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All Journal Journal of Applied Data Sciences International Journal Research on Metaverse
Joe Khosa
Department of Engineering Management, Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Exploring the Impact of Mixed Reality Technology on Anatomy Education for Medical Students Joe Khosa; Ayorinde Olanipekun
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.18

Abstract

This study investigates the effectiveness of Apple Vision Pro, a mixed reality tool, in enhancing medical students' understanding of 3D anatomical structures compared to traditional teaching methods. A quasi-experimental design was employed, involving 500 medical students who were divided into two groups: the Experiment group (n = 250), which used Apple Vision Pro, and the Control group (n = 250), which relied on conventional 2D images, textbooks, and static models. Both groups completed a pre-test to assess baseline knowledge, followed by an intervention phase over three weeks, and a post-test to measure learning outcomes. The results showed that the Experiment group demonstrated significantly greater improvement in post-test scores, with a mean improvement of 19.56 ± 5.58, nearly double the 9.40 ± 2.86 improvement observed in the Control group. Statistical analysis using an independent t-test confirmed that this difference was highly significant (t = 36.20, p < 0.0001), indicating the superior effectiveness of Apple Vision Pro in facilitating spatial visualization and comprehension of anatomical relationships. Qualitative feedback from the Experiment group further highlighted the benefits of Apple Vision Pro, including its ability to deliver an immersive, interactive, and engaging learning experience. Students reported increased motivation and a deeper understanding of anatomical structures due to the dynamic nature of the mixed reality environment. In conclusion, this study provides compelling evidence that Apple Vision Pro can transform anatomy education by addressing limitations associated with traditional teaching methods. The findings suggest that integrating mixed reality tools into medical curricula can significantly enhance learning outcomes, improve student engagement, and foster a more comprehensive understanding of complex anatomical concepts. Future research should focus on evaluating the long-term impacts of mixed reality technologies on knowledge retention and practical skill development in medical education.
How Effective are Different Machine Learning Algorithms in Predicting Legal Outcomes in South Africa? Joe Khosa; Daniel Mashao; Ayorinde Olanipekun; Charis Harley
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.215

Abstract

This study examines the effectiveness of different machine learning algorithms in predicting legal outcomes in South Africa's Judiciary system. Considering the advancement of artificial intelligence in the legal sector, this research aims to assess the effectiveness of various machine learning algorithms within the legal domain. Text classification is done using machine learning algorithms, including Logistic Regression, Random Forest, and K-Nearest Neighbours, with datasets obtained from a state legal firm in South Africa. The datasets undergo diligent data cleansing and pre-processing methods, encompassing tokenization and lemmatization techniques. This study evaluates these models' applications through accuracy metrics. The findings demonstrate that the Logistic Regression model attained an accuracy rate of 75.05%, whereas the Random Forest algorithm achieved an accuracy rate of 75.08%. On the other hand, the K-Nearest Neighbours algorithm exhibited no optimal performance, as evidenced by its accuracy rate of 62.76%. This study provides valuable insights for legal professionals by addressing a specific research question about the successful application of machine learning in South Africa's legal sector. The results indicate the possibility of using machine learning to predict the outcomes of criminal legal cases. Additionally, this study highlights the significance of responsibly and ethically implementing machine learning within the legal field. The results of this study enhance our comprehension of the prediction of legal outcomes, establishing a foundation for future investigations in this dynamic area of study. A limitation of this study is that the data was obtained from a single law firm in South Africa.
Co-Authors Ayorinde Olanipekun Ayorinde Olanipekun Charis Harley Daniel Mashao