Influence of Teacher-Student Matching on Knowledge Innovation of Graduate Students

Jin-gui, Wang; Yanming, QI

doi:10.3991/ijet.v15i20.17421

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…Eq. (1) shows the expression of the matrix O1: If the knowledge point ui is the prior knowledge point of the knowledge point uj, the value of the binary function oi,j is 1; if the knowledge point uj is the prior learning knowledge point of the knowledge point ui, the value of the binary function oi,j is 0.…”

Section: Model Constructionmentioning

confidence: 99%

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

Wu¹,

Jia²

2022

Int. J. Emerg. Technol. Learn.

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The main problems lying in the learning process of learners in different majors are that they study blindly and do not have a complex knowledge structure, which are seriously affecting their learning effects. But if a knowledge graph can be modeled for the major-specific curriculum system using the quantitative method from the perspective of knowledge network, it may be able to improve the existing teaching problems and optimize the teaching quality. The existing major-specific knowledge graphs were all constructed in an abstract form, ignoring the inherent prior learning relationship between teaching units and curriculum knowledge. To this end, taking English major as an example, this paper studied the construction and application of a major-specific knowledge graph based on the big data in education. Firstly, the English major-specific knowledge graph was modeled, the calculation process of node importance was shown, and a localized graph of the knowledge network of English major courses was given. Then, a multi-node feature selection framework for the English major-specific knowledge graph was constructed based on the context of nodes, and the importance of the top k nodes in the constructed knowledge graph was extracted using the multi-node feature extraction technology. After that, the experimental results verified the stability and connectivity of the nodes in the constructed knowledge graph.

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Section: Model Constructionmentioning

confidence: 99%

“…With the rapid development of science and technology, new knowledge and technologies are rapidly growing, leading to increasingly large and complex knowledge systems for all kinds of majors [1][2][3][4][5][6][7][8]. When studying the major-specific knowledge, however, learners currently have some problems.…”

Section: Introductionmentioning

confidence: 99%

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

Wu¹,

Jia²

2022

Int. J. Emerg. Technol. Learn.

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show abstract

“…Now the knowledge innovation has become the mainstream of the development of the times [1][2][3][4][5][6][7]. New concepts, methods and technologies brought about by the intersection and integration of various disciplines can join their forces to solve major science problems in different fields [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Structural Feature Analysis and Spatial Modeling of Knowledge System Based on Interdisciplinary Integration

Ma¹

2023

Int. J. Emerg. Technol. Learn.

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Figuring out the structural features and internal logic of knowledge system from an interdisciplinary perspective under the action of knowledge flow and revealing the changes in the spatial structure and pattern of the knowledge systems of different disciplines are meaningful works for both the theoretical and practical research on the utilization and expansion of interdisciplinary knowledge. For this reason, this paper built a spatial model for the interdisciplinary knowledge system and analyze its structural features. At first, key features of the interdisciplinary knowledge system were mined, and a knowledge system was built based on the coupling of knowledge and discipline. Then, the relationship between technical knowledge and discipline was discussed, and the complex spatial features of the interdisciplinary knowledge system were mined. After that, this paper built an evolutionary dynamics model for knowledge stickiness, and used it to study the main influencing factors of the knowledge stickiness of interdisciplinary knowledge system based on dynamics and figure out its action mechanisms on the performance of knowledge utilization and expansion. At last, experimental results verified the effectiveness of the constructed model and the proposed algorithm.

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“…In previous empirical studies, research on creativity has mainly concentrated on two principal areas: one is associated with individual factors, such as emotional intelligence ( Rodrigues et al, 2019 ), divergent thinking ( Gralewski and Karwowski, 2019 ), openness to experience ( Zhang X. et al, 2020 ), intrinsic motivation ( Zhang X. et al, 2020 ), positive affect ( Rego et al, 2014 ), and autonomous motivation ( Liu et al, 2013 ). Another aspect is related to environmental factors, such as social environments ( Carbajal and Baranauskas, 2020 ), school and family climate ( Niu, 2007 ), cultural background ( Jang, 2017 ), and teacher-student matching ( Wang and Qi, 2020 ). However, until now, it is unclear how individual and situational factors interact in the generation of creative ideas online.…”

Section: Introductionmentioning

confidence: 99%

Student Intrinsic Motivation for Online Creative Idea Generation: Mediating Effects of Student Online Learning Engagement and Moderating Effects of Teacher Emotional Support

Wang

2022

Front. Psychol.

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The online creativity generation research is a new field of creativity research. However, very little is known about the specific psychological processes of online idea generation. Against this background, this study explored the correlation between student intrinsic motivation and online creativity and possible mechanisms that may lie within this relationship. A sample of 423 Chinese students from three public universities participated in this study by completing measurements of intrinsic motivation, online learning engagement, creativity, and perceived teacher emotional support. The results indicated that student online learning engagement partially mediates the positive association between student intrinsic motivation and their online creativity. Teacher emotional support moderates the positive relationship between student intrinsic motivation and online learning engagement. Our findings suggested that student online creativity benefited from their intrinsic motivation in an online environment. The limitations of this study were also discussed.

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Influence of Teacher-Student Matching on Knowledge Innovation of Graduate Students

Cited by 8 publications

References 23 publications

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

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

Structural Feature Analysis and Spatial Modeling of Knowledge System Based on Interdisciplinary Integration

Student Intrinsic Motivation for Online Creative Idea Generation: Mediating Effects of Student Online Learning Engagement and Moderating Effects of Teacher Emotional Support

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