189 research outputs found
Editorial: Academic integrity gaining increased attention in Canada
We introduce the second issue of our first volume, highlighting the lead article from Amanda McKenzie and offering commentary on how academic integrity issues have gained attention in the national media this year
U Have Integrity: A Gamified Approach to Academic Integrity
In this article I discuss the development and initial implementation of a workshop using a gamified approach to academic integrity. The 50-minute workshop involved a scenario- based card came. The audience was university staff in Student and Enrolment Services, which included, but was not limited to the Registrar’s Office, Student Services, and Student Wellness
Book review: Coe, R., Waring, M., Hedges, L. V., & Arthur, J. (Eds.). (2017). Research methods and methodologies in education
Exploring Rogeting: Implications for academic integrity
Poor paraphrasing can be a sign of underdeveloped writing skills that can lead to plagiarism. One example of poor paraphrasing is Rogeting, which is the substitution of words with their synonyms using Roget’s thesaurus or other digital synonym providers. In this position paper, we discuss Rogeting as a form of poor paraphrasing that may lead to academic integrity breaches, such as plagiarism. We discuss methods of identifying Rogeted text, concluding with practical recommendations for educators about how to better support student writers so they can avoid Rogeting in favour of developing their writing skills
Writers’ Guide for Prospective Contributors to Canadian Perspectives on Academic Integrity
In this article we present resources and ideas for prospective writers of Canadian Perspectives on Academic Integrity. The purpose of this article is to generate ideas and build confidence among prospective contributors. Our intention is to provide support for those who have wisdom and insights to share, but who may be inexperienced or lack confidence with their professional writing skills
从教与学的学术研究角度出发审视人工智能诚信应用的建议
سمحت التطورات الجديدة في مجال الذكاء الاصطناعي (AI) بتطوير الذكاء الاصطناعي التوليدي (GenAI), القادر على إنشاء نصوص مشابهة لتلك التي يمكن للبشر إنتاجها. ونتيجة لذلك, ظهرت بسرعة مخاوف المعلمين في قطاع التعليم العالي. وقد تناولت العديد من المنظمات والخبراء هذه المخاوف من خلال التوصيات. في هذه الورقة المفاهيمية, نعتمد على النموذج المتكامل للنزاهة الأكاديمية من خلال عدسة التدريس والتعلم لفحص وتحفيز المناقشة من إحدى عشرة ورقة تركز على استخدام GenAI بنزاهة. لقد حددنا التوصيات المناسبة للمستويات الفردية (الجزئية) ,والإدارات/البرامج (المتوسطة), والمؤسسية (الكلية), والمستويات المشتركة بين المؤسسات/الوطنية/الدولية (الضخمة) فيما يتعلق بعنصرين أساسيين للنموذج: "المهنية عالية التأثير" التعلم للأفراد والمجموعات" و"القيادة والثقافات الصغيرة على المستوى المحلي".وكانت الاقتراحات حول العنصر الأساسي "المنح الدراسية والبحث والاستقصاء" مفقودة على المستويين الجزئي والمتوسط; وبالمثل, كانت التوصيات المتعلقة بالعنصر الأساسي "مساحات التعلم وطرق التدريس والتكنولوجيات" غائبة أيضًا على المستويات المتوسطة والكلي والضخمة. نحن ندرك أن هذه التوصيات تركز على التعلم، وتشرك مختلف أصحاب المصلحة, وتتجاوز سلوك الطلاب, وهو ما يتوافق مع الأساليب الحالية للنزاهة الأكاديمية. ومع ذلك, هناك بعض الثغرات تحتاج إلى مزيد من الاستكشاف. نحن نسلط الضوء على الحاجة إلى تطوير إرشادات وموارد أكثر تحديدًا وعملية لأصحاب المصلحة في مجال التعليم حول قضايا GenAI المتعلقة بالنزاهة الأكاديمية, واستكشاف أفضل السبل لدعم الشبكات والقادة في التعليم العالي فيتهيئة الظروف للاستخدام الأخلاقي لـ GenAI, والتأكيد على الحاجة إلى عدسة العدالة والتنوع والشمول في GenAINew developments in the Artificial Intelligence (AI) field allowed the development of Generative Artificial Intelligence (GenAI), capable of creating text resembling what humans can produce. As a result, educators’ concerns in the higher education sector quickly emerged. Many organizations and experts have addressed these concerns through recommendations. In this conceptual paper, we draw from the Integrated Model for Academic Integrity through a Scholarship of Teaching and Learning Lens to examine and stimulate discussion from eleven documents that focus on using GenAI with integrity. We identified recommendations suitable for the individual (micro), the departmental/program (meso), the institutional (macro), and the interinstitutional/ national/ international (mega) levels concerning two core elements of the model: “high-impact professional learning for individuals and groups” and “local-level leadership and microcultures.” Suggestions around the core element “scholarship, research and inquiry” were lacking at the micro and meso