In the fall of 1999, an international integrated product development pilot project based on collaborative engineering was started with team members in two international teams from the United States, The Netherlands and Germany. Team members interacted using various Internet capabilities, including, but not limited to, ICQ (means: I SEEK YOU, an internet feature which immediately detects when somebody comes "on line"), web phones, file servers, chat rooms and Email along with video conferencing. For this study a control group with all members located in the USA only also worked on the same project.
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The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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The pace of introduction of new technology and thus continuous change in skill needs at workplaces, especially for the engineers, has increased. While digitization induced changes in manufacturing, construction and supply chain sectors may not be felt the same in every sector, this will be hard to escape. Both young and experienced engineers will experience the change, and the need to continuously assess and close the skills gap will arise. How will we, the continuing engineering educators and administrators will respond to it? Prepared for engineering educators and administrators, this workshop will shed light on the future of continuing engineering education as we go through exponentially shortened time frames of technological revolution and in very recent time, in an unprecedented COVID-19 pandemic. S. Chakrabarti, P. Caratozzolo, E. Sjoer and B. Norgaard.
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Academic cheating poses a significant challenge to conducting fair online assessments. One common way is collusion, where students unethically share answers during the assessment. While several researchers proposed solutions, there is lack of clarity regarding the specific types they target among the different types of collusion. Researchers have used statistical techniques to analyze basic attributes collected by the platforms, for collusion detection. Only few works have used machine learning, considering two or three attributes only; the use of limited features leading to reduced accuracy and increased risk of false accusations. In this work, we focus on In-Parallel Collusion, where students simultaneously work together on an assessment. For data collection, a quiz tool is improvised to capture clickstream data at a finer level of granularity. We use feature engineering to derive seven features and create a machine learning model for collusion detection. The results show: 1) Random Forest exhibits the best accuracy (98.8%), and 2) In contrast to less features as used in earlier works, the full feature set provides the best result; showing that considering multiple facets of similarity enhance the model accuracy. The findings provide platform designers and teachers with insights into optimizing quiz platforms and creating cheat-proof assessments.
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The paper summarizes two models for engineering education, as discussed in earlier papers. The first model (Corporate Curriculum) aims to bring Industry into the school, while the second model (I3) intends to bring the school into Industry. The contribution of the presented models to the Bologna Declaration and to the Renaissance Engineer idea are discussed.
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The “Age-Friendly Cities & Communities: States of the Art and Future Perspectives”publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities. CC BY-NC-ND Book CC BY Chapters © 2021 by the authors Original book at: https://doi.org/10.3390/books978-3-0365-1226-6 (This book is a printed edition of the Special Issue Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives" that was published in IJERPH)
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Engineering students have to learn to create robust solutions in professional contexts where new technologies emerge constantly and sometimes disrupt entire industries. The question rises if universities design curricula that enable engineering students to acquire these cognitive skills. The Cynefin Framework (Kurtz & Snowden, 2003; Snowden & Boone, 2007) can be used to typify four complexity contexts a system or organisation can be found in: chaos, complex, complicated and obvious.The Cynefin framework made it possible to create the research question for a case-study: To what extend does the Business Engineering curriculum enable bachelors to find business solutions in the complexity contexts of the Cynefin framework? The results show that 80% of the methods are suitable for complicated contexts and no distinction is made between contexts. This means students are taught to approach most contexts in the same way and are not made aware of differences between the contexts. Making sense of the methods in the curriculum with the Cynefin framework was insightful and suggestions for improvement and further research could be substantiated
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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Insider ethnographic analysis is used to analyze change processes in an engineering department. Distributed leadership theory is used as conceptual framework.
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