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All Journal Brilliance: Research of Artificial Intelligence
Yaya Wihardi
Universitas Pendidikan Indonesia, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Analysis of Direct Scoring and Similarity-Based Scoring Approaches in Automatic Short Answer Scoring (ASAS) Bayu Wicaksono; Rasim Rasim; Yaya Wihardi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6275

Abstract

In the era of digital education, the need for automated scoring systems for short text answers has been steadily increasing. Automatic Short Answer Scoring (ASAS) aims to automate this assessment process with efficient and consistent approaches. Two commonly used approaches in ASAS are direct scoring and similarity-based scoring. Although these two approaches have been widely used, previous research has mostly focused on metrics like RMSE and Pearson Correlation to assess model performance. This study aims to provide a more in-depth analysis by comparing both approaches in two evaluation scenarios, specific-prompt and cross-prompt, by evaluating the accuracy and stability of the models. The dataset used in this study is the Rahutomo dataset. The results of the analysis show that direct scoring outperforms similarity-based scoring in terms of lower RMSE, higher Pearson Correlation, and fewer outliers. In the specific-prompt scenario, an RMSE of 0.0817 and a Pearson Correlation of 0.9504 were obtained, while in the cross-prompt scenario, the RMSE was 0.0917 and the Pearson Correlation was 0.9286. This study provides a more comprehensive insight into model performance by not only relying on evaluation metrics but also examining the distribution of residuals and outliers, which offers a more complete picture of model stability. Based on these findings, direct scoring is recommended for implementation in ASAS systems and for future research that can extend the analysis to other datasets or languages.
Facial Expression Recognition of Students in Classroom Using Hybrid MobileNetV3-Vision Transformer with Token Downsampling Mochamad Khaairi; Rasim Rasim; Yaya Wihardi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6323

Abstract

In large classroom environments, teachers often struggle to monitor each student’s facial expression throughout the learning process. Yet, facial expressions are important indicators of students’ emotional states and engagement, which, when detected in real time, can support a more adaptive learning experience. Most previous research on Facial Expression Recognition (FER) has relied on Convolutional Neural Networks (CNN), which tend to be limited in capturing global relationships between facial features. Additionally, many studies focus on model accuracy without evaluating their practical effectiveness in real classroom settings. This study aims to develop a facial expression recognition model that is both accurate and efficient for use in classroom contexts. A hybrid Vision Transformer (ViT) architecture is proposed, which combines MobileNetV3 for local feature extraction and a Vision Transformer for global context modeling. To reduce the number of tokens and computational cost, a Token Downsampling method is introduced within the transformer blocks. The model is trained using the FER2013 dataset and achieves a test accuracy of 71.24%, surpassing the baseline pretrained ViT model, which reached only 70.10%. Additionally, the Token Downsampling method improves inference speed. Furthermore, the model is tested on a custom dataset collected from students in a real classroom setting to evaluate its performance in practical implementation. Although the performance on the classroom dataset is not yet optimal, the results on FER2013 demonstrate the potential of this approach for further development toward real-time and accurate facial expression recognition in educational environments.
Co-Authors Bayu Wicaksono Mochamad Khaairi Rasim Rasim