Closing the gap: exploring the untapped potential of machine learning in deaf students and hearing students’ academic performance

Abstract

Modern universities use several methods to improve the learning environment. This includes analyzing the academic performance of students and planning various intervention programs to support the students to overcome the difficulties they face. The performance prediction at the time of admission and in the subsequent years helps universities effectively plan the intervention strategies which in turn can help both management and educators benefit from the students' performance prediction plans [1]. Educational data mining (EDM) and machine learning (ML) methods are gaining popularity in the educational domain.*Author for correspondence Many applications using these techniques are used to analyse student performance in academics.The EDM and ML-based applications help instructors to identify struggling students earlier and take action to improve success and retention. Researchers are using these techniques to accelerate their research to uncover discoveries and insights. Though the role of ML and EDM is explored considerably in the education of hearing students, its benefits are not used properly in deaf education. Research suggests that deaf young adults are less likely than their hearing peers to live independently[2], are underemployed and underpaid [3], and are less likely to obtain an undergraduatelevel degree [4]. The opportunities for higher