practice, law, business/management, and many other disciplines to improve student learning. This has been further accelerated during the COVID-19 pandemic through the use of technologies like Zoom, WebEx, GoToMeeting, Google Hangout Meet, etc. and the availability of PBL-ready environments in breakout rooms and asynchronous simulated projects. Student preference data from 2020 are reported as part of this study. One example of this merger between online delivery and PBLs was the development of a PBL statistical process control (PBL-SPC) module. A cross-functional academic team was created across both a college of business and college of education in which a PBL-SPC module was developed based on a real-life situation in which students immerse themselves in a potato chip factory environment. The motivation for the PBL-SPC was that this is a challenging topic to cover which students often find difficult to relate to and/or boring. Three different scenarios were developed and students, as individuals or in teams, must traverse the simulated factory to assess the situation. Learning outcomes are measured by the course instructors and the PBL environment is being used by faculty around the world. Additionally, the PBL-SPC module has now been scaled to other applications such as six-sigma simulated project training during the COVID-19 pandemic. Pedagogical methods should be interactive, stimulate learning, improve the learning outcomes / critical thinking, and enhance student experience. This paper proposes that merging the effective and tech-friendly pedagogical methods of PBLSPC, with the right modalities and model of online delivery, can help achieve these aforesaid goals. Even more, it can deliver a great opportunity to educators and institutions worldwide for advancing the reach of education

    IMPACTOS E DESAFIOS DOS MASSIVE OPEN ONLINE COURSES NO ENSINO SUPERIOR: REVISÃO SISTEMÁTICA DA LITERATURA

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    MOOCs are a recent phenomenon that has generated expectations both with respect to pedagogical issues and strategic and economic issues of higher education. Thus, systematizing knowledge about the subject through a systematic literature review (RSL) becomes relevant. Thus, the objective of this study was to perform a SLR, as proposed by Kitchenham, to identify the state of the art on the impacts and challenges of MOOCs in higher education. The main impacts of MOOCs relate to their characteristics of openness and massive attendance, which allow accessibility and democratization of education. These courses are being incorporated into higher education through the use of hybrid teaching models and, as they are a recent phenomenon, need to overcome several barriers, such as low completion rates and the limited attendance of students comprising disadvantaged groups. Many actions are being assessed and implemented in this direction.Os MOOCs são um fenômeno recente que tem gerado expectativas tanto nas questões pedagógicas, quanto nas questões estratégicas e econômicas do ensino superior. Desse modo, sistematizar o conhecimento sobre a temática, por meio de uma revisão sistemática da literatura (RSL), torna-se relevante. Assim, o objetivo desse estudo foi realizar uma RSL, conforme proposto por Kitchenham (2004), para identificar o estado da arte sobre os impactos e desafios dos MOOCs no ensino superior. Os principais impactos dos MOOCs estão relacionados as suas características de abertura e atendimento massivo, que possibilitam acessibilidade e democratização do ensino. Esses cursos estão sendo incorporados ao ensino superior por meio da utilização de modelos híbridos de ensino e, como são um fenômeno recente, precisam superar diversas barreiras, como as baixas taxas de conclusão e o impacto limitado no acesso de grupos de alunos menos favorecidos. Muitas ações estão sendo avaliadas e implantadas nesse sentido.Os MOOCs são um fenômeno recente que tem gerado expectativas tanto nas questões pedagógicas, quanto nas questões estratégicas e econômicas do ensino superior. Desse modo, sistematizar o conhecimento sobre a temática, por meio de uma revisão sistemática da literatura (RSL), torna-se relevante. Assim, o objetivo desse estudo foi realizar uma RSL, conforme proposto por Kitchenham (2004), para identificar o estado da arte sobre os impactos e desafios dos MOOCs no ensino superior. Os principais impactos dos MOOCs estão relacionados as suas características de abertura e atendimento massivo, que possibilitam acessibilidade e democratização do ensino. Esses cursos estão sendo incorporados ao ensino superior por meio da utilização de modelos híbridos de ensino e, como são um fenômeno recente, precisam superar diversas barreiras, como as baixas taxas de conclusão e o impacto limitado no acesso de grupos de alunos menos favorecidos. Muitas ações estão sendo avaliadas e implantadas nesse sentido
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