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, 2025)
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.