A Text-Based Analysis of SDGs Goal 4 Representation in Informatics and Computer Science Syllabi
DOI:
https://doi.org/10.21831/elinvo.v10i2.89955Keywords:
SDGs Goal 4, Curriculum Mapping, Informatics Education, Text Mining, machine learningAbstract
Higher education curricula play an important role in supporting the achievement of Sustainable Development Goals (SDGs), particularly SDGs Goal 4: Quality Education, through the development of relevant and sustainable learning. The purpose of this study is to look at the connections between the SDGs Goal 4 targets and the computer science and informatics curricula at universities on the island of Java. 552 course syllabi from current APTIKOM member universities are examined in this study using a text-based document analysis design. The analysis focuses on three frequently accessible syllabus components: course name, course description, and learning outcomes. The Term Frequency-Inverse Document Frequency (TF-IDF) approach was used to represent the textual data after it had been pre-processed and mapped using SDGs keywords to create initial labels. Support Vector Machine (SVM) was used as the comparison model and K-Nearest Neighbors (KNN) as the baseline model for the classification procedure. The results showed a strong relationship between most curricula and Target 4.4, which stresses the development of technical and digital skills. In terms of performance, the KNN model achieved an accuracy of 83%, while the SVM model showed a higher accuracy of 86% with more consistent performance on high-dimensional data and imbalanced class distributions. In conclusion, the syllabus analysis methodology based on keyword mapping and machine learning can be used as a systematic exploratory framework for identifying the relationship between higher education curriculum and SDGs.
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