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In recent years, with the rapid growth of science and innovation, plenty of constantly-updated scientific achievements containing innovative knowledge can be acquired and used to solve problems. However, most undergraduate students and non-researchers cannot use them efficiently. In traditional teacher-centric education, education for sustainabilit...
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... graphs can be regarded as having two parts: one is the data schema at the bottom, also called the concept level; and the other is the data level. Data schema should be designed by experts, and the data schema in this work is shown in Figure 2. In this paper, these kinds of entities, concepts, and their property relations were defined to describe scientific resources and form a knowledge graph. The entity class in this work represented objects that are important in scientific resources, such as papers, researchers, journals, and organizations, and were linked together through their properties. The concept class referred to the abstract concept. In this work, they were always the domain terminologies extracted from scientific papers, and concepts were also linked with properties. At the data level of the knowledge graph, every class in the concept level holds a variety of instances, which is the specific entity or concept of the class. Every instance holds properties defined in a corresponding class and their own values for these properties. There were two types of properties in this work: the data property links the instance to a string or number value, and the object property, such as "written_by," links the instance to another instance. In this way, the knowledge graph could be regarded as a knowledge network composed of triples of entity(concept)-property-entity(concept). "Paper," "Researcher", and their instances were selected as an example in Figure 3. "Paper" and "Researcher" are two classes, while a specific paper or an author is the instance of a corresponding class. "Is_a" represents an inheritance relationship; for example, "Researcher" is a "Person." In an inheritance relationship, a subclass holds all properties of the superclass and adds their own properties. For example, "Researcher" holds properties of "Person" such as "name" and has its own properties, such as work in some research institutions, and so on. An inheritance relationship gives a hierarchical structure to the data schema. ...
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Naturally, not all researchers can develop their own software to search for academic publications from digital libraries. Nevertheless, at several stages of their research, they will need to search digital libraries for relevant scientific publications and bibliometric information. There are typically two approaches used by researchers to search fo...
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... In the long-term, utilising Learning Analytics facilitates the development of Sustainable Education, which focuses on everyday teaching practices and learning strategies and pedagogies (Doukanari et al., 2021). Also, Sustainable Education is linked to broader societal implications, including 'sustainable development' Chi et al., 2018). The knowledge acquired through Sustainable Education provides a redirection away from traditional, teacher-centric learning. ...
Learning analytics (LA) emerged and expanded rapidly in the 2000s as a process that involves the collection and analysis of student-related data towards redesigning meaningful learning experiences. Almost two decades later, the current chapter offers a review of how learning analytics is utilized in higher education at three different levels. First, the analysis explains how LA facilitates evidence-based decision-making by administrators and educators at an institutional level. Then, the analysis adopts a pedagogical approach to delineate how LA informs current and future learning strategies. Third, the analysis adopts a societal approach to explore the potential contribution of LA to diversity, inclusion and equality in higher education. By comprising institutional, pedagogical and societal approaches, the chapter concludes with a model, which locates learning analytics in the framework of sustainable education.
... In the realm of education, the application of knowledge graphs and data analysis technologies has revolutionized the learning experience, particularly in fostering sustainability awareness. A group researchers embarked on a project that leveraged these technologies to create a scientific publication management model (Chi et al., 2018). This model facilitated the efficient retrieval of scientific resources, encouraging students and non-researchers to delve into scientific domains with ease. ...
... This model facilitated the efficient retrieval of scientific resources, encouraging students and non-researchers to delve into scientific domains with ease. The case study underscored the potential of this model in nurturing comprehensive thinking and problem-solving skills, essential attributes in addressing sustainability challenges in future careers (Chi et al., 2018). ...
... In the realm of education, the application of knowledge graphs and data analysis technologies has revolutionized the learning experience, particularly in fostering sustainability awareness. A group researchers embarked on a project that leveraged these technologies to create a scientific publication management model (Chi et al., 2018). This model facilitated the efficient retrieval of scientific resources, encouraging students and non-researchers to delve into scientific domains with ease. ...
