Computational Thinking (CT) has been formally incorporated into the National Curriculum of Thailand since 2017, where Scratch, a block-based visual programming language, has been widely adopted as CT learning environment for primary-level students. However, conducting hands-on coding activities in a classroom has caused substantial challenges including mixed-ability students in the same class, high student-teacher ratio and learning-hour limitation. This research proposes and develops ScratchThAI as a conversation-based learning support framework for computational thinking development to support both students and teachers. More specifically, it provides learning experiences tailored to individual needs. Students can learn CT concepts and practice online coding anywhere, anytime. Moreover, through its ScratChatbot, students can ask for CT concept explanations, coding syntax or practice exercises. Additional exercises may be assigned to students based on the diagnosed individual learning difficulties in a particular topic to provide possible and timely intervention. Teachers can track learning progress and performance of the whole class as well as of individuals through the dashboard and can take suitable intervention within limited school hours. Deploying ScratchThAI to several Thai schools has enabled this research to investigate its effectiveness in a school setting. The obtained results indicated positive teacher satisfaction, better learning performance and higher student engagement. Thus, ScratchThAI contributes as a possible and practical solution to CT skill development and CT education improvement under the aforementioned challenges in Thailand.

The study investigates the intricate influence of gender and age variability in individuals diagnosed with diabetes, aiming to gain a comprehensive understanding of the diverse impact and implications of this prevalent metabolic disorder. A real-world dataset, obtained from a renowned diabetologist and meticulously maintained by Dr. Reddys’ Lab, serves as the foundation for rigorous analysis. Leveraging the capabilities of ensemble learning, an advanced technique that combines multiple models, the predictive model’s efficiency is substantially enhanced, resulting in precise and reliable predictions of individuals’ diabetic status. Addressing the challenge of diabetes prediction, a novel ensemble learning model was proposed. The model combines the strengths of three distinct algorithms: Random Forest, Extra Trees, and Multilayer Perceptron (MLP). The model’s output comprises a ternary label categorizing individuals as “diabetic, non-diabetic, or pre-diabetic”, while the accompanying prediction score quantifies the likelihood of individuals belonging to each respective category. The findings of this research expand the existing body of knowledge on diabetes prediction, underscoring the untapped potential of ensemble learning methodologies in augmenting accuracy and predictive performance for diabetic patients.

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.

Taxis are commonly used by tourists to travel around unfamiliar cities they visit. These taxis today have GPS devices, which can then be used to collect a significant amount of data on the movement of tourists. One problem with this idea, however, is the question of how to extract that movement data from the raw GPS data, which includes a lot of other data, such as vehicle IDs, timestamps, and speeds, etc. The purpose of this research is to propose a data management platform to process heterogeneous data including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to extract the tourist trajectory data from the raw GPS data and produce a data integration module enriched with a knowledge base of tourist trajectories. This knowledge base is constructed through the extension of semantic trajectory ontology (STO) and mobility behavior ontology (MBO). The second objective is to extract tourist activities/point of interests (POIs) from geo-tagged Twitter data. The results of the data pipeline can readily be used for tourist behavior analysis, such as tourist descriptive analysis, popular tourist destinations/zones, and tourist movement patterns identification. We leverage the study’s results to demonstrate the real-life case study in Bangkok during the Songkran Festival in 2019. Thus, we could precisely identify tourist movement during various periods, determine popular destinations/zones, discover high density density of taxi destination points for a given trajectory type, and display the top ten tourist destinations, as well as prominent tourism keywords or trends at the time. This can provide insight to governments and businesses related to tourism regarding the trajectories and activities of tourists, and it will help predict future tourism trends.