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AfterDay Horizon: A Cross-Platform Co-op Game for Communication and Strategic Skills
(2026-01-01)
Dechakorn, Parin
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Chuenchom, Pairat
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Vittayakorn, Sirion
Cooperative skills—such as communication, teamwork, and conflict resolution—are essential in both educational and professional contexts. We present AfterDay Horizon, a two-player cooperative game designed to foster these skills through asymmetric, cross-platform gameplay. Players take on distinct but interdependent roles: the Caretaker (VR) executes survival missions in a post-apocalyptic environment, while the Leader (web) provides strategic support through interactive mini-games. Time-sensitive tasks and role-specific responsibilities promote communication, coordination, and replayability. Experimental results show improvements in survival time, mission success, and task efficiency across repeated playthroughs—indicating strengthened cooperation and joint planning. Participants also reported high satisfaction, suggesting strong engagement and a positive reception of the game’s design.
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Context-Aware Prompting for Japanese-Thai Literary Translation in a Low-Resource Setting
(2026-01-01)
Sikkhamarn, Korawit
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Vittayakorn, Sirion
Japanese-Thai literary translation remains underexplored, particularly in low-resource settings where parallel data and evaluation resources are limited. This paper investigates whether context-aware prompting can improve Japanese-Thai literary translation using large language models. To support this study, we construct JTLitCorpus-80, a Japanese-Thai literary parallel corpus derived from 80 publicly accessible online translated web novels. Using this corpus, we compare eight prompt-based translation settings, including machine translation, post-editing, and context-aware prompting variants with and without dictionary guidance. Results on a 20-title test split show that direct context-aware prompting consistently outperforms machine translation and non-context-aware baselines, while post-editing provides only limited gains once the initial draft is already generated by the same model. We further show that prompt-derived supervision can improve a smaller open model through parameter-efficient adaptation. MQM-based human evaluation supports the automatic-metric trends, indicating that the strongest context-aware setting provides the best overall translation quality. These findings suggest that context-aware prompting is a practical strategy for Japanese-Thai literary translation in low-resource conditions.
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Netverse: A Game-Based Learning Environment for Introductory Computer Networking
(2026-01-01)
Neamsri, Kanphitcha
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Arsanok, Purinprat
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Vittayakorn, Sirion
This paper presents Netverse, a 3D game-based learning environment for introductory computer networking. It was designed to support hands-on learning of cable types and connections, network devices and ports, and IP-related concepts through puzzle-based interaction. The study had two objectives: to compare Netverse with Cisco Packet Tracer in terms of learning and retention, and to examine gameplay behavior and user experience in the game environment. In a pilot comparative experiment, 30 participants were randomly assigned to either Cisco Packet Tracer or Netverse and completed pre-test, immediate post-test, and delayed post-test measures. After the delayed post-test, all participants completed the GUESS questionnaire, and gameplay telemetry from Netverse sessions was analyzed. Both conditions improved learning outcomes; Netverse showed a larger unadjusted short-term total gain, with its clearest adjusted advantage in IP-related concepts, while showing comparable retention loss over two weeks. Gameplay log analysis revealed level-specific adaptation and repeated-correction patterns, and user experience ratings were positive overall. These findings suggest that Netverse shows promise as a game-based approach for introductory networking education.
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Bugtopia: Game-Based Learning for Venomous Insect Symptom Recognition
(2026-01-01)
Wisescharoen, Supitcha
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Thongkwanjai, Sirimongkol
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Vittayakorn, Sirion
This paper presents Bugtopia, a game-based learning prototype developed to support recognition of symptoms associated with venomous insects and related arthropods. The study aimed to compare the game-based approach with a text-based learning method in terms of immediate learning, retention, and user experience. A between-group experiment with 30 participants employed pre-test, immediate post-test, and delayed post-test measures. Learning outcomes were analyzed using both all-or-nothing and partial-credit scoring. Results showed that both methods improved performance from pre-test to immediate post-test, although the text-based method yielded larger immediate gains. In contrast, delayed post-test results showed stronger retention for the game-based method, which maintained more stable performance over two weeks and achieved higher delayed scores. Subjective results further showed positive user experience for Bugtopia and more favorable ratings for the game-based method on understanding, memory support, enjoyment, and future reuse. Overall, the findings suggest that Bugtopia is a promising complementary learning tool, particularly for promoting retention and engagement.
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Enhancing Auto Insurance Fraud Detection Using Convolutional Neural Networks
(2024-01-01)
Wongpanti, Ratchanon
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Vittayakorn, Sirion
With the increasing number of vehicles in the global fleet, the size of the auto insurance market is projected to reach $1.3 billion USD by 2030. While this growth in the issuance of auto insurance policies brings prosperity to the industry, it also amplifies the risk of fraudulent activities. These fraudulent practices have a significant impact on the industry, resulting in the loss of billions of USD annually. Despite efforts to prevent such activities, the expertise available is often overwhelmed by the sheer volume of cases. In this paper, we propose an auto insurance fraud detection system that leverages a one-dimensional Convolution Neural Network (ID-CNN) model in combination with two data augmentation techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Networks (CTGAN), to address the class imbalance problem prevalent in fraud detection datasets. Furthermore, we also employ Focal Loss as the loss function in our deep learning model to effectively tackle the difficulty in classifying the minority class. By combining the ID-CNN model with these imbalance manipulation techniques and the Focal Loss function, we aim to enhance the system's ability to accurately identify fraudulent activities, even in the presence of highly imbalanced data. Our proposed approach seeks to mitigate the financial losses incurred by the auto insurance industry due to fraud and provide a more robust and efficient fraud detection system.