levels; likewise, recommendations for the core element “learning spaces, pedagogies, and technologies” were also absent at the meso, macro, and mega levels. We acknowledge that these recommendations focus on learning, involve various stakeholders, and go beyond student conduct, which aligns with current approaches to academic integrity. However, some gaps need further exploration. We highlight the need to develop more specific and practical guidance and resources for educational stakeholders around GenAI issues related to academic integrity, explore how to better support networks and leaders in higher education in creating the conditions for ethical GenAI use, and emphasizing the need for an Equity, Diversity, and Inclusion lens on GenAI.Los recientes avances en el ámbito de la Inteligencia Artificial (IA) han posibilitado el desarrollo de la IA Generativa, la que puede generar texto que se asemeja a la producción humana. En consecuencia, han surgido rápidamente inquietudes por parte de los educadores en el sector de la educación superior. Numerosas organizaciones y expertos han abordado dichas preocupaciones mediante la formulación de recomendaciones. En el presente artículo conceptual, hacemos uso del Modelo Integrado para la Integridad Académica desde un Lente del Scholarship of Teaching and Learning, con el propósito de fomentar la discusión a partir de doce documentos que se enfocan en la utilización de la IA Generativa con integridad. Identificamos recomendaciones apropiadas a nivel individual (micro), departamental/programático (meso), institucional (macro) y a niveles interinstitucionales/nacionales/internacionales (mega), enfocándonos en dos elementos fundamentales del modelo: “Aprendizaje Profesional de Alto Impacto para Individuos y Grupos” y “Liderazgo a Nivel Local y Microculturas”. Observamos una carencia de sugerencias en relación con el elemento “Investigación e Indagación” en los niveles micro y meso. Además, se constató la ausencia de recomendaciones para “Espacios de Aprendizaje, Pedagogías y Tecnologías” en los niveles meso, macro y mega. Reconocemos que estas recomendaciones se centran en el aprendizaje y van más allá de la conducta de los estudiantes, lo cual se encuentra en consonancia con las tendencias actuales en integridad académica. Asimismo, proponemos explorar formas de brindar un mayor respaldo a las redes y líderes para crear las condiciones propicias para promover el uso ético de la IA Generativa.Os novos desenvolvimentos no domínio da Inteligência Artificial (IA) permitiram o desenvolvimento da Inteligência Artificial Generativa (GenAI), capaz de criar texto semelhante ao que os humanos conseguem produzir. Consequentemente, as preocupações dos educadores no setor do ensino superior surgiram rapidamente. Muitas organizações e peritos abordaram estas preocupações através de recomendações. Neste documento conceptual, baseamo-nos no Modelo Integrado para a Integridade Académica através de uma Lente de Bolsa de Estudo de Ensino e Aprendizagem para examinar e estimular a discussão de doce documentos na utilização da GenAI com integridade. Identificámos recomendações adequadas aos níveis individual (micro), departamental/programa (meso), institucional (macro) e interinstitucional/nacional/internacional (mega) relativamente a dois elementos centrais do modelo: “aprendizagem profissional de elevado impacto para indivíduos e grupos” e “liderança e microculturas a nível local”. Faltaram sugestões em torno do elemento central “bolsas de estudo, investigação e inquérito” aos níveis micro e meso; do mesmo modo, as recomendações para o elemento central “espaços de aprendizagem, pedagogias e tecnologias” também estiveram ausentes aos níveis meso, macro e mega. Reconhecemos que estas recomendações se centram na aprendizagem, envolvem várias partes interessadas e vão além do comportamento dos estudantes, o que está de acordo com as atuais abordagens à integridade académica. Propomos também explorar formas de fornecer maior apoio às redes e aos líderes para criar as condições que conduzam à promoção do uso ético na GenAI.Os novos desenvolvimentos no domínio da Inteligência Artificial (IA) permitiram o desenvolvimento da Inteligência Artificial Generativa (GenAI), capaz de criar texto semelhante ao que os humanos conseguem produzir. Consequentemente, as preocupações dos educadores no setor do ensino superior surgiram rapidamente. Muitas organizações e peritos abordaram estas preocupações através de recomendações. Neste documento conceptual, baseamo-nos no Modelo Integrado para a Integridade Académica através de uma Lente de