Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Wu, Zhihao; Jia, Fang

doi:10.3991/ijet.v17i07.30405

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In recent years, new theories have been developed [20], [21], [22], [23], [24], [25] suggesting there are alternative ways of formalizing mathematical ideas and highlighting the relevant role of different sensory modalities in the processes of abstraction, and in general, in the construction of concepts from simple to complex [26], [27]. These theories are supported by numerous studies that were carried out during the so-called "brain decade" (1990-2000) that bring out the functioning of the motorsensory system of the brain in the development of conceptual knowledge and examine how brain physiological functions support cognitive processes [28], [29], [30], [31], [32].…”

Section: Methodsmentioning

confidence: 99%

Conceptual Metaphors and Tacit Models in the Study of Mathematical Infinity

Díaz-Chang¹,

Arredondo²

2022

Int. J. Emerg. Technol. Learn.

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This work shows the connections between conceptual metaphors and uncon-scious, tacit models that benefit our understanding of mathematical infinity in the university classroom. From the perspective of cognitive linguistics, it is argued that conceptual metaphors play a key role in explaining how this mathematical concept is grounded in our experience, simultaneously providing a mechanism to address these tacit models in a more conscious way. Moreover, it is shown that conceptual metaphors can be built from the conflicting cognitive structures un-derlying these models, specifying obstacles and difficulties that teachers must consider when designing activities aimed at achieving an adequate understanding of mathematical infinity. This type of study allows us to improve our teaching practice, making us aware and stimulating students to become aware, to reflect on the inconsistencies of their own thoughts and intuitions regarding this mathemati-cal concept. At the same time, it allows us to show the validity of these inconsist-encies by revealing how our cognitive processes are constrained by bodily-grounded experiences determined by the complexity of our human nervous sys-tem. The use of technology would also engage students in these reflections and could also help them by fostering new ways of thinking about mathematical infin-ity. This perspective would be of interest in the current context of digital technol-ogy in mathematics education research.

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Section: Methodsmentioning

confidence: 99%

Conceptual Metaphors and Tacit Models in the Study of Mathematical Infinity

Díaz-Chang¹,

Arredondo²

2022

Int. J. Emerg. Technol. Learn.

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“…If the value of the boolean expression of the conditional operator is true, then information about the partially generated solution is not displayed. If a solution is found and the process is not interrupted by the user, the source code in the if statement block (lines [15][16][17][18][19][20] redraws the graph and displays information about the values of the variables involved in the process, for example, the number of edges covered (CoverEdgeCount), the number of vertices used (CoverVertexCount), and the elapsed time determined by the difference between the values of the variables tE and tS.…”

Section: Fig 1 Global Declarations Source Codementioning

confidence: 99%

“…Many transportation problems, site allocation problems, optimal route selection problems or service center placement problems, and problems related to creating optimal schedules and timetables are also described by graphs and solved by the corresponding algorithms [9][10][11][12][13]. Graphs are also used for modeling in the field of learning [14][15][16] and assessment [17]. In this way, various physical, chemical, economic and managerial systems and processes can be successfully represented and studied with graphs.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Application for Learning and Analyzing Different Graph Vertex Cover Algorithms

2023

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This paper deals with an analysis of three algorithms for the graph vertex cover problem. Certain methods for solving this problem are analyzed. In addition, different studies on the problem and some approaches to its solution are discussed as well. An exact algorithm (based on the backtracking approach) is presented. Calculating the average time for execution of this algorithm is consistent with the multitasking way of work of the operating system. For this purpose, four different starts of the algorithm are made and then the average time of all of them is calculated. The exact algorithm found the optimal solutions for all analyzed graphs. Besides this algorithm, two other heuristic algorithms for solving the problem are discussed. For this study, an interactive application is developed to visualize the performance of the three algorithms and display the obtained results. The results show that for small graphs with no more than 25 vertices the exact algorithm can be used to solve optimally the graph vertex cover problem. For the largest graphs, none of the two heuristic algorithms found the optimal solutions, but these algorithms generated solutions that are very close to the optimal ones. In summary, when the size of the graph increases linearly, the execution time of the heuristic algorithms increases linearly, while the execution time of the exact algorithm increases exponentially.

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“…Cui and Yu [8] examined the effects of knowledge graphs on fostering deep learning in a flipped classroom, which indicated that learning with the help of knowledge graphs can achieve better performance in the students' learning products. In a similar way, Wu and Jia [9] investigated the concrete construction and the corresponding applications of the English major-specific knowledge graphs through extensive data sources in an educational domain and verified the interconnection of nodes within structured knowledge graphs. In summary, to provide a more intuitive presentation of the knowledge graph review studies, a list of existing review articles is presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Recent Developments and Trends in Knowledge Graph Research (2013–2022)

Wang,

2024

IEEE Access

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Knowledge graph has emerged as a useful resource and tool for representing real-world entities and their relations, which gained increasing importance in the fields of deep learning and machine learning. This research aims to investigate the academic publications of knowledge graphs between 2013 and 2022 based on the core collection of the Web of Science and examine hot topics and the latest developments in this subject. Thus, the present research adopted a bibliometric analysis to explore the indicators, which mainly focus on different variables from the diachronic productivity of scientific publications to the most prolific countries and the leading publication journals. By means of VOSviewer software, the most productive authors and the frequency of author keywords were further analyzed. The results manifest that dramatic growth has been identified in the past five years due to the output of publications regarding this subject, and the frequently explored themes were mainly conducted from six dimensions, focusing on ontology modelling, knowledge extraction, knowledge graph embedding, graphbased knowledge representation, multi-modal knowledge graphs and knowledge-aware applications. The findings could help researchers to gain a thorough outlook of knowledge graph research, optimize research topic choices, and detect new directions for future studies.INDEX TERMS Bibliometric analysis, knowledge graphs, VOSviewer.

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Construction and Application of a Major-Specific Knowledge Graph Based on Big Data in Education

Cited by 6 publications

References 20 publications

Conceptual Metaphors and Tacit Models in the Study of Mathematical Infinity

Conceptual Metaphors and Tacit Models in the Study of Mathematical Infinity

An Interactive Application for Learning and Analyzing Different Graph Vertex Cover Algorithms

A Bibliometric Analysis of Recent Developments and Trends in Knowledge Graph Research (2013–2022)

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