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PyraMaze VR: An Immersive Gameplay for Ancient Egypt Learning
(2024-01-01)
Tangtung, Intanon
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Lunsiay, Luksamee
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Vittayakorn, Sirion
Studying ancient Egyptian culture is challenging due to the extensive timespan and societal changes over millennia. Advanced 3D and VR technologies enable immersive virtual exploration with detailed reconstructions, allowing students to engage deeply with the material and enhance their understanding. In this work, we present PyraMaze VR: a virtual reality game that immerses players in the depths of an ancient pyramid, offering an innovative educational tool. Within the pyramid, players must decipher numerous puzzles related to ancient Egypt to navigate back to the modern world. Throughout gameplay, players gather information about ancient Egyptian culture by solving puzzles scattered across the map. Experimental results reveal that PyraMaze VR is not only an effective learning tool, enhancing players' understanding by approximately 34.7%, but also achieves satisfaction scores of 4.0±0.57 out of 5. This achievement surpasses traditional learning methods by around 12.4% and alternative games by 4.0%.
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Location-Based Score Prediction for Condominiums in Bangkok
(2023-01-01)
Bunjongsat, Sarun
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Vittayakorn, Sirion
Condominiums are one of the most popular residential properties due to the resurgence of urban living. They are typically found in more metropolitan areas, such as the national capital region, offering easy access to restaurants, shopping, and various activities. Condominiums can be both profitable investment properties and enjoyable homes. However, finding the right one can be challenging. Based on previous work, one crucial factor is the location, which requires experience, expertise, and time to consider. Thus, in this study, we aim to: 1) investigate the factors that affect the potential location of a condominium, and 2) apply machine learning algorithms to create a prediction model for condominium scores. In this research, we collected a novel dataset comprising more than 2,000 condominiums in Bangkok, Thailand, and examined the location-based factors that influence the price and purchasing decisions of individuals. We extracted several features for model training and studied the variables that could be used to measure the potential of a condominium in numerical form. We proposed a multi-layer perceptron neural network with 5-Fold cross-validation and grid-search techniques. The experimental results demonstrate that our network achieves a mean squared error (MSE) of 0.0067 when using the density-based score labeling.
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Incomplete Adventure: An Educational Game for the TOEIC Exam
(2023-01-01)
Tedsakorn, Surawee
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Aksaralikitsanti, Nattapong
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Vittayakorn, Sirion
These days, the Test of English for International Communication (TOEIC) plays an essential role in both the work environment and everyday life. In daily life, fluency in English offers tremendous benefits including exposure to new experiences, opportunities, cultures, and friends. In the work environment, it is an crucial factor in client relationships, prof-itability, team effectiveness, and employee engagement. Although TOEIC preparation sources can be seen everywhere, most of them aren't very attractive or motivating. As a result, it is very hard for test takers to fully focus on exam practice for a long period of time. Unfortunately, the lack of attractive and interesting of the traditional methods e.g, book or web-based exam, negatively effect the test takers' learning performance. To mitigate this problem, we propose a game-based application for TOEIC preparation called Incomplete Adventure. In game-play, player needs to answer a series of TOEIC questions to defeat the enemy without dying. Even though the experimental results demonstrate that Incomplete Adventure is comparable with traditional learning methods in improving English skill, the game receives a better degree of satisfaction than the web-based exam by 7.6%. Moreover, the overall performance score of our game also outperforms other TOEIC games by 2.68%. We posit that the enjoyment from Incomplete Adventure will enhance the motivation of learning among students and finally effect the students' perception of learning in the end.
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DropQuest: Game-Based Learning for Chemistry
(2022-01-01)
Kanjanapanwong, Terawat
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Kowprasert, Techin
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Vittayakorn, Sirion
Although Chemistry is one of the fundamental subjects in high school that is strongly integrated in our daily life, it is one in which most students perform poorly in the university admission exam. One of the main reasons is an inappropriate learning approach. The traditional learning approach which is widely used in schools has several limitations. Thus, a game-based learning approach has been explored to increase students' participation and interest. In this work, we aim to develop a multi-player chemistry game for Android phones that encourages student learning performance called DropQuest for Chemistry: Game-Based Learning for Chemistry. In our game, players must answer a series of questions organized in categories in a limited time to review and test their knowledge about the Atomic and Periodic Table. The experimental results demonstrate that participants love to play DropQuest with friends and they think the game provides both enjoyment and knowledge about elements of the periodic table. The experimental results demonstrate that our game enhances the players' knowledge by 7% and achieves the average satisfaction score of 4.13 ± 0.33 out of 5 which outperforms both an interactive web-based periodic table and a game-based learning application by 6.0% and 9.9%.
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Protobot: An Educational Game for Algorithmic Thinking
(2022-01-01)
Lertlapnon, Thanachote
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Lueangrungudom, Naruebes
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Vittayakorn, Sirion
Algorithm has been heavily used to provide a comfort life for us: from finding the best route in Google map to biometrics authentication on your phone. The significant of algorithm defines Computational thinking (CT) as one of the 21st Century skills. Although CT has been integrated into the education in the past years, many students still struggle with CT concept due to the complication of the topic and limited learning methods provided in school environment. To provide alternative learning approach for algorithmic thinking, we propose a game-base learning system called Protobot. Protobot requires students to apply their CT knowledge, especially the algorithmic thinking to solve problems in the gameplay. The experimental results demonstrate that Protobot fosters the algorithmic thinking skill of the players as well as provides the amusement during the gameplay.

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