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Previous research provides promising insights to the role of spherical video-based virtual reality (SVVR) applied with and for autistic users. Work already conducted in this area suggests that SVVR delivered via a range of head-mounted displays (HMDs) are useable, acceptable, can enable skill acquisition, can be relevant for delivering training, can help to reduce discomfort and promote skills generalization. However, to date very little research articulates methods or approaches to the design and development of SVVR. Here, the authors share the experiences of working in this space and designing SVVR content with and for autistic groups.

The authors draw upon two case studies/projects that were previous worked on with the intention to extrapolate key parts of the production process of SVVR development. The authors also outline key theoretical contexts as related to SVVR development in this field.

The goal of this primer on SVVR is to provide researchers and practitioners with an overview of using this technology. The authors provide a set of recommendations that should inform others in creating their own content and developing SVVR for/with/by autistic people.

This work combines and outlines theoretical, conceptual and practical considerations for practitioners and stakeholders seeking to build and deploy SVVR content; aspects not reported in previous research.

The purpose of this paper is to present evaluation findings from a proof-of-concept virtual reality adaptive skills intervention called Virtuoso, designed for adults with autism spectrum disorders.

A user-centric usage test was conducted to investigate the acceptability, feasibility, ease-of-use and relevance of Virtuoso to the unique needs of participants, as well as the nature of participants’ user experiences. Findings are presented from the perspectives of expert testers and participant testers with autism.

This paper offers findings that suggest Virtuoso is feasible and relevant to the unique needs of the target population, and that user experience was largely positive. Anecdotal evidence of skills transfer is also discussed.

The research was conducted in limited settings and with a small number of participants. Multiple VR hardware systems were used, and some experienced instability. This could be accounted for in future research by deploying across multiple settings and with a larger number of participants. Some evidence of cybersickness was observed. Future research must carefully consider the trade-offs between VR-based training and cybersickness for this vulnerable population.

This paper reports on cutting-edge design and development in areas that are under-represented and poorly understood in the literature on virtual reality for individuals with autism.

The purpose of this study is to examine the continuance intentions of working professionals to pursue e-learning courses as a path for career advancement. The primary objective of this study is to ascertain the predictors of continued intentions of working professionals to pursue e-learning courses and examine if this is a trend in career development.

Perceived usefulness of e-learning, motivation and satisfaction are independent variables which are examined using a regression model as potential determinants of continued intentions to use various e-learning platforms. Data from 240 working professionals in different sectors was collected. In addition, satisfaction, motivation and perceived usefulness among the male and female respondents are compared using ANOVA.

The findings showed that motivation, satisfaction and perceived usefulness of e-learning are significant predictors and have a strong influence on the continued intentions of working professionals to pursue e-learning courses. In addition, the results showed that motivation levels while pursuing e-learning and satisfaction derived from them were higher for female professionals.

This study identifies the antecedents of the continued intentions of working professionals to pursue e-learning courses on the path of career advancement. The outcome of the study can be used by educators and e-content creators to make e-learning more engaging. Corporates can also use the results of this study to identify initiatives that can encourage the pursuit of e-learning.

This study provides an important insight exploring the antecedents of continued intentions of working professionals to pursue e-learning courses as a path of career advancement. The research contributes significantly to the understanding thought process of working professionals towards their careers.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.