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<p>The traditional intellectual property law course cannot adapt to the needs of cultivating students’ active learning ability, practical ability and innovation ability in the digital era due to the teaching concept, teaching means, teaching method and assessment form. This study proposes an information-based teaching model, which builds a course knowledge organization model using a formal expression language, through atomized knowledge points and knowledge granularity. The k-means algorithm has been improved from the perspective of center selection and constraint rules to adapt to the characteristics of the intellectual property law course and complete the clustering and extraction of knowledge points. The frequent item-based method analyzes the students’ answers and generates the corresponding association rules to further develop the analysis of learning situations. For a college law school study, it was found that 925 simultaneous wrong answers and 8152 simultaneous correct answers were successfully mined using clustering. In applying this college students’ evaluation scores of this information technology teaching in terms of content design, presentation design, structural design and relationship design of knowledge clustering are 0.796, 0.818, 0.788, 0.794, and overall good results are achieved.</p>
</abstract>

The traditional intellectual property law course cannot adapt to the needs of cultivating students’ active learning ability, practical ability and innovation ability in the digital era due to the teaching concept, teaching means, teaching method and assessment form. This study proposes an information-based teaching model, which builds a course knowledge organization model using a formal expression language, through atomized knowledge points and knowledge granularity. The k-means algorithm has been improved from the perspective of center selection and constraint rules to adapt to the characteristics of the intellectual property law course and complete the clustering and extraction of knowledge points. The frequent item-based method analyzes the students’ answers and generates the corresponding association rules to further develop the analysis of learning situations. For a college law school study, it was found that 925 simultaneous wrong answers and 8152 simultaneous correct answers were successfully mined using clustering. In applying this college students’ evaluation scores of this information technology teaching in terms of content design, presentation design, structural design and relationship design of knowledge clustering are 0.796, 0.818, 0.788, 0.794, and overall good results are achieved.

Informatization Teaching Mode of Intellectual Property Law Course Based on Clustering Algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The traditional intellectual property law course cannot adapt to the needs of cultivating students’ active learning ability, practical ability and innovation...

Informatization Teaching Mode of Intellectual Property Law Course Based on Clustering Algorithm

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 22 apr 2024

Accettato: 27 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0962

Parole chiave
Knowledge granularity, Knowledge organization, k-means algorithm, Cluster extraction, Association rules, Intellectual property law

© 2024 Jinghan Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Informatization Teaching Mode of Intellectual Property Law Course Based on Clustering Algorithm

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 22 apr 2024

Accettato: 27 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0962

Parole chiaveKnowledge granularity, Knowledge organization, k-means algorithm, Cluster extraction, Association rules, Intellectual property law

© 2024 Jinghan Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Knowledge granularity, Knowledge organization, k-means algorithm, Cluster extraction, Association rules, Intellectual property law