Biology is a science about living organisms. Organisms have complex systems consisting of complex organs. Focusing on the human body, if the organ or its structure is visually presented, the learners are more likely to understand it and its function. This research aims to explore the bilingual (Thai and English language), development of an augmented reality tool for use in teaching students about the human heart. The augmented reality application was evaluated by five experts, who analyzed its content consistency by using the Index of Item Objective Congruence (IOC), Diffusion of Innovation (DOI), and the content validity index (CVI), indicating that the augmented reality can be used for publicizing. A sample of 30 subjects were evaluated after AR training. It was determined that the learning result post AR obtained higher ratings when compared with the ratings prior to the use of augmented reality tool. The before and after augmented reality learning results were analyzed for statistical significance at p value < 0.001 with the use of a T-Test. Afterwards, the effectiveness of the tool was evaluated by users focusing on the acceptance of the augmented reality tool to teach the anatomy of the heart; the evaluation of which was based on the theory of Unified Theory Acceptance and Use of Technology (UTAUT) in which the results of the arithmetic mean and the standard deviation were 4.65 and 0.48, respectively. This demonstrated that the users generally accepted the augmented reality tool to teach about the heart at the highest level.

This research has developed a one-stop service supply chain mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers and consumers in accordance with the Thailand 4.0 economic model. This is an investigation into the agricultural product distribution supply chain which focuses on marketing, distribution and logistics using the Dijkstra’s and Ant Colony Algorithms to respectively explore the major and minor product transport routes. The accuracy rate was determined to be 97%. The application is congruent with the product distribution, supply chain, in a value-based economy. The effectiveness of the mobile application was indicated to be at the highest level of results of learning outcomes, user comprehension and user experience of users. That is, the developed mobile application could be effectively used as a tool to support elderly farmers to distribute their agricultural products in the one-stop service supply chain which emphasizes marketing, distribution and location-based logistics for elderly farmers and consumers with respect to Thailand 4.0.

Focusing on electrical circuits as the topic for a STEM project, this research aims to develop virtual reality media for student learning. Using virtual reality media to perform STEM electrical circuit activities, data was collected and analyzed. The virtual reality media was then evaluated by five experts who used the Index of Item-Objective Congruence (IOC) to assess its effectiveness, where the IOC for each criteria was 0.8 or higher. Moreover, after evaluating it with the Diffusion of Innovation Theory (DOI), the arithmetic mean of its effectiveness on the diffusion of innovation was 4.32, with a 0.48 standard deviation. This demonstrates that the use of virtual reality media was a beneficial innovation at a high level. Afterwards, the developed virtual reality media was evaluated by a sample of 30 subjects in the areas of VR features, usability, learning experience, and VR measurement outcomes. The findings show that its arithmetic mean was 4.67 with a 0.47 standard deviation, meaning the developed virtual reality media was an effective for electrical circuit learning at the highest level. Furthermore, after evaluating its effectiveness in the area of information technology acceptance, it was found that, for these criteria, the arithmetic mean was 4.64 with a standard deviation of 0.48. This indicates that the subjects generally accepted the use of virtual reality media for use in learning STEM activities at the highest level. It can be said that the development of the virtual reality media for learning STEM activities with a focus on electrical circuits enhances the learning process of the learners who confirmed such statements at the highest level, with an increase in subject understanding of the learning activity and self-satisfaction, while at the same time reducing the prejudice learners have towards the study of electrical circuit installation.

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Analysis of the symptoms of rose leaves can identify up to 15 different diseases. This research aims to develop Convolutional Neural Network models for classifying the diseases on rose leaves using hybrid deep learning techniques with Support Vector Machine (SVM). The developed models were based on the VGG16 architecture and early or late fusion techniques were applied to concatenate the output from a fully connected layer. The results showed that the developed models based on early fusion performed better than the developed models on either late fusion or VGG16 alone. In addition, it was found that the models using the SVM classifier had better efficiency in classifying the diseases appearing on rose leaves than the models using the softmax function classifier. In particular, a hybrid deep learning model based on early fusion and SVM, which applied the categorical hinge loss function, yielded a validation accuracy of 88.33% and a validation loss of 0.0679, which were higher than the ones of the other models. Moreover, this model was evaluated by 10-fold cross-validation with 90.26% accuracy, 90.59% precision, 92.44% recall, and 91.50% F1-score for disease classification on rose leaves.