Bolsa de Estudo de Ensino e Aprendizagem para examinar e estimular a discussão de doce documentos na utilização da GenAI com integridade. Identificámos recomendações adequadas aos níveis individual (micro), departamental/programa (meso), institucional (macro) e interinstitucional/nacional/internacional (mega) relativamente a dois elementos centrais do modelo: “aprendizagem profissional de elevado impacto para indivíduos e grupos” e “liderança e microculturas a nível local”. Faltaram sugestões em torno do elemento central “bolsas de estudo, investigação e inquérito” aos níveis micro e meso; do mesmo modo, as recomendações para o elemento central “espaços de aprendizagem, pedagogias e tecnologias” também estiveram ausentes aos níveis meso, macro e mega. Reconhecemos que estas recomendações se centram na aprendizagem, envolvem várias partes interessadas e vão além do comportamento dos estudantes, o que está de acordo com as atuais abordagens à integridade académica. Propomos também explorar formas de fornecer maior apoio às redes e aos líderes para criar as condições que conduzam à promoção do uso ético na GenAI.人工智能(AI)领域的新发展促进了生成式人工智能(GenAI)的出现,这种新技术可以创造出与人类能力相似的文本。针对这一现象,高等教育阶段的教育者们反应迅速地表达了他们的担忧。众多的专家和组织通过建议的方式展示他们的忧虑。因此,在这篇理论性论文中,我们通过“基于教与学学术研究的学术诚信模型“来审视十一篇关于生成式人工智能诚信应用的文章,并在此基础上进行相应的讨论。我们识别出适用于个人(微观)、部门/项目(中观)、机构(宏观)、机构间/国家/国际(巨型)层面的建议,其中建议涉及模型的两个核心元素:具有深远影响力的个人及小组专业化学习、地方级别领导力和微观文化。在微观和中观层面缺少关于“学术、研究及调查”的核心元素建议。而在中观、宏观和巨型层面欠缺关于“学习空间、教学法及技术”的核心元素建议。我们发现这些建议都聚焦于学习,且涉及多个利益相关者,并不仅仅限于学生的行为,这与现在的学术诚信方法一致。然而我们也发现仍有差异需要更深入的研究和探索。在与学术诚信相关的生成式人工智能问题上,我们坚信应该为教育利益相关者们提供更具体更实用的指导和资源。我们也坚持提倡为高等教育网络和领导者提供更好的支持,为符合道德规范地使用生成式人工智能创建条件,强调对平等、包容及多样化的生成式人工智能的需求
Academic Integrity in Canada: An Enduring and Essential Challenge
In this session, Dr. Julia Christensen Hughes and Dr. Sarah Elaine Eaton will reflect on academic integrity in Canada, synthesize their various contributions, and challenge future practice. Drawing from their book, Academic Integrity in Canada: An Enduring and Essential challenge, they will highlight their experience and insights, since the seminal work of Christensen Hughes and McCabe (2006), focusing on the enduring and essential challenge of building cultures and practices aligned with academic integrity in Canadian higher education today. Participants will gain insight into current challenges and strategies for supporting academic integrity in their own administrative and teaching and research practice
Exploring academic integrity and mental health during COVID-19: Rapid review
Purpose: The goal of this study was to understand the relationship between academic integrity and students’ mental health during the COVID-19 crisis. Methods: We employed a rapid review method to identify relevant data sources using our university library search tool, which offers access to 1026 individual databases. We searched for sources relating to the concepts of (a) COVID-19 crisis; (b) academic integrity; and (c) mental health. We delimited our search to sources published between 01 January and 15 May 2020. Results: Our search resulted in a preliminary data set of sources (N=60). Further screening resulted in a total nine (n=9) sources, which were reviewed in detail. Data showed an amplification of students’ anxiety and stress during the pandemic, especially for matters relating to academic integrity. E-proctoring of examinations emerged as point of particular concern, as there were early indications in the literature that such services have proliferated rapidly during the crisis, with little known about the possible impact of electronic remote proctoring on students’ well-being. Implications: Recommendations are made for further research to better understand the impact of e-proctoring of remote examinations on students’ mental health, as well as the connections between academic integrity and student well-being in general. (DIPF/Orig.
The Academic Integrity Technological Arms Race and its Impact on Learning, Teaching, and Assessment
This essay discusses the technological arms race that has developed in response to academic cheating. The author highlights three technological advances that impact academic integrity, from oldest to newest: a) text-matching software, b) online exam proctoring software, and c) artificial intelligence and Large Language Models (LLMs). This essay argues that there is no “silver bullet” to preventing or investigating academic misconduct and that our ethical obligations for learning, teaching, and assessment must include a human focus to promote student success
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