ABSTRACT
As education is an essential enabler in achieving Sustainable Development Goals (SDGs), it should “ensure inclusive, equitable quality education, and promote lifelong learning opportunities for all”. One of the frameworks for SDG 4 is to propose the concepts of “equitable quality education”. To attain and work in the context of SDG 4, artificial intelligence (AI) is a booming technology, which is gaining interest in understanding student behavior and assessing student performance. AI holds great potential for improving education as it has started to develop innovative teaching and learning approaches in education to create better learning. To provide better education, data analytics is critical. AI and machine learning approaches provide rapid solutions with high accuracy. This paper presents an AI-based analytics tool created to predict student performance in a first-year Information Technology literacy course at The University of the South Pacific (USP). A Random Forest based classification model was developed which predicted the performance of the student in week 6 with an accuracy value of 97.03%, sensitivity value of 95.26%, specificity value of 98.8%, precision value of 98.86%, Matthews correlation coefficient value of 94% and Area Under the ROC Curve value of 99%. Hence, such a method is very useful in predicting student performance early in their courses of allowing for early intervention. During the COVID-19 outbreak, the experimental findings demonstrate that the suggested prediction model satisfies the required accuracy, precision, and recall factors for forecasting the behavioural elements of teaching and e-learning for students in virtual education systems.
ABSTRACT
BACKGROUND: There has been a dramatic change in the pattern of patients being seen in hospitals and surgeries performed during the ongoing COVID-19 pandemic. The objective of this study is to study the change in the volume and spectrum of surgeries performed during the ongoing COVID-19 pandemic compared to pre-COVID-19 era. METHODS: Details of all patients who were operated under department of neurosurgery at our institute since the onset of COVID-19 pandemic in India were collected and compared to the same time period last year. The demographic profile, diagnosis, surgery performed, type of surgery (routine/emergency, cranial/spinal and major/minor) in these two groups were compared. They were further categorized into various categories [neuro-oncology (brain and spine tumors), neuro-trauma (head injury and spinal trauma), congenital cases, degenerative spine, neuro-vascular, CSF diversion procedures, etc.] and compared between the two groups. RESULTS: Our study showed a drastic fall (52.2%) in the number of surgeries performed during the pandemic compared to pre-COVID era. 11.3% of patients operated during COVID-19 pandemic were non-emergent surgeries compared to 57.7% earlier (p = 0.000). There was increase in proportion of minor cases from 28.8% to 41.5% (p = 0.106). The proportion of spinal cases decreased from 27.9% to 11.3% during the COVID-19 pandemic (p = 0.043). CONCLUSIONS: The drastic decrease in the number of surgeries performed will result in large backlog of patients waiting for 'elective' surgery. There is a risk of these patients presenting at a later stage with progressed disease and the best way forward would be to resume work with necessary precautions and universal effective COVID-19 testing.