... This model facilitated the efficient retrieval of scientific resources, encouraging students and non-researchers to delve into scientific domains with ease. The case study underscored the potential of this model in nurturing comprehensive thinking and problem-solving skills, essential attributes in addressing sustainability challenges in future careers (Chi et al., 2018). ...
This comprehensive study explores the dynamic intersection of engineering innovations and sustainable entrepreneurship, a nexus that promises to redefine the contemporary business landscape. The research employs a systematic approach to literature review, critically analyzing recent scholarly works to unearth the prevailing trends, challenges, and opportunities in the sector. The primary aim of this paper is to delineate the transformative role of engineering tools in fostering sustainable entrepreneurship, with a keen focus on artificial intelligence, Internet of Things, and blockchain technology. Through a meticulous methodological approach, the study evaluates the impact of these innovations on sustainable practices, offering advanced interpretations and forecasts based on data analysis. The conclusion underscores a paradigm shift towards a more sustainable and future-oriented economy, driven by the integration of engineering innovations. It highlights the significant research gaps, indicating further exploration necessary to foster a deeper understanding of the sector's intricate dynamics. In light of the findings, the paper proposes strategic suggestions for the engineering sector, emphasizing the need for collaborative efforts, educational reforms, and policy initiatives to navigate the complexities of the evolving landscape. Furthermore, it outlines prospective developments and implications, highlighting the potential growth trajectories and the consequent socioeconomic impacts. The study culminates in a set of robust recommendations, advocating for a harmonized approach that integrates technological advancements with sustainability principles, thereby fostering a landscape that thrives on innovation, sustainability, and entrepreneurship.
... Educational management is the planning and integration of existing educational resources through various means or ways in the process of education, and the level of school educational management directly affects the quality of school operation and the quality of talent cultivation in schools [12][13]. In today's booming development of higher vocational education, studying the education management mode of higher vocational colleges can effectively improve the schooling effect and social service level of higher vocational colleges, which has positive practical significance for cultivating socialist builders and successors [14]. ...
... represents the rubric affiliation matrix for teaching runs. , , , , (14) represents the rubric affiliation matrix for teaching effectiveness. Of which: (15) represents the number of experts who give the same score to a certain judgment level, represents the total number of experts, 12 in total, and is the number of secondary indicators under each primary indicator. ...
Subject-specific education and quality management in higher education institutions are crucial to the cultivation of innovative talents. This paper constructs a GCEF talent cultivation model consisting of government, school, enterprise, and family to improve the quality of cultivation. It evaluates the teaching management system, promotes the construction and development of faculties, determines the weights with an index system and hierarchical analysis, and adds special quantitative items to make the evaluation more relevant. The study shows that the school plays the biggest role in talent cultivation, reaching 50%. The new model of teaching management in colleges and universities is able to develop sustainably and give full play to its service and professionalism.
... EduKGs are used for various educational purposes, e.g., to support learning and scientific discovery [3], predict prerequisite dependencies among courses in MOOCs [10,16], provide computer-aided education [17], support scientific resource retrieval [18], and recommend learning resources [19], learning paths, knowledge levels [20], and curricula [21]. A variant of EduKG, the course knowledge graph (CKG), integrates scattered courses with knowledge points, and fully reflects the relationship between courses and knowledge points [22,23]. ...
Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs have incorporated concept extraction and weighting modules. However, these studies face limitations in terms of accuracy and performance. To address these challenges, this work aims to improve the concept extraction and weighting mechanisms by leveraging state-of-the-art word and sentence embedding techniques. Concretely, we enhance the SIFRank keyphrase extraction method by using SqueezeBERT and we propose a concept-weighting strategy based on SBERT. Furthermore, we conduct extensive experiments on different datasets, demonstrating significant improvements over several state-of-the-art keyphrase extraction and concept-weighting techniques.
