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.
Highlights
Artificial intelligence (AI), connectivity, information digitisation, additive manufacturing, virtual or augmented reality, machine learning, blockchain, robotics, quantum computing, and synthetic biology are all examples of areas where the digital revolution can help to facilitate Sustainable DevelopmentGoals (SDGs) [1,2]
data mining (DM) is one of the most popular techniques which is widely applied in education systemic approach, model features and response variables are used in classifying at-risk analyse student performance [25,32]
The use of ICT tools contributes to an excellent learning environment among students and learning pedagogies
Summary
Artificial intelligence (AI), connectivity (the Internet of Things), information digitisation, additive manufacturing (such as 3D printing), virtual or augmented reality, machine learning, blockchain, robotics, quantum computing, and synthetic biology are all examples of areas where the digital revolution can help to facilitate Sustainable Development. Universities strive to maximize successful completion of courses and programmes with student support services, tools and technologies that have been shown to enhance student learning This warrants the use of new and innovative pedagogies to captivate interest and maximize the potential of the learners. Student enrolment and attendance records, as well as their examination results, are the most conventional form of data mining (DM) in higher education institutions [11,20,21,22] In this age of big data, education data mining (EDM) is an interdisciplinary field where machine learning, statistics, DM, psycho-pedagogy, information retrieval, cognitive psychology and recommended systems methods and techniques are used in various educational data sets to resolve educational issues [23].
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