The evaluation of computer science curriculum for high school education based on similarity analysis

Ramadhani, Syaifudin, Hariyadi, M. Amin ORCID: and Crysdian, Cahyo (2023) The evaluation of computer science curriculum for high school education based on similarity analysis. International Journal of Advances in Data and Information Systems, 4 (2). pp. 201-213. ISSN 2721-3056

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The government is currently developing regulations to regulate educationcurriculum For High School Students. In this regulation, curriculum standards have been created that can be developed by educators in schools. Computer science teachers at the school level develop a curriculum that has been set as a standard curriculum. However, measurable evaluation tooptimizethe development of the new curriculum has not been available yet. This research proposes a form of evaluation that can be used as a benchmark by analyzing the similarity of curriculum content developed by teachers using a text mining approach. This is conductedby comparing computer science documents with applicable documents, namely knowledge field documents.It is expectedthat the results of optimizing competency development in the computer science curriculum can be achieved better. The average similarity checking performance using Cosine Similarity and Word2Vec are 40.9850 and 97.3558 respectively.Meanwhile, in the process of fulfilling the knowledge sector, with Cosine Similarity an average percentage of 40.98% was obtained, and with Word2Vec an average percentage of 97.36% was obtained. The results of this trial will be used as a basis for measurable evaluation of teacher contributions to be able to develop the curriculum better according to the applicable curriculum. The results of this evaluation are also used by the government to make future curriculum evaluations more measurable and the standards used are clear and help facilitate curriculum development in schools.

Item Type: Journal Article
Keywords: text mining; similarity document; computer science; curriculum; curriculum document; cosine similarity; word2vec similarity
Subjects: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions: Faculty of Technology > Department of Informatics Engineering
Depositing User: Mokhamad Amin Hariyadi
Date Deposited: 14 Nov 2023 11:24


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