Technology-enhanced education can potentially enhance teaching and learning outcomes for rural educators since they face limited educational resources and low job satisfaction. Recently, there has been a surge in extended reality (XR) as an immersive learning technology to improve teaching and learning in rural areas, but without focusing on rural educators’ perspectives. This study aims to bridge this gap by investigating rural educators’ interactions with XR educational applications and exhibiting their insights on using XR education to improve education quality in rural areas. After educators’ hands-on experiences in a pre-designed XR education workshop, qualitative data was collected from their discussions in focus groups. As a result, educators believed that XR could transform traditional educational practices and create opportunities for new patterns of rural education (e.g., public engagement with rurality and rural vocational education). Limitations include a lack of school infrastructure to apply XR and an absence of well-structured curriculum design to use XR in the classroom setting. We suggest that future studies explore the integration of effective XR practices into primary and elementary education in those areas with limited educational resources.

Thyroid disease is a health concern related to the thyroid gland, which is vital for controlling the metabolism of the human body. Predominantly affecting women in their fourth or fifth decades of life, thyroid disease can result in physical and mental issues. This research focuses on improving the diagnostic process by creating a classification model that utilises various machine learning models and a deeplearning model to categorise three types of thyroid disease conditions. This research developed an automated system capable of classifying three thyroid conditions using five machine learning models and a deep learning model. Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. Finally, a web-based application is developed utilising the most effective model, GBC, which facilitates easy classification of thyroid diseases. The experimental analysis showed that the Gradient Boosting Classifier (GBC), using oversampling techniques, achieved the highest level of performance in classifying thyroid diseases, obtaining an accuracy and F1-Score of 99.76%. This study demonstrated that TSH was the most indicative biomarker for thyroid disease classification. The experimental results proved that the Gradient Boosting Classifier (GBC) utilising the oversampling technique achieved a superior performance compared to other classifier models, with an accuracy and F1-Score of 99.76%. This research presented insights that can assist healthcare practitioners in promptly diagnosing thyroid diseases.

This chapter presents the results of a systematic review that investigated the use of mixed reality (MR) technology to assist social competence learning in mainstream education. Social competence represents our ability to interact with others and, therefore, is a key determinant for many of the possibilities that are presented to us across the lifespan. In today's digital world, MR technology has been widely used in special and inclusive education for social competence training purposes but has not yet been generally applied to mainstream education. This systematic review bridges this research gap. Findings support (1) the potential for MR technology as a medium for social competence learning activities and (2) how the MR approach can contribute to a universally designed (UD) learning environment.