The purposes of the research were 1) to design the Digital Educational Computer Games Environments Supporting Education (DECGE-SE) and 2) to evaluate the DECGE-SE. The research procedure was divided into two phases. The DECGE-SE as follows: (1) Learning: (1.1) Subject area, (1.2) Learning objectives/Player goals, (1.3) Learning Theories, (1.4) Platform and (1.5) Type of game; (2) Instruction: (2.1) Game Cycle and (2.2) Components for educational computer games online; (3) Outcomes and Impacts comprised four elements: (3.1) Knowledge acquisition/content understanding, (3.2) Perceptual and cognitive skills, (3.3) Affective and motivational outcomes and (3.4) Behavioural change. The expert opinions on the DECGE-SE evaluated that it was a very high appropriate level.

These objectives of the study are 1) to design STEAM education using Design Thinking Process through Virtual Communities of Practice (STEAM-DT-VCoPs), and 2) to evaluate the designed STEAM-DT-VCoPs. It divides the research procedures into two phases. The first phase is to design STEAM-DT-VCoPs, and the second phase is to evaluate the STEAM-DT-VCoPs. The sample group of this study comprises fourteen experts selected by purposive sampling. The arithmetic mean and standard deviation analyzed data. The research findings are: 1) The STEAM-DT-VCoPs comprise three steps are 1.1) the role of virtual communities of STEAM practice 1.2) Design Thinking Process through Virtual Communities of Practice, and 1.3) the various disciplines in STEAM education. 2) The experts agree that STEAM-DT-VCoPs is the highest level of appropriateness.

This research aims to 1) Develop a common framework for artificial intelligence in higher education (AAI-HE model) and 2) Assess the AAI-HE model. The research process is divided into two stages: 1) Develop an AAI-HE model, and 2) Assessment the model. The sample consists of five experts chosen through purposive sampling. The data is analyzed by means and standardized deviations statistically. The research result shows that 1) the AAI-HE model consists of seven key components which are 1.1) User Interactive Components and Technology of AI, 1.2) Components and Technology of AI, 1.3) Roles for Artificial Intelligence in Education 1.4) Machine Learning and Deep Learning 1.5) DSS Modules 1.6) Applications of Artificial Intelligence in Education, and 1.7) AI to enhance campus efficiencies, and 2) The result of the assessment of the AAI-HE model is rated as absolutely appropriate overall.

The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: 1) context analysis and 2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: i) data source, ii) machine learning methods and attributes, and iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.

This study aims to: (1) a design for design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs; and (2) evaluate the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs. The sample is made up of ten specialists who were chosen using purposive sampling. The instrument used in the study was an evaluation form of the leaning model. The arithmetic mean and standard deviation were employed in the investigation. The results show that: (1) the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs consists of four key components: 1) Design thinking process, 2) Digital Workspace, 3) Design thinking tools and 4) Sustainability metrics. (2) All ten specialists agreed that the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs designed through this study shows the highest level of appropriateness.