Search

The use of simulation tools has been widely used to learn something. Simulation tools have the advantage of imitating a process similar to the actual situation. But there are only a few researches that examine the students' engagement in using simulation tools in the learning process so that it affects the student learning outcomes. This research aims to analyze the factors of Graphical User Interface (GUI) simulation as learning media for learning signal coding techniques. The Partial Least Square -- Structural Equation Modeling (PLS-SEM) method was used to investigate the impact and the relationship of factors of GUI simulation such as Easiness of Use (EU), Media Attractiveness (MA), and Learning Content (LC) toward Learning Outcome (LO). The results showed that the learning content factor gave a large and significant contribution toward learning outcomes, while easiness of use and media attractiveness made a small contribution to the learning outcome.

Copyright of Journal of Syntax Literate is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Clustering the distribution of student graduates is an approach used to analyze and understand the success of Vocational High School education programs in preparing graduates to enter the workforce or start their own businesses. The purpose of clustering is to evaluate the effectiveness of educational programs, identify entrepreneurial potential, formulate career planning, and develop entrepreneurial skills, all contributing to the fulfilment of Sustainable Development Goals (SDGs) related to quality education (SDG 4), decent work and economic growth (SDG 8), and industry, innovation, and infrastructure (SDG 9). Through this clustering, schools can evaluate the extent to which quality high school graduates achieve career or entrepreneurial success, supporting the objectives of SDG 4. This information helps in designing educational programs that are more in line with the needs of the job market, providing better career guidance to students, and promoting entrepreneurial skills among high school students, contributing to SDG 4 and SDG 8. Clustering the distribution of vocational high school students by working, continuing, and entrepreneurial status plays an important role in strengthening the link between education and the world of work, aligning with the aims of SDG 4 and SDG 8. Self-Organizing Map (SOM), as an Artificial Neural Network, assists in data clustering or mapping tasks, aiding in the discovery of patterns and trends within the vocational high school graduate population. The result of clustering using Z-Score and Min-Max normalization techniques is 5.31% and 3.98%, respectively, providing insights into the career and entrepreneurship trends and patterns of vocational high school students. This valuable information can be used for the development of educational programs, career guidance initiatives, and improved alignment between education and the needs of the world of work, ultimately contributing to the realization of SDGs 4, 8, and 9.

AES has been widely used in assessing student learning outcomes. However, few studies use Automated Essay Scoring (AES) to simultaneously determine the community academy's competency test scores and levels. This study aims to apply AES to assess essays on the competency certification test. The AES can predict the examinees' scores and classify examinees' competency levels. The method used to build AES uses Back Propagation Neural Networks (BPNN). BPNN was chosen because of its simplicity and ease in building the model. The results showed that the AES for predicting the examinee's competency value showed the MAE value is 0.061621 and the accuracy value is = 97.9665 %. The results of the classification of student competency levels show Accuracy= 0.9063, Precision= 0.9167, Recall= 0.8888, and F1 Score= 0.8857. [ABSTRACT FROM AUTHOR]