... In the field of education, Chi et al. (2018) assessed learning behaviors using a curriculum knowledge graph to help teachers optimize Massive Open Online Course (MOOC) teaching effects. Dang et al. (2019) adopted an acknowledged educational ontology to model online learning resource knowledge and constructed a MOOC-related knowledge graph to improve the utilization of online learning resources. ...
Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.
... Research groups from Stanford University 19) , the Massachusetts Institute of Technology 20) , the University of Bari 21) , the University of Leipzig 22) , and the University of Manchester 23) are focusing on the problems of the development of the Semantic Web 24) , related issues of Machine Learning (ML) and Natural Languages Processing (NLP). The global giants of the IT industry are actively developing knowledge representation models and machine learning technologies, including: IBM Watson Studio 25) , Google AI and Machine Learning 26) , Amazon Comprehend NLP 27) , AWS Machine 19) https://nlp.stanford.edu/software/. 20) https://web.mit.edu/. ...
... 25) https://www.ibm.com/cloud/watson-studio. 26) https://developers.google.com/learn/topics/datascience. 27) https://aws.amazon.com/en/comprehend/features/. ...
... For example, the SoLeMiO tool [25] uses semantic annotation for this purpose. In [26] and [27], a knowledge graph-based design of a model for managing scientific publications and associated metadata is presented. The ELODIE system [28] offers an effective approach for involving the user in information search and knowledge management. ...
The paper deals with the issues of finding and researching optimum algorithms for classification and semantic annotation of textual network content in the interests of filling and updating nuclear knowledge graphs in Russian and English. Testing of the studied algorithms is carried out by the method of cross-validation. The novelty of the presented research is due to the application of the Pareto’s optimality principle for multi-criteria evaluation and ranking of the studied machine learning algorithms, provided that there is no a priori information about the comparative importance of the criteria. The features of the software implementation of efficient classification and semantic annotation algorithms as part of a scalable semantic web portal hosted on a cloud platform are discussed. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services.
... Smart Education systems are expected to contribute to sustainable education systems that should be more efficient and accessible, and providing contemporary teachers with a more sustainable teaching lifestyle and allow for learners to gain the necessary knowledge and skills [315][316][317][318][319]. ...
Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. On one side, trends show a lack of appeal for rural environments as people prefer to move to cities, looking for better opportunities and lifestyles. On the other side, recent studies and reports show that the attractiveness of rural areas as places with opportunities is increasing. Sustainable solutions are needed to enhance development in the rural context, and technological innovation is expected to lead and support the stability for people and organizations in rural regions. While Smart City is progressively becoming a reality and a successful model for integrating Smart Technology into different aspects of everyday life, its effective application in a rural context according to a Sustainable Development approach is not yet completely defined. This study adopts comparative and categorial content analysis to address the different applications and the specific characteristics of rural regions, which often present significant peculiarities depending on the country and the context. The main goal is to investigate and discuss how the Smart City model may be adopted and effectively applied within rural contexts, looking at major gaps and challenges. Additionally, because of the complexity of the topic, we provide an overview of the current adoption of Smart Technology in the different applications in rural areas, including farming, education, business, healthcare, and governance. The study highlights the huge difficulties in rural life and the potentiality of Smart Technology to enhance their Sustainable Development, which is still challenging. While the holistic analysis clearly points out a gap, there is no specific strategic roadmap to re-use or adapt existing models, such as Smart City. The study does not address fine-grained indicators.
... In addition to constructing academic knowledge graphs, many researchers also take advantage of knowledge graphs to develop various applications beneficial to scientific research. Chi et al. (2018) proposed a scientific publication management model to help nonresearchers learn methods for sustainability from research thinking. They built a knowledge graph-based academic network to manage scientific entities. ...
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.
... In addition to constructing academic knowledge graphs, many researchers also take advantage of knowledge graphs to develop various applications beneficial to scientific research. Chi et al. (Chi et al, 2018) proposed a scientific publication management model to help non-researchers learn methods for sustainability from research thinking. They built a knowledge graph-based academic network to manage scientific entities. ...
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.
