157 research outputs found

    Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance

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    [EN] Mass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle. The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.The work presented has evolved during the IMPACT project, funded by the Swedish Knowledge Foundation, and has been partly presented on three conferences [8-10]. The three conference papers show the rendering of graphs for CAD-models, spread sheets and KBE-rules together with the first case example in this article. The work has also been partially supported by grant DPI2017-84526-R (MINECO/AEI/FEDER, UE), project CAL-MBE.Johansson, J.; Contero, M.; Company, P.; Elgh, F. (2018). Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance. Advanced Engineering Informatics. 38:252-263. https://doi.org/10.1016/j.aei.2018.07.005S2522633

    Recent Work in Connectivism

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    Abstract Since the introduction of connectivism as a learning theory in 2004 a body of literature has developed both offering criticisms and expanding on applications and empirical validation. This article surveys recent literature on the topic, grouping it into themes, and developing an understanding of current perspectives in connectivism. It surveys current perspectives and criticisms of connectivism, views of connectivism as a pedagogy and as a theory of learning, recent evidence supporting connectivism, and a wider understanding of connectivism as it is developing today

    CACDA: A knowledge graph for a context-aware cognitive design assistant

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    The design of complex engineered systems highly relies on a laborious zigzagging between computer-aided design (CAD) software and design rules prescribed by design manuals. Despite the emergence ofknowledge management techniques (ontology, expert system, text mining, etc.), companies continue tostore design rules in large and unstructured documents. To facilitate the integration of design rules andCAD software, we propose a knowledge graph that structures a large set of design rules in a computableformat. The knowledge graph organises entities of design rules (nodes), relationships among design rules(edges), as well as contextual information. The categorisation of entities and relationships in four sub-contexts: semantic, social, engineering, and IT – facilitates the development of the data model, especiallythe definition of the “design context” concept. The knowledge graph paves the way to a context-awarecognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cog-nitive design assistant will capture the design context in near real time and run reasoning operationson the knowledge graph to extend traditional CAD capabilities, such as the recommendation of designrules, the verification of design solutions, or the automation of design routines. Our validation experi-ment shows that the current version of the context-aware cognitive design assistant is more efficientthan the traditional document-based design. On average, participants using an unstructured design rulesdocument have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precisionscore. Finally, designers supported by the design assistant spend more time designing than searching forapplicable design rules compared to the traditional design approach.Capgemini DEM

    MOOC (Massive Open Online Courses)

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    Massive Open Online Courses (MOOCs) are free online courses available to anyone who can sign up. MOOCs provide an affordable and flexible way to learn new skills, advance in careers, and provide quality educational experiences to a certain extent. Millions of people around the world use MOOCs for learning and their reasons are various, including career development, career change, college preparation, supplementary learning, lifelong learning, corporate e-Learning and training, and so on

    Adapting Collaborative Learning Tools to Support Group Peer Mentorship

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    Group peer mentorship is a relatively new addition to the area of collaborative learning. We see an untapped potential in supporting this model of mentorship with the existing collaborative learning tools like peer review and wiki. Therefore, we proposed to use a modified peer review system and a modified wiki system. From our preliminary studies using both peer review and wiki systems, we found that participants preferred the peer-review system to the wiki system in supporting them for mentorship. Therefore, this dissertation specifically addresses how to adapt the peer review system to support group peer mentorship. We proposed a modified peer review system, which comprises seven stages – initial submission of the first draft of the paper by the author, the review of author’s paper by peer reviewers, release of review feedback to the author, back-evaluation of their reviews by the authors, modification of the paper by the author, submission of the final paper and the final stage where both authors and reviewers provide an evaluation of the peer review process with respect to their learning, their perception of the helpfulness of the process, and their satisfaction with the process. We also proposed to use our group matching algorithm, based on some constraints and the principles of the Hungarian algorithm, to achieve a diversified grouping of peers for each peer review session. With these, we conducted six peer review studies with the graduate and undergraduate students at the University of Saskatchewan and teachers in Chile. This dissertation reports on the findings from these studies. We found that peer review, with some modifications, is a good tool to facilitate group peer mentorship. An evaluation of the performance of our group matching algorithm showed an improvement over three other algorithms, with respect to three metrics – knowledge gain of peers, time and space consumption of the algorithm. Finally, this dissertation also shows that wiki has the potential to support group peer mentorship, but needs further research

    Effective practices of high school principals\u27 leadership in developing traditionally underrepresented students\u27 higher education and future career readiness

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    Political initiatives in response to government reports have focused on students’ preparation for higher education and their future careers, and students fall short. School districts and school programs give attention to the application of instructional practices to ensure students’ college and career preparation, providing professional development in various instructional methods that address Language Arts and Math skills development, and students fall short. Teachers work tirelessly to use instructional strategies that develop students’ critical and computational thinking, communication, collaboration, and creative skills, and students fall short as research indicates that students entering higher education continue to require remedial classes before beginning their college degree programs. This qualitative study design’s purpose was to analyze the effective practices that early college high school principals employ that influence the academic achievement of students traditionally underrepresented in higher education. Thirteen (13) research participants’ responses to leadership style, challenges, and solutions in program planning, development, and implementation with their recommendations yielded sixty themes of practices and strategies employed by early college high school principals. This study’s results corroborate the literature on effective educational leadership practices that affect student achievement and inform educational leadership practice for underrepresented student populations in higher education. Implications for further research address the academic needs of other underrepresented student populations in higher education, including students with moderate to severe educational needs, foster youth and homeless youth

    Cloud eLearning - Personalisation of learning using resources from the Cloud

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    With the advancement of technologies, the usage of alternative eLearning systems as complementary systems to the traditional education systems is becoming part of the everyday activities. At the same time, the creation of learning resources has increased exponentially over time. However, the usability and reusability of these learning resources in various eLearning systems is difficult when they are unstandardised and semi-standardised learning resources. Furthermore, eLearning activities’ lack of suitable personalisation of the overall learning process fails to optimize resources’ and systems’ potentialities. At the same time, the evolution of learning technologies and cloud computing creates new opportunities for traditional eLearning to evolve and place the learner in the center of educational experiences. This thesis contributes to a holistic approach to the field by using a combination of artificial intelligence techniques to automatically generate a personalized learning path for individual learners using Cloud resources. We proposed an advancement of eLearning, named the Cloud eLearning, which recognizes that resources stored in Cloud eLearning can potentially be used for learning purposes. Further, the personalised content shown to Cloud Learners will be offered through automated personalized learning paths. The main issue was to select the most appropriate learning resources from the Cloud and include them in a personalised learning path. This become even more challenging when these potential learning resources were derived from various sources that might be structured, semi- structure or even unstructured, tending to increase the complexity of overall Cloud eLearning retrieval and matching processes. Therefore, this thesis presents an original concept,the Cloud eLearning, its Cloud eLearning Learning Objects as the smallest standardized learning objects, which permits reusing them because of semantic tagging with metadata. Further, it presents the Cloud eLearning Recommender System, that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. And it concludes with Cloud eLearning automated planner, which generates a personalised learning path using the output of the CeL recommender system
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