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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.

With a lifetime prevalence of 2.4%, 1.5 million Thai people suffer from depression in their lifetime. Depression is the leading cause of disability and a major contributor to the overall burden of disease. With the lack of response to this health, the challenge has been compounded by negative perception of mental illness, less than half of depression individuals in Thailand access mental health services. This results in increased number of depression by about 18%. More proactive service should be considered. The sooner depression is detected, the less complicated and shorter course of therapy it would be. This research applies Natural Language Processing (NLP) techniques in psychological domains to develop a depression detection algorithm for the Thai language. Since Facebook is the most popular social network in Thailand. It is often used for sharing opinions and life events. This research proposes Facebook utilization as a large-scale resource to develop depression screening model. The features for depression detection in the Thai community are recognized.

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Depression is a major mental health problem in Thailand. The depression rates have been rapidly increasing. Over 1.17 million Thai people suffer from this mental illness. It is important that a reliable depression screening tool is made available so that depression could be early detected. Given Facebook is the most popular social network platform in Thailand, it could be a large-scale resource to develop a depression detection tool. This research employs techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. To establish the reliable result, Thai Mental Health Questionnaire (TMHQ), a standardized psychological inventory that measures major mental health problems including depression. Depression scale of the TMHQ comprises of 20 items, is used as the baseline for concluding the result. Furthermore, this study also aims to do factor analysis and reduce the number of depression items. Data was collected from over 600 Facebook users. Descriptive statistics, Exploratory Factor Analysis, and Internal consistency were conducted. Results provide the optimized version of the TMHQ-depression that contain 9 items. The 9 items are categorized into four factors which are suicidal ideation, sleep problems, anhedonic, and guilty feelings. Internal consistency analysis shows that this short version of the TMHQ-depression has good to excellent reliability (Cronbach's alpha > .80). The findings suggest that this optimized TMHQ-depression questionnaire holds a good psychometric property and can be used for depression detection.