Researchers are increasingly exploring educational games in immersive virtual reality (IVR) environments to facilitate students’ learning experiences. Mainly, the effect of IVR on learning outcomes has been the focus. However, far too little attention has been paid to the influence of game elements and IVR features on learners’ perceived cognition. This study examined the relationship between game elements (challenge, goal clarity, and feedback) as pedagogical approach, features of IVR technology (immersion and interaction), and learners’ perceived cognition (reflective thinking and comprehension). An experiment was conducted with 49 undergraduate students who played an IVR game-based application (iThinkSmart) containing mini games developed to facilitate learners’ computational thinking competency. The study employed partial least squares structural equation modelling to investigate the effect of educational game elements and learning contents on learner’s cognition. Findings show that goal clarity is the main predictor of learners’ reflective thinking and comprehension in an educational game-based IVR application. It was also confirmed that immersion and interaction experience impact learner’s comprehension. Notably, adequate learning content in terms of the organisation and relevance of the content contained in an IVR game-based application significantly moderate learners’ reflective thinking and comprehension. The findings of this study have implications for educators and developers of IVR game-based intervention to facilitate learning in the higher education context. In particular, the implication of this study touches on the aspect of learners’ cognitive factors that aim to produce 21st-century problem-solving skills through critical thinking.

With the push to integrate Artificial Intelligence (AI) from kindergarten to twelfth-grade levels comes a need for equipped teachers. However, there needs to be more initiative in professional learning opportunities, which demands the required effort to ensure teachers learn the AI content they will be teaching. To design an effective professional development program, understanding teachers' existing knowledge, interest and disposition towards AI is crucial to devise strategies that could best support the teachers. As a result, this study aims to investigate in-service teachers' perspectives of teaching AI in schools from the perspective of planned behavior theory. Using a 7-factor scale of AI Anxiety, AI Readiness, AI Relevance, Attitude towards using AI, AI for Social Good, Confidence in AI and Behavioral Intention. This study sampled 159 in-service teachers in Namibia. The teachers' responses were analyzed with SmartPLS using Structural Equation Modelling and various Multigroup Analysis techniques. This study found that teachers' behavioral intention to teach AI depends on a combination of factors, including the relevance of AI, attitude towards using AI, the use of AI for social good and confidence. Meanwhile, AI Anxiety and readiness could not be linked to the intention to teach AI. We discussed our findings, highlighted the study implication, and suggested future directions.

This study uses the quantitative research approach to examine the connection between students’ teamwork experience, self-regulated learning, technology self-efficacy, and performance in an online educational technology course. Sixty-three (63) students participated in this study. The study data were collected through an online questionnaire that included background information, course satisfaction, motivation strategies for learning, and online technology self-efficacy, to study the variables’ interactions using quantitative research. To realize this study’s aims, multivariate regression and correlation approaches were employed to analyze the online students’ data. The multivariate regression analysis results show a relationship between self-regulated learning, the online course level, and the number of online courses that the students have completed. Right self-regulated learning strategies in online courses motivate students to strive for a good teamwork experience, leading to increased interest in online learning. In addition, the results also show that there is a relationship between satisfaction and the level of the online course. Achieving good grades makes the student more satisfied and improves the level of technology use. Finally, this study established a relationship between the students’ motivation and the online course level. Therefore, teachers and course designers should implement learning objects that promote students’ engagement and motivation in online learning environments.

The objective of this study is to conduct a bibliometric review of literature on electronic invoicing to provide an understanding of the growing field and valuable sources for future research. A total of 191 papers within the period of 1997 to July 2021 were included in our analysis. The systemic analysis revealed several insights in research progression over two decades, relevant authors and leading institutions including countries, most frequent keywords, and the principal methodologies and theories adopted. Although the field of electronic invoicing is still emerging, it is interesting to see trending keywords such as 'data mining', 'automation', 'blockchain', 'digital storage', and 'machine learning' as demonstrated in recent publications. The study also attempted to develop a framework and proposed an integrated theory of electronic invoicing since the general theoretical framework does not exist in the literature. Several research gaps were exposed related to more studies in the emerging field of electronic invoicing and how future studies could further shape the field by addressing yet unanswered questions. We anticipate that the findings in this study will be a valuable contribution and resource for e-invoicing research.