Machine learning and stem education : challenges and possibilities

Abstract

Science, technology, engineering, and mathematics (STEM) fields are important in national and international economies in driving innovation and improving the economy and workforce pattern to meet 21st century realities. For this goal to be achieved, there is a need for innovation that will drive the economy of the future, which can only be acquired from advances in science and technology. The major rationale behind STEM education is to foster critical thinking skills, which would result in our having more creative problem-solvers in the workforce. The world is gravitating towards a knowledge-based economy, therefore having creative problem-solvers will provide answers to the complex problems of the future. This paper relied on literature review to critically address the topic under consideration. A theoretical analysis of STEM education and machine learning was conducted to clarify the nexus between the two. The key point of this study is the impact of machine learning on STEM education, as properly enacted. Findings from this research revealed that, with the current changes manifested in the global sphere, generally, it is important to leverage STEM education. With more focus on some emerging technologies. such as artificial intelligence and machine learning, the multi-versatility of machine learning has been brought to fore in many areas of computing. This includes spam filtering, and optical character recognition. There are thus ample benefits of STEM education, in that it increases innovation and creativity. STEM reduces the time and stress associated with the rigours of teaching, by providing a better standardization system. STEM education also minimises the stress associated with scoring students, predicting future behaviour and performance of students, and changing the old methods of education. The study recommended that adequate support be provided to stakeholders in the educational value chain, such as teachers, students, policymakers, etc. to familiarise themselves more with machine learning as a concept and a practice. Capacity-building workshops should also be provided for these stakeholders to ensure that they are properly oriented to adopt machine-learning approaches in their classrooms, with minimal rigour and stress.